rafmacalaba/datause-encoder-data huggingface dataset:
by digitspan recall), and location of business. Table 3 reports the resulting treatment effects according to these expectations, and compares them to the estimated experimental treatment effects.

Unfortunately for the purpose of this exercise, in this context there is not significant heterogeneity in treatment effects according to any of these observable differences. We see two key results in Table 3. First, every single group dramatically overestimates what the employment impact of winning the competition would be – so it is not the case that expectations are accurate for one gender and not for another, or accurate for university‐educated participants and for less educated participants, etc. Second, the rankings of treatment effects according to counterfactuals are inaccurate with respect to the rankings according to the experimental treatment effects. For example, males expect a higher treatment effect than females, but if anything the female effect is larger; those in the cities of Abuja and Lagos expect higher treatment effects, but if anything have lower treatment effects. While limited, this evidence casts doubt on the ability of this method to target interventions at who benefits most.

# **5. Conclusions**

I find that participants in a business plan competition do not provide accurate counterfactuals when asked what would have happened had they had the alternative treatment status. Instead both the treatment and control groups appear to systematically overestimate how important winning the competition is or would be for the growth of their businesses. In addition, the counterfactuals also appear inaccurate for generating relative rankings of which types of businesses benefit most from treatment.

Although these results only apply to one experiment, they are consistent with a broader body of literature showing that policy makers and program participants tend to have over‐optimistic ex ante expectations about how effective different policy interventions are likely to be. The difference here is that these beliefs are not corrected even after having experienced the program. Replication in other experimental settings in which the intervention had less impact would be welcome, but the current results suggest that eliciting counterfactuals directly from program participants is unlikely to provide an accurate way of assessing the impact of a program in settings in which change would occur even without a program.

# **References**

Andrade, Gustavo Henrique de, Miriam Bruhn, and David McKenzie (2015) “A Helping Hand or the Long Arm of the Law? Experimental Evidence on What Governments Can Do to Formalize Firms”, _World Bank Economic Review_ , forthcoming.

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38 Our interviews with enterprise managers also suggest that wage supplements have become more important as base wages began to fall relative to inflation. This development is important for two reasons: First, from an analytical standpuint we clearlY must include supplements and fringe benefits in order to obtain a realistic picture of the evolution of worker earnings. Second, the preeminence of wage supplements in recent years has greatly decreased union influence on wages because many supplements are granted and adjusted at the employer's discretion.

# _4.4.2 Evoluion of Wages by Skill Groups_

Because base wages have been changed infrequently by the Tripartite Commission, the evolution of base wages has been very discontinuous. Percentage increases in base wages have tended to be related to the price increases of basic commodities, and the adjustments have generally favored low-income workers.

Data on the evolution of base wages suggest that, while in the past wages tended to catch up with inflation, since 1975 real wages have fallen significantly. For example, Bloch's (1985) calculation of the real minimum wage (real SMIG) suggests that it declined from an index value of 100 in 1975 to 90 in 1980 and 76 in 1984. Our calculations indicate that it decreased further, to 70 in 1985 and 66 in 1986.

Our data from the worker sample and data compiled by the Directorate of Labor tell the same story. The Directorate of Labor data on the evolution of the base wages of various categories of workers between November 1974 and April 1983 indicate that the cumulative percentage increase ranged between 70 and 83 percent for the unskilled workers and between 35 and _45_ percent for the professionals of the highest category. During the same period, the African and European cost of living indices registered increases of 107 and 114 percent, respectively, thus substantially exceeding the rise in the base wages of all categories.

The survey data on workers in 17 enterprises, presented in Table 4.12, indicate that average annual growth of the total compensation (measured as base wage plus bonuses and supplements) in nominal terms was _6.55_ percent a year over the
deprivation (starvation or meal rationing) in both periods is much higher among the poor (moderate, extreme, or food poor) than among the non-poor. This also suggests that the subjective measure of deprivation well captures the objective measure of food poverty or deprivation.

# **5. Does PRIME Reach the Seasonal and Hard-core Poor?**

According to the 2008–09 InM survey, some 62 percent of the ultra-poor in the northwest region’s 5 districts participate in some form of microfinance. Of these, 29 percent are PRIME participants, while 33 percent participate in regular microfinance. However, the annual growth rate of participation among the ultra-poor is much lower for regular microfinance (3.3 percent) than for PRIME (11.2 percent) (Figure 3).<sup>17</sup> <!-- Start of picture text --> .4<br>.3<br>.2<br>MFI1<br>.1<br>MFI2<br>PRIME<br>0<br><2000 2000 2002 2004 2006 2008<br>MFI1: MFI in PRIME area MFI2: MFI in non-PRIME area<br><!-- End of picture text --> 17 About 6.1 percent of PRIME participation came from those who had participated in regular microfinance. Of the 33 percent who participated in regular microfinance, some 14 percent participated before 2006 (the year PRIME was introduced); after 2006, the participation rate fell to 19 percent.

22
Policy Research Working Paper 8135

# **Abstract**

In developing countries, younger and better-educated cohorts are entering the workforce. This developing worldled education wave is altering the skill composition of the global labor supply, and impacting income distribution, at the national and global levels. This paper analyzes how this education wave reshapes global inequality over the long run using a general-equilibrium macro-micro simulation framework that covers harmonized household surveys representing almost 90 percent of the world population. The findings under alternative assumptions suggest that global income inequality will likely decrease by 2030. This increasing educated labor force will contribute to the closing of the gap in average incomes between developing and high income countries. The forthcoming education wave would also minimize, mainly for developing countries, potential further increases of within-country inequality.

This paper is a product of the Development Prospects Group, Development Economics. 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 sahmed20@worldbank.org, mbussolo@worldbank.org; marciocruz@worldbank.org; dgo@worldbank.org; and iosoriorodarte@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
# **Measuring the Performance and Achievement of Social Objectives**

# **of Development Finance Institutions**

Manuela Francisco, Yira Mascaró, Juan Carlos Mendoza, and Jacob Yaron<sup>1</sup>

**Key Words** : Metrics, Performance, State-Owned Banks

## **JEL: G21, H23**

The views expressed in this working paper are those of the authors and do not necessarily represent those of the World Bank or World Bank policy.

> 1 The authors are thankful for earlier inputs by Ilias Skamnelos, Radwa El-Swaify, and, more recently, Marco Arena. Also, this document benefits from useful comments from participants to the presentation to LCSFF and from peer reviewers.
Should we think of these as “large” or “small” effects? We consider two ways of putting the magnitude in context. First, the average infant mortality rate for countries and years in our sample is 106.7 per 1,000 children born; this implies that a 1 percent change in per capita GDP is associated with a 0.32 to 0.58 percent change in mortality, implying an elasticity of mortality with respect to GDP of between -0.32 and -0.58. Second, the average annual decline in infant mortality for countries and years in our data is 2.56 per thousand. A 1 percent decline in GDP from expected trends is therefore associated with an increase in infant mortality that is equivalent to between 0.13 (0.34/2.56) and 0.24 (0.62/2.56) of the average annual decline in infant mortality in the sample. More meaningfully, perhaps, in countries in which per capita GDP growth was below trend, the average deviation from trend was -2.6 percentage points. The average event of belowtrend growth in our data was therefore associated with an increase in mortality of between 0.34 and 0.62 percent of the average annual decline in IMR.

<u>Changes in the composition of women giving birth: The second and third rows of Table 3</u> focus on possible changes in the composition of women giving birth. We first re-estimate our basic model, but limit the sample to women who have had at least two live births. The coefficients on log per capita GDP for this somewhat smaller sample are very similar to those for the full sample of women—a little bit larger in the linear specification, and a little bit smaller in the quadratic and cubic specifications. In the third row of the table we then present the results from regressions that include mother fixed effects. These results are reasonably similar to those in the second row, which suggests that changes in the composition of women giving birth explain at most a small fraction of the increases in infant mortality we observe during negative shocks to GDP in Africa.

<u>Heterogeneity by gender, place of residence, education: In Table 4, we consider the</u> possibility that shocks to per capita GDP have different effects on the mortality of boys and girls (upper panel), on the mortality of children born to women living in urban and in rural areas (middle panel), and on the mortality of children born to women with more or less education (lower panel). The table suggests that, in Africa, the mortality of girls is substantially more sensitive to income shocks than is the mortality of boys: A 1 percent deviation in GDP results in approximately 0.33 more boy deaths per thousand, but 0.62 more girl deaths per thousand; note, moreover, that the differences in percentage terms are even larger because of the higher average mortality of boys than girls—a well-known finding in the demographic literature.<sup>7</sup> The middle and lower panels of the table suggest that the mortality of children in rural areas, and of children

> 7 For example, the World Health Organization (2006) estimates that the male-to-female ratio in neonatal mortality and in early neonatal mortality in developing countries is 1.3.

6
Figure 27: Mean Adjusted R<sup>2</sup> , by Weather Product, Rainfall Metric, and Model <!-- Start of picture text --> .05 .12<br>.04<br>.03 .1<br>.02 .08<br>.01<br>* Weather Product * 0 * Weather Product * .06<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Model * * Model *<br>Weather + Weather2 + FE + Inputs Weather + Weather2 + FE + Inputs<br>Weather + Weather2 + FE Weather + Weather2 + FE<br>Weather + Weather2 Weather + Weather2<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Specification # - sorted by model & effect size Specification # - sorted by model & effect size<br>.27<br>.26<br>.25<br>.24<br>.23<br>.22<br>.21<br>.2<br>* Weather Product *<br>TAMSAT<br>ERA5<br>ARC2<br>MERRA-2<br>CPC<br>CHIRPS<br>* Model *<br>Weather + Weather2 + FE + Inputs<br>Weather + Weather2 + FE<br>Weather + Weather2<br>Weather + FE + Inputs<br>Weather + FE<br>Weather 0 1 2 3 4 5 6 7 8 9 10 11 12 mean adjusted R2<br>Specification # - sorted by model & effect size 95% C.I.<br><!-- End of picture text --> _Note_ : The figure presents the mean adjusted R<sup>2</sup> , by weather product and model specification, aggregated over rainfall metric, country, and outcome variable. Only one extraction method is included. Across panels, the figure includes 1,728 regressions, with each panel including 576 regressions. Each column includes 48 regressions, which are for each specification model and each rainfall metric. The northwest panel presents results from the linear and quadratic model specifications. The northeast panel presents results from the linear and quadratic model specifications with fixed effects. The southwest panel presents results from the linear and quadratic model specifications with fixed effects and input controls.

88
- Beaman, L., Chattopadhyay, R., Duflo, E., Pande, R., and Topalova, P. (2009). Powerful Women: Does Exposure Reduce Bias? _The Quarterly Journal of Economics_ , 124(4):1497–1540.

- Beaman, L., Duflo, E., Pande, R., and Topalova, P. (2012). Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India. _Science_ , 335(6068):582– 586.

- Belloni, A., Chernozhukov, V., and Hansen, C. (2013). Inference for High-dimensional Sparse Econometric Models. _Advances in Economics and Econometrics: 10th World Congress_ , 3:245–195.

- Belloni, A., Chernozhukov, V., and Hansen, C. (2014a). High-dimensional Methods and Inference on Structural and Treatment Effects. _Journal of Economic Perspectives_ , 28(2):29–50.

- Belloni, A., Chernozhukov, V., and Hansen, C. (2014b). Inference on Treatment Effects after Selection among High-dimensional Controls. _Review of Economic Studies_ , 81:608–650.

- Belloni, A., Chernozhukov, V., Hansen, C., and Kozbur, D. (2016). Inference in High-Dimensional Panel Models With an Application to Gun Control. _Journal of Business and Economic Statistics_ , 34(4):590–605.

- Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. _Journal of the Royal Statistical Society_ , 57(1):289– 300.

- Benjamini, Y., Krieger, A. M., and Yekutieli, D. (2006). Adaptive Linear Step-up Procedures that Control the False Discovery Rate. _Biometrika_ , 93(3):491–507.

- Bertrand, M., Black, S. E., Jensen, S., and Lleras-Muney, A. (2019). Breaking the Glass Ceiling? The Effect of Board Quotas on Female Labor Market Outcomes in Norway. _Review of Economic Studies_ , 86:191–239.

- Bertrand, M., Kamenica, E., and Pan, J. (2015). Gender Identity and Relative Income within Households. _The Quarterly Journal of Economics_ , 130(2):571–614.

- Bettinger, E. P. and Long, B. T. (2005). Do Faculty Serve as Role Models? The Impact of Instructor Gender on Female Students. _American Economic Review: Papers & Proceedings_ , 95(2):152– 157.

- Blattman, C. and Dercon, S. (2018). The Impacts of Industrial and Entrepreneurial Work on Income and Health: Experimental Evidence from Ethiopia. _American Economic Journal: Applied Economics_ , 10(3):1–38.

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# Figure 4: External positions in LICs

# (A) Current account balance

<!-- Start of picture text --> Percent of GDP<br>0<br>All LICs<br>-2<br>Commodity exporters<br>-4 Others<br>-6<br>-8<br>-10<br>-12<br>-14<br>-16<br>2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

## (B) Current account funding

<!-- Start of picture text --> Ratio of current account deficit<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>LICs Metal exporting LICs<br>Foreign direct investment<br>Official development assistance<br><!-- End of picture text -->

# (C) Foreign direct investment

<!-- Start of picture text --> Standard deviation of FDI flows<br>6<br>Median for non-LIC EMDEs<br>5<br>4<br>3<br>2<br>1<br>0<br>LICs Commodity Commodity Metal exporting<br>exporting LICs importing LICs LICs<br><!-- End of picture text -->

# (D) Median foreign reserves

<!-- Start of picture text --> Months of import coverage<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>LIDCs LICs<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. LICs= Low-income countries. Figure shows median current account balance in percent of GDP. The sample represents a total of 30 low-income countries, of which 2 are oil exporters, 8 are metals exporters, and the remaining 20 are non-resource-intensive. It excludes Somalia, South Sudan, and Syria due to data restrictions.

B. The sample represents a total of 21 low-income countries, including 6 metals-exporting LICs, with current account deficits in 2016.

C. EMDEs= Emerging market and developing economies. Standard deviation represents the median standard deviation of foreign direct investment in percent of GDP from 2000 to 2017.

D. LIDCs=Low-income developing countries; LICs=Low-income countries. See Annex for details. Orange lines indicate interquartile range. Data is as of the last reported year, mostly 2016.

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Potential impacts of the US-Vietnam FTA and loss of AGOA preferences on trade, growth and welfare were estimated using a global dynamic computable general equilibrium (CGE) model. This modeling framework allows for the incorporation of complex interactions between producers, consumers and the government. It captures productivity differences at the country, sector and factor levels, shifts in demand as income rises, as well changes in comparative advantage and trade flows following trade liberalization. The analysis includes 17 sectors and 35 trading partners (see Table 2) and simulates the impacts of policy changes up to 2030, including reductions in tariffs, NTMs, and regulatory barriers in trade in services. For this analysis the 2010 social accounting matrix (SAM) for Lesotho has been updated to 2011 and incorporated into the Global Trade Analysis Project (GTAP) database<sup>7</sup> version 9.2, benchmarked to 2011.<sup>8</sup> The key macroeconomic variables and trade flows were then updated to the most recent data available for Lesotho (primarily form 2015).

**Table 2: Sectors and countries/regions included in the global CGE model.**

|**Sectors**|**Countries/Regions**|
|---|---|
|**Agriculture**|Australia|
|**Natural resources / mining**|Brunei Darussalam|
|**Food, beverages, tobacco**|Canada|
|**Textiles**|Chile|
|**Wearing apparel and leather**|Japan|
|**Chemical, rubber,plasticproducts**|Malaysia|
|**Metals**|Mexico|
|**Transport equipment**|New Zealand|
|**Electronic equipment**|Peru|
|**Machinery and equipment**|Singapore|
|**Other manufacturing**|United States|
|**Utilities**|Vietnam|
|**Construction**|Brazil|
|**Trade and transport**|Russian Federation|
|**Finance and other business services**|India|
|**Communication and business services**|China|
|**Social services**|South Africa|
||EU28|
||Egypt,Arab Rep.|
||Colombia|
||Turkey|
||Thailand|
||Korea,Rep.|
||Philippines|
||Indonesia|
||Bangladesh|

> 7 https://www.gtap.agecon.purdue.edu/

8 We are grateful to Badri Narayanan for the incorporation of Lesotho SAM to the GTAP database.

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locations i and j. But it does not include the temporal and user components of accessibility (e.g. mode preference or subset of opportunity preferences).

We utilize an urban accessibility tool, which utilizes the road network (in this case OpenStreetMap), and transit attributes (specifically, the cities’ General Transit Feed Specification (GTFS) files and/or a transit network GIS layer), to calculate the travel times between origin and destination pair in the city. The tool then combines the estimated travel times and location data for employment opportunities (or other opportunity inputs) to calculate the accessibility value for every point in the city.

This flexible open source tool provides, with ease and detailed granularity, the basis for the accessibility analysis of the city. It is developed by Conveyal,<sup>6</sup> and uses a simple web-based interface to generate detailed raster accessibility maps. Raster maps offer a representation of the area divided into a regular, standardized grid of cells and are useful for storing high resolution data that vary continuously. This output can be processed and analyzed in greater depth and collated with other sources of data with other GIS software.

When averaged over the whole urban area, localized accessibilities are weighted by population n�, to account for population distribution within the city. This produces an average accessibility for the urban area, A̅.

While the time threshold can be set to any value, this analysis takes 60 minutes as the threshold, which has been recognized as meaningful to capture labor market access in urban areas (Bertaud 2014).

A second set of metrics and visuals complement the average accessibility indicator. They provide information on the distribution of accessibility – spatially through maps and in aggregate through Lorenz curves and the calculation of Gini coefficients. The distribution of accessibility is an important component of the overall picture, in tandem with the average accessibility, which brings to light the equity distribution of access within a city.

Similar to the way income inequality is often captured, we compute Lorenz curves and Gini coefficients to represent how equally accessibility is distributed for each urban area in our sample. When looking at income inequality, it is common to sum the total incomes of a country and explore what proportion of that total “income pot” goes to a given share of the country’s population. The higher the inequality the lower the share of the total incomes that goes to the poorest.

In the framework of this paper, and following the methodology proposed in Avner and Lall (2016), we replace the “income pot” by the “accessibility pot”, which is the total of accessibility to opportunity levels when summed over the entire population of a given urban area. And the Lorenz curves will capture what share of the population has access to what share of the “accessibility pot”, sorting the population within a city on the x-axis by increasing accessibility levels.

> 6 <u>https://www.conveyal.com/</u>

6