rafmacalaba/datause-encoder-data huggingface dataset:
growth incidence is upward sloping). Expenditure of the poorest 81 percentiles grew at a rate below the overall growth rate.

Table 3.4. Distribution Adjustment Factor

|Focal Parameter|1993-1996|1996-1999|1999-2002|
|---|---|---|---|
|1|-1.88|1.86|-1.02|
|2|-2.64|3.04|-1.64|
|3|-2.78|3.45|-1.82|
|4|-2.83|3.64|-1.89|
|5|-2.84|3.73|-1.91|
|6|-2.84|3.79|-1.91|

Source: Computed Table 3.4 shows the distribution correction factors associated with the rate of propoor growth defined by equation (2.18). These factors depend on the focal parameter ν and are computed as a weighted average of points on the relative growth incidence curve. These factors adjust the overall growth rate up or down according to the pattern of inequality and the degree of aversion to inequality. When the pattern of distributional shifts is favorable to the poor (as was the case during the crisis), the adjustment factor would be positive. Otherwise, it would be negative. After adjustment, we get the rates of pro-poor growth presented in table 3.5. When the focal parameter is equal to 1, the rate of pro-poor growth is equal to the average growth rate within the population (and not the growth rate of the mean). For instance, between 1993-1996, mean expenditure grew at a rate of 7.8 percent per year. The adjustment factor for ν **=1** , is equal to –1.88 percent. Hence the average growth rate was 5.92 (i.e. 7.8-1.88).

Finally, these results clearly demonstrate the difference between the relative and the absolute interpretation of the concept of pro-poor growth. As we noted earlier, the relative approach would declare growth pro-poor if the associated distributional shifts are favorable to the poor. Thus the relative incidence curve is an indicator of pro-poor 23
fast economic growth and 'formalization" of gray economies, most countries of group I may fail to raise a sufficient amount of revenue to sustain their current expenditure heights. This conclusion is reinforced by the aging trends of the population, which are likely to result in increasing pressures on social security expenditures. The implicit deficit and potential for fiscal crisis applies to several countries of group 2 as well; the difference being only that the governments of these have so far easier access to credit. Countries of group 3 show levels of the implicit deficit similar to the OECD countries (about 8 percent of GDP on average). Only group 4, except for the Kyrgyz Republic, which is the only country there with a high actual deficit, shows implicit surpluses, which are typical for middle-income developing countries.

Kazakhstan, Turkmenistan, as well as Lithuania, could be considered happy examples. They do not run excessive deficits and, yet, do not exhaust their revenue capacities above the predicted levels. However, these countries belong to the lowest spenders arnong the transition countries and, as we noted earlier, their governments have postponed spending on some of their current liabilities. Thus, they appear accumulating a hidden debt, to be repaid as soon as revenues recover. Moreover, if reforms and private initiatives are lacking in the social sectors, as is the case of Kazakhstan and Turkmenistan, a continuing financial constraint on government expenditure may annihilate their social security systems as well as pensioners.

Table **4: Index of Tax Effort and Actual and Predicted Deficits, General Government, 1993-94.**

||Tax Effort<br>Index|Predicted<br>Revenue<br>'|Actual<br>Revenue<br>.-|Predicted<br>Expenditure|Actual<br>Expenditure|Actual Deficit|Deficit for<br>Predict. Rev.<br>and Predict.<br>Expenditure|Deficit for<br>Predict. Rev.<br>and Actual<br>Expenditure|
|---|---|---|---|---|---|---|---|---|
|OECD|1.03<br>.|44.0|45.3|50.|50.8|_-5.5_|-6.4|-6.8|
|OECD w/o U.S.& Japan|1.07|43.9|46.8|52.1.|52.6|-5.8|-8.2|-8.7|
|WesternEurope|1.10|44.3|48.7|54.9|54.9|62|-10.6|-10.5|
|Transition 1<br>3|<br>1.43|22.3|31.8|39.3|50.1|-18.2|-17.0|-27.8|
|Transition2|1.33|31.7|42.5|46.|479|5.4|_-15.1_|-16.2|
|Transition3|1.18|25.5|30.2|46.1<br>.|33.3|-3.0|-20.6|-7.7|
|Transition4|0.77|25.0|19.3|25.6|23.1|-3.9|-0.6|1.8|
|Developing countries|_0.90_|271|244|26.4|261|17|07|1.0|

We can see another interesting 'Warning" when we consolidate our predictions for the expenditure and revenue, and analyze the predicted deficits (the second column from the right, in Tables 3 and 4). The largest predicted deficits surface not only for high spenders, such as Georgia, Ukraine, Belarus and 28
-1-

# CHAPTER I

# THE PURPOSE OF THE PRESENT STUDY AND PREVIOUS RESEARCH ON PURCHASING POWER AND PRODUCTIVITY

# The Two Basic Problems - Measuring Real Output and Purchasing Power

This study is concerned with the conceptual and measurement problems which arise in comparisons of levels of per capita output and productivity in different countries. The most direct way of doing this is to use the exchange rate to convert GDP in one country's prices into the prices of another country, and, in multicountry comparisons, to use some key currency, such as US dollars, as the num6raire. However, the essence of our pr-oblem is thet exchange rates do not indicate the average purchasing power of currencies over all goods and services, but mainly reflect their purchasing power over tradeable goods and services. Furthermore exchange rates are subject to fluctuation, and capital movements may play a major role in determining their level, so that even for tradeables, they may be substantially misleading as indicators of purchasing power. Hence the measurement of real output across countries is closely intertwined with the assessment of purchasing power.

# The Expenditure Approach to the Problem

Research on purchasing power parities (PPPs) to replace _xv,-ange rates has been under way for over three decades in international agencies concerned with burden sharing or with relative need for aid. Hence the early work of OEEC (1954, 1958, 1959) fcr Western countries, of Gosplan (1965) for the CMEA countries, and ECLA (1963) for Latin America. This kind of measure is also useful for analysing military or geopolitical power potential (see the CIA studies of Block (1981) and Schroeder and Edwards (1981); and the US Congress Joint Economic Committee studies (1981) and (1982) on Eastern Europe and the USSR).

Most of the above studies estimate purchasing power parities (PPPs) for final demand components (consumption, investment, etc.). The largest and most sustained scholarly effort using this "expenditure approach" has been the International Comparisons Project (ICP) of the United Nations. The results of the first four phases are published in Kravis, Kenessey, Heston and Summers (1975), Kravis, Heston and Summers (1978) and (1982), and UN (1986). ICP methods are now used on a regional basis by Eurostat (1983) and OECD (Ward, 1985).
economic contraction affects child survival is shorter than an annual frequency.<sup>23</sup> The empirical estimate of the mortality semi-elasticity with respect to GDP downturns will be mapped in the model to the increased mortality from lower consumption. However, the estimated coefficient may capture other mechanisms, such as lower availability or utilization of health services. Unfortunately, there is insufficient health system capacity time series data to isolate this effect empirically. The available health capacity data would be _de facto_ absorbed by the country fixed effects in estimation. Going further, we tested for heterogeneous semi-elasticities when stratifying by hospital capacity and found no statistically significant differences in the semi-elasticity estimates across countries with different levels of hospital capacity.

Further we believe it unlikely that the health capacity channel is an important one for the magnitude of the semi-elasticity estimate. This is because the semi-elasticity is estimated on past (pre-pandemic) data on “regular” economic downturns, rather than those brough on by pandemics. In a regular economic downturn, there is less reason to believe that the recession in and of itself leads the health system capacity to be overrun. To the extent that lower utilization of health services is responsible for part of the child mortality response in regular recessions, it is likely due to lower demand for health services by the households suffering negative income shocks, rather than sharp reductions in availability.

# **3.4 Demographic and Economic Parameters**

Country-specific age distributions are obtained from the 2020 World Population Prospects.<sup>24</sup> The age distribution is used to compute _s_ 0 _−_ 5, which is then used to rescale the semi-elasticity of under-5 mortality to the age group 0 - 15 using the formula in the previous section. In addition, we use the age distribution of the three age groups to compute the masses of the different age groups within the household ( _l_<sup>1</sup> _, l_<sup>2</sup> _, l_<sup>3</sup> ) in each country.

> The weekly discount factor equals to _β_ = 0 _._ 96<sup>1</sup><sup>_/_52</sup> to reflect an annual risk-free rate of 4 percent. We assume that at _t_ = 0, _ε_ = 0 _._ 1 percent of population is infected. We set

> 23Source: WHO fact sheet, https://www.who.int/news-room/fact-sheets/detail/newborns-reducingmortality

> 24population.un.org/wpp

28
|2010)<br>t<br>White-collar Employment<br>(5)<br>(6)<br>-0.018***<br>-0.004<br>(0.005)<br>(0.005)<br>0.911<br>0.910<br>81,250<br>81,229<br>-0.010*<br>0.0002<br>(0.006)<br>(0.005)<br>0.911<br>0.910<br>81,254<br>81,233<br>-0.063***<br>-0.010<br>(0.019)<br>(0.013)<br>81,250<br>81,229<br>**orking Age Pop.)**<br>0.288***<br>0.328***<br>(0.023)<br>(0.025)<br>151.57<br>446.21<br>82,738<br>82,719<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>No<br>Yes<br>s and i) armed attacks by illegal armed groups<br>nment transfers to health, education, and other<br>2000 GDP share in agriculture, services, and<br>vel are reported in parentheses. _∗∗∗_signifcant|
|---|
|y Type (2000-<br>Employmen<br>(4)<br>-0.003<br>(0.004)<br>0.919<br>79,804<br>0.001<br>(0.004)<br>0.919<br>79,808<br>-0.007<br>(0.011)<br>79,804<br>**IDP (% W**<br>0.328***<br>(0.025)<br>446.21<br>82,719<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>of year dummie<br>) central gover<br>s in 1995, viii)<br>rs at the frm le|
|abor Demand b<br>Blue-collar<br>(3)<br>-0.020***<br>(0.005)<br>0.919<br>79,823<br>-0.006<br>(0.005)<br>0.919<br>79,827<br>-0.068***<br>(0.017)<br>79,823<br>**ble: Share of**<br>0.288***<br>(0.023)<br>151.57<br>82,738<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>ude interactions<br>itures in 1995, v<br>collection offce<br>ed standard erro|
|Infows in L<br>oyment<br>(2)<br>-0.001<br>(0.004)<br>0.932<br>82,657<br>0.0003<br>(0.004)<br>0.932<br>82,661<br>-0.004<br>(0.010)<br>82,657<br>**Dep. Varia**<br>0.328***<br>(0.025)<br>446.21<br>82,719<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>al controls incl<br>) public expend<br>number of tax<br>n 1995. Cluster|
|**Table (VI)**<br>Impacts of IDP<br>**Dependent Variables (in logs)**<br>Empl<br>(1)<br>**Panel A. Reduced Form**<br>Predicted Infows<br>-0.016***<br>(0.004)<br>R-squared<br>0.932<br>Observations<br>82,676<br>**Panel B. OLS**<br>Share of IDP (% Working Age Pop.)<br>-0.008*<br>(0.005)<br>R-squared<br>0.932<br>Observations<br>82,680<br>**Panel C. 2SLS**<br>Share of IDP (%Working Age Pop.)<br>-0.055***<br>(0.016)<br>Observations<br>82,676<br>**Panel D. First Stage**<br>Predicted Infows<br>0.288***<br>(0.023)<br>First Stage F-statistic<br>151.57<br>Observations<br>82,738<br>**Controls (for all panels)**<br>Year FE<br>Yes<br>Firm FE<br>Yes<br>Year_×_Department FE<br>Yes<br>Homicides Rates<br>Yes<br>Confict Controls<br>Yes<br>Additional Controls<br>No<br>_Notes:_ Each coeffcient corresponds to a separate regression. Addition<br>in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv<br>expenditures in 1995, vi) number of fnancial institutions in 1995, vii)<br>industry, ix) municipal tax income in 1995, and x) night light density i<br>at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|

37
> I( y RFSEAR H WORKIN(G PAPFR 1521

# Summary findings

What, if anv, is the link between labor market policies that btenefit insiders -for example, regulationis guaranteeing high minimium wages and strict job security **-and** political regimes.

Is it true that in a democracy outsiders vote and impose limits on wvhat insiders cani achieve, whereas in a dictatorship the governimienit need woriryv only about insiders who have real power?

Or are demilocratic governments more likely to sticcumiib to trade unlioIn pressure and use labor policies to give them special privileges?

To test these competing hypotheses, Banerji and (Chanem designed a two-sector political economy model that demonstrates that labor market distortions depcnd directly on the trade regime: The more open the trade regime, the fewer distortions in the labor market.

Thev use cross-country regressions to test the relationship between political and civil liberties and trade and labor policies. Using data for 90 developing countries, they apply existing indices of openness and political freedom and twso different constructed measures of labor market distortion.

Their conclusion, based on the regression results: Authoritarian systems that repress lahor are more likely than democratic systems to adopt inefficient labor policies inimical to development.

This paper _-a_ prodILct of the Office of thc Vice President, Developmenit Economics - was prepared _as_ a backgrounid paper for _World Development Report 1995_ on labor. (Copies of the paper are available free from the World Bank, 1818 H Streer NW, Washington. DC' 20433. Please contact Hafez Ghalemlii, room F15- 16;. telephlione 202-458-5557, fax 202-676-0652, liternet address hghanc(ns.worldbank.org. October _1995._ (30 pages)

> _T 1he l'olicv Research Workinig Paper series dissemninates the finditigs of work in progress to encouragc the exchange of ideas about Ideuelopment issues. An ohbjecttie of the series is to get the findings_ **_out_** _quickliy. even if the presentations are less than fully polished. The_

> _papers carry the names of the aithbors and should be used and_ ci _ted accordingly. The findings, interpretations, and conclusions are thr aulhors owun and shldi not be attributed to the World Bank. its Lxecu,tive Board of l)irectors, or atny of its niember countries._

P'roduceLl by the Policy Research Disseniination Center
# **Figure 5: Medium-term cycle component**

<!-- Start of picture text --> Log scale<br>1.0<br> Energy Base metals<br> Precious metals Fertilizers<br> Annual agriculture Perennial agriculture<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text -->

# **Figure 6: Permanent shock component**

<!-- Start of picture text --> Weighted index, January 1970 = 100<br>180<br> Energy Base metals<br> Precious metals Fertilizers<br> Annual agriculture Perennial agriculture<br>140<br>100<br>60<br>20<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 25 —_
buys infrastructure bonds issued by some corporations. The BNDES is financed through a combination of retained earnings, foreign funding (including form bilateral and multilateral lenders) and government resources (Walsh, Park and Yu 2011).

54. Several developing countries have also started to tap into international financial markets to finance infrastructure projects.<sup>34</sup> International bond issuances are attractive for infrastructure investments, not only because they enable countries to augment domestic savings and broaden the investor based, but also because they have a back-loaded repayment profile. But they also entail significant risks, which include a reversal in the confidence of international investors, exchange rate exposure, high refinancing needs and potentially large costs-of-carry.

55. But the private sector’s role in infrastructure investments has not been limited to these types of financing. It often engages in infrastructure financing through public-private partnerships (PPPs), which are established through a long-term contract between a government and a private investor, bundling investment and service provision into a single long-term contract. The investor (usually a group of private investors) finances and manages the construction of the project, and maintains and operates it over the time of the contract (usually around 20 to 30 years), before transferring the assets to the government. During the operation, the investor receives a stream of payments (for example, through user fees or government payments) as a compensation (seem for example, Engel, Fischer and Galetovich 2010).

56. Governments in developing countries have been increasingly interested in attracting Private Public Partnerships (PPPs) for infrastructure investments. One source of information is the World Bank’s data base on Private Participation in Infrastructure (PPI)<sup>35</sup> which provides information on infrastructure investment commitments in which private parties assume operating risks. These commitments reached a record high of US$170 billion in 2010, but have been historically heavily concentrated in a few countries and in one sector, telecommunications. India has been the top recipient of private sector flows in infrastructure since 2006 (see Figure 6). Excluding the top recipients of private sector investments in infrastructure Brazil, China, India, Russia and Turkey, private investment commitments actually fell by around 30 percent between 2007 and 2010 as a

> 34 Senegal and Sri Lanka, for example, issue bonds for infrastructure projects.

> 35http://ppi.worldbank.org

26
Regarding education subsidies, we estimate the log of public expenditure per student as percent of GDP per capita in 1990, ln(�90); as a function of:

- the log of the high skilled emigration rate, ln(m90); so as to capture the policy trade-o¤ predicted by our theoretical model;

- the investment rate in 1990, I90; since investments in public education are mainly pro...table where there are machines to complement skills;

- the "rule of law" dimension of governance measuring the extent to which agents have con...dence in and abide the rules of society, RLAW . It ranges from -2.5 (bad governance) to 2.5 (good governance). We average the annual scores provided in Kaufmann et al. (2003);

- dummies for middle-income (MINC) countries based on the World Bank classi...cation;

- the level of remittances in 1990, R90; so as to capture the political demand for public transfers (as remittances are potential determinants of liquidity constraints);

- the same set of regional dummies, REGDi, as above.

Our speci...cations are: where �H and �� are error terms.

The potential correlation between unobserved characteristics and the emigration rate can a¤ect both the growth rate of human capital and level of public subsidies 15
We quantify this effect using the following illustrative calculation. The bottom panel of Figure 5 is based on the assumption that the household survey data comes from a simple random sample of n= 5000 observations, and a standard deviation of log income of σ= 0.74 , which corresponds to a population Gini coefficient of 40, which is the average observed value in our data. The variance of the growth rate of measured average income is the sum of the variance of true income growth, plus sampling variation, which under our assumptions is simply 2σ<sup>2</sup> /n .<sup>9</sup> Based on this we can arrive at an 0.74<sup>2</sup> estimate of the true standard deviation of income growth, which is 4.0 percent, i.e. −2 = �0.042<sup>2</sup> 2500 0.0396 , as opposed to the observed standard deviation which is 4.2 percent.

Similarly, the observed standard deviation of the growth rates of the _cumulative_ income shares of the bottom 10, 20 and 40 percent will reflect both true variation as well as well as sampling variation. Using the results in Appendix A, we can calculate the amount of sampling variation corresponding to our assumption of n= 50000 and σ= 0.74 , and subtract this from the observed variance in income shares to arrive at an estimate of their true standard deviations equal to 4.5 percent, 2.0 percent, and 1.6 percent for the income shares of the 10<sup>th</sup> , 20<sup>th</sup> and 40<sup>th</sup> percent (as opposed to the observed standard deviations of 5.2 percent, 3.4 percent, and 2.3 percent, respectively). Finally, we use these estimates of the true standard deviations of income growth and growth in income shares of the poorest to recalculate the share of the variance of growth in social welfare due to growth in average incomes.<sup>10</sup> This gives us larger variance shares, equal to 47 percent, 71 percent, and 86 percent for the income shares of the 10<sup>th</sup> , 20<sup>th</sup> and 40<sup>th</sup> percent (as opposed to the 41 percent, 61 percent, and 78 percent as reported in Table 2).

To sum up, we have seen that the share of the variance of growth in social welfare due to growth in average incomes is smaller for more bottom-sensitive social welfare functions. This mechanically reflects the fact that growth in income shares of lower deciles exhibits greater variability in our dataset than those of higher deciles. However, we have seen that some of this greater variability in income shares of the poorest may simply reflect measurement error due to sampling variation.

> 9 The growth rate of average income is the difference between the mean of log income in the first sample and the second sample.  The variance of mean log income is σ<sup>2</sup> /n , and since the two samples are drawn indpendently, the variance of the difference in means is 2σ<sup>2</sup> /n .

> 10 For this calculation, we assume that the true correlation between growth in the mean and changes in equality is the same as observed in the data.  Since these observed correlations are small this assumption has little effect on our conclusions.

18