rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> c. Progressivity of direct transfers and indirect subsidies<br>(Kakwani Index)<br><!-- End of picture text --> <!-- Start of picture text --> All indirect subsidies -0.08<br>Agriculture 0.44<br>Water -0.26<br>All direct transfers 1.03<br>NUSAF 1.23<br>UWEP 1.15<br>YLP 1.18<br>SAGE 0.40<br>-0.50 0.00 0.50 1.00 1.50<br>Kakwani Index<br><!-- End of picture text --> <!-- Start of picture text --> d. Redistributive and Poverty Reduction Effects direct<br>transfers and indirect subsidies<br>0.4<br>0.32<br>0.3 0.26<br>0.2<br>0.10<br>0.07 0.10 0.07<br>0.1 0.01 0.03 0.01 0.06 0.07<br>0.01 0.01 0.01<br>0.0<br>-0.03<br>-0.1<br>-0.10<br>-0.2<br>Redistributive Effect<br>Poverty Reduction Effect<br>SAGE YLP UWEP NUSAF All direct transfers Water Agriculture All indirect subsidies<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ _Note for d: A positive redistributive effect means inequality declines, while a negative redistributive effect means inequality increases (in Gini points). A positive poverty reducing effects means poverty is declining while a negative poverty reducing effect means poverty is increasing (in percentage points)._

# 5.2.3. In-kind Transfers

Except for the tertiary education transfer and, to a lesser extent, the technical education transfer, all inkind transfers are concentrated relatively evenly (Figure 11a). Taken together, all in-kind transfers are progressively distributed with a Kakwani index of 0.16 (Figure 11b). Primary education and primary health are the most progressively distributed. Given that the primary education transfer is a large proportion of households’ income, its marginal contribution to equality is sizeable at around 1.24 Gini points. This effect on redistribution is partially offset by the tertiary and technical transfers and, overall, the marginal contribution of the net education transfers to inequality reduction is 0.95 Gini points. Health transfers are small in size and their marginal contribution to reducing inequality is small, around 0.09 Gini points. We lack data on the distribution of _quality_ in the public education and health systems. However, studies in the Africa region and elsewhere have demonstrated a marked variability in the quality of such public services as well as a tendency for lower-quality public services to be produced in areas with higher concentrations of poor individuals.<sup>24</sup> That is to say, including a variable quality dimension to our analysis of in-kind transfers might significantly alter the estimated impact of these services on inequality.<sup>25</sup>

> 24 World Bank (2016) shows that in the case of education and health, more and better inputs seem to be available in better-off locations (e.g. lower number of pupils per teacher or lower number of pupils per classroom). Similarly, World Bank (2019c) points to an overall low quality of education in Uganda, and highlights that quality varies across urban and rural areas as well as regions.

> 25 Following standard CEQ methodology, we only analyze the effect of in-kind transfers on inequality.

32
brought out sharply where, despite a remarkably weaker growth of the former, the slope of the line representing the latter is much steeper in the second period than that in the first period. An even weaker growth of the agricultural sector further highlights this difference.

Despite the various arguments for or against this swift fall in poverty, one simple but perhaps not unreasonable hypothesis is that the questionnaire design of the consumption module in the 2011/12 (68<sup>th</sup> ) round of the NSS is not comparable to that in the 2009/10 (66<sup>th</sup> ) round (and 2004/05 or 61<sup>th</sup> round), which in turn leads to inconsistently constructed and incomparable consumption data. Indeed, there are several major changes to the questionnaire in the 68<sup>th</sup> round that include: i) changing the consumption code, ii) aggregating consumption items in broader groups, iii) disaggregating consumption items in smaller groups, iv) using/ providing somewhat different item names, v) dropping some consumption items in previous rounds, and vi) adding new consumption items. These changes may not be harmless in affecting the comparability of the consumption data over time.<sup>8</sup> To further investigate whether these changes may lead to different consumption aggregates over time, we explore the raw item-by-item consumption data at the household level and examine a variety of alternative consumption aggregations over time. The results shown in Appendix 1, Table 1.1 confirm that these questionnaire revisions could be a source of concern. While most consumption groupings make up rather similar shares in total household consumption, the share of the items with some change in code (grouping number 2) are two percentage points lower, and the share of the new items added in the 68<sup>th</sup> round are three percentage points higher than those

> 8 We use data from Type 1 Schedule for all survey rounds. An Excel file that provides a comparison and detailed tracking of the change to each consumption item for the 61<sup>th</sup> , 66<sup>th</sup> , and 68<sup>th</sup> rounds of the NSS are available upon request. Survey design issues that compromise the comparability of poverty estimates are found in various countries such as China (Gibson, Huang, and Rozelle, 2003), Tanzania (Beegle et al., 2012), and Vietnam (World Bank, 2012). See also Deaton and Grosh (2000) and Crossley and Winter (forthcoming) for general reviews on the influence of survey design on the quality of consumption data in developing and richer countries respectively.

7
# **IN PURSUIT OF BALANCE: RANDOMIZATION IN PRACTICE IN DEVELOPMENT FIELD EXPERIMENTS**<sup>**#**</sup>

Miriam Bruhn, _World Bank_ David McKenzie, _World Bank, BREAD, CReAM and IZA_ _Keywords:_ Randomized experiment; Program evaluation; Development. _JEL codes:_ C93, O12.

> # We thank the leading researchers in development field experiments who participated in our short survey, as well as colleagues who have shared their experiences with implementing randomization. We thank Esther Duflo, David Evans, Xavier Gine, Guido Imbens, Ben Olken and seminar participants at the World Bank for helpful comments, We are also grateful to Radu Ban for sharing his pair-wise matching Stata code, Jishnu Das for the LEAPS data, and to Kathleen Beegle and Kristen Himelein for providing us with their constructed IFLS data.. All views are of course our own.

- 1 -
repeat this residual bootstrap 1,000 times and the standard deviation of the estimate, which functions as our estimate of the standard error.

Importantly, the drawing of observations from the vectors of residuals is done by weighting based on aggregate sampling weights at the subarea and area level. As such, we are taking into account the fact that sampling is informative and some (sub) areas have a higher probability of inclusion than others. Since only in-sample (sub)areas are included in the calculation of the residual bootstrap vector, this method works when only a household survey is available.

The proposed bootstrap consists of the following steps:

1. Predict an outcome (asset index or poverty) using XGBoost, BRF, or Cubist.

2. Calculate subarea residuals for in-sample subareas by differencing the prediction and the direct estimate from the survey. Call this vector of residuals _R_<sup>ˆ</sup> _sa_ = _y_ ˆ _sa_<sup>_ML_</sup> _− y_ ˆ _sa_<sup>_direct_</sup> .

3. Aggregate predictions to the area level, using estimated population from WorldPop as weights.

4. Calculate area residuals for in-sample subareas by differencing the prediction and the direct estimate from the survey. Call this vector of residuals _R_<sup>ˆ</sup> _a_ = _y_ ˆ _a_<sup>_ML_</sup> _− y_ ˆ _a_<sup>_direct_</sup> .

5. With original subarea predictions, bootstrap with replacement from _R_<sup>ˆ</sup> _sa_ for all subareas, with sampling probability determined through aggregated sampling weights.

6. Aggregate these new predictions to the area level.

7. Bootstrap with replacement from _R_<sup>ˆ</sup> _a_ for all areas, with sampling probability determined through aggregated samplign weights.

8. Repeat steps five through seven 1,000 times, saving new area estimates after each replication.

9. Calculate percentiles across the 1,000 replications.

Although residuals can only be calculated for sampled sub-areas and areas, these are used to generate uncertainty estimates for both samples and non-sampled areas meaning that, unlike EBP – which conditions the random effect on the sample for in-sample areas – estimated confidence intervals are approximately equal in size for in-sample and out-of-sample areas Importantly, the poverty rate is a variable bounded by zero below and by one above. In order to respect these restrictions throughout the process, we do not estimate the poverty rate in levels. Instead, we estimate an arcsin (square root) transformed poverty rate: _p_<sup>_transformed_</sup> _sa_ = _sin_<sup>_−_1</sup> (<sup>_√_</sup> _<u>psa</u>_ ). We carry over this transformation 14
Maloney, Tim (1987) "Employment Constraints and the Labor Supply of Married Women: A Reexamination of the Added Worker Effect" _The Journal of Human Resources_ 22(1), 51-61.

Maloney, William (1998) _The Structure of Labor Markets in Developing Countries: Time Series Evidence on Competing Views._ Policy Research Working Paper #1940 (Washington DC: The World Bank).

Marquez, Gustavo (1994) _Regulacion del Mercado de Trabajo en America Latina_ (Chile: CINDE).

Mincer, Jacob (1962) "Labor Force Participation of Married Women" in _Aspects of Labor Economics_ H. Greg Lewis, ed., Universities NBER Conference Series no. 14 (Princeton Univ. Press: Princeton, New Jersey).

Nakamura, Alice and Masao Nakamura (1992) "The Econometrics of Female Labor Supply and Children" _Econometric Reviews_ 11(1), 1-71.

Pradham, Menno and Arthur Van Soest (1997) "Household Labor Supply in Urban Areas of Bolivia" _Review of Economics and Statistics_ 79(2), 300-311.

Psacharopoulos, George and Zafiris Tzannatos (1992) _Women's Employment and Pay in Latin America: Overview and Methodology_ (Washington, DC: The World Bank).

Ros, Jaime et. al. (1996) "Prospects for Growth and the Environment in Mexico in the 1990s" _World Development_ 24(2),307-323.

Smith, James P. (1983) _Female Labor Supply: Theory and Estimation_ (Princeton, NJ: Princeton University Press).

Stiglitz, J.E. (1986) "Alternative Theories of Wage Determination and Unemployment in LDCs: The Labor Turnover Model," _Quarterly Journal of Economics_ 88, 194-227.

Standing, Guy (1989) "Global Feminization through Flexible Labor" _World Development_ 17(7), 1077-1096.

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Valletta, Robert G. (1993) "Union Effects on Municipal Employment and Wages: A Longitudinal Approach" _Journal of Labour Economics_ 11 (3).

48
<!-- Start of picture text -->
1.00<br>G<br>2007 G<br>G<br>0.75<br>2010 G<br>0.50<br>Displacement<br>0.2 0.5 1.0 2.0 5.0<br>G Ever Displaced<br>0.25<br>2007 G<br>0.00<br>2006 2007 2008 2009 2010 2011 2010 G<br>Year<br>G % Ever Displaced<br>Variable % Good/Very Good Relationship with Community<br>% Has Ability to Contribute to Events in Community 0.2 0.5 1.0 2.0 5.0<br>Odds ratio (log scale)<br>(a) General Trends in Uganda Data (b) Temporal Variation in Relationships<br>Relationship with Community<br>Proportion<br>Year<br>Contribute to Events in Community<br><!-- End of picture text -->

Figure 3: Uganda Temporal Analysis communities. When we re-sampled the same area in 2010, experience with displacement became positively correlated with perceptions of being able contribute to the communities. In a span of three years, therefore, the relationship between experience with displacement and perception of empowerment within their community flips when sampling the same area.

While there is some risk of conflating group attachment with empowerment as northern Uganda has high community identification/attachment in general (as highlighted by Figure 8), we focus on an explicit measure of perceived empowerment. We direct ask respondents whether they perceive themselves as able to contribute to their communities in the bottom graph of Figure 3b. We do not believe that the variation in responses that we highlight – the change between survey waves – is likely driven by a community pressure mechanism that changes over time. Instead, we find it much more plausible that changing dynamics in the conflict and thus the experience of displacement (in particular the return of IDPs from camps to their communities between 2007 and 2010) changed perceptions of empowerment.

18
fades away when diverse social groups are introduced. However, since non-Urdu speakers constitute a relatively small share of the sample, some covariates were imbalanced at conventional significance levels and therefore the results for non-Urdu speakers should be interpreted with caution.<sup>17</sup> Table 4. Treatment Effects on Perceptions Toward Outgroups

|VARIABLES|(1)<br>Index Ste<br>(z-sc|(2)<br>reotypes<br>ore)|(3)<br>Et|(4)<br>hnicity<br>(=1)|(5)|(6)<br>Sex<br>(=1)|
|---|---|---|---|---|---|---|
|[T1] Urban Design|-0.02|-0.00|-0.01|-0.00|-0.02|-0.01|
||(0.057)|(0.054)|(0.028)|(0.027)|(0.029)|(0.029)|
|[T2] Diversity|-0.07|-0.05|-0.02|-0.01|-0.08***|-0.07***|
||(0.058)|(0.055)|(0.028)|(0.027)|(0.029)|(0.028)|
|[T3] Urban Design + Diversity|-0.05|-0.06|-0.02|-0.03|-0.04|-0.05|
||(0.058)|(0.055)|(0.028)|(0.027)|(0.029)|(0.028)|
|Observations|1,924|1,924|1,924|1,924|1,924|1,924|
|Control Mean|0.00|0.00|0.31|0.31|0.38|0.38|
|Control Mean (No z-score)|0.35|0.35|||||
|F-test T1=T2 (p-value)|0.41|0.30|0.78|0.67|0.04|0.03|
|F-test T1=T3 (p-value)|0.63|0.22|0.70|0.30|0.47|0.24|
|F-test T2=T3 (p-value)|0.74|0.86|0.92|0.54|0.17|0.31|
|Selected Controls||X||X||X|

Notes: Specifications in odd-numbered columns include experiment location fixed effects while even-numbered columns include all the covariates selected. Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

Table 4.A. Subgroup Analysis: Perceptions Toward Outgroups (by ethnicity)

|VARIABLES<br>Index Stereotypes(z-score)|(1)<br>Urdu Speakers|(2)<br>Non- Urdu<br>Speakers|(3)<br>F-test<br>(p-value)<br>(1)-(2)|
|---|---|---|---|
|[T1] Urban Design|-0.08<br>(0.063)|0.31***<br>(0.112)|0.00|
|[T2] Diversity|-0.09<br>(0.064)|0.07<br>(0.118)|0.23|
|[T3] Urban Design + Diversity|-0.06<br>(0.064)|-0.07<br>(0.121)|0.97|
|Observations|1,473|451||
|Control Mean|-0.02|0.07||
|Selected Controls|X|X||

Notes: Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

> 17 Tables available upon request. For non-Urdu, five of the 33 covariates were imbalanced at conventional significant levels, including age, studying/working status and if participant had worked with outgroups on projects.

18
Policy Research Working Paper 6459

# **Abstract**

This paper examines why some countries experience economic recoveries without pick-up of bank credit (credit-less) and how different this recovery pattern is from the case where credit is increased as an economy recovers (credit-with). To answer these questions, the paper uses quarterly data covering 96 countries and identifies 272 recovery episodes. It finds that more than 25 percent of all recoveries are credit-less and around 45 percent of all credit-less recoveries occurred in 2009–10. It also finds that output and investment growth tends to be lower in credit-less events but, by eight quarters after the trough date, the gap between credit-less and credit-with episodes is mostly exhausted. Results of the probit estimations show that the size of the downturn and the extent of external adjustment are associated with the likelihood of credit-less recoveries. Moreover, fiscal loosening tends to be related to credit-less events while monetary easing and a country’s decision to seek an International Monetary Fund-supported program reduce the probability of credit-less recoveries. Finally, the model suggests that many countries in the Europe and Central Asia region were likely to experience credit-less recoveries following the global financial crisis in 2008/09. What is more worrisome for them is the fact that they are facing another negative external shock.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nsugawara@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# D.1 Supplementary Figures

a) <!-- Start of picture text --> Fertilizers Difference between Stochastic and Deterministic Solutions<br>1500 1<br>Fertilizers (million tons)Ratio of Fertilizers to Crop Land (tons per Ha) 0.03<br>Range of all sample paths<br>10% quantile<br>0.025 25% quantile<br>50% quantile<br>0.02<br>1000 0.5 0.015<br>0.01<br>0.005<br>0<br>500 0<br>2000 2020 2040 2060 2080 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>b)<br>Consumption of Biofuels Difference between Stochastic and Deterministic Solutions<br>80 2%<br>0%<br>Biofuels (million tonnes of oil equivalent)<br>Share of Biofuels in Liquid Fuel Consumption (%) −0.1%<br>60 1.5% −0.2%<br>−0.3%<br>−0.4%<br>40 1% −0.5%<br>−0.6%<br>−0.7%<br>20 0.5%<br>−0.8% Range of all sample paths<br>10% quantile<br>−0.9% 25% quantile50% quantile<br>0 0% −1%<br>2000 2020 2040 2060 2080 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>million tons tons per Ha<br>Ratio of Fertilizers to Crop Land (tons per Ha)<br>Biofuels (million toe)<br>Share of Biofuels in Liquid Fuel Consumption Share of Biofuels in Liquid Fuel Consumption<br><!-- End of picture text --> Figure D.1: Consumption of Fertilizers and Biofuels under Climate and Technology Uncertainty

# References

- Antoine, B., Gurgel, A. and Reilly, J.: 2008, Will Recreation Demand for Land Limit Biofuels Production?, Journal of Agricultural & Food Industrial Organization 6(2), 5.

- Brazee, R. and Mendelsohn, R.: 1990, A Dynamic Model of Timber Markets, Forest Science 36(2), 255264.

22
# 1. Introduction

The global economy has suffered two major adverse shocks to start the 2020s—the COVID-19 pandemic and Russia’s invasion of Ukraine.<sup>2</sup> After a strong rebound in 2021 from the pandemic-induced recession of 2020, global growth in 2022 slowed precipitously (figure 1). The war in Ukraine has disrupted activity and trade, pent-up demand in the wake of COVID-19 lockdowns has faded, and macroeconomic policy support for demand is being withdrawn amid high inflation.

While the growth slowdown in EMDEs in 2022 was partly cyclical, it also reflected underlying, structural weakness. Potential growth—the rate of increase of potential output, or the level of output an economy would sustain at full capacity utilization and full employment—slowed in the past decade (2011-21) relative to the preceding one in a wide swath of EMDEs and in almost all EMDE regions (Kilic Celik, Kose, Ohnsorge, and Ruch, 2023). If the drivers of current trends do not undergo major reversals, potential growth is expected to continue slowing down over the remainder of this decade.

Yet, there have been wide differences in these trends, as well as in prospects for long-term growth, across EMDE regions and these have implications for regional policy priorities. This paper examines differences across the World Bank's six EMDE regions by addressing the following questions for each region.

- How have potential growth and its drivers evolved since the turn of the century?

- What are the prospects for potential growth?

- Which policies would lift potential growth?

Contributions. Drawing on a rich body of region-specific studies and using the comprehensive new database introduced in Kilic Celik, Kose, Ohnsorge, and Ruch (2023), this paper is the first study to systematically analyze potential growth in all six EMDE regions in a consistent manner. Other major cross-country studies of potential growth have largely focused on advanced economies (Dabla-Norris et al. 2015; IMF 2015; OECD 2014) or Asian economies (ADB 2016). This paper examines data for up to 6 EMDEs in EAP, 9 in ECA, 16 in LAC, 5 in MNA, 3 in SAR, and 14 in SSA over the past two decades (2000-21) and considers prospects for the remainder of this decade (2022-30).

The estimates of potential growth that the paper primarily utilizes are based on the production function approach to estimating potential growth, described in Kilic Celik, Kose, Ohnsorge and Ruch (2023). Briefly, the production function approach utilizes a Cobb-Douglas production function where:

- Potential capital stock is assumed to match the actual capital stock.

> 2 For the purposes of this paper, the 2000s are assumed to cover the period 2000-10, the 2010s the period 2011-21, and the 2020s the period 2022-30. The 2000s and 2010s are selected to ensure that the averages include both the global recession and the subsequent rebound. The 2020s are selected to cover projections. Data for 2022-23 and 2022-30 are forecasts.