rafmacalaba/datause-encoder-data huggingface dataset:
_support_ provided; (c) the quality of _inputs_ in terms of human resources and medical supplies; and (d) facility _infrastructure_ . Principal component (PC) analysis is used to construct, for each of these categories, a single index that is a composite of multiple indicators from the facility surveys. Each index (with a mean of 0 and a standard deviation of 1) is the first PC and increases with higher levels of quality (see Appendix B for the definition of each index). In addition, we use reported perceptions of households on the quality of service at the health facility they have last visited, namely: (e) whether the household faced any discrimination in the facility, (f) the speed of service provided and (g) the waiting time to receive services.

The institutional and governance environment in which health services function is captured by the following indicators derived from the household survey: (i) _level of accountability_ (proxied by the responsiveness of service provider to complaints); (ii) an index of _active participation_ in community level initiatives providing and improving health services.<sup>14</sup> Finally, binary variables for rural/urban areas and regional location are included in all models to take into account any effect of location of a household on expectations about quality and unobserved differences in facility quality, both of which may systematically vary across regions and rural/urban areas. See table 2 for the detailed results from the estimation of models (1) and (2) for pooled, rich district and poor district samples and Appendix B for the description of variables.

After conditioning for expectation, satisfaction varies significantly with various indicators of objective quality and governance in the expected directions, with distinct differences across rich and poor districts. _Firstly_ , satisfaction is significantly correlated with the

> 14 The index of active participation is comprised of multiple indicators related to participation in community level initiatives for providing and improving health services – namely, being present in the community meetings, providing and sharing ideas, and making monetary contributions.

16
some improvements in business practices, but no increases in business profits or in business survival rates.

Finally, a number of recent papers attempt to profile successful entrepreneurs in terms of preferences or behavioral skills (Elston et al., 2005, Djankov et al., 2006; de Mel et al., 2010). Unlike our paper, only a few studies have provided evidence on employment programs that shape these behavioral skills or attitudes. Carneiro et al. (2010) show that that participants in _Chile Solidario_ have greater self-esteem and higher perceived self-efficacy in the labor market as well as greater optimism towards the future.<sup>5</sup> In Jordan, Groh et al. (2012) find that a soft skills training course improved female graduates’ mental health and positive thinking. Blattman et al. (2011) show that in Northern Uganda, the NUSAF Youth Opportunities Program that provides cash grants for investments in seed capital or vocational training had some limited effects on dimensions of social cohesion. Likewise, Blattman and Annan (2011) show that reintegration and agricultural livelihoods program for high-risk Liberian youth have positive impacts on social inclusion, through improvements in social engagement, citizenship and stability. In this paper, we measure how the entrepreneurship training program affected individual’s behavioral skills and their attitudes towards the future.

The rest of the paper is organized as follows. Section 2 briefly sets the country context and describes the entrepreneurship track. Section 3 describes the randomized assignment and take-up of the entrepreneurship track. Section 4 describes the empirical strategy. Section 5 discusses the main effects of the program on labor market outcomes, while Section 6 discusses some of the main channels behind these impacts. Section 7 concludes.

# **<u>2. The Tunisian entrepreneurship track: Business training and coaching</u>**

In Tunisia, both the graduation rate from university and the unemployment rate among tertiary educated youth have been increasing steadily. Access to post-secondary education has soared over the past decade. Gross enrollment rates in tertiary education reached 34 percent in 2009, up from 12 percent in 1995 (EduStats, 2011). At the same time, unemployment rates among youth with a higher education degree have reached alarming levels. According to data from the

> 5 An ongoing World Bank evaluation of the youth training program _Juventud y Empleo_ , a comprehensive training program in the Dominican Republic shows similar results: preliminary evidence shows significant effects on future expectations, job satisfaction, and job search attitudes.

6
year 2004. If there was no discrepancy between the Landsat data for the year 2000 and the MODIS data for the year 2004, the area was selected as training area.<sup>11</sup> For each training area, the spectral reflectance derived from MODIS 500 meters and the elevation data were extracted and used for the training of the algorithm. During this phase the algorithm identifies the relationships between above ground forest biomass and the predictors (remotely sensed and elevation data). Once the relationships are defined, they were applied to the entire set of 2004 data to predict forest biomass for the area where remotely sensed and elevation data were available.

An important step in assessing biomass is to define and identify areas as forests and non-forests. To separate forested from non-forested areas, we use a MODIS product called MOD44B (Vegetation Continuous Field) that provides information on the density of the vegetation defined as ground cover from 0 to 100%. The combined visual analysis of the vegetation density product with the high resolution Landsat data indicates that a threshold of 20% best separates forested from non-forested land. We estimate the amount of forest area in Malawi as areas with vegetation density (according to the MOD44B) above the threshold of 20%.<sup>12</sup> We estimate biomass density for the same areas.<sup>13</sup> The regression tree algorithm allows us to estimate biomass per unit area for Malawi in all 463.3 m x 463.3 m pixels.

Our estimates of biomass or forest area estimates are not directly comparable with the estimates provided in the Government of Malawi 1993 report and cannot be used to assess land cover change between 1990 and 2004. This is because we used different definitions for forested land and the remote sensing data have different spatial characteristics.

# <u>Household Welfare and Biomass Use</u>

> 11 We extracted MODIS observations taken at the beginning and end of the drying season.  At the beginning of the drying season most vegetation still have leaves on, while at the end of the dry season only evergreen trees have leaves on them. This information is useful in separating evergreen vegetation from deciduous vegetation.

> 12 We do not attempt to classify the landscape in land-cover type or forest classes as done in the 1993 report (e.g., Evergreen forests, Brachystegia, Grass, etc.).  This is because our interest is mainly in estimating the volume of biomass per unit of land area, and land-cover type provides only limited information about biomass.

> 13 Setting the tree density threshold to 20%, may result in a value of zero biomass for regions characterized by a mixture of agriculture and trees.

10
Page 40

# BOX: 10

# Agricultural Extension That Misses Female Farmers

Rubber in Malaysia: About 75 percent of "settler wives" spend 5 to 6 hours daily tapping rubber trees and 40 percent help with maintenance. But extension services work systematically only with male settlers. Women settlers wanted extension and reported they would have no problem with extension visits from male extension officera. Women were successfully trained in rubber tapping with much success in Cote d'Ivoire.

Source: World Bank OED, "The Jengke Triangle Projects in Malaysia," 1988.

Cotton and Food in Cote d'Ivoire. Togo. and Burkina Faso: The introduction of cotton improved men's incomes. But it increased women's work at the expense of their traditional endeavors and weakened their economic position. Cotton fields and the income derived therefrom are controlled by men, but women are obliged to work there. Hence, women in Burkina Faso gradually shifted away from work on ttneir household plots -- only 10 percent of women report cultivating such plots. Women there now constitute about half of the agricultural labor force, provide 60 percent of the labor for mechanized production, and 35 percent of hired labor. By contrast, most women in Cote d'Ivoire and Togo still also cultivate household plots, primarily for food for home consumption but also for income. Women have more control over the income from those plots, particularly in Cote Ivoire, where they are gradually expanding the plots and growing more cash crops. On the whole, in these three countries, women supply 40 to 60 percent of the labor for agricultural activities. In the M'Bingue Region of Cote d'Ivoire, women provide 41 percent of all agricultural labor, including 67 percent of the labor requirements for groundnuts, 25 percent for rainfed rice, 90 percent for irrigated rice, and 44 percent for cotton. In Burkina Faso, women have some access to extension and credit through small-scale programs involving development/extension agents and women's groups. But in Togo and Cote d'Ivoire, where women are more active in cashcropping and other production on their own plots, they lack access to extension (except for a special program for rubber in Cote d'Ivoire). They can get access to inputs only in Togo. "If women as cultivators have no direct access to extension services, one of the principles of the methodology used in all three countries -- the feedback from the farmer to the research center -- is unlikely to occur." Although women work more in the "family" cotton fields, men control the income from cotton, and women lose income by putting less time into their household plots. Reduced access to the household plots affects the status of women and their daughters -- these fields are often a source of food and income for school fees, clothing, and medical supplies. They are often important to the survival of polygamously married women and their children in the dry season, when the output from the family fields is exhausted. Loss of income from this source is not made up by revenues from the family fields -- the proceeds from which are often allocated to the purchase of another wife, a mobylette, or a
# **Table 1: Survey design and interview completion rates**

||Weekly in person<br>interview|Weekly phone<br>interview|Triweekly phone<br>interview|Total|
|---|---|---|---|---|
|**Baseline**(Aug-Sept 2013)|||||
|Targeted # participants|320|320|320|960|
|Interviews Completed|318|315|321|954|
|**High Frequency Surveys**(Aug–Oct|2013)||||
|Interviews Attempted|3180|3150|9630|15,960|
|Interviews Completed|2977|2797|8593|14,367|
|_Non-completion rate_|6.4%|11.2%***|10.8%***|10.0%|
|**Endline (**Oct-Nov 2013)|||||
|Interviews Attempted|318|315|321|954|
|Interviews Completed|318|314|320|952|
|_Non-completion rate_|0.0%|0.3%|0.3%|0.2%|
|**3-Month Follow up**(March 2014)|||||
|Interviews Attempted|318|315|321|954|
|Interviews Completed|281|289|295|865|
|_Non-completion rate_|11.6%|8.3%|8.1%|9.3%|

Note: ***, ** , * indicate the differences in completion rate are different from the weekly in person group at the 1%, 5%, and 10% significance levels respectively 21
seen in the 1960s and 1970s, this time, mentions to inequality increased visibly in every country.

# **3.1 ROBUSTNESS OF THE ENLIGHTENMENT**

To assess the robustness of the poverty and inequality political enlightenment to different methodologies, we perform a similar analysis to the one presented in the previous section, but with some modifications. The first of these involves increasing the threshold of the number of times a speech must mention a keyword to be counted as referencing the subject. In other words, instead of considering that a presidential speech discusses poverty and inequality if the associated keywords are mentioned at least once, we increase the threshold to higher values. Figure 3 shows the percentage of presidential speeches on each decade that contained at least “�” times the relevant keywords, for �∈�1,2,3,4,5�.

## **Figure 3. Different thresholds to assess prevalence of poverty and inequality** _(% of speeches that contain each keyword at least “t” times by decade)_

<!-- Start of picture text --> (a) Poverty (b) Inequality<br>100 100<br>75 75<br>50 50<br>25 25<br>0 0<br>t = 1 t = 2 t = 3 t = 4 t = 5<br>% of speeches % of speeches<br>1830 1850 1870 1890 1910 1930 1950 1970 1990 2010 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010<br><!-- End of picture text --> Note: This Figure shows the percentage of presidential speeches on each decade that contained at least “t” times the keywords “poverty” or “poor” (panel a), and the keywords “inequality,” “inequity,” “equity” or “equality” (panel b). The percentage was calculated by pooling the presidential speeches of 10 Latin American countries by decade. See Appendix A for more details about the data.

Increasing the threshold of words diminishes the percentage of speeches with the keywords, but the enlightenment remains. For example, with a threshold of at least five mentions, the percentage of speeches during the 2000s that mentioned poverty and inequality plummets from 98% to 78%, and from 81% to 38%, respectively. Nonetheless, the evolution of the frequencies tells the same story: the last decades saw a clear rise in the discussion of these subjects in political speech.

7
15 Adjustmof Trad **e** Flows.nt Soviet energy exports are the residual of domestic production and consumption, equivalent to around 20 percent of production in 1989. As production is dropping and domestic consumption stagnating, the size of this residual has declined substantially since 1988 (by 5 percent for the first nine months of 1990 compared to the same period in 1989; by another 10 percent for 1991). In 1990, the USSR changed its allocation policy in anticipation of the new “MEA rules and in response to its own balance of payment problems. Earlier, volumes of deliveries to the Five were kept stable and adjustment was made in convertible currency exports. In early 1990, however, the USSR reversed the priority. Deliveries to the Five started to drop sharply and the year as a whole may average 15-25 percent below 1989 levels. Informal estimates for 1991 indicate a further decline, perhaps to one-third of the 1989 levels.

Table 7. Estimates of Implicit Soviet Subsidies’ (in billions of current US$)

|ae|1982<br>e|1987<br>e|1988<br>|1989<br>e|(a)<br>|()<br>e|1990<br>()<br>|Percent of<br>___GpP|
|---|---|---|---|---|---|---|---|---|
|Hungary|2.6|0.1|0.1|1.0|1.1|1.9|2.6|3.4|
|Bulgaria|3.7|0.2|0.1|1.4|1.6|2.7|3.7|7.1 (1989)|
|Czechoslovakia|49|0.2|0.2|18|2.1|3.5|49|7.0 (1989)|
|GDR|5.9|0.3|0.2|2.2|2.5|42|5.9|n.a.|
|Poland|43|0.2|0.2|1.6|1.8|3.1|43|5.0|
|Romania|0.4|0|0|0.1|0.2|0.3|0.4|na.|
|CMEA Six|21.7|1.0|0.9|8.1|9.4|15.6|21.8|na.|
|Five (ex. GDR)|15.9|0.8|“1.1 _|5.8|6.8_|_<br>11.4|15.9|na|

(a) based on oil price of $15.8/barrel (b) _ based on oil price of ($15.8+$26.0)/2 = $20.9/barrel (c) based on oil price of $26.0/barrel Source: Michael Marrese, OECD, forthcoming; GDP: World Bank estimates.

As a result of the terms-of-trade adjustment and reduced Soviet crude oil exports, the volume and value of intra-CMEA trade are expected to fall. A second volume effect is likely in manufactured exports to the USSR. The USSR could use the convertible currency receipts from raw materials exports left after its debt service payments to switch to imports of higher quality and/or lower prices by buying from CC countries. Since the available export supply of the Five can arguably not be diverted easily to other markets, a sizable share of their exports would collapse. Alternatively, the USSR could demand delivery of fully competitive manufactures as a condition for continued supply of fuels and other crucial raw materials. In this case, the Five would have to divert some of their exportables from OECD markets to the USSR, thus reducing their own capacity to maintain import from convertible currency markets. In this case some present trade of the Five with OECD markets would be destroyed.2’ The USSR, in contrast, is likely to be a net gainer in either case: the income effect of improved terms with CMEA countries and increased convertible currency revenues
- 52 -

<!-- Start of picture text --> (8) ex ** *T,'<br>rns, -nica t- et+ dt<br>.*T . 8 _ 0<br>rgdpt<br><!-- End of picture text --> nica is the non-interest current account in US dollars as ratio of real GDP, superscript T denotes total external debt (private and public sector)<sup>2 6</sup> and the rest of variables are as defined before. The real net savings of the public sector is the real PS deficit with opposite sign and the real net savings of the private sector is estimated as a residual using equation (7).

In equation (7) the net savings identity is defined in real terms. This identity in real terms provides a more accurate estimate of the resources transferred between sectors because it classifies only the real interest payments on the domestic and external debt as an expense of the public sector and as an income of the private and external sectors. As is well-known, when nominal interest payments on the external and domestic debt are classified as an expense, the PS exp.nditureu and the savings of the external and private sectors will be overestimated because the compensation for the erosion of inflation is classified as an expense rather than as a capital amortization. Moreover, using the real net savings id'antity, we will be able to make comparisons among countries that experienced different domestic and external inflation rates and different levels of domestic and external debts.

26/ In most countries the private external debt accounted for a very small proportion of the total external debt.
|P064983|2002|2002|6/28/2002|1 Closed|Algeria|
|---|---|---|---|---|---|
|P064983|2002|2003|4/15/2003|3 Closed|Algeria|
|P064983|2002|2004|6/29/2004|6 Closed|Algeria|
|P064983|2002|2005|5/11/2005|8 Closed|Algeria|
|P064983|2002|2005|6/24/2005|9 Closed|Algeria|
|P064983|2002|2006|12/23/2005|10 Closed|Algeria|
|P064983|2002|2006|6/23/2006|11 Closed|Algeria|
|P064983|2002|2007|12/28/2006|12 Closed|Algeria|
|P065035|2004|2004|6/22/2004|2 Closed|China|
|P065035|2004|2005|6/24/2005|4 Closed|China|
|P065035|2004|2006|4/17/2006|5 Closed|China|
|P065035|2004|2007|5/13/2007|6 Closed|China|
|P065035|2004|2008|5/15/2008|7 Closed|China|
|P065035|2004|2009|4/18/2009|8 Closed|China|
|P065035|2004|2010|4/23/2010|9 Closed|China|
|P065035|2004|2021|7/23/2020|10 Closed|China|
|P065041|2000|2000|6/29/2000|1 Closed|Romania|
|P065041|2000|2001|6/22/2001|3 Closed|Romania|
|P065041|2000|2002|6/27/2002|5 Closed|Romania|
|P065041|2000|2003|6/19/2003|7 Closed|Romania|
|P065041|2000|2004|5/20/2004|9 Closed|Romania|
|P065059|2001|2001|6/29/2001|1 Closed|Poland|
|P065059|2001|2002|6/27/2002|3 Closed|Poland|
|P065059|2001|2003|6/11/2003|5 Closed|Poland|
|P065059|2001|2004|5/26/2004|7 Closed|Poland|
|P065059|2001|2005|6/2/2005|10 Closed|Poland|
|P065059|2001|2006|6/6/2006|11 Closed|Poland|
|P065059|2001|2007|6/28/2007|12 Closed|Poland|
|P065059|2001|2008|6/26/2008|13 Closed|Poland|
|P065059|2001|2009|12/29/2008|14 Closed|Poland|
|P065111|2003|2003|2/28/2003|1 Closed|Yemen,Republic of|
|P065111|2003|2004|3/31/2004|4 Closed|Yemen,Republic of|
|P065111|2003|2005|4/29/2005|6 Closed|Yemen,Republic of|
|P065111|2003|2006|12/9/2005|7 Closed|Yemen,Republic of|
|P065111|2003|2006|1/9/2006|8 Closed|Yemen,Republic of|
|P065111|2003|2007|12/28/2006|9 Closed|Yemen,Republic of|
|P065111|2003|2007|6/27/2007|10 Closed|Yemen,Republic of|
|P065111|2003|2008|12/7/2007|11 Closed|Yemen,Republic of|
|P065111|2003|2008|6/11/2008|12 Closed|Yemen,Republic of|
|P065111|2003|2009|12/31/2008|13 Closed|Yemen,Republic of|
|P065111|2003|2009|6/29/2009|14 Closed|Yemen,Republic of|
|P065111|2003|2010|10/12/2009|15 Closed|Yemen,Republic of|
|P065111|2003|2010|6/30/2010|16 Closed|Yemen,Republic of|
|P065113|2000|2000|6/26/2000|1 Closed|Philippines|
|P065113|2000|2001|6/4/2001|3 Closed|Philippines|
|P065113|2000|2002|6/6/2002|5 Closed|Philippines|
|P065113|2000|2003|6/2/2003|7 Closed|Philippines|
Table 1: The average capital stock as a fraction of _k_<sup>_ss_</sup> for different levels of

|Infation|_E_(_k_)<br>_k_<sup>_ss_</sup> <sup>: </sup>|<sup>Machinery investment</sup>|_E_(_k_)<br>_k_<sup>_ss_</sup> <sup>: Land and building investment</sup>|
|---|---|---|---|
|-3%||1.01|1.04|
|7.4%||0.94|0.93|
|15.7%||0.86|0.86|
|24%||0.84|0.84|
|30%||0.76|0.945|
|40%||0.77|0|

_E_ ( _k_ ) is the average capital stock of the firm during its steady state cycle; _k_<sup>_ss_</sup> is given by the condition _αk_<sup>_α−_1</sup> = _ρ_ + _δ_ , and corresponds to the steady state capital level of the standard neoclassical growth model with divisible investment (which, in this model, can be achieved by taking the limit _C →_ 0). The length of a period is specified as a day, and annual discount, inflation and depreciation rates are adjusted accordingly. For the case of machinery, the size of the indivisible investment is calibrated to match a steady state with 7.4% inflation and a maximum ratio of investment over sales of 1.373. Similarly, for land and buildings, the indivisible investment is calibrated to match a maximum ratio of investment over sales of 2.25.

# **4 Suggestive evidence**

In this section, we present some evidence from firm level data that is broadly consistent with McKinnon’s mechanism. We begin by describing the data used for the analysis. We use the World Bank Enterprise Survey (WBES), a rich firm-level survey database that provides information about firms’ characteristics such as ownership, size, sector and their activities such as employment, investment and sales. Other information such as the degree of access to finance and the interactions with government agencies is also collected. In our sample, there are 41,602 observations (consisting of 33,012 unique firms) in 57 countries, spanning from 2002 to 2011. The sample was selected using stratified random sampling. Three levels of stratification were used in all countries: industry, establishment size, and region.<sup>6</sup>

> 6Sampling details can be found at “BEEPS 2008-2009: A report on methodology and Observations”.

14
|(1)<br>Table 5: Export participation and share of workers trained|(2)<br>, OLS estim|(3)<br>ates for s|(4)<br>witchers|
|---|---|---|---|
|A: Dependent variable: share of workers receiving technical upgradi|ng|||
|Export (=1 if yes)<br>0.002***|0.001**|0.001**|0.001**|
|(0.001)|(0.000)|(0.000)|(0.000)|
|Log employment|0.002***||0.001|
||(0.000)||(0.000)|
|B: Dependent variable: share of workers receving training in other c|ategories|||
|Export (=1 if yes)<br>-0.000|-0.001|-0.001|-0.001|
|(0.001)|(0.001)|(0.001)|(0.001)|
|Log employment|0.001||0.002|
||(0.000)||(0.001)|
|Firm effects<br>No|No|Yes|Yes|
|Sector effects<br>No|Yes|No|No|
|State effects<br>No|Yes|No|No|
|Firm controls<br>No|Yes|No|Yes|
|Observations<br>29,347<br>Notes: Robust standard errors clustered by firm in parentheses. <br>regressions include year effects. Firms controls are: average age <br>female workers ratio; share of workers with less than high schoo<br>exactly high school completed; share of workers with more than <br>wage; share of white workers; share of workers in each of skill level|29,347<br> *** p<0.01, <br>of workers; <br>l completed<br>high school <br>s 1 to 4.|29,347<br> ** p<0.05, <br>average ten<br>; share of <br> completed;|29,347<br> * p<0.1. All<br>ure; male to<br>workers with<br> average log|