EXCHANGERATE AND

PPP Date US/INDIA

Exchange Rate US CPI INDIA CPI RI (Rate of inflation)

PPP

Deviation

2

0

00 Jan 43.540 1

69.300 35.737 0.211 31.906 11.634 2000 Feb 43.603 17

0.000 35.654 0.210 31.701 11.902 2000 Mar 43.572 17

1.00

0 35.986

0.210

31.809 11.763 2000 Apr 43.622 170.900 36.317 0.213 32.121 11.500 2000 May 43.988 171.200 36.483

0.213

32.211 11.777 2000 Jun 44.660 172.200 36.649

0.213

32.170 12.490 2000 Jul 44.763 172.700 36.898 0.214 32.294 12.469 2000 Aug 45.671

172.700

36.732

0.213

32.149 13.522 2000 Sep 45.834 173.600 36.815 0.212 32.055 13.779 2000 Oct 46.318 173.900 37.230

0.214

32.360 13.959 2000 Nov 46.784 174.200 37.312

0.214

32.376 14.408 2000 Dec 46.746 174.600 36.981

0.212

32.015 14.731 2001 Jan 46.536 175.600

36.898 0.210

31.761 14.775 2001 Feb 46.515 176.000

36.732

0.209 31.546 14.968 2001 Mar 46.609 176.100

36.898 0.210

31.671 14.939 2001 Apr 46.756 176.400 37.147

0.211

31.830 14.926 2001 May 46.899 177.300 37.395

0.211

31.881 15.018 2001 Jun 46.972 177.700 37.893

0.213

32.232 14.740 2001 Jul 47.122 177.400 38.390 0.216 32.710 14.411 2001 Aug 47.108

177.400

38.639 0.218 32.922 14.186 2001 Sep 47.589 178.100 38.556

0.216

32.722 14.867 2001 Oct 47.997 177.600 38.805

0.218

33.026 14.970 2001 Nov 47.984 177.500 39.137 0.220 33.327 14.657 2001 Dec 47.905

177.400

38.888 0.219 33.134 14.771 2002 Jan 48.271

177.700

38.722

0.218

32.937 15.334 2002 Feb 48.665 178.000

38.639

0.217 32.811 15.853 2002 Mar 48.734 178.500

38.805 0.217

32.860 15.874 2002 Apr 48.909 179.300

38.888 0.217

32.783 16.126 2002 May 49.002 179.500

39.137 0.218

32.956 16.046 2002 Jun 48.963 179.600 3

9.468

0.220

3

3.217 15.747 2002 Jul 48.788 180.000 39.883 0.222 33.491 15.297 2002 Aug 48.603 180.500 40.132

0.222

33.607 14.997 2002 Sep 48.444 180.800 40.215

0.222

33.620 14.824 2002 Oct 48.363 181.200 4

0.380 0.223 33.684 14.678 2002 Nov 48.251 181.500 40.546

0.223

33.767 14.484 2002 Dec 48.176 181.800

40.132

0.221 33.366 14.810 2003 Jan 47.938 182.600 40.049

0.219

33.152 14.786 2003 Feb 47.740 183.600

40.132 0.219

33.039 14.701 2003 Mar 47.665 183.900

40.380 0.220

33.190 14.475 2003 Apr 47.404 183.200 40.878

0.223

33.727 13.676 2003 May 47.098 182.900 40.961 0.224 33.851 13.247 2003 Jun

46.746

183.100 41.210 0.225 34.019 12.727 2003 Jul 46.246 183.700 41.541 0.226 34.181 12.065 2003 Aug 45.944 184.500 41.375

0.224

33.897 12.047 2003 Sep 45.847 185.100

41.375 0.224

33.787 12.060 2003 Oct 45.385 184.900 41.707

0.226

34.095 11.290 2003 Nov 45.486 185.000 41.790

0.226

34.144 11.342 2003 Dec 45.565 185.500 41.624

0.224

33.917 11.648 2004 Jan 45.432 186.300

41.790 0.224

33.906 11.526 2004 Feb 45.236 186.700

41.790 0.224

33.833 11.403 2004 Mar 45.055 187.100

41.790 0.223

33.761 11.294 2004 Apr 43.821 187.400

41.790 0.223

33.707 10.114 2004 May 45.124 188.200 42.122

0.224

33.830

11.294
2004 Jun 45.471 188.900 42.453

0.225

33.970 11.501 2004 Jul 45.997 189.100 42.868 0.227 34.265 11.731 2004 Aug 46.304 189.200 43.282 0.229 34.579 11.725 2004 Sep 45.860 189.800 43.365 0.228 34.535 11.325 2004 Oct 43.929 190.800 43.614

0.229

34.551 9.378 2004 Nov 45.111 191.700 43.531

0.227

34.324 10.787 2004 Dec 43.761

191.700

43.200

0.225

34.062 9.698 2005 Jan 43.735 191.600

43.614 0.228

34.407 9.328 2005 Feb 43.618 192.400

43.531 0.226

34.199 9.419 2005 Mar 43.639 193.100

43.531 0.225

34.075 9.564 2005 Apr 43.696 193.700 43.863

0.226

34.228

9.468
2005 May 43.453 193.600 43.697

0.226

34.116 9.337 2005 Jun 43.557

193.700 43.863 0.226 34.228

9.329 2005 Jul 43.490 194.900 44.609

0.229

34.596 8.894 2005 Aug 41.961 196.100 44.775

0.228

34.512 7.448 2005 Sep 43.877 198.800 44.941

0.226

34.170 9.707 2005 Oct 44.730 199.100 45.438

0.228

34.496 1

0.234 2005 Nov 45.630 198.100 45.853 0.231 34.986 10.643 2005 Dec 45.694

198.100

45.604 0.230 34.796 10.898 2006 Jan 44.351 199.300 45.521

0.228

34.524 9.827 2006 Feb 44.255 199.400

45.521 0.228

34.507 9.748 2006 Mar 44.371 199.700

45.521 0.228

34.455 9.916 2006 Apr 44.801 200.700 45.904

0.229

34.571 10.230 2006 May 45.254 201.300 46.286

0.230

34.756 10.498 2006 Jun 45.894 201.800 47.051 0.233 35.242 1

0.65

1 2006 Jul 46.385 202.900 47.434

0.234

35.336 11.048 2006 Aug 46.491 203.800

47.434 0.233

35.180 11.311 2006 Sep 46.120 202.800 47.816 0.236 35.639 10.481 2006 Oct

45.438

201.900 48.581 0.241 36.371 9.068 2006 Nov 44.812 202.000

48.581 0.241

36.353 8.459 2006 Dec 44.633 203.100

48.581

0.239 36.156 8.477 2007 Jan 44.306 203.437

48.581 0.239

36.096 8.210 2007 Feb 44.124 204.226 48.964 0.240 36.239 7.884 2007 Mar 44.014 205.288

48.581

0.237 35.770 8.244 2007 Apr 42.277 205.904

48.964

0.238 35.944 6.332 2007 May 40.871 206.755 49.347

0.239

36.076 4.795 2007 Jun 4

0.754 207.234 49.729

0.240

36.271 4.482 2007 Jul 40.421 207.603 50.494 0.243 36.764 3.657 2007 Aug 40.843 207.667 50.877 0.245 37.031 3.812 2007 Sep 4

0.388 208.547

50.877

0.244 36.875 3.514 2007 Oct 39.548 209.190 51.259

0.245

37.038 2.510 2007 Nov 39.439 210.834

51.259 0.243

36.749 2.690 2007 Dec 39.465 211.445

51.259

0.242 36.643 2.822 2008 Jan 39.268 212.174

51.259 0.242

36.517 2.751 2008 Feb 39.674 212.687 51.642

0.243

36.701 2.973 2008 Mar 40.145 213.448 52.407 0.246 37.112 3.033 2008 Apr 39.967 213.942 52.789 0.247 37.296 2.670 2008 May 42.002 215.208 53.172

0.247

37.346 4.656 2008 Jun 42.763 217.463 53.554

0.246

37.224 5.539 2008 Jul 42.703 219.016 54.702 0.250 37.752 4.950 2008 Aug 42.906 218.690 55.467 0.254 38.337 4.568 2008 Sep 45.530 218.877 55.850 0.255 38.569 6.961 2008 Oct 48.616 216.995 56.615 0.261 39.436 9.179 2008 Nov 48.852 213.153

56.615

0.266 40.147 8.705 2008 Dec 48.513 211.398 56.232

0.266

40.207 8.306 2009 Jan 48.700 211.933

56.615

0.267 40.378 8.321 2009 Feb 49.248 212.705

56.615 0.266

40.232 9.017 2009 Mar 51.129 212.495

56.615 0.266

4

0.271 10.858 2009 Apr 49.966 212.709 57.380 0.270 40.774 9.191 2009 May 48.510 213.022 57.762

0.271

40.986 7.524 2009 Jun 47.674 214.790 58.527 0.272 41.187 6.487 2009 Jul 48.362 214.726 61.205 0.285 43.084 5.278 2009 Aug 48.243 215.445 61.970 0.288 43.477 4.765 2009 Sep 48.292 215.861 62.353 0.289 43.661 4.631 2009 Oct 46.652 216.509 63.118 0.292 44.065 2.588 2009 Nov 46.531 217.234 64.265 0.296 44.716 1.814 2009 Dec 46.527 217.347 64.648 0.297 44.959 1.568 2010 Jan

45.894

217.488 65.795 0.303 45.727 0.167 2010 Feb 46.273 217.281 65.030 0.299 45.239 1.035 2010 Mar 45.451 217.353

65.030 0.299

45.224

0.227
2010 Apr 44.444 217.403

65.030 0.299

45.213 -0.769 2010 May 45.769 217.290

65.795 0.303 45.769 0.000

my

base year global crisis 2010 Jun 46.498 217.199 66.560 0.306 46.321 0.178 2010 Jul 46.762 217.605 68.091 0.313 47.297 –

0.535 2010 Aug 46.461 217.923

68.091

0.312 47.228 -0.768 2010 Sep 45.873 218.275 68.473 0.314 47.417 -1.544 2010 Oct 44.354 219.035 69.238 0.316 47.780 -3.426 2010 Nov 44.932 219.590 69.621 0.317 47.923 -2.991 2010 Dec 45.100 220.472 70.768 0.321 48.518 -3.418 2011 Jan 45.375 221.187 71.916 0.325 49.145 -3.770 2011 Feb 45.380 221.898

70.768

0.319 48.206 -2.826 2011 Mar 44.914 223.046

70.768 0.317

47.958 -3.044 2011 Apr 44.301 224.093 71.151 0.318 47.992 -3.691 2011 May 44.902 224.806 71.533

0.318

48.097 -3.194

base year global crisis
2011 Jun 44.811

224.806

72.298 0.322 48.611 -3.800 2011 Jul 44.396 225.395 73.828 0.328 49.510 -5.114 2011 Aug 45.314 226.106 74.211

0.328

49.610 -4.297 2011 Sep 47.691 226.597 75.359 0.333 50.268 -2.578 2011 Oct 49.202 226.750 75.741 0.334 50.489 –

1.28

7 2011 Nov 50.679 227.169 76.124 0.335 50.651 0.028 2011 Dec 52.382 227.223

75.359

0.332 50.130 2.253 2012 Jan 51.002 227.842

75.741 0.332

50.247

0.754
2012 Feb 49.181 228.329

76.124 0.333

5

0.394 -1.212 2012 Mar 50.364 228.807 76.889 0.336 50.794 –

0.430 2012 Apr 51.690 229.187 78.419 0.342 51.719 -0.029 2012 May 54.331 228.713 78.801 0.345 52.079

2.253
2012 Jun 55.942 228.524 79.566 0.348 52.628 3.315 2012 Jul 55.425 228.590 81.097 0.355 53.624 1.801 2012 Aug 55.494 229.918 81.862 0.356 53.817 1.676 2012 Sep 54.350 231.015 82.244

0.356

53.812 0.538 2012 Oct 53.100 231.638 83.009 0.358 54.167 -1.067 2012 Nov 54.785 231.249 83.392 0.361 54.508 0.277 2012 Dec 54.647 231.221 83.774 0.362 54.765 -0.118 2013 Jan 54.229 231.679 84.539 0.365 55.155 -0.926 2013 Feb 53.808 232.937 85.304 0.366 55.354 -1.546 2013 Mar 54.423 232.282 85.687 0.369 55.759 -1.336 2013 Apr 54.324 231.797 86.452 0.373 56.375 -2.051 2013 May 54.985 231.893 87.217 0.376 56.850 -1.865 2013 Jun 58.384 232.445 88.365

0.380

57.461 0.922 2013 Jul 59.761 232.900 89.895 0.386 58.342 1.419 2013 Aug 62.811 233.456 90.660

0.388

58.698 4.113 2013 Sep 63.648 233.544 91.042 0.390 58.924 4.724 2013 Oct 61.606 233.669 92.190 0.395 59.635 1.971 2013 Nov 62.518 234.100 92.955 0.397 60.019 2.499 2013 Dec 61.811 234.719 91.425

0.390

58.875 2.936 2014 Jan 62.106 235.288

90.660

0.385 58.241 3.864 2014 Feb 62.164 235.547

91.042

0.387 58.423 3.741 2014 Mar 60.948 236.028

91.425 0.387

58.549 2.399 2014 Apr 60.346 236.468 92.573 0.391 59.173 1.173 2014 May 59.284 236.918 93.338

0.394

59.549 -0.265 2014 Jun 59.737 237.231 94.103

0.397

59.958 -0.221 2014 Jul 6

0.096 237.498 96.398 0.406 61.351 -1.256 2014 Aug 60.874 237.460 96.780 0.408 61.605 -0.731 2014 Sep 60.898 237.477

96.780 0.408

61.600 -0.703 2014 Oct 61.367 237.430

96.780 0.408

61.612 -0.246 2014 Nov 61.683 236.983

96.780 0.408

61.729 -0.046 2014 Dec 62.707 236.252

96.780

0.410 61.920 0.788 2015 Jan 62.130 234.718 97.163 0.414 62.571 –

0.441 2015 Feb 61.991 235.236

96.780

0.411 62.187 -0.196 2015 Mar 62.481 236.005

97.163

0.412 62.229 0.251 2015 Apr 62.641 236.156 97.928 0.415 62.679 -0.038 2015 May 63.715 236.974 98.693 0.416 62.951 0.764 2015 Jun 63.781 237.684 99.841 0.420 63.493

0.288
2015 Jul 63.605 238.053 100.606 0.423 63.880 -0.275 2015 Aug 65.097 238.028 100.988 0.424 64.130 0.967 2015 Sep 66.167 237.506 101.753 0.428 64.757 1.409 2015 Oct 65.026 237.781 102.901 0.433 65.412 -0.386 2015 Nov 66.100 238.016 103.283 0.434 65.590 0.510 2015 Dec 66.502 237.817

102.901 0.433

65.402 1.100 2016 Jan 67.333 237.833

102.901 0.433

65.398 1.935 2016 Feb 68.240 237.469 102.136

0.430

65.011 3.228 2016 Mar 66.891 238.038 102.518 0.431 65.099 1.792 2016 Apr 66.422 238.827 103.666

0.434

65.610 0.812 2016 May 66.890 239.464 105.196 0.439 66.401 0.488 2016 Jun 67.266 240.167 105.961

0.441

66.688 0.577 2016 Jul 67.158 240.150 107.109 0.446 67.415 -0.257 2016 Aug 66.904 240.602 106.344 0.442 66.808

0.096
2016 Sep 66.714 241.051

105.961

0.440 66.444

0.270
2016 Oct 66.742 241.691

106.344 0.440

66.507 0.235 2016 Nov 67.640 242.029

105.961

0.438 66.175 1.464 2016 Dec 67.805 242.772

105.196 0.433

65.496 2.309 2017 Jan 68.047 243.780 104.814

0.430

64.988 3.059 2017 Feb 66.973 243.961

104.814 0.430

64.940 2.033 2017 Mar 65.801 243.749

105.196

0.432 65.234 0.567 2017 Apr 64.536 244.051

105.961 0.434

65.627 -1.091 2017 May 64.420 243.962

106.344

0.436 65.888 -1.468 2017 Jun 64.448 244.182

107.109 0.439

66.302 -1.854 2017 Jul 64.424 244.390 109.021

0.446

67.429 -3.005 2017 Aug 63.968 245.297

109.021

0.444 67.179 -3.211 2017 Sep 64.478 246.418

109.021 0.442

66.874 -2.396 2017 Oct 65.036 246.587 109.786 0.445 67.297 -2.261 2017 Nov 64.844 247.332 110.169

0.445

67.328 -2.484 2017 Dec 64.245 247.901 109.404

0.441

66.707 -2.462 2018 Jan 63.645 248.884

110.169

0.443 66.908 -3.263 2018 Feb 64.430 249.369

109.786 0.440

66.546 -2.116 2018 Mar 65.046 249.498

109.786 0.440

66.512 -1.466 2018 Apr 65.673 249.956

110.169 0.441

66.621 -0.948 2018 May 67.510 250.646 11

0.55

1

0.441

66.668 0.842 2018 Jun 67.790 251.134 111.317

0.443

66.999 0.791 2018 Jul 68.687 251.597 115.142 0.458 69.174 -0.487 2018 Aug 69.632 251.879

115.142

0.457 69.097

0.535
2018 Sep 72.278 252.010

115.142 0.457

69.061

3.217
2018 Oct 73.561 252.794 115.524

0.457

69.075 4.486 2018 Nov 71.738 252.760

115.524 0.457

69.085 2.653 2018 Dec 70.83

3 252.723

115.142

0.456 68.866 1.967

Exchange Rate and PPP

US/INDIA Exchange Rate 2000 Jan 2000 Feb 2000 Mar 2000 Apr 2000 May 2000 Jun 2000 Jul 2000 Aug 2000 Sep 2000 Oct 2000 Nov 2000 Dec 2001 Jan 2001 Feb 2001 Mar 2001 Apr 2001 May 2001 Jun 2001 Jul 2001 Aug 2001 Sep 2001 Oct 2001 Nov 2001 Dec 2002 Jan 2002 Feb 2002 Mar 2002 Apr 2002 May 2002 Jun 2002 Jul 2002 Aug 2002 Sep 2002 Oct 2002 Nov 2002 Dec 2003 Jan 2003 Feb 2003 Mar 2003 Apr 2003 May 2003 Jun 2003 Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2004 Jan 2004 Feb 2004 Mar 2004 Apr 2004 May 2004 Jun 2004 Jul 2004 Aug 2004 Sep 2004 Oct 2004 Nov 2004 Dec 2005 Jan 2005 Feb 2005 Mar 2005 Apr 2005 May 2005 Jun 2005 Jul 2005 Aug 2005 Sep 2005 Oct 2005 Nov 2005 Dec 2006 Jan 2006 Feb 2006 Mar 2006 Apr 2006 May 2006 Jun 2006 Jul 2006 Aug 2006 Sep 2006 Oct 2006 Nov 2006 Dec 2007 Jan 2007 Feb 2007 Mar 2007 Apr 2007 May 200 7 Jun 2007 Jul 2007 Aug 2007 Sep 2007 Oct 2007 Nov 2007 Dec 2008 Jan 2008 Feb 2008 Mar 2008 Apr 2008 May 2008 Jun 2008 Jul 2008 Aug 2008 Sep 2008 Oct 2008 Nov 2008 Dec 2009 Jan 2009 Feb 2009 Mar 2009 Apr 2009 May 2009 Jun 2009 Jul 2009 Aug 2009 Sep 2009 Oct 2009 Nov 2009 Dec 2010 Jan 2010 Feb 2010 Mar 2010 Apr 2010 May 2010 Jun 2010 Jul 2010 Aug 2010 Sep 2010 Oct 2010 Nov 2010 Dec 2011 Jan 2011 Feb 2011 Mar 2011 Apr 2011 May 2011 Jun 201 1 Jul 2011 Aug 2011 Sep 2011 Oct 2011 Nov 2011 Dec 2012 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 2012 Jun 2012 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2013 Jan 2013 Feb 2013 Mar 2013 Apr 2013 May 2013 Jun 2013 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2014 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 Jun 2014 Jul 2014 Aug 2014 Sep 2014 Oct 2014 Nov 2014 Dec 2015 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Jul 2015 Aug 2015 Sep 2015 Oct 2015 Nov 2015 Dec 2016 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Jun 2016 Jul 2016 Aug 2016 Sep 2016 Oct 2016 Nov 2016 Dec 2017 Jan 2017 Feb 2017 Mar 2017 Apr 2017 May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017 Oct 2017 Nov 2017 Dec 2018 Jan 2018 Feb 2018 Mar 2018 Apr 2018 May 2018 Jun 2018 Jul 2018 Aug 2018 Sep 2018 Oct 2018 Nov 2018 Dec 43.540399999999998 43.602899999999998 43.572000000000003 43.62149 9999999997 43.988199999999999 44.6601 44.763199999999998 45.671100000000003 45.8339 46.318199999999997 46.7836 46.745800000000003 46.536000000000001 46.514499999999998 46.609299999999998 46.755600000000001 46.898699999999998 46.972200000000001 47.121600000000001 47.1083 47.589100000000002 47.996600000000001 47.984299999999998 47.904800000000002 48.271099999999997 48.664700000000003 48.733699999999999 48.9086 49.002200000000002 48.9634 48.787700000000001 48.603200000000001 48.444400000000002 48.362499999999997 48.251100000000001 48.176299999999998 47.937600000000003 47.740099999999998 47.664499999999997 47.403570000000002 47.098300000000002 46.746400000000001 46.245600000000003 45.9437 45.846899999999998 45.384999999999998 45.4861 45.564700000000002 45.432200000000002 45.235900000000001 45.054600000000001 43.820799999999998 45.123899999999999 45.470500000000001 45.996499999999997 46.304000000000002 45.860399999999998 43.929099999999998 45.110599999999998 43.7607 43.735100000000003 43.6175 43.638800000000003 43.696100000000001 43.4529 43.557299999999998 43.490099999999998 41.960599999999999 43.876600000000003 44.729599999999998 45.629600000000003 45.694400000000002 44.351399999999998 44.2547 44.3705 44.801200000000001 45.253599999999999 45.893500000000003 46.384900000000002 46.491 46.119500000000002 45.438200000000002 44.811500000000002 44.6325 44.306199999999997 44.123699999999999 44.014299999999999 42.276499999999999 40.870600000000003 40.753571000000001 40.420952 40.843125000000001 40.388438000000001 39.548260999999997 39.439205000000001 39.465375000000002 39.267600000000002 39.673499999999997 40.145200000000003 39.966799999999999 42.001899999999999 42.763300000000001 42.7027 42.905700000000003 45.53 48.615499999999997 48.851700000000001 48.513199999999998 48.6995 49.248399999999997 51.129100000000001 49.965499999999999 48.51 47.6736 48.362400000000001 48.242600000000003 48.292400000000001 46.6524 46.530500000000004 46.527299999999997 45.894399999999997 46.273200000000003 45.450899999999997 44.444000000000003 45.768999999999998 46.4983 46.761699999999998 46.460500000000003 45.872900 000000001 44.353999999999999 44.9315 45.1 45.375 45.3795 44.914299999999997 44.301000000000002 44.9024 44.810899999999997 44.396000000000001 45.313499999999998 47.6905 49.201999999999998 50.6785 52.382399999999997 51.0015 49.181199999999997 50.363500000000002 51.69 54.331400000000002 55.942399999999999 55.424799999999998 55.493499999999997 54.35 53.099499999999999 54.784500000000001 54.646999999999998 54.228999999999999 53.807899999999997 54.422899999999998 54.323599999999999 54.984499999999997 58.383499999999998 59.760899999999999 62.810899999999997 63.648000000000003 61.605899999999998 62.517899999999997 61.811 62.105699999999999 62.164200000000001 60. 947600000000001 60.346400000000003 59.284300000000002 59.736699999999999 60.095599999999997 60.873800000000003 60.897599999999997 61.366799999999998 61.6828 62.707099999999997 62.13 61.990499999999997 62.480499999999999 62.641399999999997 63.715000000000003 63.780900000000003 63.604500000000002 65.097099999999998 66.166700000000006 65.026200000000003 66.099999999999994 66.502300000000005 67.333200000000005 68.239500000000007 66.890900000000002 66.421899999999994 66.889499999999998 67.265500000000003 67.158000000000001 66.903499999999994 66.713800000000006 66.741500000000002 67.639499999999998 67.805199999999999 68.047399999999996 66.9726 65.800899999999999 64.536000000000001 64.419499999999999 64.4482 64.42400000000000 7 63.968299999999999 64.477500000000006 65.035700000000006 64.843500000000006 64.244500000000002 63.645200000000003 64.430000000000007 65.045500000000004 65.672899999999998 67.510000000000005 67.790000000000006 68.686700000000002 69.631699999999995 72.277900000000002 73.560900000000004 71.738

70.8331

00000000002 PPP 2000 Jan 2000 Feb 2000 Mar 2000 Apr 2000 May 2000 Jun 2000 Jul 2000 Aug 2000 Sep 2000 Oct 2000 Nov 2000 Dec 2001 Jan 2001 Feb 2001 Mar 2001 Apr 2001 May 2001 Jun 2001 Jul 2001 Aug 2001 Sep 2001 Oct 2001 Nov 2001 Dec 2002 Jan 2002 Feb 2002 Mar 2002 Apr 2002 May 2002 Jun 2002 Jul 2002 Aug 2002 Sep 2002 Oct 2002 Nov 2002 Dec 2003 Jan 2003 Feb 2003 Mar 2003 Apr 2003 May 2003 Jun 2003 Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2004 Jan 2004 Feb 2004 Mar 2004 Apr 2004 May 2004 Jun 2004 Jul 2004 Aug 2004 Sep 2004 Oct 2004 Nov 2004 Dec 2005 Jan 2005 Feb 2005 Mar 2005 Apr 2005 May 2005 Jun 2005 Jul 2005 Aug 2005 Sep 2005 Oct 2005 Nov 2005 Dec 2006 Jan 2006 Feb 2006 Mar 2006 Apr 2006 May 2006 Jun 2006 Jul 2006 Aug 2006 Sep 2006 Oct 2006 Nov 2006 Dec 2007 Jan 2007 Feb 2007 Mar 2007 Apr 2007 May 2007 Jun 2007 Jul 2007 Aug 2007 Sep 2007 Oct 2007 Nov 2007 Dec 2008 Jan 2008 Feb 2008 Mar 2008 Apr 2008 May 2008 Jun 2008 Jul 2008 Aug 2008 Sep 2008 Oct 2008 Nov 2008 Dec 2009 Jan 2009 Feb 2009 Mar 2009 Apr 2009 May 2009 Jun 2009 Jul 2009 Aug 2009 Sep 2009 Oct 2009 Nov 2009 Dec 2010 Jan 2010 Feb 2010 Mar 2010 Apr 2010 May 2010 Jun 2010 Jul 2010 Aug 2010 Sep 2010 Oct 2010 Nov 2010 Dec 2011 Jan 2011 Feb 2011 Mar 2011 Apr 2011 May 2011 Jun 2011 Jul 2011 Aug 2011 Sep 2011 Oct 2011 Nov 2011 Dec 2012 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 2012 Jun 2012 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2013 Jan 2013 Feb 2013 Mar 2013 Apr 2013 May 2013 Jun 2013 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2014 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 Jun 2014 Jul 2014 Aug 2014 Sep 2014 Oct 2014 Nov 2014 Dec 2015 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Jul 2015 Aug 2015 Sep 2015 Oct 2015 Nov 2015 Dec 2016 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Jun 2016 Jul 2016 Aug 2016 Sep 2016 Oct 2016 Nov 2016 Dec 2017 Jan 2017 Feb 2017 Mar 2017 Apr 2017 May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017 Oct 2017 Nov 2017 Dec 2018 Jan 2018 Feb 2018 Mar 2018 Apr 2018 May 2018 Jun 2018 Jul 2018 Aug 2018 Sep 2018 Oct 2018 Nov 2018 Dec 31.906388260216772 31.701285095628666 31.809068602688544 32.12102396642846 32.211151450140484 32.169658888882068 32.294235432916963 32.149092801756332 32.054616319509989 32.359670638686765 32.37588857790611 32.014590638421303 31.760902387612528 31.546297311723404 31.670723789124136 31.830009398354889 31.880501164518154 32.231914764622282 32.710313702805458 32.922259579915199 32.722491764 621729 33.026323096162066 33.327364770035302 33.134205457057483 32.937208304336281 32.811285671218855 32.859803820046963 32.783090061807009 32.956029229422093 33.216812424744617 33.491138013804679 33.606670587755595 33.620227363316978 33.684344589895993 33.766773399948093 33.366358861880549 33.151538745269825 33.039237696568001 33.189794669326552 33.727083170815845 33.850927846348071 34.019300391569246 34.181089637992372 33.897019155001473 33.787142269571973 34.094820437537258 34.144137108640351 33.916976995671291 33.90587957648129 33.833237092118182 33.760905211643319 33.706858938625743 33.829955525511949 33.969983561165776 34.265442758914325 34.57854416866487 34.535266717149675 34.551325060347359 34.323733726220397 34.062219564494356 34.407060655084948 34.198855277112528 34.074882212928273 34.228146722320751 34.116352725785653 34.228146722320751 34.596149902118505 34.512267966977753 34.16962716662858 34.495832636514677 34.986297874461293 34.796498790134912 34.524101074399894 34.50678708188515 34.454949144356036 34.57137000729999 34.755561732748717 35.242496578180052 35.336404267195384 35.180355376908452 35.638940050055062 36.370570950101957 36.352565716958345 36.155678359554834 36.09578530368411 36.239454776388868 35.770324007372984 35.944123917761651 36.075836170830222 36.271461844278498 36.764022468421118 37.031121809670474 36.874862610581012 37.037920499001139 36.749113469298344 36.642921748852174 36.517021827302344 36.700800733111357 37.111729499445644 37.296300186662194 37.3455711259437 37.224200379877502 37.752256164941954 38.337323923797825 38.568739452900729 39.436167736155078 40.146989335861903 40.206769229461784 40.378096935856959 40.231547062396139 40.271306232650048 40.774449837954144 40.985968899543707 41.186993340439571 43.084203188964047 43.477174437224868 43.661247112563728 44.064688745685103 44.716128896264685 44.958909766356904 45.727332128669168 45.238676079940632 45.22369038994438 45.213289500722539 45.768999999999998 46.320596515980348 47.297027678698797 47.2280103890973 47.41674641786949 47.7801792272234 47.922729511234976 48.517789208533117 49.145184262164328 48.205995648377709 47.957883227601997 47.9918365978452 48.096826842068857 48.611231407224672 49.510321671205595 49.610357838441246 50.26836 8153286389 50.489446680341544 50.650848712212657 50.129878658543525 50.24746111238246 50.393522728626834 50.793655280559918 51.718580674854621 52.078574306948511 52.627681110690929 53.62426468125625 53.817499700601026 53.812230861632344 54.16673320083239 54.507887002804864 54.764554161814146 55.15543579920169 55.354011785150774 55.759025923381387 56.374583330281638 56.849928738184033 57.461173068447813 58.341970112684358 58.698367452190581 58.923828864178965 59.634647887684885 60.01883712064015 58.875196744162267 58.241330080236153 58.42276355995115 58.548677718715673 59.173288367156019 59.54900170640925 59.957895970397985 61.351233766081137 61.604547853591967 61.600137837828278 61.612331774897633 61.728545648058912 61.919543255989133 62.570559350587331 62.186977900125605 62.229344927654743 62.679236531468916 62.950868091014172 63.492624089175628 63.879985115441087 64.129609596447722 64.757454319881347 65.412063178762082 65.590407413982859 65.402161303477996 65.397761432220207 65.011031174742399 65.09853543708175 65.609780415881403 66.401085310245008 66.688224133236417 67.415251194747583 66.807970597684786 66.443660160741871 66.506950369455836 66.175172088497618 65.49630720483627 64.988304522091013 64.940088278025371 65.233783493398988 65.626900628995543 65.887848688501293 66.302072120052387 67.428600417793689 67.179279225202919 66.873668547365043 67.296803516726655 67.327873081996032 66.706855851496826 66.908027455024197 66.546029734165344 66.511622893887235 66 .621075329722999 66.668361439853641 66.999288643225867 69.174137678509567 69.096691337904204 69.060773451450231 69.075318772744353 69.08461043613363 68.8659343134577

Deviation

Deviation 2000 Jan 2000 Feb 2000 Mar 2000 Apr 2000 May 2000 Jun 2000 Jul 2000 Aug 2000 Sep 2000 Oct 2000 Nov 2000 Dec 2001 Jan 2001 Feb 2001 Mar 2001 Apr 2001 May 2001 Jun 2001 Jul 2001 Aug 2001 Sep 2001 Oct 2001 Nov 2001 Dec 2002 Jan 2002 Feb 2002 Mar 2002 Apr 2002 May 2002 Jun 2002 Jul 2002 Aug 2002 Sep 2002 Oct 2002 Nov 2002 Dec 2003 Jan 2003 Feb 2003 Mar 2003 Apr 2003 May 2003 Jun 2003 Jul 2003 Aug 2003 Sep 2003 Oct 2003 Nov 2003 Dec 2004 Jan 2004 Feb 2004 Mar 2004 Apr 2004 May 2004 Jun 2004 Jul 2004 Aug 2004 Sep 2004 Oct 2004 Nov 2004 Dec 2005 Jan 2005 Feb 2005 Mar 2005 Apr 2005 May 2005 Jun 2005 Jul 2005 Aug 2005 Sep 2005 Oct 2005 Nov 2005 Dec 2006 Jan 2006 Feb 2006 Mar 2006 Apr 2006 May 2006 Jun 2006 Jul 2006 Aug 2006 Sep 2006 Oct 2006 Nov 2006 Dec 2007 Jan 2007 Feb 2007 Mar 2007 Apr 2007 May 2007 Jun 2007 Jul 2007 Aug 2007 Sep 2007 Oct 2007 Nov 2007 Dec 2008 Jan 2008 Feb 2008 Mar 2008 Apr 2008 May 2008 Jun 2008 Jul 2008 Aug 2008 Sep 2008 Oct 2008 Nov 2008 Dec 2009 Jan 2009 Feb 2009 Mar 2009 Apr 2009 May 2009 Jun 2009 Jul 2009 Aug 2009 Sep 2009 Oct 2009 Nov 2009 Dec 2010 Jan 2010 Feb 2010 Mar 2010 Apr 2010 May 2010 Jun 2010 Jul 2010 Aug 2010 Sep 2010 Oct 2010 Nov 2010 Dec 2011 Jan 2011 Feb 2011 Mar 2011 Apr 2011 May 2011 Jun 2011 Jul 2011 Aug 2011 Sep 2011 Oct 2011 Nov 2011 Dec 2012 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 2012 Jun 2012 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2013 Jan 2013 Feb 2013 Mar 2013 Apr 2013 May 2013 Jun 2013 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2014 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 Jun 2014 Jul 2014 Aug 2014 Sep 2014 Oct 2014 Nov 2014 Dec 2015 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Jul 2015 Aug 2015 Sep 2015 Oct 2015 Nov 2015 Dec 2016 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Jun 2016 Jul 2016 Aug 2016 Sep 2016 Oct 2016 Nov 2016 Dec 2017 Jan 2017 Feb 2017 Mar 2017 Apr 2017 May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017 Oct 2017 Nov 2017 Dec 2018 Jan 2018 Feb 2018 Mar 2018 Apr 2018 May 2018 Jun 2018 Jul 2018 Aug 2018 Sep 2018 Oct 2018 Nov 2018 Dec 11.634011739783226 11.901614904371332 11.762931397311458 11.500476033571537 11.777048549859515 12.490441111117931 12.468964567083034 13.522007198243671 13.779283680490011 13.958529361313232 14.40771142209389 14.731209361578699 14.775097612387473 14.968202688276595 14.938576210875862 14.925590601645112 15.018198835481844 14.740285235377719 14.411286297194543 14.186040420084801 14.866608235378273 14.970276903837934 14.656935229964695 14.770594542942519 15.333891695663716 15.853414328781149 15.873896179953036 16.125509938192991 16.046170770577909 15.746587575255383 15.296561986195321 14.996529412244406 14.824172636683024 14.678155410104004 14.484326600051908 14.809941138119449 14.786061254730178 14.700862303431997 14.474705330673444 13.676486829184157 13.247372153651931 12.727099608430755 12.064510362007631 12.046680844998527 12.059757730428025 11.29017956246274 11.34196289135965 11.647723004328711 11.526320423518712 11.402662907881819 11.293694788356682 10.113941061374256 11.29394447448805 11.500516438834225 11.731057241085672 11.725455831335132 11.325133282850324 9.3777749396526389 10.786866273779601 9.698480435505644 9.3280393449150552 9.4186447228874712 9.5639177870717305 9.4679532776792499 9.3365472742143467 9.3291532776792465 8.8939500978814934 7.4483320330222469 9.7069728333714238 10.23376736348532 10.64330212553871 10.89790120986509 9.827298925600104 9.7479129181148494 9.9155508556439642 10.229829992700012 10.498038267251282 10.651003421819951 11.048495732804618 11.310644623091548 10.48055994994494 9.067629049898045 8.458934283041657 8.4768216404451664 8.2104146963158868 7.8842452236111313 8.2439759926270142 6.3323760822383477 4.7947638291697814 4.4821091557215027 3.6569295315788821 3.8120031903295271 3.5135753894189889 2.5103405009988577 2.6900915307016575 2.8224532511478273 2.7505781726976579 2.9726992668886396 3.0334705005543583 2.6704998133378055 4.6563288740562996 5.5390996201224993 4.9504438350580457 4.5683760762021777 6.9612605470992719 9.1793322638449197 8.7047106641380978 8.3064307705382134 8.3214030641430412 9.0168529376038578 10.857793767349953 9.1910501620458547 7.5240311004562912 6.4866066595604295 5.2781968110359543 4.7654255627751354 4.6311528874362722 2.5877112543148968 1.8143711037353185 1.5683902336430933 0.16706787133082912 1.034523920059371 0.22720961005561691 -0.76928950072253599 0 0.17770348401965208 -0.53532767869879905 -0.7675103890972963 -1.5438464178694886 -3.4261792272234004 -2.9912295112349767 -3.4177892085331152 -3.770184262164328 -2.8264956483777084 -3.0435832276019994 -3.6908365978451982 -3.1944268420688573 -3.8003314072246752 -5.1143216712055946 -4.2968578384412481 -2.5778681532863885 -1.2874466803415459 2.7651287787342937E-2 2.2525213414564718 0.7540388876175399 -1.2123227286268374 -0.43015528055991581 -2.8580674854623567E-2 2.2528256930514914 3.31471888930907 1.8005353187437478 1.6760002993989715 0.53776913836765772 -1.0672332008323906 0.27661299719513721 -0.11755416181414802 -0.92643579920169117 -1.546111785150778 -1.3361259233813882 -2.0509833302816389 -1.8654287381840362 0.9223269315521847 1.4189298873156417 4.1125325478094155 4.7241711358210381 1.9712521123151134 2.4990628793598475 2.9358032558377332 3.8643699197638455 3.7414364400488509 2.3989222812843281 1.1731116328439839 -0.26470170640924806 -0.22119597039798577 -1.2556337660811394 -0.73074785359196426 -0.70253783782828094 -0.24553177489763556 -4.574564805891157E-2 0.78755674401086395 -0.44055935058732842 -0.19647790012560762 0.2511550723452558 -3.7836531468919077E-2 0.76413190898583139 0.28827591082437465 -0.27548511544108578 0.96749040355227578 1.4092456801186586 -0.38586317876207943 0.50959258601713486 1.1001386965220092 1.9354385677797978 3.2284688252576075 1.7923645629182516 0.81211958411859086 0.48841468975498969 0.57727586676358555 -0.25725119474758 174 9.552940231520779E-2 0.27013983925813534 0.23454963054416567 1.4643279115023802 2.3088927951637288 3.0590954779089827 2.0325117219746289 0.56711650660101043 -1.0909006289955414 -1.468348688501294 -1.8538721200523867 -3.0046004177936823 -3.2109792252029195 -2.3961685473650363 -2.2611035167266493 -2.4843730819960257 -2.4623558514968238 -3.262827455024194 -2.1160297341653376 -1.4661228938872313 -0.948175329723 0.84163856014636451 0.79071135677413906 -0.48743767850956488 0.53500866209579101 3.2171265485497713 4.4855812272556506 2.6533895638663694 1.9671656865423017

TRADABLES

Exchange Rate

PPP

1.00

1 0

1.28

0.65

70.8331

	Country		Local Price		Dollar Price		Value
of Van Shoes
	United States	£ 54.99	$ 54.99
	United Kingdom	£ 35.95	£ 28.17	-0.49
	India	रु 2,799.00	£ 39.52	50.90	-0.28

NON-TRADABLES

Country Local Price Exchange Rate Dollar Price PPP Value

United States

1.00

1 0

United Kingdom

1.28

0.55

India

70.83

of 2LT Fanta
£ 2.27	$ 2.27
£ 1.25	£ 0.98	-0.57
रु 95.00	£ 1.34	41.85	-0.41

PURCHASING POWER PARITY THEORY AND EXCHANGE RATE

1

Purchasing Power Parity Theory and

Exchange Rate

Student’s Name

Professor’s Name

Institution

Table of Contents

3

Purchasing Power Parity Theory and Exchange Rate

3

Overview of Purchasing Power Parity Theory

4

Purchasing Power Parity and the Law of One Price

5

Types of Purchasing Power Parity

6

Long Run Exchange Rate

7

Relationship between Purchasing Power Parity and Ongoing Inflation

8

Relationship between Purchasing Power Parity and Interest Rate

8

Strengths and Weaknesses of Purchasing Power Parity

9

Empirical Analysis of Purchasing Power Parity Theory

9

Relative Purchasing Power Parity

11

Absolute Purchasing Power Parity

12

Traded Goods

13

Non-Tradables

14

Factors Explaining the Problem with Purchasing Power Parity

14

Monopolies and Oligopolies

15

Price Measurement Levels

15

Consumption Patterns

15

Price Changes in the Long Run

16

Price Changes in the Short Run

16

Conclusion

17

Reference List

Purchasing Power Parity Theory and Exchange Rate

No nation is rich enough to rely on free gold standard. All countries across the globe have paper currencies that are not convertible into other valuable things including gold. Hence, nowadays nations have standard paper currencies, which complicate exchange situations. In such cases, the exchange rate(ER)between two currencies can be measured their purchasing powers. The purchasing power parity theory infers that the rate of exchange between two nations depends on their currencies’ relative purchasing power. In essence, the ER between two nations equals the ratio of their price levels. The purchasing power parity, thus, predicts that a decline in an economies domestic purchasing power due to an increase in domestic prices, will lead to a proportional depreciation of the currency in foreign exchange market (Paul, Kimata & Khan 2

0

17).

Conversely,

PPP

holds that an growth in the domestic PP of a currency will result in a proportional currency appreciation. For instance, if a certain good can be purchased for $1 in the

United States

and 60 rupees in

India

, the purchasing power of$1 in the United States equals to purchasing power of 60 rupees in India. If in the United States $1 can buy a collection of goods that cost 80 rupees in India, then the exchange rate will be $1 equals to 80 rupees. This report tests the validity of absolute purchasing power parity and relative purchasing power by comparing the prices of Van shoes and Fanta orange in the United States and India, and the exchange rates between the two countries.

Overview of Purchasing Power Parity Theory

Purchasing power parity theory, at its core, holds that the nominal exchange rate between different currencies is the same as the ratio of aggregate commodity price levels between the two nations. This way, the unit of currency of one nation has the same PP in another nation. Purchasing power parity has a long history, dating back many years ago (Pilbeam 2013). The primary idea behind the theory is that a unit of currency ought to buy the same basket of commodities in country A as the equivalent amount of foreign currency buy in country B. Hence, the one unit of currency across the two economies leads to parity in purchasing power.

The simplest means of determining existence of discrepancy in purchasing power parity is to compare the price of identical commodities from the basket in two different nations. For instance, the Economist newspaper normally compares the prices of MacDonaldBig Mac hamburgers around the globe with the United States dollars at prevailing market exchange rates. By doing so, it is easy to ascertain whether or the currency of country A is overvalued or undervalued against the United States’ currency at prevailing exchange rate. For instance, in July 2019, a Big Mac burger was selling at £3.29 in

United Kingdom

against $5.74 in the United States, implying an exchange rate of 0.57. The variance between the 0.57 and the actual exchange rate of 0.80, demonstrates an undervaluation of the British pound by 28.5 percent (Economist 2019).

Purchasing Power Parity and the Law of One Price

The purchasing power parity holds due to arbitrage of international commodities associated with the law of one price. This law asserts that the price of a globally traded commodity must remain the equivalent anywhere around the world as long as it is measured using a common currency. This is due to the fact that people to earn riskless profits by moving commodities from areas with low prices to areas with very high prices. If a similar commodity enters each economy’s market basket used to compute the aggregate price level, the law of one price infers that a purchasing power parity exchange rate must stay true between the two nations concerned (Lee & Yoon 2013). Proponents of purchasing power parity theory contend that the theory valid in the long run and does not need law of one price.

Even if the law does not hold for each individual good, proponents of the theory posit that prices(P) of goods and ER must not to deviate very much from the relation determined by PPP. When commodities are more expensive in one nation than in other countries the demand for its commodities and currency declines, which decreases both the domestic price and the ER to equate PPP. Conversely, low-priced domestic products lead to appreciation of currency as well as price level inflation. Purchase power parity, therefore, holds that whether or not the law of one price is untrue, the resultant economic factors will ultimately equalize the economy’s purchasing power in different nations (Krugman, Obstfeld, & Melitz 2012).

Critics of law of one price assert that the existence of transaction costs including transportation costs, tariffs and non-tariff barriers, as well as taxes, would invalidate the law of one price. In addition, some commodities are not traded between nations and different countries do not attach similar weights to same commodities in aggregate price indices. Moreover, different economies produce differentiated goods and services rather than substitutable products. Also, given that PPP is anchored on traded commodities, the law of one price can be effectively tested by using producer price indices containing the price of tradable goods instead of consumer price indices (Bahmani-Oskooee & Nasir 2015).

Types of Purchasing Power Parity

Notwithstanding the aforementioned objections, it is always held that the PPP theory of ER holds due to arbitrage with internationally manufactured goods. Commonly, PPP can hold in two ways Absolute Purchasing Power Parity (APPP) and Relative Purchase Parity (RPPP) (Liang 2013). Absolute purchasing power parity remains true when a unit currency’s purchasing power parity remains the same not only in the domestic economy, but also in foreign economy. This can only happen after conversion of the unit currency into the foreign currency at the current market exchange rate. Nonetheless, it is usually challenging to ascertain if the same basket of products can be found in different countries.

Hence, it is important to analyze relative purchasing power parity. Generally, this type of PPP assumes that a percentage change in exchange rate at a given time must balance inflation rate variations between the concerned economies over the same period of time. Generally, if APPP holds, the RPPP must also hold (Zhang & Bian 2015). However, APPP must not necessarily remain true if the RPPP remains true because it is common for nominal exchange rates variances to happen at different purchasing power levels for the two economies (Findreng 2014).

Long Run Exchange Rate

The theory of PPP, together with money demand and supply relationship, can result in a significant theory of the interaction between exchange rates and monetary factors. Since the factors that do not affect money supply and money demand do not impact this theory, this is usually known as the monetary approach to exchange rate. This approach helps in understanding the long run theory of exchange rates (Al-Gasaymeh & Kasem 2016). The monetary model to exchange rates is a long-run theory since it does not accommodate price rigidities. This is particularly important in understanding short run macro-economic developments. On the contrary, the monetary approach to exchange rate continues as though prices can adjust immediately to maintain not only purchasing power parity, but full employment as well (Abbas Ali, Johari & Haji Alias 2014).

To derive the monetary approach to ER predictions for dollar/rupee exchange rate, it is important to have an assumption that in the long-term, foreign market dictates the rate so that PPP holds.

Rupee (dollar/rupee) = Price (USA)/ Price (India)————–(i)

It is assumed that the above equation should hold if in the absence of market rigidities to avoid immediate adjustment of ER and prices to positions that are in congruence with full employment.

In the United States;

P (US) = Money supply in US/Long term aggregate money demand in the United States

While in India;

P (India) = Money supply in India/Long term aggregate money demand in India

The long term aggregate money demand falls with an increase in interest rates but also rises with an increase in real output.

Relationship between Purchasing Power Parity and Ongoing Inflation

Although a permanent rise in a nation’s level of monetary supply leads to an increase in its price levels, it does not pose any long run effects on interest rates and real output. Whereas a conceptual examination of a short run money supply change is important in determining the long run impacts of money, it is an unrealistic description of monetary policies. The reasoning is that constant growth in money supply will need an uninterrupted increase in price level or ongoing inflation. Other factors remaining constant, constant growth of money supply leads to ongoing inflation of price levels. Nonetheless, long-run changes in the rate of consumer price index do not affect the long run relative price of commodities or full employment output level (Saadon & Sussman 2018).

Link between Purchasing Power Parity and Interest Rate

Unlike ongoing inflation, there is a negative relationship between interest rate and long-run monetary supply growth rate. Whereas the long-run rate is independent of absolute monetary supply levels continuous increase in monetary supply affects interest rate (Taylor & Sarno 2004). The interest parity assumes that if people assume the RPPP to be true, the variance between interest rates provided by currency deposits of different countries must balance the difference between expected interest rates(EIR) existing between the two countries.

Strengths and Limitations of Purchasing Power Parity

The primary strength of PPP is that it always remains stable over long duration. The RPPP, in particular, proves ER should equal PPP in the long-run. The outcome can be attributed to a decline in inflation rates between two nations. It also corrects trade imbalances between a country’s imports and exports. There is need to erect trade hindrances which may distort markets. But, economists can easily correct the imbalance by observing the difference between a country’s purchasing power and strength of currency. Readjusting the currency of a country to equate PP can easily resolve the issue without the contribution of the government . Purchasing power parity also explains factors affecting balance of payment. It indicates that trade and payment between economies change due to variations in relative price(RP) levels of concerned economies. Thus, in the long run, ER are hinged on RP and changes in prices.

Nonetheless, conditions relating to prices and tariffs tend to change all the time hindering people’s ability to arrive at a stable conclusion regarding exchange rates. The purchasing power parity can only apply to a static world, but the world is dynamic. This is because, with time, the exchange rate will increase while price level continue decline leading to a situation where exchange rate is greater than price levels. Rose, Marquis and Lu (2012) indicate that internal prices and production costs are always changing. Hence, a new equilibrium between two different currencies changes on daily basis. Differences in two nation’s economic performance, especially in relation to transport and tariffs, can deviate normal exchange rates to certain levels from a currency’s intrinsic purchasing power. The exchange rate of a nation’s currency will rise while its price levels will remain constant if it decides to raise its tariffs. Shapiro (2014) indicates that PPP can only hold in the case of prices of commodities entering into foreign trade. It is illogical to apply the theory for general indices because of the lack of any relationship between domestic and international prices of products confined to domestic markets. The other limitation of the theory is that it does not take into account other balance of payments items instead of merchandise trade. This implies that the theory only works for current account transactions but not for capital account transactions.

Empirical Analysis of Purchasing Power Parity Theory

Overall, the absolute and relative purchasing power parity theories do not effectively explain the relationship between actual exchange rate data and price levels. Relative purchasing power parity, which is always considered a reasonable estimation to exchange rate data well for a given duration. An analysis of figure 1 demonstrates strengths of relative purchasing power parity by plotting the United States dollar against Indian rupee exchange rates, Erupee/$, and the ratio of India’s and the United States price levels, Pindia/Pus, between 2008 and 2018.Price levels are illustrated by indexes reported by both the Indian and the United States governments.

Relative Purchasing Power Parity

Relative purchase power parity predicts that Erupee/$ and the ratio of India’s and United States’ price levels will move proportionately, which is not the case. From 2008 to 2018, the Indian rupee gained significant strength against the United States dollar. In 2018, the exchange rate was

70.83

, which was equal to the purchasing power price. Between January 1, 2008 and July 1, 2015, the United States had higher rate of inflation than India which explains the devaluation of the United States currency against the Indian rupee. Nonetheless, the United States dollar started appreciating against Indian rupee between August 1, 2015 and December 31, 2018, due to a decline in the United States’ rate of inflation relative to India’s rate of inflation. This validates the relative purchasing power parity assertion that exchange rates and price levels in two economies should equal out after a given duration. As the rate of inflation continued to decrease from 1.74 on January 1, 2008 to December 1, 2018, relative purchasing power parity also decreased from 133.68 to 70.83.

Figure 1: the Indian rupee/United States dollar exchange rate and India-U.S price levels
between 2008 and 2018

In the long-run the difference between exchange rate and purchasing power parity declined from negative 94.41 on January 1, 2008 to 0.0 in December 31, 2018 (see figure 2). The deviation can be attributed to the increase in exchange rates as well as the decline in purchasing power parity between the United States and India. The United States/India exchange rate increased from 39.27 to 70.83 from the beginning of 2008 to December 31, 2018. Purchasing power parity, on the other hand, declined from 133.68 to 70.83 over the same time period. Overall, the data validates relative purchasing power parity’s assertion that, in the long-run, exchange rate between two countries must equal purchasing power parity. Even so, the theory can only apply partially even after taking into consideration the long run understanding of both purchasing power parity and exchange rate.

Figure 2: deviation between exchange rate and purchasing power parity between 2008
and 2018

Absolute Purchasing Power Parity

To test the effectiveness of APPP theory, there is need to compare international prices of a large number of baskets of commodities by having necessary adjustments for inter-country quality variations between the identified goods and services. These comparisons normally hold that APPP is ineffective. The prices of identical goods, when converted into one currency, tend to vary across different economies. According to Çağlayan and Filiztekin (2012), even the law of one price is ineffective in explaining the relationship between PPP and ER. For instance, manufactured commodities that tend to be the same in different countries normally sell at different prices. Since the assertion that leads to absolute purchase power parity theory is anchored on the law of one price, there is no doubt that PPP is incongruent to the data. To understand the impact of APPP, there is need to compares prices of traded and non-traded commodities in different economies.

Traded Goods

In order to assess the validity of APPP, the local price of Vans men’s Doheny shoes in India, the United States, and the United Kingdom were taken into consideration. This product has been chosen because a trader can export to or import it from one country and still earn some profit. The three countries have been chosen because they have different trade policies, taxation policies, and demand and supply levels. The Vans men’s Doheny shoes costs 2,799 rupees in India,

$54.99

in the United States, and

£35.95

in the United Kingdom (see table 1). When converted to the United States dollars, the product costs $28.17 in the United Kingdom and $39.52 in India. The prices are based on the December 31, 2018 exchange rates between dollar and Indian rupee which stood at 70.833, and the exchange rate between dollar and pound which stood at 1.2763.

The differences in the price of the products, when measured in United States dollars, invalidates the APPP’s assertion that the price of the same good in different economies ought to be equal when measured using a single currency. The difference in the price of Vans men’s Doheny shoes in India, the United Kingdom, and the United States can be attributed to cost of transportation and trade barriers. Regarding transportation cost, American traders must import the product from the other countries making the price of van shoes to cost more than in India and the United Kingdom. Tariffs can also affect the price imports, where the same commodity is cheaper in the India and the United Kingdom than in the United States. These factors sever the association between prices of goods and exchange rates inferred by the law of one price.

 

 

 

 

	Nation		Local Price		Exchange Rate		Dollar Price		PPP		Value
										of Van Shoes
	United States	$54.99		1.00	$ 54.99		1		0
	United Kingdom	£35.95		1.28	$ 28.17	0.65	-0.49
	India	रु2,799.00	70.8331	$ 39.52	50.90	-0.28

Table 1: Big Mac Index for Van Shoes

Non-Tradables

Just as it is for tradable goods, APPP is irrelevant for nontradable goods. For instance, whereas a two-liter Fanta orange drink costs

$2.27

in the United States, the same product costs

£1.25

in the United Kingdom and 95 rupees in India. When converted to the United States dollar at the December 31, 2018 exchange rates, the product costs $0.98 in the United Kingdom and $1.34 in India. In the real world it costs more to acquire a two liter bottle of Fanta orange drink in the United States than in India and the United Kingdom, which invalidates absolute purchasing power parity’s assertion.

The difference in prices of the same commodity in the three counties can be attributed to such items such as insurance cost, utility costs, as well as labor costs. Generally, the higher the utility cost the higher the cost of production. The same applies to insurance costs and labor costs. These can greater affect the price that producers of Fanta in the three different countries price their products. The price of Fanta costs more in the United States than in India and the United States because of high cost of production.

Nation

Local Price

Exchange Rate

Dollar Price

PPP

Value

 

 

 

 

 

United States

1.00

1

0

United Kingdom

1.28

India

of 2LT Fanta
$2.27	$ 2.27
£1.25	$ 0.98	0.55	-0.57
रु 95.00	70.83	$ 1.34	41.85	-0.41

Table 2: the Big Mac currency table for Fanta

Factors Behind the Problem with Purchasing Power Parity

There are several factors explaining the negative empirical outcome described above. First, Lee (2010) indicates that contrary to the law of one price assumptions, other factors such as trade barriers and transportation costs exist in trade. These factors may be high enough to hinder trading of goods and services between two countries. Monopolistic practices can also interact with trade tariffs and quotas to weaken the link between prices of similar commodities sold in different economies. Furthermore, since the inflation data reported between India and the United States are based on different baskets of commodities, ER variances cannot offset official inflation measures, even in the absence of trade barriers and tradable products.

Monopolies and Oligopolies

Existence of monopoly and oligopoly, and trade hindrances can weaken linkages between price levels of different countries. An extreme scenario happens when a single organization decides to charge different prices for a given good or services in different markets. This pricing to market mechanism may result in different demand levels in different nations. For instances, economies characterized with inelastic demand tend to charge higher markup prices over a monopolistic seller’s cost of production. Bastos, Ferreira and Arruda (2018) in an analysis of company level export data found strong evidence of pervasive pricing technique to manufacturing trade markets. In the present report, it costs more to buy a Van men’s shoes in the United States than in the United Kingdom and India, when the price of the product is measured using the United States dollar. This could be due to the fact that there are fewer traders of Vans men’s shoes which hinders the cogency of absolute PPP. Shifts in demand as well as market structures can violate relative purchasing power parity.

Price Measurement Levels

Different governments across the world have different measures of price levels. One of the primary reasons for the difference in measures of price levels is that residents of different countries always spend their incomes differently. Ghosh (2018) asserts that people tend to consume higher proportions of domestic products, both tradable goods and non-tradable, than foreign made products.

Consumption Patterns

An average Indian is, therefore, more likely to consume more Indian produced Fanta than her American counterpart while an American resident is more likely to consume American produced Fanta than an Indian. As a result it is likely that the Indian government is likely to have relatively high weight on Indian produced Fanta when constructing a commodity basket for measuring purchasing power.

Price Changes in the Long Run

The aforementioned factors associated with purchasing power parity’s poor empirical test performance can lead to divergence of national prices in the long run, after all prices have adjusted to their clearing levels. But many prices may take time to adjust fully and any PPP departures can be greater in the short run rather than in the long run (AbuDalu & Ahmed 2013). A depreciation of the Indian rupee against the United States, for instance, makes van men’s shoes in the country less costly relative to similar product produced in the United States.

Price Changes in the Short-Run

Short-run departures from PPP tend to disappear over time. Even when such temporary purchasing power parity departures are not taken into consideration, the cumulative impacts of some long run trends tend to cause predictable deviations from purchasing power parity for many economies. Choji and Sek (2017) associate this trend with the positive correlation between levels of prices and real income per capita. This is to say that, a pound, when converted into local currency such as Indian rupee at the current market exchange rate performs better in poor nations than in rich ones.

Conclusion

Purchasing power parity theory, especially the absolute one, holds that exchange rate between different currencies is the same as their price ratios, as measured by reference commodity basket prices. However, assessment of tradable and non-tradable goods invalidates the assertion that similar products ought to cost the same in different economies if measured using a common currency. This is attributed to various factors such as demand and supply, government policies, and transportation cost. Absolute purchasing power parity proposes another version of purchasing power parity theory, the relative purchasing power parity, which asserts that percentage changes in exchange rate is the same as the differences in inflation rates. Overall, relative purchasing power parity theory validates the notion that exchange rate should equal price level after a given period of time. Nonetheless, the theory can hold for only a given duration because with time the exchange rate should be greater than the purchasing power parity.

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So the calculation we did before where wrong as we had to choose a base year and we didn’t before so I had to do the calculation all again …

You have to show knowledge and understanding for this part ? for the history behind the numbers ? the graph what they mean ? you have to interpret the graph in a professional way

…

you have to the calculation ready so use the number in there when u explain .

Say that in this relative ppp im looking at monthly data from 2000-2018 and is monthly data and I will test the theory if hold or not

I chose May 2010 as A BASE YEAR YOU HAVE TO EXPLAIN WHAT HAPPENED IN 2010 in the economic one thing can be the global crisis . And that’s why I chose is as base year and find other relevant reasons .. and I did the calculation based on that .. my calculation are right so base you explanations on the numbers I have found please.

According to that you have to explain exchange rate and ppp before and after the base period may2010 …equilibrium .. what happened before the base year ?and what make it change after the base year ???

What is the relationship between exchange rate and ppp and how this explain the theory is the real words and data been explained as the theory stated ? yes (.you can say ppp holds because exchange rate converge (move forward)to ppp , even if we have some deviations ) they move In the same direction? the first graph what it shows ?

What happened to the prices of product before and after base year ?be critical

Then u have to explain why I had this deviation numbers and explain the deviation graph ( it can be because comparing developed and developing country >/ ? transportation cost and tariffs..and check all other reasons please we have to focus on explaining the deviation in the graph in details by referring them to the history behind that and critically discuss . talk about the main picks in the deviation graphs and what cause the deviation in what year that happened and why what happened at that time causing deviation ?

For example what happened in 2000 to have a dramatical decrease ,2006,2008, 2009, 2010, 2013 etc you have to focus on the main picks increase or decrease or constant ,?history behind data with references please .

The relationship between the deviation and the exchange rate and PPP?

…

And check why the deviation is like this ????

Explain in detail the graphs and the history behind that ?

HOW THE RELATIVE ppp explain the 2 graph ?

AND ADD ANY RELAVENT INFORMATIONS TO EXPLAIN THE 2 GRAPH THAT YOU SEE IS NEEDED IN THIS PART …..

And you have to check and change anything you have wrote that referes to the old calculations and change it to the new one and the new results