rafmacalaba/datause-encoder-data huggingface dataset:
Chile: Martinez-Aguilar, Sandra and Eduardo Ortiz-Juarez. 2016. “CEQ Master Workbook: Chile. Version: October 7, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Colombia: Melendez, Marcela and Valentina Martinez. 2015. “CEQ Master Workbook: Colombia. Version: December 17, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and Inter-American Development Bank).

Costa Rica: Sauma, Pablo and Juan D. Trejos. 2014. “CEQ Master Workbook: Costa Rica. Version: February 14, 2014,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

Dominican Republic: Aristy-Escuder, Jaime, Maynor Cabrera, Blanca Moreno-Dodson and Miguel Sanchez-Martin. 2016. “CEQ Master Workbook: Dominican Republic. Version: August 4, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Ecuador: Llerena Pinto, Freddy Paul, Maria Cristhina Llerena Pinto, Roberto Carlos Saa Daza and Maria Andrea Llerena Pinto. 2017. “CEQ Master Workbook: Ecuador. Version: January 5, 2017,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

El Salvador: Beneke, Margarita, Nora Lustig and Jose Andres Oliva. 2014. “CEQ Master Workbook: El Salvador. Version: June 26, 2014,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and Inter-American Development Bank).

Ethiopia: Hill, Ruth, Eyasu Tsehaye and Tassew Woldehanna. 2014. “CEQ Master Workbook: Ethiopia. Version: September 28, 2014,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Georgia: Cancho, Cesar and Elena Bondarenko. 2015. “CEQ Master Workbook: Georgia. Version: December 31, 2015” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Ghana: Younger, Stephen, Eric Osei-Assibey and Felix Oppong. 2016. “CEQ Master Workbook: Ghana, February 10, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

Goraus, Karolina and Gabriela Inchauste. 2016. “The Distributional Impact of Taxes and Transfers in Poland”, Policy Research Working Paper 7787, World Bank: Washington DC.

Guatemala: Cabrera, Maynor and Hilcias E. Moran. 2015. “CEQ Master Workbook: Guatemala. Version: May 6, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University, Instituto Centroamericano de Estudios Fiscales (ICEFI) and International Fund for Agricultural Development (IFAD)).

Honduras: Castaneda, Ricardo and Ilya Espino. 2015. “CEQ Master Workbook: Honduras. Version: August 18, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University, Instituto Centroamericano de Estudios Fiscales (Icefi) and International Fund for Agricultural Development (IFAD)).

34
# **Appendix Table 3: Summary Statistics for LV sample**

||Mean|Std.<br>Dev.|Min.|Max.|
|---|---|---|---|---|
|Net disbursements (constant 2007 $mil)|3359|4041|8.04|29611|
|Net aid transfers (constant 2007 $mil )|2802|3398|-333.8|24286|
|Gross disbursements (constant 2007 $mil)|3830|4677|11.78|30475|
|GDP per capita (2007 $)|25253|10417|3943|67110|
|Population (millions)|38.4|57.2|0.258|302|
|Trade share of GDP (%)|72.1|43.8|16.1|326.6|
|Government spending (% of GDP)|26.2|11.8|10.1|63.0|
|Surplus/deficit (% of GDP)|-2.2|4.3|-16.8|18.5|
|Unemployment rate (% of labor force)|7.0|3.9|1.5|23.9|
|Inflation rate (%)|4.4|4.1|-3.8|24.7|
|Real exchange rate|105.2|13.0|54.9|145.9|
|Inequality (Gini coefficient)|29.0|5.5|19.7|43.9|
|Corruption (0 worst, 6 best, from ICRG)|4.9|1.1|2|6|
|Leftist ruling party (0-1 dummy)|0.38|0.48|0|1|
|Rightist ruling party (0-1 dummy)|0.47|0.50|0|1|
|No. years from start of systemic crisis|2.3|5.6|0|31|
|No. years from start of crisis (systemic or not)|6.1|8.2|0|34|

25
effects of the negative external environment invites a combination of macroeconomic and microeconomic policy actions. The burden of alleviating heightened social costs associated with the economic slowdown must be shared among multiple policy instruments. Macro policies will play a supporting role in aggregate demand management given lack of appetite from the private sector, but they cannot replace the engines of growth.

# **References**

Arbache, J.S. and J. Page (2007a), More Growth or Less Collapses? An Investigation of the Growth Challenges of Sub-Saharan African Countries, World Bank Policy Working Paper # 4384.

Bagliano, F. C. and Favero, C. A. (1999) “Information from financial markets and VAR measures of monetary policy”. European Economic Review, 43(4-6), pp. 825-837.

Bernanke, B. and Blinder, A. (1992) “The Federal Funds Rate and the Channels of Monetary Transmission”. American Economic Review, v. 82, pp. 901-921.

Bernanke, B. and Mihov, I. (1998) “Measuring Monetary Policy”. _Quarterly Journal of Economics_ , Aug., pp. 869-902.

Elliott, G., T.J. Rothenberg e J.H. Stock Efficient Tests for an Autoregressive Unit Root. Econometrica 64, 813-836, 1996.

Kim, H. (2009) “Generalized Impulse Response Analysis: General or Extreme?”. Mimeo, Auburn University.

Kim, S. (2001) “International transmission of US monetary policy shocks: Evidence from VAR's”. Journal of Monetary Economics, 48(2), pp. 339-72.

Lee, J. and M. C. Strazicich (2001) “Break Point Estimation and Spurious Rejections with Endogenous Unit Root Tests”. Oxford Bulletin of Economics and Statistics 65, 535-558.

Lee, J. and M. C. Strazicich (2003) “Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks”. The Review of Economics and Statistics 85, 1082-1089.

Pesaran, M. H. and Y. Shin, (1998) “Generalized Impulse Response Analysis in Linear Multivariate Models”. Economics Letters 58, pp. 17-29.

Ng, S. and Perron, P. (2001) “Lag Length Selection and the Construction of Unit Root Testes with Good Size and Power”. Econometrica, v. 69, pp. 1519-54.

Perron, P. (1997) “Further Evidence on Breaking Trend Functions in Macroeconomic Variables”. Journal of Econometrics 80, 355-385.
External Wealth of Nations Mark II database developed by Lane and Milesi-Ferretti (2007) as the ratio of the sum of total financial assets and financial liabilities to GDP. Finally, we relied on Calvo and Reinhart (2000) to construct banking crisis and currency crisis.<sup>2</sup>

# Table 1 [about here]

Table 1 shows summary statistics for financial sector real value added growth rates (calculated as log differences of dollar amounts at 1995 prices) for each country. The mean growth rate for each country is positive; however, the series for 17 out of the 28 countries exhibit negative skewness. These countries, mostly emerging markets, are: Argentina; Bolivia; Chile; Colombia; Costa Rica; Denmark; Hong Kong SAR, China; India; Indonesia; Italy; Korea; Mexico; Philippines; Singapore; Sweden; Thailand; United States; and Venezuela. In contrast, as can be seen from Table 1, countries with positively skewed financial sector growth series include mostly OECD economies.

# Figure 1 [about here]

Furthermore, the average level of kurtosis is higher for countries with negative skew in the series (6.619 compared to 5.527). Figure 1 illustrates the significance of the difference in the skew via a probability plot. The difference is seen in a greater degree of deviation from the normal distribution (Gaussian bell curve) in the tails of the distribution of financial sector growth rates for the subsample of countries with negative skew. The difference is substantial: for instance, an annual growth rate of minus 20 percent in financial sector real value added has 0.0 probability of occurrence under normality, while the empirical probability in countries with positive skew is 0.1% (0.001 on the vertical axis) and an order of magnitude greater at 1.0% (0.01 on the vertical axis) in countries with negative skew. Overall, the predominance of a large number of outliers in the tails of the negative skew subsample indicates that the estimated difference in kurtosis between the two series is economically significant.

> 2 The period from 2000 through 2005 represents a time of stable economic growth in most countries in our sample, also known as the ―Great Moderation‖ (Stock and Watson (2002)). The exception to this is Argentina, where a crisis lasted from 1999 through 2002.

6
# **The Microfinance Business Model:**

# **Enduring Subsidy and Modest Profit**

Robert Cull (World Bank) Asli Demirgüç-Kunt (World Bank) Jonathan Morduch (New York University)

# **Introduction**

Microfinance institutions aim to serve customers ill-served by traditional commercial banks. The success of microfinance in achieving wide scale reach – one count includes 211 million customers globally -- has inspired social business initiatives in energy, health, education and other sectors.<sup>1</sup> Microfinance, though, has taken a beating in recent years. Six prominent randomized controlled trials, for example, found only a small average impact of microcredit access on marginal borrowers, though the studies found some “potentially important” (though modest and not clearly robust) impacts on “occupational choice, business scale, consumption choice, female decision power, and improved risk management” (Banerjee et al 2015, p. 14).<sup>2</sup> While perhaps disappointing to microfinance advocates, these modest impacts could nonetheless feed into sizable benefit-cost ratios if the costs are proportionally small too. This is indeed a fundamental premise of microfinance.

> 1 Data are as of December 31, 2013, reported as part of the Microcredit Summit’s _State of the Campaign Report 2015_ . Data are from https://stateofthecampaign.org/data-reported/, accessed 4-15-16. 2 As Banerjee et al (2015) describe, the six studies do not provide the final word on microfinance/microcredit impacts. Most important, the studies measure impacts only on marginal borrowers. Some borrowers were determined to be not creditworthy and would have been excluded from being served, for example, but were instead served for the purposes of the study. Other studies measured impacts in new regions for the microlenders, or new populations. Still, the studies are not far from earlier studies that credibly attend to selection biases (see, e.g., Armendàriz and Morduch 2010).

2
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underreporting of work done in connection with the household business or farm, as well as underreporting of teenage employment.<sup>7</sup> The 1991 CPS study noted above also pointed to the existence of gender dimensions of these effects. In particular, the revision of the questionnaire, aimed at better capturing unpaid work in a household business or farm, increased the female employment rate. In developing countries, the gender effects may be even more dramatic than in developed countries. Several studies have argued that because female employment patterns tend to include multiple activities in comparison to men, the structure and wording of the questionnaire is likely to produce larger variations in female than in male employment statistics. In particular, concerns about the underreporting and undervaluing of women’s work when using the most common methods of employment data collection have been expressed in several studies (Anker, 1983; Dixon-Mueller and Anker, 1988; Charmes, 1998; Mata Greenwood, 2000). In developing countries, women workers tend to have a prominent role in agriculture (especially in subsistence agriculture), often as contributing family members, and in the so-called invisible household economy – that is, in fetching water and wood, carrying out domestic tasks, while providing care to children, the elderly, and persons with disabilities. They generally work on simultaneous (and often seasonal) activities, paid and unpaid, within and outside the System of National Accounts (SNA). They tend to engage in informal sector activities and, because of

> 7 Other indirect evidence on the impact of questions is available from comparing the “main activity” question in a household roster reported by the head and the more detailed set of questions in a labor module of the same questionnaire. Of the more than 60 Living Standard Measurement Surveys done by the World Bank, only two (Malawi and Timor Leste) ask both the “main activity” in the household roster _and_ collect activity-specific information from each member of the household (such as number of hours in a specific activity in the last 7 days). In Malawi, for women, 33 percent are reported inactive by the “main activity” question, compared to 25 percent from the labor module. This difference can be driven by some combination of the shorter question (and, therefore, different interpretation of work across survey sections) and the proxy response.

7
# **Understanding Economic Growth in Ghana in Comparative Perspective**

**_Michael Geiger Jan Trenczek_** _The World Bank Group University of Mainz_ <u>mgeiger@worldbank.org jtrencze@uni-mainz.de</u>

**_Konstantin M. Wacker_** _University of Mainz_ <u>kwacker@uni-mainz.de</u>

## **<u>JEL Classifications</u>** <u>: O47, O55</u>

**<u>Keywords</u>** <u>: Economic growth; Africa; Ghana; determinants of growth; structural change</u>

## **<u>Highlights:</u>**

- We investigate factors that have driven Ghana’s recent growth performance in comparative perspective.

- Structural change provided relatively little impetus to aggregate productivity growth.

- Stabilization policies, infrastructure, and job creation in higher-productivity sectors offer viable sources of income gains.
incomes in the numerator by the share of the household that is under 18. Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our balanced, backward interpolated panel and Column (7) reports synthetic DiD estimates. We find similar patterns to those documented in Panel A but with much stronger and more precisely estimated effects. We find significant reductions in relative incomes of households with both types of children. We note that these are _relative_ income effects and, as such, are still entirely consistent with income effects driving child labor market response to RTAs without child labor prohibitions. It is likely the case that household incomes across the population rise in response to trade liberalization but that for households with children, this facilitates pulling those children out of the labor market which reduces per capita earnings relative to the population. These relative income effects are significant at the 1% level in some specifications and correspond to a more than 3% reduction in household equality relative to the mean.

We find robust evidence of increases in relative household incomes with the inclusion of child labor provisions with significantly positive increases in relative incomes across most specifications of up to 3.3%. These positive impacts are entirely consistent with increases in child employment and decreases in school enrollments that we document in Tables 2 and 3. Our theoretical model suggests that a demand-side restriction on child labor implemented as a fine on employers of underage children will drive down wages earned by the youngest children and, ceteris paribus, lower household income. This pushes 14-17 year old children into employment with potentially ambiguous results for under-14 children for which the provisions create both income and substitution effects. Hence, the ex ante impact of child labor provisions on per capita income for households with both younger and older children is ambiguous. Panel B of Table 4 suggests that the net effect on household incomes is positive—child labor provisions lead to income equality between households with children and the general population, but apparently through increases in child employment. We also note that our identification strategy cannot identify the longer-run impacts that will arise from changes in schooling behavior. Reductions in primary and secondary school enrollment rates attributable to child labor provisions are likely to have longer-term negative impacts on household 24
35

**Table SI-8:** Difference in storm exposure in storm-year migration, high damage storms

||Mean|SE|_t-stat_|
|---|---|---|---|
|Receiving county storms|7.65|4.38||
|Sending county storms|7.72|5.07||
|Diference|-0.06||-0.62|

Notes: For counties that experience a high-damage storm, we calculate the total number of storms each county experiences over our full sample (Sending county storms). In each year a sending county experiences a storm, we calculate the weighted average total number of storms that the counties receiving migrants from that county in that year experience over our sample (Receiving county storms). Receiving counties are weighted based on the number of migrants they receive from a given county in a given year. We report the difference (a positive difference indicating that receiving counties experience more storms on average) and a t-test of the significance of that difference. We restrict the sample to those counties that have at least $20 million in approved FEMA compensation (the top 90% of storm impacted counties).

**Table SI-9:** Return migration after high damage storms

||_>_10_MM_|_>_20_MM_|_>_40_MM_|_>_80_MM_|_>_160_MM_|
|---|---|---|---|---|---|
|High Damage County=1|-0.247|-0.346|-0.453|-0.721<sup>_∗_</sup>|-1.089<sup>_∗∗_</sup>|
||(0.152)|(0.208)|(0.279)|(0.327)|(0.328)|
|N|410,616|283,635|180,036|119,259|72,765|
|Fixed Efects:||||||
|County|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of return-migrants to a county in the five years following a high damage storm as the outcome. We only include migration from counties that received migrants from the storm-impacted county in the year of the storm. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damage begins to appear - during a hurricane in the year and if the damage payments from FEMA in response to the storm exceeded the totals indicated in the column headings.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.
preparatory. More strikingly, families of the highest wealth quintile spend about ten times more than families in the bottom wealth quintile, and the same is true when comparing the expenditures of parents with a college education with those of uneducated parents. For instance tutoring expenditures reach 340 pounds for preparatory pupils whose mothers have a college degree. Expenditures in tutoring are also much higher for pupils born in urban governorates (210 pounds in average at the preparatory level) than in rural ones (35 pounds in rural Egypt).

Thus tutoring expenditures, which are large and strongly linked with a pupil’s background, are likely to contribute to learning achievement inequities. To investigate, we re-estimated the effects of pupils’ circumstance characteristics on differentials in learning achievements after controlling for families’ expenditures in tutoring. We use as control the logarithm of total household expenditures in tutoring divided by the number of enrolled children.<sup>19</sup>

**Table 11: Opportunity Share of Inequality in National Exam Scores Controlling for the Expenditures on Tutoring**

||**Primary**|**Preparatory**|
|---|---|---|
|Overall inequality|128.1|177.6|
|Base results|0.223|0.298|
|Tutoring household expenditures control|0.211|0.259|

_Source:_ SYPE 2009 survey. _Sample:_ 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates.

**Table 11** reports the estimates of the equity-shares of learning achievements after controlling for the levels of household expenditures in tutoring at the primary and preparatory levels. These are obtained using the SYPE 2009 data. Taking the baseline uncorrected estimates as reference, the opportunity share of inequalities in achievements at the primary exam decreases only slightly from 22.3 to 21.1 percent when controlling for tutoring expenditures. But, even if it remains high, the corresponding share in achievements at the preparatory exam decreases much more

> 19 Of course, there are quite serious identification issues here. In particular, although averaging at the family level should mitigate this concern, tutoring expenditures are likely to depend on a child’s scholastic ability.

34
conceptual analytical framework came from Loewe et al. (2021) in their introduction to a recent special issue of _World Development_ .<sup>25</sup> From a methodological standpoint, our objective is to contribute to making operational this conceptual framework for the purpose of characterizing the main features of an SC, in particular, that of MENA countries. An attractive feature of the operational framework that we propose in this first attempt is that it allows us to analyze the impact of digital transformation of society on this SC, a question that has recently drawn much attention.

# **3. Data and preliminary analysis**

This section will detail the construction of the MENA database and preliminary analysis. We first visualize the 3-P model of the SC and describe the raw data used to proxy the three dimensions of Provision, Protection, and Participation or those used as controls. We then examine descriptive statistics between MENA and OECD countries as an initial base of comparison, and proceed to process the data, including reducing dimensionality using a principal component approach (PCA). Finally, we discuss descriptive evidence from the transformed data on the three indices. Table A.6 in the appendix lists the MENA and OECD countries on which we collected data over the 2007-2020 period.

# **_Raw data for the 3-P model of the social contract_**

Following Loewe et al. (2021), we represent a given SC as a vector in a 3-dimensional space (see Figure 1) whose components are levels of Protection, Provision, and Participation, noted respectively P1, P2, and P3 hereafter.<sup>26</sup> For our empirical analysis, we collected data on variables that are good candidates for proxying each of the three dimensions. Table 1 lists these variables, along with control variables that may plausibly impact at least one of the three fundamental dimensions of the SC.<sup>27</sup>

> 25 ' We must indicate here that while each of the papers published in this special issue makes use of Loewe et al. s (2021) framework in an interesting way, e.g., El-Haddad (2020), Furness and Trautner (2020), Revkin and Aram (2020), and Sobhy (2020), none of them brings this framework to actual statistical data. Contributing to filling this gap is also one of our contributions to this literature.

> 26 In fact, Loewe et al. (2021) also discuss a 4th dimension of an SC that is meant to capture factors that are external to a given country but have a significant impact on its economic and political development pathway. As indicated in the previous section, geopolitics and market globalization have indeed played a role in the way MENA's societies have functioned throughout the second half of the 20<sup>th</sup> century. However, this international dimension, it seems to us, has significantly less weight nowadays than it used to. For this and other reasons related to data collection, we do not consider it in this first empirical attempt to analyze an SC.

> 27 The definition of each of these variables and the sources from which we obtained the data on them are given in the appendix.

13