rafmacalaba/datause-encoder-data huggingface dataset:
_First, we observe that the significance of the relationship between debt service paid and disbursements is largely driven by a handful of countries entering and exiting non-accrual status with IDA_ . In our sample there are a few cases of countries that go into non-accrual status with IDA. This occurs more or less automatically if scheduled debt service payments to IDA are late by more than sixty days, and this in turn more or less automatically triggers a halt to disbursements on IDA lending. Conversely, exits from non-accrual status generally involve a significant payment of debt service including past arrears, combined with a resumption of disbursements. Episodes such as these will thus naturally feature a positive correlation between disbursements and repayments. Figure 2 illustrates one such episode in our sample. Around the time of the 1994 genocide, Rwanda briefly entered, and then exited from non-accrual status with IDA (the period indicated with a gray rectangle). Around this time per capita disbursements and debt service as a share of GDP both fell sharply and then recovered quickly afterwards, generating a strong positive correlation between the two measures.<sup>17</sup> We note first that these episodes are rare. In the full set of 1687 country-year observations on IDA-eligible countries between 1984 and 2003 we find that 154 observations, or slightly less than 10 percent of the sample, correspond to countries in non-accrual status, with most of these observations are concentrated in a handful of countries. For our purposes the within-country over-time fluctuations in non-accrual status are relevant. In our regression sample we have just 14 cases of countries entering non-accrual status and 14 cases of countries exiting, yet it turns out that these cases are highly influential in driving the partial correlation between debt service paid and new disbursements.<sup>18</sup> To see this we construct a dummy variable taking on the value of one in country j in year t if a country entered into or exited from non-accrual status in years t-1, t or t+1. We then interact this dummy variable with total debt service and enter it into our baseline regressions, thus allowing the slope of the relationship between debt service and disbursements to differ in these episodes from the rest of the

> 17 Note that disbursements on an annual basis do not fall to zero as Rwanda entered non-accrual status near the end of the 2003 and exited again in early 2004, so that it did not spend a full calendar year in non-accrual status.

> 18 This is smaller than the total number of entries into and exits from nonaccrual status because our regression sample is restricted to observations where debt service and disbursements are both positive (since both variables are entered as logarithms).  We thus do not have in our regression sample episodes of nonaccrual status that lead to complete halts in disbursements.

14
# **Figure 6**

Median forecasted share of RE in t+30 by energy project and according to year of publication Note: US scenarios are depicted as large squares

|15<br>16<br>17<br>18<br>19<br>20||
|---|---|
|14||
|12<br>13<br>**100)**||
|7<br>8<br>9<br>10<br>11<br>**RE share (%x**||
|4<br>5<br>6||
|0<br>1<br>2<br>3||
|197|0<br>1975<br>1980<br>1985<br>1990<br>1995<br>2000<br>2005<br>2010|
||Department of the Interior<br>EUOGEP<br>Energy Research and Development Agency<br>European Union / DGTREN<br>Ford Foundation<br>Greenpeace<br>Harvard Business School<br>IEA|
||IIASA<br>IPCC<br>OECD<br>PEW|
||Shell International BV<br>Union of Concerned Scientists<br>WEC<br>WEC and IIASA|

13
# **Table 11: Heterogeneity of Returns Amongst Female-Owned Enterprises**

Dependent Variable: Real profits adjusted for own hours worked

||FE<br>(1)|FE<br>(2)|FE<br>(3)|FE<br>(4)|
|---|---|---|---|---|
|Amount|**-0.82**|**-0.81**|**1.00**|**0.81**|
||(2.80)|(2.87)|(2.58)|(2.44)|
|Amount*Ever had formal loan||||-1.18<br>(6.63)|
|Amount*Number of Wage Workers||||1.39<br>(3.26)|
|Amount*Household Asset Index||||-2.95*<br>(1.51)|
|Amount*Years of Education||0.55<br>(0.81)||0.08<br>0.91|
|Amount*Digitspan recall|||6.41***<br>(1.89)|6.89***<br>(2.33)|
|Amount*Risk aversion||||-0.61<br>(1.66)|
|Observations|1827|1827|1805|1805|
|Number of enterprises|182|182|177|177|
|% with mean treatment less than or equal to 0||47.9|48.3|59.0|
|% with mean treatment between 0 and 200||51.6|10.1|5.0|
|% with mean treatment between 200 and 500||0.5|19.6|9.0|
|% with mean treatment of more than 500||0.0|22.0|27.0|

# _Notes:_

Robust Standard errors in parentheses, *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. All variables used in the interaction apart from gender are standardized to have mean zero by subtracting off their mean.

43

-  -
and science, and almost 0.3 of a standard deviation in reading. In other words, these are large effect size effects, indicating that school choice contributes to achievement in Netherlands.

In the case of math outcomes, the private school attendance is associated with a significant effect size of 0.17 of a standard deviation. This is much higher than what one obtains with OLS. It is also interesting that there is a substantial change in at least one of the institutional variables. That is, the coefficient on firing teachers is reduced by 20 percent, or 9 points, while at the same time private schooling increases three-fold, by 12 points. In reading, the impact of private schooling is much higher in the IV results. In fact, when instrumented private school attendance has a six times higher effect size than in OLS. Also, for reading, the impact of firing teachers is substantially reduced in the IV results, by more than 87 percent or points; at the same time, the impact of private schooling increased by 24 points. In science, when estimated using OLS, private schooling had no effect, and the coefficient was insignificant.

Moreover, the impact of firing teachers was huge, at over 70 points. However, in the IV results, the firing teachers variable is substantially reduced, falling by more than 24 points. At the same time, private schooling increases from a small, even _negative_ , but insignificant effect in OLS, to a large, positive and significant effect in the IV results. The private school effects are large and significant, suggesting that private schools have a positive effect on learning outcomes, even controlling for institutional factors and a host of other controls.

Therefore, one may reject the null hypothesis that private schools have no impact on cognitive ability. The private school effect implies that school choice is beneficial for improving cognitive ability. The difference between the OLS and IV results further suggests that private schooling’s true impact works through the competition that the Dutch system promotes. Parents are able to choose among a variety of providers, thus ensuring that tastes and preferences are 20
which appears to have been due to low demand for the program.<sup>15</sup> Khandker et al. (1998) assess the village-level impacts of the three programs using a regression model applied to village level data, with program placement indicators for whether a particular village has a particular kind of program. The data used came from a household survey administered in 1991-1992. A limitation of the analysis is that it does not allow program placement to be based on unobservable village attributes, which could lead to bias in the estimated program impacts. Also, the study does not distinguish between impacts on male and female borrowers within the village.

Khandker et al. (1998) find that all of the programs have positive impacts on income, production and employment, especially in the non-farm sector. For example, average household income increased by about 20-30% in villages with the programs. However, only the Grameen Bank, which on average provided the largest loans, increased household labor supply (by 7%); the other programs appear to have reduced labor supply (by 11-12%). Also, only the Grameen Bank had an effect on the average village level wage, which the authors hypothesize was due to a general equilibrium effect stemming from the decrease in the supply of wage workers and an increase in self-employment. For this reason, the Grameen Bank appears to have had important positive spillovers on wage workers not directly participating in it.

# **4.1.2  Gender-targeted Grants in Sri Lanka**

A recent study examining the differential impacts of providing grants to women and men is de Mel, McKenzie, and Woodruf (2007), which estimates the returns to capital for Sri Lankan microenterprises owned by women and men using a randomized social experiment. The experiment randomly provided cash or equipment grants in the amount of 10,000 or 20,000 rupees. 10,000 rupees was equivalent to about three months of median profits. The study finds that about 75% of the grants were invested in businesses, both for men and for women.

De Mel. et al. (2008) estimate the average return to capital to have been in the range of 4.6%5.3% per month, or about 60% per year. But they also document substantial heterogeneity in return across different types of enterprises and find the return to capital for female owned enterprises to have been substantially lower than that for male owned enterprises. In fact, they estimate a return of zero for female owned enterprises and a return in excess of 9 % per month for male owned enterprises. After exploring various possible explanations for the gender

> 15 Roodman and Morduch (2009) note that the credit-consumption relationship is ”U”-shaped and that the poorest are often excluded (or self-excluded) from micro-credit programs.

31
# **<mark>Table IA1. Classification of Local Public Indebtedness in the Alert System</mark>**

<mark>This table displays the classification of indebtedness of states given their financial health indicators as defined by the Mexican</mark> <u>Ministry of Finance.</u>

|Ministry of Finance.|**Indicator 1**|**Indicator 2**|**Indicator 3**|
|---|---|---|---|
||_Total public debt to freely_|_Debt service and obligations to_|_Short-term obligations to total_|
||_disposable income_|_freely disposable income_|_income_|
|**Classification of Ind**|**ebtedness**|||
|Sustainable|Low|Low|Low|
||Low|Low|Medium|
||Low|Medium|Low|
|Under-Watch|Low|Medium|Medium|
||Low|High|Low|
||Low|High|Medium|
||Low|Low|High|
||Low|Medium|High|
||Medium|Medium|Medium|
||Medium|High|Low|
||Medium|High|Medium|
||Medium|Low|High|
||Medium|Medium|High|
|High|High|-|-|
||Low|High|High|
||Medium|High|High|

74
# 9.9 Honduras

As for El Salvador and Guatemala, we take advantage of the estimate of the total population from Denevan (1992, p. 291), which is 750,000 for both Honduras and Belize. We assume a similar population density in both areas and therefore, we get a total estimated population of 622,843 people for Honduras. To distribute this population across the states we proceed as follows. First, we consider the coastal states of Choluteca and Valle and parts of the state of El Para��so. For these areas, we apply a population density of 17.70, which corresponds to the simple average of the coastal states of La Uni�on and Moraz�an in El Salvador. This leaves us with a total estimated population of about 220,000 people. The remaining population corresponds to the eastern regiones of the country, which were populated by several peoples, such as the Lencas. Thus, we apply an estimate of 8.19 people per square kilometer to these areas, to get the total population estimated in Denevan.

# 9.10 Mexico

Estimates for Central Mexico come from Sanders (1992), in particular for Mexico, DF, Hidalgo, Puebla, Tlaxcala, Tamaulipas, and Morelos. In addition, Denevan (1992) presents population estimates for the following regions: (i) Baja California Norte and Sur; (ii) Campeche, Quintana Roo, and Yucat�an; (iii) Chiapas; (iv) Chihuahua, Durango, Sinaloa, and Sonora; (v) Coahuila de Zaragoza and Nuevo Le�on; (vi) Colima, (vii) and Tabasco. In the cases in which a region includes more than one state, we impute the same population density for each region. As in all the other cases, we adjust the population estimates so to match the total estimate for Mexico from Denevan (1992, Table 00.1).

# 9.11 Panam�a

As for all other Central American countries, we take advantage of the information that coastal areas were more densely populated. In this case, we use a population density of 0.01 people per square kilometer in the mountain areas. For the coastal regions we apply a population density of 30.88 people per square kilometer, so that we generate the total population of 1,000,000 estimated by Denevan (1992, p. 291). Using estimates for the area of each state belonging to each region, we estimate population density in each state.

31
2 trade statistics with those reported by its main trading partners to assess whether there was in fact significant growth to countries outside the Middle East. We then present a simple model focusing on the role of export incentives, relative prices and foreign income growth in export determination. This model is estimated econometrically and then used for an assessment of the contributions of the various factors mentioned to the Turkish export miracle, a miracle, by the way, the existence of which we do confirm.

# 2 ExRort incentives and Export Performance

# 2.1 Export Developments in Turkey.

Since 1980, Turkey's trade strategy has shifted from interventionist import substitution towards a more market-based outward orientation. Export growth over the same period has been impressive. In volume terms growth averaged 24 percent per annum. As overall exports expanded, the structure of exports changed drastically too, both in terms of commodity composition, and markets reached.

Industrial exports grew spectacularly during 1980-1987, from $ 1.0 billion in 1980 to $ 8.1 billion in 1987. Industrial exports, which in 1980 represented 36 percent of total Turkish exports, had more than doubled their share by 1987. But agricultural exports remained at the same level in value, with their share in total exports falling from 57 percent to 18.2 percent.

The pattern of exports markets has also changed very rapidly, with the Middle Eastern Countries taking an increasing share (cf figure 1). Exports to Oil-exporting Countries (RXO in Fig.l) jumped from $ 0.4 billion ( 13.8 percent of total exports) to $ 2.9 billion ( 36.4 percent of total exports) in 1985. Iran and Iraq have become Turkey's major export markets, mainly because of Turkish exporters easy access to these markets, although supply factors too must have played a role. 1/ However since then exports to oil-exporting countries have stagnated, as falling oil prices forced these countries to curtail their expenditure (cf Fig. 1).

However Turkish export growth was much more than a spill-over from the Middle East, triggered by buoyant OPEC expenditure. Turkish exports to other countries, mostly the major OECD Countries, grew at an equally impressive rate of 17.5 percent per year on average (again, in volume terms). Exports to the OECD Countries expanded in both labor-intensive industries (such as textile and apparel) and specialized products (mostly specialized industrial and electrical machinery). Low value added exports consisting mostly of raw materials (such as processed food), diminished in importance. Exports of textiles, leather products, chemicals, machinery, electrical equipment expanded at a higher growth rate than the average of total exports. Textiles and leather products remain the largest exporting industries, while the iron and steel industry has emerged as the second most important export industry.

**1** The economic boom in the Middle East started well before 1980.
|Tax rates: Obstacle severity (0-|0.300|0.297|0.231|
|---|---|---|---|
|4 scale)||||
||(0.229)|(0.219)|(0.213)|
|Firm has own website Y:1 N:0|0.457|0.306|0.251|
||(0.548)|(0.536)|(0.528)|
|Fertility rate||0.097<br>|23.356**<br>|
|||(0.969)|(11.772)|
|Gender parity in workplace||0.072***|0.433**|
|laws||(0.022)|(0.200)|
|Gender parity in primary<br>||19.854|-113.067|
|education enrollment||||
|||(12.639)|(171.920)|
|Freedom from business<br>||0.204|0.323|
|regulations||||
|||(0.517)|(0.535)|
|Urbanization rate (%)||-0.002|-0.007|
|||(0.036)|(0.037)|
|Women in Parliament (%)||0.086|0.078|
|||(0.061)|(0.062)|
|Paternity leave (logs)||-0.070|0.283|
|||(0.452)|(0.448)|
|Square of Maternity leave<br>|||-0.488|
|(logs)|||(0.524)|
|Maternity leave (logs)*GDP|||-9.429***|
|per capita (logs)||||
||||(2.440)|
|Maternity leave (logs)*Number|||0.559*|
|of workers (logs)||||
||||(0.331)|
|Maternity leave (logs)*Female<br>|||0.377|
|owner present Y:1 N:0||||
||||(0.691)|
|Maternity leave (logs)*Fertility|||-5.024*|
|rate|||(2.574)|
|Maternity leave (logs)*Gender<br>parity in workplace laws|||-0.071<br>(0.046)|
|Maternity leave (logs)*Gender|||27.781|
|parity in primary education||||
|enrollment|||(37.391)|
|Govt. Funded Y:1 N:0*GDP|||0.526|
|per capita (logs)||||
||||(1.643)|

58
longitudinal surveys in the United States. Her results indicate that differences in these noncognitive traits explain about 8 percent of the wage gap. Similar results have been reported for the Netherlands, where adding the factor of personality was found to reduce the gender wage gap from 75 percent to 63 percent (Nyhus and Pons, 2012); and for Russia, where the authors found that the noncognitive skills used (locus of control and challenge-affiliation) explain about 8 percent of the gender wage gap (Semykina and Linz, 2007). Sakellariou (2012) used data from the International Adult Literacy Survey (IALS) for Norway, Denmark, Finland, Hungary, and the Czech Republic to examine whether cognitive skills matter in gender wage decomposition analysis. His results indicate that using the IALS score underestimates the effect of cognitive skills on earnings, but when cognitive skills are specified in terms of origin (i.e., acquired at school or outside of school), the size of the unexplained component changes substantially in Finland, Hungary, and the Czech Republic, with differing patterns in the different countries.

# **III. Methodological Approach**

As a first step toward decomposing earnings differentials, we express earnings as a function of observed characteristics. In our first set of empirical examinations, we use the approach popularized by Mincer (1974); the standard wage function includes experience and schooling. We add a dummy variable for type of employment to this standard formulation in order to control for differences among workers: In the equation above, �� indicates (log) hourly earnings and _Observed Characteristics_ represents a vector of variables that includes years of potential experience calculated as ( _Age_ – _Years of education_ – 6), completed years of education, and a dummy variable for self-employed workers (wage workers serve as the reference group). The above equation can be estimated separately for men and women to get estimates of returns to schooling for men and women, respectively.

We add skills measures to the model above to estimate the association between skills and earnings, controlling for completed years of education. The resultant model is expressed as follows: where _Skills_ represents scores on each of the selected personality traits that are used to estimate noncognitive skills’ associations with earnings. The standardized score on the reading proficiency assessment is then added to estimate the association between cognitive skills and earnings, 7
extend back to 1976, the quality of the data in the mid-1970s is very poor—in 1976 only 14 countries report industry data. By 1985, the number of countries reporting disaggregated data increases dramatically to 65. The sample includes countries reporting data at least 14 years during 1985-2000. The choice of the industries was also guided by the quality of reporting: we kept industries that report fully during the same period.<sup>5</sup> With these conditions, the sample includes on average 85 percent of world trade.<sup>6</sup> We use the disaggregated data to look for evidence of compositional shifts or industry trends that would affect total trade. Compositional effects would occur if, for example, the share of trade increases for products that typically trade at a short distance, pulling down average distance. Alternatively, for some industries there may be a trend in distance as, for example, a country increases trade in that product to more distant places.

Figure 2 shows weighted, unweighted, and median average distances for the 769 industries. The weighted distance multiplies the average distance in a given industry by the industry’s share of total trade in each year. If a changing composition of imports has large effects on trade, then movements in the weighted and unweighted average distance would not coincide. No matter how we measure distance traveled, these results show that the distance that a typical product travels has remained roughly constant for the last 15 years.

The averaged industry data could obscure important changes within industries that offset each other. For example, it is possible that short-distance and long-distance products both increased their weight in trade, leading to little movement in weighted and un-weighted distance. To examine this issue, we also report the distribution of industry

> 4 We also tried using all reporters and there were no significant differences.

> 5 There are 911 industries in total at the 4-digit level.

8
TABLE 1. Descriptive statistics, overall provision of childcare, 2018–2022

||2018|2019|2020|2021|2022|
|---|---|---|---|---|---|
|N of LF women|0.58|0.57|0.43|0.36|0.32|
|N. of employed women|0.45|0.46|0.38|0.29|0.27|
|Labor Force Participation|0.25|0.23|0.18|0.16|0.15|
|Employed|0.19|0.19|0.16|0.13|0.13|
|Coverage rate|0.41|0.46|0.56|0.60|0.64|
|Age|36.47|36.96|37.24|37.20|37.27|
|Married|0.78|0.77|0.77|0.74|0.71|
|Higher education|0.09|0.09|0.09|0.08|0.07|
|Extended household|0.81|0.82|0.84|0.84|0.85|
|Mother|0.53|0.50|0.50|0.50|0.50|
|Grandmother|0.26|0.27|0.27|0.27|0.26|
|Other|0.21|0.22|0.23|0.24|0.23|
|Household size|7.40|7.46|7.42|7.47|7.45|
|N. of women|2.50|2.50|2.47|2.47|2.43|
|N. of children 7-15|0.82|0.88|0.85|0.88|0.91|
|N of children 1-6|1.62|1.63|1.61|1.61|1.61|
|N of employed men in the household|0.98|1.05|0.96|0.87|0.80|
|Households with children aged 1-2|0.26|0.27|0.23|0.21|0.20|
|Households with children aged 3-6|0.43|0.42|0.47|0.52|0.53|
|Households with children aged 1-2 and 3-6|0.31|0.31|0.30|0.26|0.27|
|HH income:||||||
|Lowest income|0.39|0.38|0.37|0.37|0.36|
|Middle income|0.34|0.35|0.34|0.35|0.35|
|Highest income|0.27|0.27|0.29|0.28|0.29|

_Note:_ The sample consists of working-age women aged 15 to 64 living in households with at least one child aged 1 to 6, excluding households with infants under the age of 1; _Source_ : World Bank: Listening to Citizens of Uzbekistan; Uzbekistan Statistical Agency: data on children preschool participation rate; own calculations.

summary statistics of respondents in the HBS datasets, which cover a longer period and are presented in Appendix table 1, demonstrate a considerable resemblance to the respondents in the L2CU dataset. It is evident that the household structure in Uzbekistan did not undergo significant changes over time.

# **4. Methodology**

_Empirical model._ Our analyses are among a sample of female respondents aged between 16-64, who are living with at least 1 youngest child aged between 1-6 years old in the household. We estimate the effect of this policy reform on these women’s labor supply since 2018. The applied generalized staggered difference-in-differences regression 9