rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2. Methodology and Variables of Interest**

To be able to evaluate whether trade restrictions affect export performance, one has to use the trade barriers imposed by trading partner countries on the products of the exporting country. Kee et al. (2009) constructed such an indicator. The market access-trade tariff restrictiveness index (MA-TTRI) which is presented in World Trade Indicators (WTI) from 2005 to 2007 measures the equivalent uniform tariff of trading partners facing the exporter country that would maintain the imports of the trading partners constant, including preferential tariffs. It is weighted by import values and import demand elasticities of trading partners and expressed as a tariff rate. A low value of the index indicates low trade barriers (or high market access) faced by the country’s exporters when selling their products to other countries. I use this indicator as a proxy for the restrictiveness of trade policies in accessing to foreign markets.

The tariff rates adopted at home can also contribute to the export performance of a country if exporters are more likely to use imported intermediate goods. In their analysis on U.S. firms, Bernard et al. (2007) find that 41 percent of exporting firms also import while 79 percent of importers also export. Using a firm level dataset from 43 developing countries, Seker (2010) shows that 35 percent of firms that are engaged with international markets through exporting or importing perform both activities. Low domestic tariff rates decrease the cost of imports which can stimulate exports. I include trade tariff restrictiveness index (TTRI) in the analysis which is also constructed by Kee et al. (2009) and presented in WTI.<sup>5</sup> This index summarizes the impact of each country’s non-discriminatory trade policies on its aggregate imports. It indicates the degree of domestic inefficiency caused by the trade regime. It is calculated as an equivalent uniform tariff of a country’s tariff schedule that would keep domestic import levels constant. Product level tariffs are weighted by import shares
Figure 2: Total area enrolled across all years of the PES program by state (in hectares, both common and private properties) <!-- Start of picture text --> Aguascalientes<br>Baja California<br>Baja California Sur<br>Campeche<br>Chiapas<br>Chihuahua<br>Coahuila<br>Colima<br>Distrito Federal<br>Durango<br>Estado de Mexico<br>Guanajuato<br>Guerrero<br>Hidalgo<br>Jalisco<br>Michoacan<br>Morelos<br>Nayarit<br>Nuevo Leon<br>Oaxaca<br>Puebla<br>Queretaro<br>Quintana Roo<br>San Luis Potosi<br>Sinaloa<br>Sonora<br>Tabasco<br>Tamaulipas<br>Tlaxcala<br>Veracruz<br>Yucatan<br>Zacatecas<br>0 500000 1.0e+06<br>Total area of PSA recipients, 2004−2015<br><!-- End of picture text -->

- 1 Source: authors calculations from CONAFOR program data.

26
14 lightings by station. The consultant also identified the main sources of uncertainty which could invalidate the forecast.

The stated preference survey had a crucial role. It generated data on the preferences, recent trips and demographic characteristics of 800 potential commuter rail users in the corridorincluding quotas for time-of-day, age and gender of users. The respondents were recruited at 16 bus stops in or near the corridor from 5 am to 8.30 pm and were offered a fee of up to 200 pesos to participate in the interview. They were offered the option of being interviewed at their home or and an interview site next to the bus station. On the interview day, they were guided though a 30 minutes computer-based interview.

The questionnaire was designed to estimate the trade-off between time, cost ad other service factors. It essentially asked the user to pick among of alternatives differentiated according to walking time, waiting time, time in mode (bus, rail, subway), total trip time, total tip cost and total number of transfers. The experiments were customized based on the attributes of the intercepted tip to ensure realism. The choice experiments clearly show that potential users place different values on walking time, waiting time, in-vehicle time and transfers between modes. They value their time at 3 to 9 pesos per hour (roughly UScts25 to 75) depending on the use of time. In contrast to other locations where in-vehicle time seems to be preferred to walking time, Mexican seem to generally perceive time spent walking comparable to time spent traveling. Mexicans prefer to spend time walking to travelling than waiting (as anywhere else). They see transfers as equivalent to 13-15 minutes of traveling. This means that the speed advantage of subway or commuter rail transfers is offset by 15 to 30 minutes for 1 to 2 transfers. Overall, they prefer the commuter rail to bus or metro (but this may be biased by the perception of additional security).

The network data is used to assign each trip in the total market to a specific mode or
<br>day was 2.2, 34% reported<br>that blackouts lasted 30 to 60<br>minutes, followed by 25%<br>reporting 15 to 30 minutes,<br>17% reporting 1 to 3 hours,<br>14% reporting less than 15<br>minutes, and 11% reporting<br>more than 3 hours.|The bottom 2 in urban and<br>the bottom 4 quintiles in rural<br>areas had too few sampled<br>households for meaningful<br>statistics. The frequency of<br>blackouts increased with<br>quintile in urban areas.|Meaningful comparison was<br>not possible because even the<br>top quintile in rural areas had<br>a total of only 31 samples.|
|Nigeria|57% of households reported<br>daily blackouts in their areas,<br>and 31% reported several<br>blackouts over the last 7 days.|The percentage of households<br>reporting daily blackout<br>declined from 62 in the<br>second to 54 in the top<br>quintile.|55% of urban and 59% of rural<br>households reported daily<br>blackouts.|
|Senegal|Over the last 4 weeks,<br>households connected to the<br>grid reported blackouts 15<br>days on average, or 53% of<br>the time.|The number of days with<br>outages decreased slightly<br>from the second to the top<br>quintile.|The average number of days<br>with blackouts over the last 4<br>weeks was 14 in urban and 18<br>in rural areas.|

Source: World Bank staff analysis of household surveys _Note_ : All percentages are percentages of households connected to the grid who replied to the questions on blackouts.

# Gender of Head of Household
quality of public administration as well as property rights and rule-based governance.<sup>5</sup> The trends on public sector management and institutions need, however, to be put in historical and comparative perspective to be better apprehended: as illustrated by Andrews et al. in their 2017 book on “Building State Capability”, institutional development has historically been an extremely slow-moving process that unfolds over decades, and even centuries. Rather than being an anomaly, slow institutional development that is also subject to reversals appears to be the norm and warrants more realistic and gradual policy prescriptions for governance reform.

**Figure 5. Trends in CPIA governance indicators, SSA, 2005-2021**

<!-- Start of picture text --> Property rights and rule-<br>3.5<br>based governance<br>3.4<br>Quality of budgetary and<br>3.3 financial management<br>3.2<br>Efficiency of revenue<br>mobilization<br>3.1<br>3.0 Quality of public<br>administration<br>2.9<br>Transparency,<br>2.8<br>accountability, and<br>corruption in the public<br>2.7 sector<br>2.6<br>2 0 0 5 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5 2 0 1 7 2 0 1 9 2 0 2 1<br>CPIA governance indicators<br><!-- End of picture text --> Source: Country Policy and Institutional Assessment (CPIA), 2022, World Bank. Note: CPIA values range from 1=low to 6=high. Q12: Property rights and rule-based governance; Q13: Quality of budgetary and financial management; Q14: Efficiency of revenue mobilization; Q15: Quality of public administration; Q16: Transparency, accountability, and corruption
13 The data in Table 4 can be used to answer the question: Why does nonfarm income make a smaller contribution to rural income inequality than agricultural income? On the one hand, nonfarm income has a larger share in total rural income (Sk) than agricultural income. However, the source Gini (Gk) for nonfarm income is lower than that for agricultural income (0.634 vs. _1.155);_ this shows that nonfarm income is distributed more equally than that of agricultural income. 1<sup>4</sup> Moreover, the correlation of source income with total income rankings (Rk) for nonfarm income is lower than that for agricultural income (0.590 vs 0.750). For these reasons then - a low source Gini and a low degree of correlation with total income -- nonfarm income makes a smaller contribution to overall income inequality than agricultural income.

The decomposition results in Table 4 can also be used to distinguish between inequality-increasing and inequality-decreasing sources of income. According to the relative concentration coefficients (gk) for the various income sources, three sources of income - nonfarm, transfer and livestock - represent inequality-decreasing sources of income. This means that, ceteris paribus, additional increments of nonfarm, transfer and livestock income will reduce overall income inequality. By contrast, additional increments of two sources of income - agricultural and rental - represent inequalityincreasing sources of income.

# IV. Exogenous Increases in Sources of Income

Using the decomposition formulations in Table 4 it is also possible to measure how much an increase in any particular income source will increase or decrease overall income inequality. Taking household labor and production decisions as given, suppose
Figure 5: Test for discontinuity of selected covariates: survey data <!-- Start of picture text --> Ln(km to road) Ln(km to major city)<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>Ln(members with rights to comm. land) Ln(poverty index 2010)<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>Ln(average slope in deg) Mean canopy cover 2000<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>Household head educ: secondary school or above Household depends upon subsistence agriculture<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>2 1<br>1<br>0 0<br>-1<br>-1<br>-2<br>de-meaned value de-meaned value<br>-3 -2<br>3 2<br>2 1<br>1<br>0<br>0<br>-1 -1<br>de-meaned value de-meaned value<br>-2 -2<br>1 50<br>0<br>0<br>-1<br>de-meaned value de-meaned value<br>-2 -50<br>.1 .1<br>.05<br>.05<br>0
-0.372|-1.2<br>-9.7|-1.7<br>-8.7|
|In-kind Health|0.464|0.1<br>3.6|0.8<br>3.3|
|In-kind Education|0.396|2.2<br>5.6|4.4<br>6.1|

Notes: Kakwani index is the measure of progressivity. The higher the value is the more progressive is the program. Marginal contributions are measured at the level of final income. National - below the national poverty line (1562.8 MDL per month per adult equivalent) International - below the international line (3.2 USD/day in 2011 PPP or 759.4 MDL per month per capita) International - below the international line (5.5 USD/day in 2011 PPP or 1305.2 MDL per month per capita) Source: authors’ calculations using HBS-2017 data.

At the level of specific direct transfers, the most progressive transfers are social assistance to poor families (Table 14). However, all other analyzed direct transfers are also highly progressive. The highest effect on inequality and poverty reduction comes from pensions – labor and social, assistance to poor families and other benefits. Despite a very high level of progressivity, social assistance transfers to poor families have a moderate effect on poverty – on their own they decrease poverty by 1.5-2.5 percentage points; this is 2 times lower than social pensions and 10 times smaller than labor pensions. This moderate effect is mainly explained by the relatively small size of the program.

**Table 14. Progressivity and redistributive effect by direct transfers.**

||Progressivity<br>Kakwani index t|o Gini index to|nationalpoverty to internationalpoverty (3.2)<br>to internationalpoverty (5.5)<br>Marginal contributions, p.p.|
|-
2022).<sup>28</sup> Using face-to-face interviews, each respondent was asked to answer a set of questions on “how easy or difficult it is to” behave in a particular way relating to each of the scales. Responses were recorded using a five-point scale ranging from one (very difficult) to five (very easy). Details on the conscientiousness-related skill measures used are provided in Appendix D and Appendix Table D.1. While not perfect, the reliability metrics of the psychometric scales (Cronbach’s alpha and the interitem covariance, reported in Appendix Table D.2) are acceptable, and the reliability of the overall conscientiousness skill scale is satisfactory (Taber, 2018). Despite our efforts in carefully validating our measures for the local context, several factors might explain the imperfect scores of the scales: translation issues,<sup>29</sup> cultural differences, social desirability bias or the fact that we asked the questions in an interview format rather than have respondents self-report. These are in line with the concerns raised by Laajaj et al. (2019) that psychometric surveys, often designed with Western, educated, industrialized, rich and democratic (WEIRD) cultures in mind, may not be easily translatable to different contexts, especially in developing countries and for low-skilled workers.

With these caveats in mind, in Figure 2 we show the effect of the treatment on each of the measures of conscientiousness that we collected. While the effect of the intervention is statistically insignificant for most measures, in around 53 percent of cases the effect is positive. The training had a significantly positive impact on four of the 32 conscientiousness items and a significantly negative effect on only one measure. Specifically the four measures which were significantly higher due to the treatment are: how easy or difficult is it for you to (1) work towards reaching your goals; (2) focus on your most important goals; (3) live up to your responsibilities; and (4) fulfill your
declined from 82 to 34 percentage points of GDP between 2000 and 2006, with the bulk of the drop between 2004 and 2006. Since 2006, however, the debt-to-GDP ratio begun to increase, reaching slightly over 50 percentage points of GDP in 2010.<sup>11</sup> Panel B of Figure 5 displays the evolution of the six determinants of foreign debt, as identied by equation (2.1). The average growth rate of real GDP between 2001 and 2010 was 4 percentage points, although with signicant volatility. The GDP deator in U.S. dollars (that is, the reciprocal of the real exchange rate) was highly volatile, and the country had a persistent current account decit net of interest payments. In addition, the large negative values of the debt shock during 2005 and 2006 (-9 and -26 percent respectively) reect the realization of the debt relief agreed under the HIPC initiative. FDI ows remained roughly constant, at around 1.5 percentage points of GDP, and the implicit interest rate was virtually constant at less than one percentage point.

The top panel of Figure 6 displays the evolution of foreign debt / GDP and the probabilities that debt will cross the proposed thresholds during 2011-2020. Here, the country-specic intercept is set at its estimated value based on historical data. The median foreign debt trajectory decreases from 52 to just over 5 percentage points of GDP by 2020. Likewise, the upper quartile of foreign debt decreases to 27 percentage points of GDP over the same horizon. These trajectories imply that the probability of foreign debt increasing to 60 percentage points of GDP during the next ten years is always smaller than 8 percentage points. Similarly, the probabilities that debt will increase to more than 80 or 100 percent of GDP never exceed 4 and 2 percentage points respectively. These predictions for foreign debt are, to a large extent, driven by the proposed intercept. Evaluating equation (3.3) at the estimated intercept implies very
of varieties of inputs imported from abroad varies across firms since the firm pays a fixed cost of importing each variety of the foreign intermediate input. The elasticity of substitution within

> 7Intermediate inputs are commonly modeled as CES functions in the trade and growth literature. See Ethier (1982), Kasahara and Rodrigue (2008), Halpern et al. (2011) and Kasahara and Lapham (2013).

> 8An alternate approach in the growth literature has been that of quality ladder type models where intermediate inputs are vertically differentiated (see Aghion and Howitt (1992) and Chapter 4 of Grossman and Helpman (1991)). The data set used in the paper does not have information about firm-specific actual varieties and prices of imported materials. This makes it difficult to make inferences about the quality of imports. The horizontally differentiated varieties framework allows me to derive an expression relating varieties to import intensity (observed in the data) to use in the production function. Moreover, empirical evidence points to the importance of the extensive margin or number of varieties in imports. Goldberg et al. (2010), using HS6-level data on imports into India, find that between 1987 and 2000, the extensive margin (varieties not imported in 1987) accounted for over 67 percent of the growth of imported intermediates in India.

5
# **_Summary statistics_**

The main outcome variables of interest in this study are (1) the proportion of project playgroups that remained open in 2016, three years after the end of project funding, and (2) the proportion of project playgroups that remained open in 2016 with two teachers that were trained as part of the project. This latter measure captures a more rigorous definition of sustainability compared to our first measure because it requires the continued employment of teachers who were trained under the project.

Of the full sample of 432 project playgroups, 92% were still operating in 2013 at the end of the project and 86% remained open in 2016, see Appendix Figure 1. As noted above, data on teacher training were not available for the full sample of 432 project playgroups so the second measure of sustainability cannot be assessed for this group.

For the main analytic sample, the 245 project playgroups with data from all three time points, about 96% were still open in 2013 and by 2016 92% remained in operation (shown as a solid line in Figure 1). Because the project was rolled out in phases and the training was provided in two blocks of 100 hours each, in 2010 our first survey of playgroups showed that only about 29% of the 245 project playgroups reported that they had two trained teachers. However, the proportion of open playgroups with two trained teachers _increased_ between 2013 and 2016 even though the teacher training component of the project ended in 2013 (shown as a dashed line in Figure 1).<sup>11</sup> We argue that open project playgroups with two trained teachers in 2013 and 2016 were those that were particularly successful at attracting and hiring teachers from other project playgroups who had also been trained under the project. This view is supported by the fact that the proportion of open project playgroups with at least one trained teacher _declined_ between 2010 and 2016 (shown as a dotted line in Figure 1). Thus, our second measure of sustainability –
. As an extension of our main database, we present two scenarios where those elements are explicitly taken into account. We find that the implementation of complementary policy reforms magnifies the impact on shipment times and trade costs, especially along the corridors. For instance, if border delays were reduced by half, the reduction of shipment times along corridors would range between 7.7 percent for the China‐Indochina Peninsula Economic Corridor (CICPEC) and 25.5 percent of the China‐Central Asia‐West Asia Economic Corridor 3
***|(0.000351)|(0.000194)||(0.000197)|(0.000312)|||||
|Share of hhlds with computer||-0.0771|-0.104|-0.124||0.0100|-0.0126|-0.0226|||||
|||(0.0794)|(0.0763)|(0.0837)||(0.0528)|(0.0392)|(0.0398)|||||
|IDN exports per worker *||||-0.00156||||-0.0026|||||
|Share of hhlds with computer||||(0.00123)||||(0.00101)***|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.974|0.974|0.974|0.974|0.843|0.843|0.843|0.843|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Low-educated workers: workers with uncomplete secondary education or less. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level
education targeted to university students<sup>4</sup> , as well as the first evidence on entrepreneurship-support interventions in the Middle East and North Africa (Angel-Urdinola et al., 2010). Furthermore, we analyze the effectiveness

> 2 The program was implemented in all Tunisian universities delivering _licences appliquées_ , including Ez-Zitouna, Jendouba, Gabès, Gafsa, Tunis, Kairouan, Mannouba, Monastir, Carthage, Sfax, Sousse, Tunis, and Tunis El-Manar. 3 As we will discuss below, the rate of self-employment in the control group is 4%. This is in line with the low rate of self-employment among university-educated in Tunisia in general.  Among 25 – 34 year old with a university degree, 4.6 percent were classified as independent workers and 5.6 percent as employers according to the Labor Force Survey of 2010.

> 4 Oosterbeek et al. (2008) analyze the impact of entrepreneurship education on students’ entrepreneurship competencies and intentions in the Netherlands. They find few positive effects, but their study used a quasiexperimental approach with a small sample size and high attrition rates.

3
identify impacts on business startup; (ii) we focus on slightly larger businesses than micro‐ enterprises, some of which have employees, own operational assets, make business investments, and are formally registered; and (iii) we utilize very detailed and high quality administrative loan data to study impacts on default rates and loan terms, in addition to analyzing survey measures on business outcomes.

Our research design is a randomized control trial with 445 Partner loan clients, two‐thirds of whom received an invitation to attend a comprehensive business and financial education program run by a highly experienced and reputable training institute in the city. The remaining one‐third of the sample is our control group. The randomization was stratified by baseline financial literacy level, gender, industry, and baseline profits.

We find that financial literacy is a strong predictor of baseline financial and business outcomes, consistent with the existing literature. Further, our experimental results show that the training program led to significant improvements in basic financial knowledge for those who start out with low levels of financial literacy at baseline.

Our results on business outcomes, on the other hand, are quite stark. We do not find any significant treatment effects on the extensive margin. Specifically, treatment businesses were no more likely to survive than control businesses in a period where 36 percent of businesses 4