rafmacalaba/datause-encoder-data huggingface dataset:
services and nonmarket services), and show that employment shares during the transition have generally moved towards benchmarks calculated from a sample of market economy comparators: in particular, the share of industry has fallen and the share of market services has risen in all TEs. These patterns are also evidenced in the relative growth rates of firm employment and jobs in the BEEPS surveys, but with a twist. The employment growth regressions for 1999, 2002 and 2005 show that employment in trade and services firms has grown consistently faster than in manufacturing firms in the EU8 countries.<sup>9</sup> The twist is that, for the lower-income TE country groups (SEE and CIS), the differential switches size and manufacturing firms grow as fast as services firms in 199699 and then faster than services firms in 1999-02 and 2002-05. When we look at aggregate employment growth (i.e., job growth), however, the pattern is different for TEs as a whole – net job growth is slower in manufacturing throughout the period – which is consistent with the findings by Raiser at al. (2004).<sup>10</sup> In short, we have evidence at the firm level of two different Kuznets-Chenery-type patterns. In the higher-income TEs, the lower rate of employment growth in manufacturing relative to services reflects primarily a convergence to market economy benchmarks driven by industrial sectors that were “too large” at the start of transition, and market services sectors that were “too small”. In the lower-income TEs, the observed pattern of relatively higher rates of employment growth in manufacturing relative to services is consistent with a bigger impact of the standard Kuznets-Chenery-type pattern in which, as a country develops and productivity growth, employment in manufacturing first increases and then decreases.

In sum, the picture painted by the BEEPS data is broadly consistent with both the basic macroeconomic trends in the region, and with previous sectoral and firm-level studies: following the “transformational recession” (Kornai 1994) of the mid-1990s, TEs have been growing, and at a faster rate than that observed in the developed market economies – convergence is under way. The pattern of growth at the country, sectoral and firm level

> 9 These and other results discussed in this paragraph are not shown in the paper, but are available on request from the authors.

> 10 The explanation for this contrast is as follows: Raiser et al. (2004) and other studies that have looked at structural change in this framework use shares of total employment, whereas the faster growth of manufacturing firms relative to services firms in the lower-income TEs that we report is based on firm level

22
Ferroni, F., and B. Mojon. 2014. “Domestic and Global Inflation,” Federal Reserve Bank of Cleveland, OH.

Förster, M., and P. Tillmann. 2014. “Reconsidering the International Comovement of Inflation.” Open Economies Review 25 (5): 841-63.

Gonçalves, C. E. S., and J. M. Salles. 2008. “Inflation Targeting in Emerging Economies: What Do the Data Say?” Journal of Development Economics 85 (1-2): 312-18.

Ha, J., M. A. Kose, and F. Ohnsorge. 2019. Inflation in Emerging and Developing Economies: Evolution, Drivers and Policies. Washington, DC: World Bank.

Ha, J., M. A. Kose, C. Otrok, and E. S. Prasad. 2017. “Global Macro-Financial Cycles and Spillovers.” Paper presented at the 18th Jacques Polak Annual Research Conference, International Monetary Fund, November 2-3, Washington, DC, November 2-3.

Hakkio, C. S. 2009. “Global Inflation Dynamics.” Research Working Papers 09-01, Federal Reserve Bank of Kansas City, Kansas City, MO.

Hartmann, P., and P. McAdam. 2018. “Price and Wage-Setting in Advanced Economies: TakeAways from the ECB’s 2018 Sintra Forum.” European Central Bank, Frankfurt am Main.

Henriksen, E. R., F. E. Kydland, and R. Šustek. 2013. “Globally Correlated Nominal Fluctuations.” Journal of Monetary Economics 60 (6): 613-31.

Huidrom, R., M. A. Kose, and F. Ohnsorge. 2017. “How Important Are Spillovers from Major Emerging Markets?” Policy Research Working Paper 8093, World Bank, Washington, DC.

IMF (International Monetary Fund). 2018. “House Price Synchronization: What Role for Financial Factors?” In Global Financial Stability Report. April 2018. Washington, DC: IMF.

Johnson, R. C. 2014. “Trade in Intermediate Inputs and Business Cycle Comovement.” American Economic Journal: Macroeconomics 6 (4): 39-83.

Kabukçuo ğ lu, A., and E. Martínez-García. 2018. “Inflation as a Global Phenomenon—Some Implications for Inflation Modeling and Forecasting.” Journal of Economic Dynamics and Control 87 (C): 46-73.

Karagedikli, Ö., H. Mumtaz, and M. Tanaka. 2010. “All Together Now: Do International Factors Explain Relative Price Comovements?” Working Paper 381, Bank of England, London.

Knight, G., and L. Johnson. 1997. Tradables: Developing Output and Price Measures for Australia’s Tradable and Non-Tradable Sectors. Canberra, Australia: Australian Bureau of Statistics.

Kose, M. A., C. Otrok, and E. Prasad. 2012. “Global Business Cycles: Convergence or Decoupling?” International Economic Review 53 (2): 511-38.

Kose, M. A., C. Otrok, and C. H. Whiteman. 2003. “International Business Cycles: World, Region, and Country-Speci fi c Factors.” American Economic Review 93 (4): 1216-39.

———. 2008. “Understanding the Evolution of World Business Cycles.” Journal of International Economics 75 (1): 110-30.

15
_Table A3: Decomposition of the evolution of poverty 2017/18 versus 2015_

|Region|P(z0,y0)|P(z1,y0)|P(z0,y1)|P(z1,y1)|Δ<br>total<br>expenditure<br>effect|Δ<br>poverty<br>line<br>effect|Total<br>change|
|---|---|---|---|---|---|---|---|
|Metropolitan|11.0%|11.0%|25.6%|25.3%|14.4%|-0.2%|14.3%|
|Lower Urban|9.6%|6.9%|17.4%|12.6%|6.8%|-3.8%|3.0%|
|Lower Rural|17.1%|11.8%|32.8%|25.3%|14.6%|-6.4%|8.2%|
|Upper Urban|23.9%|20.5%|31.6%|28.2%|7.7%|-3.4%|4.3%|
|Upper Rural|54.8%|51.7%|54.9%|51.4%|-0.1%|-3.3%|-3.4%|
|Borders|15.5%|10.9%|59.5%|40.7%|36.9%|-11.7%|25.2%|
|National|24.8%|21.6%|35.3%|31.0%|9.9%|-3.7%|6.2%|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18.

24
chain rule and depends on the benefit pass-through elasticity:<sup>40</sup> We can see that the response is a linear combination of two terms. The first is the increase in demand contributed by SNAP consumers. The second is the reduction in aggregate quantity demanded in response to the price rise.

In Section 5.4, we examine whether the magnitude estimated for the left-hand-side responses are comparable to what would be predicted by terms on the right-hand sides calibrated independently based on estimates from separate regressions reported in previous sections or from existing literature.

# **5.3 Incidence**

We now present the changes in consumer and producer surplus of the program. The change in consumer surplus can be derived from the construction of consumer surplus as the integral of the market demand curve, � _p∞_ ( _Q,b_ )<sup>_QD_(</sup><sup>_x, b_)</sup><sup>_dx_.Tomaketheanalysistractable,</sup> we additionally assume that the inverse aggregate-market-demand curve _p_ ( _Q, b_ ) shifts up by a constant amount in response to an increase in benefits.<sup>41</sup> The change can then be decomposed into that for SNAP consumers and non-SNAP consumers: For the change in producer surplus, we again start with perfect competition and then proceed to symmetric imperfect competition as before. Under perfect competition, Under symmetric imperfect competition, whereas the formula for<sup>_<u>dCS</u>_</sup> _db_<sup>remains unchanged,</sup>

> 40The derivation is given in equation (27) in Appendix Section K.

> 41That is, we assume the shift magnitude<sup>_∂p_</sup> _∂b_<sup><u>(</u></sup><sup>_x,b_</sup><sup><u>)</u></sup> is constant in _x_ , so that<sup>_<u>dCS</u>_</sup> _db_<sup>=</sup><sup>_−ρQ_+</sup> � _p∞ ∂Q_<sup>_D_</sup> _∂b_ <u>(</u> _x,b_ <u>)</u> _dx_ = _−ρQ_ + �0 _Q ∂p∂b_ <u>(</u> _x,b_ <u>)</u> _dx_ = _−ρQ_ +<sup>_∂_</sup> _∂b_<sup>_<u>p</u>Q_.SeeAppendixSectionKfordetails.</sup>

26
_What has been the marginal impact on African countries of one additional dollar in_ **_ODA_** _debt relief?_ Table 6 reports the results of panel data regressions of import **capacity on** total grants (pure grants plus ODA debt relief), for the sample three groups of countries. The results include a more restrictive model (A), that forces the relationship to **be the** same for the entire sample period, and less restrictive ones (B and C) that allow for a break-point in 1989, when ODA debt forgiveness began increasing.<sup>2 1</sup> It is important to note that rather than implying any causality running from the independent variable (grants) to the dependent variable (import capacity), as is the case in standard regression analysis, our results are intended to be interpreted just as simple correlations between the variables (for a methodological discussion see Appendix 2). Constants for all countries are not reported because they are of no interest to our analysis. Similarly, results for the less restrictive models are usually reported only when they differ from the results obtained under the more restrictive one.

Table **6. Impact of incremental total grants on import bill capacity**

||_(A)_<br>_Total period_|_(B)_<br>_First period_<br>_(1984-88)_|_(C)_<br>_Second period_<br> <br>_(1989-93)_|
|---|---|---|---|
|Low group (16 countries)|1.285<br>(4.916)******|stable|stable|
|Middle group (8 countries)|0.964|0.997|0.974|
||(5.913)******|(4.296)******|(5.626)*|
|High group (7 countries)|0.364<br>(1.880)******|0.523<br>(1.117)|0.400<br>(1.819)*|
|Nigeria|-15.087<br>(-1.456)<br>*****|||
|R<sup>2</sup>|0.83|0|.79|
|Adjusted R<sup>2</sup>|0.81|0|.76|
|IF-test|40.67*******|2|9.13***|

Note: t-statistics are in parentheses.

***:** Significant at I0 percent on one-tail test

****:** Significant at 5 percent on one-tail test

*****:** Marginal significance level of less than I percent

Two main conclusions emerge from Table 6.22 First, the middle and the low groups are not significantly different from each other from a statistical point of view. That is, in both cases a $1 increase in total grants has been accompanied by a $1 **increase** in import capacity (the results do not reject the null hypothesis that the coefficient is the same for both groups). Furthermore, for both groups this result is stable over time; for the low group the estimated coefficient is around 1.3 and significantly different from zero in

> **21** The results of the unrestricted models reported in the text refer only to the 31 countries that result **from excluding** Nigeria, which again in preliminary estimates and analysis was an outlier.

> A third conclusion that emerges from Table 6 is that Nigeria is a clear outlier. This **result, which repeated** consistently across several regressions, is illustrated by the plot of residuals shown in Appendix 4.

14
differences in the impact of price changes across households by imposing the same

# basket for all households.

Table 9: Percentage Changes in Inequality Indices between 1996 and 1999

||||based on|Per Capita<br>I|Real Expe<br>|nditures||||
|---|---|---|---|---|---|---|---|---|---|
|Inequality||Feb-96|||Feb-99||Perce|ntage Cha|nges|
|Measure|Urban|Rural|Total|Urban|I Rural|Total|Urban|Rural I|<br>Total|
||||Deflato<br>|r based on<br>|consumpti<br>|on of botto<br>|m 30%<br>|||
|Gini|0.37|0.28|0.36|0.33|0.25|0.32|-10.91|-10.44|-12.62|
|GE(-I)|0.24|0.13|0.22|0.19|0.11|0.17|-22.87|-19.26|-24.82|
|GE(0)|0.22|0.13|0.21|0.18|0.10|0.16|-21.28|-20.04|-23.93|
|GE(1)|0.26|0.15|0.26_|0.20|0.12|0.19|-23.16|-23.38|-26.60|
|GE(2)|0.45|0.25|0.48|0.32|0.17|0.31|-29.08|-29.80|-34.19|
|||||Househo<br>|ld specific<br>|deflator<br>||||
|Gini|0.37|0.28|0.36|0.35|0.27|0.33|-6.37|-6.03|-8.07|
|GE(-1)|0.24|0.13|0.22|0.21|0.12|0.19|-13.58|-10.50|-15.89|
|GE(0)|0.22|0.13|0.21|0.20|0.12|0.18|-12.73|-11.79|-15.61|
|GE(1)|0.26|0.15|0.26|0.23|0.13|0.22|-13.89|-14.54|-17.55|
|GE(2)|0.45|0.25|0.48|0.37|0.21|0.37|-16.16|-16.66|-21.47|

The _GE(a)_ indices offer the advantage of being more sensitive to differences in different parts of the expenditures distribution depending on the value of the sensitivity parameter a. The larger a _is,_ the more sensitive GE(a) is to consumption differences at the _top_ of the distribution. On the other hand, the more negative a is, the more sensitive GE(a) is to consumption differences at the bottom of the distribution. 17 The Generalized Entropy index GE(a) is given by the expression: GE(0) equals the standard deviation of ]ln(PCE), GE(1) is the Theil index of inequality, and GE(2) is half the square of the coefficient of variation of ln(PCE). For more details on this and other inequality indices, see Cowell (1995).

31
studies is henceforth likely to be determined by the fact that immigrants from Mexico form a large share of the overall US immigrant population. Nearly 32 percent of immigrants in my sample are Mexicans. Taking the unweighted average across the 93 countries in the sample leads to a return to home country experience of 3.1 percent with a standard deviation of 3.7.

Returns to home country experience change with years of experience. If a country with a higher log earnings experience slope �1j has a larger coe¢ - cient on the curvature of the log earnings experience pro...le in absolute value then the ranking in returns to experience of two countries may be reversed when evaluating returns at di¤erent years of experience.<sup>15</sup> Illustrating this fact ...gure 2 shows that the di¤erence between returns to experience of immigrants from Korea and Taiwan, for example, increases with years of home country experience, while the contrary holds for Belgium and Portugal. For the analysis below, I use ...ve years of labor market experience as the year at which to evaluate returns to home country experience experience.<sup>16</sup> The second step of the estimation strategy consists of regressing estimated returns to home country experience of US immigrants on home country openness in order to understand how trade relates to on-the-job accumulation of human capital. Table 5 presents the results for three di¤erent samples: all countries, non-Oil countries and non-OECD/non-Oil countries. The dependent variable is the estimated return to home country experience evaluated at ...ve years of labor market experience in percent. As in section 4, the statistic of openness is the natural logarith of the mean of Open, where the mean is calculated from 1970 to 1980 in order to control for short-term ‡uctuation.<sup>17</sup> Regressing estimated returns to home country experience on openness yields a positive and signi...cant coe¢ cient, as shown in speci...cation (I) of Table 5: The coe¢ cient amounts to 1.290 when using the entire sample and reduces slightly, when dropping the oil exporting countries. Increasing Open from 0.2 to 0.3 raises the annual return to home country experience by 0.5 percentage points in the non-Oil sample. Given the low returns to home country experience of US immigrants, this e¤ect is quite large.

Restricting the sample to non-OECD countries depresses the e¤ect of openness on returns to experience. Still it remains positive and signi...cant.<sup>18</sup>

> 15 This may arise, for example, if the introduction of new technologies in a country induces young workers to invest more in on-the-job learning, but makes knowledge of older workers obsolete at a faster rate.

> 16 Results for other years of experience are available upon request.

> 17 A detailed description of variables can be found in the Data Appendix.

> 18 Standard errors are calculated using the White estimator in order to correct for het-

11
is shown in Equation (6). Positive components (errors) ������� imply that domestic prices are temporarily above equilibrium, and that the processed adjustment implies a price decrease. Negative components (errors) ������� imply the converse.

This strategy was followed for all goods. We focus on series that appear to be cointegrated with the international prices or show to be stationary, otherwise any relation between local and international prices could be spurious. In order to evaluate this, we perform the Augmented Dickey-Fuller GLS test, which is better suited for short samples (in our case, at most series can be 144 observations long). In the descriptive statistics, we present passthrough indicators at city level, while in the regressions with the control variables we use the city-outlet level indicators.

# DESCRIPTIVE STATISTICS

We describe and analyze how markets function in two ways. First, we use heat maps that characterize how price indicators behave for each good and city across the time series. Heat maps provide a visual idea of which goods and in which cities prices have a tendency toward higher efficiency. In the following section, the results of fixed effects regressions provide information on which characteristics play an important role in the way markets work.

However, it is important first to understand the macroeconomic environment in which Mexico experienced during the period of study. Figure 2 indicates the evolution of prices, GDP, exchange rates and unemployment rates; low and falling CPI and exchange rates, a modestly falling GDP rate of growth and a very modestly falling unemployment rate.

Figure 2. Macroeconomic indicators of Mexico during the period of study <!-- Start of picture text --> a) Inflation and Exchange rate b) GDP growth and Unemployment<br>6.0 18.0 7.0<br>GDP - annual growth rate<br>Unemployment rate - %<br>17.0<br>6.0<br>5.0<br>16.0<br>5.0<br>4.0<br>15.0<br>4.0<br>3.0 14.0<br>3.0<br>13.0<br>2.0<br>2.0<br>12.0<br>1.0 CPI - annual growth rate 11.0 1.0<br>Exchange rate - MXN/USD (right)<br>0.0 10.0 0.0<br>Jan-2010 Jul-2010 Jan-2011 Jul-2011 Jan-2012 Jul-2012 Jan-2013 Jul-2013 Jan-2014 Jul-2014 Jan-2015 Jul-2015 Q1 2010 Q3 2010 Q1 2011 Q3 2011 Q1 2012 Q3 2012 Q1 2013 Q3 2013 Q1 2014 Q3 2014 Q1 2015 Q3 2015<br><!-- End of picture text --> Source: INEGI, Bank of Mexico.

Source: INEGI.

18
# **School Inputs, Household Substitution, and Test Scores**

Jishnu Das<sup>*</sup> Stefan Dercon James Habyarimana Pramila Krishnan Karthik Muralidharan Venkatesh Sundararaman _JEL Classification_ : H52, I21, O15 _Keywords_ : school grants, school inputs, household substitution, education in developing countries, randomized experiment, India, Zambia, Africa, education production function

* Jishnu Das (World Bank and Center for Policy Research, Delhi: jdas1@worldbank.org ) Stefan Dercon (Oxford University, BREAD, and CEPR: stefan.dercon@economics.ox.ac.uk ) James Habyarimana (Georgetown University, IZA, and Center for Global Development: jph35@georgetown.edu ) Pramila Krishnan (Cambridge University and CEPR: pk237@cam.ac.uk ) Karthik Muralidharan (UC San Diego, NBER, BREAD, and J-PAL: kamurali@ucsd.edu) Venkatesh Sundararaman (World Bank: vsundararaman@worldbank.org)

We thank Julie Cullen, Gordon Dahl, Roger Gordon, Gordon Hanson, Hanan Jacoby and several seminar participants for comments. The World Bank and the UK Department for International Development (DFID) provided financial support for both the Zambia and India components of this paper. The experiment in India is part of a larger project known as the Andhra Pradesh Randomized Evaluation Study (AP RESt), which is a partnership between the Government of Andhra Pradesh, the Azim Premji Foundation, and the World Bank. We thank Dileep Ranjekar, Amit Dar, Samuel C. Carlson, and officials of the Department of School Education in Andhra Pradesh for their continuous support. We are especially grateful to DD Karopady, M Srinivasa Rao, and staff of the Azim Premji Foundation for their leadership in implementing the project in Andhra Pradesh. Vinayak Alladi provided excellent research assistance. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent.
4 The meaning of z* can be understood as follows. Consider countries with "typical" values of the non-income factors (E-O), and with A > z*. A resident with the average income in such a country is not poor by the standards of .nat country. Or, equivalently, if the average income could be equally distributed, nobody would be poor in that country. Consider instead a resident with the mean income of some country with A < z ; that person is poor - by the standards of that country if the average income could be equally distributed everyone would be poor. For cross-country comparisons, z* is thus one possible candidate for an absolute poverty line, interpreted as the level of income below which a "typical" citizen of a country would be deemed "poor"f by the standards of that country.1 In the empirical work we will quantify each of these approaches to the definition of an absolute poverty line.

# 3. An International Comparison of Poverty Lines

Many countries now have reasonably well established (though rarely uncontentious) local poverty lines. From a wide range of sources within and outside the World Bank, we have compiled the local poverty lines for 33 countries, both developing and developed. These should not be considered as "official" poverty lines, either of the governments or the Bank. Many are the estimates of independent researchers. Nor has our survey been exhaustive; there are undoubtedly credible poverty lines we do not know about. When more than one pove;ty line was found (such as for urban and rural areas), the lowest was used. Appendix 1 gives the results and sources.