rafmacalaba/datause-encoder-data huggingface dataset:
functions we consider, this decomposition will be additive, allowing for a straightforward quantification of the relative importance of growth and inequality changes, and the costs or benefits in terms of growth of the latter. In particular, the increase (decrease) in the relevant inequality measure in percentage points per year can be interpreted as the amount by which average income growth would have to be higher (lower) to deliver the observed growth rate of social welfare absent any changes in income distribution.

We work with a large dataset of income distributions and average incomes covering 117 countries over the past four decades. This dataset combines the high-quality household survey data for developing countries underlying the World Bank’s global poverty estimates (Ravallion and Chen (2010)), with the Luxembourg Income Study (LIS) data for advanced economies. We focus on within-country changes in average incomes and income inequality observed over episodes at least five years long. In a sample of 285 such non-overlapping episodes, we calculate the contribution of growth in average incomes, and the contribution of changes in inequality, to growth in social welfare, for a wide variety of social welfare functions.

Our basic findings are easily summarized. For all of the social welfare functions we consider, social welfare on average increases equiproportionately with average incomes. This reflects the fact that changes in the relevant inequality measures are not systematically correlated with changes in average incomes. For all but the most bottom-sensitive social welfare functions, the relationship between growth in social welfare and growth in average incomes is also quite precisely estimated. This reflects the fact that changes in inequality are small, in the sense that variation across episodes in inequality accounts for only a small fraction of the variation across episodes in changes in social welfare.

Although changes in social welfare driven by changes in the relevant inequality measures are on average small and uncorrelated with growth in average incomes, it is nevertheless useful to understand their correlates. In particular, if there were some combination of policies and institutions that supported a given growth rate of overall per capita incomes, but in addition led to declines in the relevant inequality measure, then from the standpoint of promoting social welfare, such policies would dominate others that did not lead to the same declines in inequality. In the last part of the paper, we consider a range of variables identified as important for growth and inequality in the large empirical cross-country literature. In the spirit of comprehensive data description, we use Bayesian Model Averaging to systematically document the partial correlations between these variables and growth in average incomes and in inequality, and through these channels, their correlations with social welfare.

4
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control for the extent of aid. To measure the extent of outside intervention in a village, we use the RAN [Recovery Aceh-Nias] database to count aid projects in a village

[http://rand.brr.go.id/RAND/reference]. From table 1 there were on average 11 different “first level implementers,” or different aid agencies actually delivering aid on the ground per village, each with different projects, each negotiated separately. The mean number of projects from these aid agencies was about thirty-two in 2009.

# _(b) Democratization_

For twenty years prior to the tsunami, Aceh had an insurgency movement, the Free Aceh Movement [GAM]. The national government imposed effective military rule, with villages caught in the struggle between the army and the insurgents. While free, formal elections in villages in Indonesia commenced in 1979, in Aceh these were suspended. Village heads were chosen and certified by the subdistrict ( _kecamatan_ ) government, in close consultation with the village council of elders ( _tuhapeut_ ), who represent the elites in the village. The few elections that occurred were irregular in timing and informal, usually with lack of contestation, secret ballots, and/or full voting rights; they tended to rubberstamp the selection of the head chosen by elites. An election, for example, might involve a show of hands by males in a post-Friday prayer meeting at the mosque. The introduction of regularly scheduled, contested, and certified elections with secret ballots and full voting rights followed major election reforms enacted by early 2006, after settlement of the insurgency.

Table 2 shows that elections phased in over time. During 2006 and 2007, 47.4% of our villages had formal elections under the new procedures and another 23.1% in the 9
<!-- Start of picture text --> Central Government General Government Public Sector Private Sector<br>1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010<br>year year year year<br>EAP ECA EAP ECA EAP ECA EAP ECA<br>LAC MENA LAC MENA LAC MENA LAC MENA<br>NA SA NA SA NA SA NA SA<br>SSA SSA SSA SSA<br>35 35 35 100<br>30 30 30<br>25 25 25<br>80<br>20 20 20<br>15 15 15<br>60<br>10 10 10<br>5 5 5<br>0 0 0 40<br><!-- End of picture text --> Source: calculations based on ILO Stats

# **v. Determinants of public employment**

This section explores the potential factors that explain these differences across regions and over time, and has two parts. The first one focuses on Rodrik’s (2000) hypothesis of public employment as a tool to mitigate the country exposure to undiversifiable external risk. The second one expands the list of potential determinants of public employment.

We initially replicate Rodrik’s baseline econometric specification, but with more recent data and additional countries. The summary statistics and cross correlations of the main variables (Tables 2 and 3) are presented to facilitate comparisons with the original work. The central variable in Rodrik’s model is exposure to external risk, which is calculated as the product of the volume of trade with the unanticipated component of variability in the external terms of trade, which he argues, it is the theoretically appropriate measure of external risk, as it yields the unpredictable variation in the streams of incomes associated with foreign trade. Hence, let x, m, and y stand for the volumes of exports, imports, and GDP, respectively, and T represent the terms of trade. The measure of exposure to external risk corresponds to: 11
Sahn and Stifel (2000) offer a well-cited study that constructs wealth indexes for two or more periods from the DHS for 11 Sub-Saharan African countries between 1986 and 1998. They find that poverty generally declined, largely due to improvements in rural areas. A recent study by Harttgen, Klasen, and Vollmer (2013) analyzes 160 DHS surveys from 33 African and 34 nonAfrican countries and constructs wealth indexes in different ways. This study argues, however, that employing wealth indexes as a proxy for trends in household consumption is subject to various types of biases. These include changing preferences for certain assets (e.g., the increasing ownership of smart phones) and changing relative prices among different assets leading to more demand for one asset at the expense of others (e.g., the dramatically decreasing price of smart phones).

Other additional challenges exist with using wealth indexes to measure poverty. First, as suggested by the title of this section, wealth indexes offer a non-monetary or relative measure of poverty. Put differently, a wealth index can only identify a household as poor by its relative position on the population’s wealth distribution. This renders it difficult, if not impossible, to compare the poverty rate among different countries without assuming that all the countries under comparison have comparable distributions of wealth. Practically speaking, the list of assets must be either identical or comparable for all these countries, which is certainly harder to satisfy than comparing a consumption distribution denominated on a monetary basis such as the international Purchasing Power Parity (PPP) dollars. For example, an air-conditioner may be a valuable asset in countries in a tropical climate, but it may not even be found in those in a frigid climate, and vice versa with assets such as a heater. Another inconvenience is that the poverty line (threshold) is relative and would need to be fixed for the whole distribution.<sup>8</sup> Furthermore, wealth indexes tend

> 8 A similar concern applies to comparing the wealth index within a country (or countries) over time. In this case, assets for all countries from two periods must be pooled together to construct the wealth index.

10
decrease by 1 percentage point then the exports growth decreases by 1.18 percentage points. Obviously, the effect of the same demand shock of importer _p_ will have a different impact across sectors dependent on the relative importance of this importer in each industry.<sup>9</sup> To see whether a demand shock differentially affects sectors based on their dependence on external finance, in columns 2-4, we interact the demand shock with each of our three core financial measures. Unlike for dependence on trade credit and tangibility, where no significant differential effect is observed, a positive demand shock abroad also leads to a higher growth in industries relatively more dependent on external finance. This could imply that sectors relying heavily on external finance are also more pro-cyclical as durable goods tend to. It is a well established fact that during downturns the consumption of durables decreases disproportionately more than of non-durables. To test whether our external dependence measure is not just a proxy for durables in column 5 of Table 6 we include in addition to the interactions with external dependence also similar interactions with a dummy variable taken from Kroszner, Laeven, and Klingebiel (2007) which equals one when the sector is producing predominantly durable goods. Even though, the coefficient on the main interaction slightly decreases in its in magnitude, it still remains negative and significant at 1 percent level. Exports of durables are not significantly more affected by a domestic crisis, however, consistent with previous literature they tend to be much more affected than non-durable goods by demand shocks.

In Figure 2 below we visualize the results from column 5 of Table 6. It depicts a situation when a country that faces a banking crisis simultaneously experiences a drop in demand from the only importer. The drop of 2.8% that we choose for our simulation corresponds to the projection of IMF for the United States in 2009. As the figure shows, the effect of finance is amplified by the demand shock and the it is particularly pronounced in sectors producing durable goods whose exports growth drops as much as 10 percentage points.

Most importantly for us, controlling for the demand does not affect the coefficient of the interaction of crisis and external dependence which remains strongly significant and with a magnitude that is almost unchanged compared to the baseline regressions. We also run similar tests using a recession dummy instead of the GDP growth variable and find the results to be consistent with the findings above. Therefore, from our analyses we can conclude that in a situation when a domestic banking crisis is accompanied by a demand shock abroad the industries that depend heavily on external finance will be double hit.

> 9The importance of demand shocks is shown to be very large also in recent works by Freund (2009) and (Freund and Klapper 2009) who find that this has been particularly important in the context of the current crisis.

12
# **4.3 University graduates:**

Among the university graduates in the sample, we find that the proportion of females have increased during this period. At the regional level, most of the graduates come from Accra and other urban areas, but the number of graduates coming from rural areas has increased during this period. Most of them come from smaller households, perhaps an indication that they are becoming independent from the nuclear family. Even though most of the graduates are non-poor, the share of poor graduates has increased during this period, which is also reflected in the increased unemployment rates for university graduates.

**Table 28: Characteristics of University graduates (in percent).**

|**Characteristics**|**1987**|**1988**|**1991**|
|---|---|---|---|
|**Gender**||||
|Male|85.11|85.00|81.75|
|Female|14.89|15.00|18.25|
|**Sector**||||
|Rural|19.15|8.51|25.00|
|Urban|80.85|91.49|75.00|
|**Hhsize**||||
|1-5 persons|57.45|59.57|55.36|
|6-10 persons|40.43|40.43|37.51|
|10-15 persons|2.13|0.00|7.13|
|> 15 persons|0.00|0.00|0.00|
|**Poverty status**||||
|poor|10.64|2.13|16.90|
|Non-poor|89.36|97.87|83.04|
|**Unemployment rate**|0.55|0.54|1.51|
|**Locality-5**||||
|Accra|27.66|40.43|34.82|
|Other Urban|53.19|51.06|40.18|
|Rural.coastal|2.13|0.00|7.14|
|Rural forest|14.89|8.51|10.71|
|Rural Savanah|2.13|0.00|7.14|

**Source: Authors' own calculations using GLSS 1,2,3.**
of every child enrolled in EP2. The attendance rate triggering transfers in the conditional transfers arms was calculated by the implementing NGO solely based on the information contained in the attendance report cards described in Section I-C. No incentive was paid on the basis of the presence or absence of pupils during the attendance spot checks and therefore there is no reason to expect the data to be manipulated. In addition, the enumerators were blind to the treatment arms, so that there is no reason either to expect the data to be affected by social desirability bias.

In addition to the spot check data, a baseline household survey and an endline household survey collected basic household information as well as, for each girl in the household who had completed, at least, 5<sup>th</sup> Grade, and, at most, 6<sup>th</sup> Grade, as of the end of 2015 (and was therefore potentially eligible for the conditional transfers): data on self-reported quality of attendance monitoring by the parent or guardian, degree of agreement about statements regarding returns to education for each child, self-reported girl empowerment, expenditure on personal goods consumed by the eligible girl, cognitive tests (at endline only), and household expenditure data (at endline only).

_Household survey sample_ . The household data used in the analysis is based on a sample drawn from the universe of girls enrolled in the 173 schools included in our study within three years of the data collection (based on school records), as in Benhassine et al. (2015), and who still lived with their parent or guardian at baseline (given our analytical framework based on asymmetric information between parents and children). The target was to interview 20 potentially eligible girls per school, sampling those enrolled in 2015 (the last academic year before the trial) and recent drop outs who were not enrolled in 2015 but were enrolled in 2013 or 2014, proportionally to the size of each of the two groups (“enrolled in 2015” and “recent drop outs”) in the school.<sup>15</sup> During 15 Ahead of the baseline survey, the survey team visited each school and, based on school records, drew the list of all girls who (i) were enrolled in grade 5 or 18
Page 11 Table 1.

WOMEN'S EOUITY AND DEVELOPMENT STAGE Life ExDectancy at Birth (1986)

||(Years)|||
|---|---|---|---|
||Male|Female|_Gap_|
|Low Income<br>ex. China&India|52|54|2|
|Low Income|60|61|1|
|Lower Mics|57|61|4|
|Upper Mics|64|70|6|
|Industrial|73|79|6|

Note: The "normal" situation is for women to outlive men.

# Primary School: Gross Enro2lment Ratios (1985)

||(Percentage)|||
|---|---|---|---|
||Male|Female|Gm|
|Low Income<br>ex. China & India|75|56|19|
|Low Income|110|88|22|
|Lower Mics|111|100|11|
|Upper Mics|108|132|6|
|Industrial|101|101|0|

|Secondary School: Gross Enrollment Ratios (198<br>(Percentage)|5)|
|---|---|
|Male<br>Female|Gap|
|Low Income<br>ex. China & India<br>28<br>16|12|
|Low Income<br>41<br>26|15|
|Lower Mics<br>50<br>41|9|
|Upper Mics<br>66<br>63|3|
|Industrial<br>91<br>92|-1|

Source: World Bank, World Development Report, 1988, Tables 30 and 33.
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selection procedure (stepwise search using the Bayesian Information Criterion or BIC), for bureau indicators and phone indicators (CDR).<sup>9</sup> Random forests are a generalization of decision trees designed to reduce overfitting, by combining multiple trees which each have access to a subset of the sample (Breiman, 2001).

However, these straightforward estimation routines may muddle the individual factors that explain repayment with common temporal shocks that lead to differences in the proportion of credit repaid in different time periods. High frequency indicators such as our phone indicators are particularly susceptible to picking up these shocks. For phone indicators, we develop two new models that improve intertemporal stability by basing predictions off of only within-week variation (CDR-W). The first is an OLS model with week fixed effects; these absorb week-to-week variation in repayment.<sup>10</sup> Predictions are formed differently from a standard fixed effect model. A standard model would include the fixed effect for each offer week in its predicted repayment, but that is not feasible in this setting: a lender would not know the fixed effect for future weeks. Instead, predictions are based on the average of the past weeks’ fixed effects, weighted by the proportion of loans granted in that past week. The second model is an analogous version of random forests: we fit separate random forest models to each past week of data, and combine them in an ensemble. When making a prediction for an individual, each submodel is weighted by the proportion of transitions granted in that past week.<sup>11</sup> This approach reduces the discrepancy between within-time and out-of-time performance; it may also lead to selecting indicators that are more stable over time.

To illustrate the features that the models select, we first estimate these models on the entire sample. For random forest importance plots see Figure S1, and for regression parameter estimates see Table S1, in the supplementary appendix. Standard models tend to place substantial weight on various periodicities of behavior. While some of these patterns are related to repayment, others pick up high frequency artifacts in

> 9 The stepwise search is initialized from multiple sets of starting variables; the highest within-fold fit is kept. This article uses the randomForest R package with default tuning of 500 trees, sample sizes of 63.2% drawn for each tree, and √K variables considered at each node (Breiman & Cutler, 2006).

> 10 If few transitions are made in a week, it is combined with adjacent weeks.

> 11 When giving out loans, one could upweight more recent models to capture changes in conditions.

13
access to sanitation facilities, which are located just below the line<sup>15</sup> . The results suggest that Egypt has attained (or exceeded for some cases) the level of infrastructure performance, expected to achieve at a given level of development in comparison with the rest of the world.

In Appendix 2, we provide two additional sets of figures to examine whether these results remain the same when we compare Egypt with a group of fast growing countries<sup>16</sup> (in Figure 1-A), and when we use per capita real income growth (in Figure 1- B) instead of per capita real income level. The former confirms that infrastructure performance in Egypt has achieved what is expected (or more than expected in some cases) at a given level of development even compared with a group of fast growing countries.

> 15 As for telephone faults, waiting list, and power loss, the lower value means high quality in services. Thereby, Egypt being below the predicted regression line indicates that the performance of these indicators in Egypt is better than the expected level at a given level of economic development. 16 There is a criteria used to select the sub-group of countries. Using real GDP per capita data for 19832005, we calculate average growth rates for each country. The sub-group consists of countries that satisfy greater than median real growth rates, which are close to the average real growth rates of Sweden (0.019).

9