rafmacalaba/datause-encoder-data huggingface dataset:
- 15 -

for only 35 per,nt of the growth in total factor productivity.1t / The inclusion of monetary balances has cut the unexplained residual in half. Hence, even if our coefficients are off by a factor of two, it appears inescapable that the change in the structure of financial technology has made a very substantial contribution to economic growth in Colombia.

# B. Income Distribution Issues

4.05 Measuring the incidence of lower inflationary taxes or a more competitive financial system is, as Urrutia (1985) shows, a very difficult task, and our aggregate results shed little direct light on this important issue. Rather than trying to tease out the possible effects that financial policy may have had on income or wealth distribution, as Berry and Soligo (1980) have creatively done, it is perhaps of more interest to consider briefly in the words of Sherlock Holmes, "a dog that did not bark." The dog in this case is the observation that the share of income of the lowest income group did not deteriorate over the 1958-84 period. In fact, it improved.12 1 The improvement is surprising because the traditional view of the development process is that during development there is generally an initial deterioration in the earnings of lower income groups. Chenery and Syrquin's (1975) model allows for a rough quantification of how income level shares might be expected to behave.<sup>221</sup> It indicates that based on Colombia's income level and population growth over this 1E/ The growth in output per unit of labor is decomposed into growth in capital per unit of labor, growth in monetary balances per unit of labor and the remainder is attributed to "technical change." When monetary balances are not included, growth due to capital per labor is 28 percent and technical change, 72 percent. However, when monetary balances are included, growth due to capital is 21.5 percent, technical change, 35 percent and monetary balances, 43.5 percent. For more detail, see Solow (1957).

IV/ See Reyes (1987) for the most recent data on income distribution. Urrutia (1985) provides the most comprehensive evidence for the 1970s.

ZQ/ The Chenery and Syrquin work is a regression model of the behavior of 101 countries over the 1950-1970 period. It "explains" various characteristics of an economy, e.g. employment in different sectors, urbanization, saving, and income distribution measures as a function of the level of per capita income and population. The basic perspective of the work is that development processes are sufficiently uniform among countries to produce a consistent pattern of change. The analysis provides measures for the behavior of a representative developing country. For Colombia, the model's predictions for employment distribution among primary, manufacturing and service sectors were very close to the observed values; so too were its estimate of savings and investment rates and the distribution of income.
controlling for spatial price variation<sup>15</sup> – governorates in which income levels are lower would have a relatively lower incidence of households reporting that their income is sufficient to satisfy needs. On the other hand, as shown in Figure 4, while average household income is by far the lowest in Idleb governorate, the share of households in Idleb that report income being sufficient to satisfy household needs is the third highest after Damascus, Aleppo and Ar-Raqqa. This finding, coupled with background information of high levels of both cash and in-kind humanitarian assistance being provided to households in Idleb underscore the limitations of a partial welfare metric not comprehensive of in-kind income receipts.

_Figure 3: Contribution to total household monetary income, by source_ <!-- Start of picture text --> IDLEB<br>QUNEITRA<br>TARTOUS<br>AS-SWEIDA<br>RURAL DAMASCUS<br>ALEPPO<br>HAMA<br>DAR'A<br>LATTAKIA<br>DAMASCUS<br>HOMS<br>DEIR-EZ-ZOR<br>AR-RAQQA<br>AL-HASAKEH<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Labor Remittances (domestic) Remittances (Intl.) Pension Cash Assistance<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP June2021 Demographic and WASH survey data _Figure 4: Average household income and share of households reporting income sufficiency, by governorate_ <!-- Start of picture text --> 500,000 45%<br> 450,000 40%<br> 400,000 35%<br> 350,000 30%<br> 300,000 25%<br> 250,000<br>20%<br> 200,000<br> 150,000 15%<br> 100,000 10%<br> 50,000 5%<br> - 0%<br>Income % income sufficient<br>(SYP)<br>Average household income<br>sufficient to satisfy needs<br>% household reporting income<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP June2021 Demographic and WASH survey data Overall, the fact that the income metric used in the HNAP report is solely based on labor income and that it does not include in-kind income receipts leads to the conclusion that the 98 percent extreme poverty estimate is significantly over-estimated.

> 15 Due to data limitation, a basic food spatial deflator was constructed using the cost of the WFP basic food basket at the governorate level.
Figure 8: Kernel density of market access (with _θ_ = 40) <!-- Start of picture text --> 9 10 11 12<br>ln market access<br>2007 2008 2009 2010<br>1<br>.8<br>.6<br>density<br>.4<br>.2<br>0<br><!-- End of picture text --> restrictions around Nablus in late 2009 arguably denotes the most prominent change. Not only because Nablus was surrounded by some of the most restrictive obstacles, effectively amounting to a blockade, but also because the city is central to the West Bank, both geographically and economically.<sup>14</sup> Another notable change is the gradual improvement in connectivity between the north and the south of the West Bank (from late 2007 to 2010) resulting from a reduction in Wadinar checkpoint traversal time (which controls North-South traffic). As some of the restrictions to north-south traffic were eased, more centrally located localities were able to re-connect to the south’s large market/population. The improvements in market access brought about by the above mentioned changes are also apparent in Figure 9 which shows a time-series of log market access by means of a box plot. The width of the boxes show the cross-sectional variation in (log) market access (across locations).

14It is the second largest city, home to the Palestinian Stock Exchange (which is small by international standards), and enjoys close geographic proximity (in the absence of obstacles) to all but the two most southern governorate capitals (which are Hebron and Bethlehem).

20
national location, and country heterogeneity. Evidence from the BEEPS-AMADEUS dataset confirms that a good business environment encourages firms to operate efficiently and promote productivity growth by lowering risks, costs, and barriers to entry. In a global economy where technology diffuses rapidly, the persistence of productivity differences across countries may be largely explained by differences in the business environment in which firms operate. These microeconomic foundations of economic development—infrastructure quality, financial development, governance, labor market flexibility, labor quality, and competition—are critically linked to the success and growth of firms.

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Table S2.9 Share of Formal Employment, Informal Employment, and Self-employed in Total Employment (percent)

|||||||Share|in tota|l emplo|yment||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2002|2003|2004|2005|2006|2007|2008|2009|2010|2011|2012|2013|2014|2015|
|Argentina|||||||||||||||
|Share of formal workers|57|54|55|55|57|60|60|61|63|62|63|62|62|62|
|Share of informal workers|24|28|27|27|26|24|22|21|22|22|21|21|21|22|
|Share of self-employed|19|18|18|18|17|16|18|18|15|16|16|17|17|16|
|Bolivia|||||||||||||||
|Share of formal workers|12|13|13|15|19|18|17|18|20|21|22|24|21|21|
|Share of informal workers|40|41|42|35|34|39|39|39|36|33|33|29|32|31|
|Share of self-employed|48|46|44|50|47|43|44|42|44|45|46|47|47|49|
|Brazil|||||||||||||||
|Share of formal workers|54|55|56|57|58|60|62|62|63|64|65|67|66|66|
|Share of informal workers|23|22|22|22|21|20|19|18|17|16|15|14|14|13|
|Share of self-employed|23|23|22|21|21|21|19|19|20|20|20|19|20|21|
|Chile|||||||||||||||
|Share of formal workers|69|69|70|70|71|71|70|69|71|73|74|76|75|75|
|Share of informal workers|15|14|14|13|13|13|14|15|13|11|11|10|10|11|
|Share of self-employed|17|17|17|16|16|16|16|16|16|16|15|14|14|15|
|Costa Rica|||||||||||||||
|Share of formal workers|62|63|64|63|63|65|65|67|65|66|66|67|68|66|
|Share of informal workers|20|21|19|22|21|21|19|19|20|20|19|19|19|20|
|Share of self-employed|18|16|17|15|16|14|15|15|15|15|15|15|14|14|
|Mexico|||||||||||||||
|Share of formal workers|37|38|38|39|40|40|41|39|38|37|36|38|39|41|
|Share of informal workers|45|46|47|48|49|49|50|51|53|53|54|53|52|51|
|Share of self-employed|18|17|15|13|12|11|9|9|10|10|10|9|9|8|
|Nicaragua|||||||||||||||
|Share of formal workers|25|24|24|24|24|25|25|25|27|28|29|31|32|33|
|Share of informal workers|44|44|43|42|41|40|39|38|38|39|40|40|41|42|
|Share of self-employed|31|32|33|34|35|36|36|37|35|33|31|29|27|25|
|Peru|||||||||||||||
|Share of formal workers|29|30|16|31|33|37|38|42|42|45|47|49|50|48|
|Share of informal workers|67|65|79|64|63|60|59|54|54|51|49|48|46|48|
|Share of self-employed|4|5|5|5|4|4|4|4|4|4|3|3|3|4|
|Paraguay|||||||||||||||
|Share of formal workers|7|6|7|9|7|9|10|11|11|12|13|14|13|13|
|Share of informal workers|17|18|20|21|21|20|20|20|18|20|20|20|20|20|
|Share of self-employed|77|76|73|70|72|71|70|70|71|68|67|67|67|67|
|El Salvador|||||||||||||||
|Share of formal workers|34|34|32|35|33|34|36|34|34|33|34|36|37|38|
|Share of informal workers|32|31|35|33|36|33|32|33|34|35|35|33|35|30|
|Share of self-employed|34|35|33|33|31|33|31|33|32|32|31|30|29|32|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : The shares shown in this table were calculated based on total employment. If a specific country did not have information available for a particular year, a simple interpolation was applied. The sample was restricted to individuals between ages 18 and 65 years.

61
Table 6: Spatial Disparity, Access to Infrastructure and Sorting of household Attributes Regression Coefficients

|||2000|||2010||
|---|---|---|---|---|---|---|
||All|Urban|Rural|All|Urban|Rural|
|Years of Education|0.052***|0.051***|0.048***|0.070***|0.120***|0.026*|
||(7.700)|(6.085)|(4.486)|(6.609)|(6.634)|(1.957)|
|% of workers skilled|-0.037|-0.021|-0.345|0.136|-0.074|-0.149|
||(-0.330)|(-0.170)|(-1.453)|(1.301)|(-0.489)|(-0.941)|
|% of workers semi-skilled|-0.040|-0.004|-0.211|-0.114|0.042|-0.213*|
||(-0.443)|(-0.038)|(-1.402)|(-1.415)|(0.373)|(-1.761)|
|% of workers in manufacturing|-0.076|-0.156|0.278*|-0.275***|-0.353**|-0.155|
||(-0.720)|(-1.030)|(1.666)|(-3.021)|(-2.432)|(-1.197)|
|% of workers in services|-0.181**|-0.335***|0.132|-0.113|-0.164|0.049|
||(-2.270)|(-2.847)|(1.088)|(-1.610)|(-1.463)|(0.470)|
|% children|-0.637***|-0.502**|-0.655***|-0.119|0.416|-0.330*|
||(-4.364)|(-2.453)|(-3.277)|(-0.788)|(1.403)|(-1.930)|
|% of household with phone|0.911***|0.925***|2.045***|0.218***|0.364***|0.204***|
||(6.933)|(6.973)|(2.819)|(3.466)|(2.583)|(2.874)|
|% household with electricity|0.223***|0.339***|0.306***|0.138***|0.197**|0.204***|
||(4.983)|(4.296)|(5.010)|(3.699)|(1.977)|(4.940)|
|% of hh with sanitary latrine|0.088|0.059|-0.114|0.084|0.059|-0.006|
||(1.578)|(0.710)|(-1.250)|(1.539)|(0.576)|(-0.086)|
|% with water seal latrine|0.086|0.149|-0.008|0.092|0.009|0.154**|
||(0.939)|(1.332)|(-0.030)|(1.636)|(0.088)|(2.160)|
|% with paved pit latrine|-0.045|-0.065|0.042|0.042|-0.014|0.055|
||(-0.742)|(-0.754)|(0.441)|(0.786)|(-0.136)|(0.866)|
|% unpaved permanent latrine|0.062|-0.143|0.098*|-0.048|-0.185*|0.056|
||(1.286)|(-1.597)|(1.675)|(-0.982)|(-1.655)|(1.061)|
|% using tap water|-0.000|-0.097|-0.211|-0.013|0.017|0.050|
||(-0.004)|(-0.330)|(-0.891)|(-0.194)|(0.146)|(0.349)|
|% using tubewell water|-0.111|-0.278|-0.067|-0.068|-0.000|-0.064|
||(-1.138)|(-0.970)|(-0.670)|(-1.229)|(-0.001)|(-0.983)|
|Log(distance to large cities)|0.041**|0.018|0.111***|-0.051**|-0.045*|-0.027|
||(2.286)|(0.665)|(3.321)|(-2.569)|(-1.738)|(-0.821)|
|Rain Standard Deviation|0.000|0.000|-0.000|0.000|0.000|0.000|
||(1.352)|(0.134)|(-1.166)|(1.472)|(0.938)|(0.888)|
|Monsoon Rain Coeff. Of variation|-1.545*|-1.373|1.287|-2.010***|-1.485|-2.019**|
||(-1.947)|(-0.825)|(1.242)|(-3.248)|(-1.623)|(-2.352)|
|Rice price index|0.038***|0.039***|0.045***|0.005|0.004|0.009**|
||(4.686)|(2.757)|(4.728)|(1.643)|(0.858)|(2.290)|
|log(housing cost)|0.213**|-0.034|0.572***|0.160*|0.222|0.110|
||(2.213)|(-0.185)|(3.486)|(1.958)|(1.632)|(1.093)|
|Non-traded price index|0.002**|0.002|0.001|0.001***|0.000|0.002***|
||(2.471)|(1.561)|(1.020)|(2.950)|(0.339)|(2.728)|

Robust z statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
# **A6.2 Robustness: Choice of elasticity** σσ

**Table A4: Counterfactual change in market output: Varying elasticity** σσ

||Labor ma|rket only|Human<br>on|capital<br>ly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995<br>(1)<br>|2005<br>(2)<br>|1995<br>(3)<br>|2005<br>(4)|1995<br>(5)|2005<br>(6)|1995<br>(7)|2005<br>(8)|
|**Panel A: Market**|**output, relati**|**ve to 2015**(σ|= 2)||||||
|Costa Rica|1.388|<br>1.011|<br>0.604|0.810|0.983|1.049|0.701|0.858|
|El Salvador|1.012|<br>1.001|<br>0.972|1.014|1.065|1.030|1.031|1.054|
|Panama<br>|2.602<br>|1.624<br>|0.146<br>|0.328|0.727|0.826|0.150|0.403|
|**Panel B: Market**|**output, relati**|**ve to 2015**(σ|= 4)||||||
|Costa Rica|1.529|<br>1.006|<br>0.576|0.790|0.980|1.075|0.680|0.852|
|El Salvador|1.014|<br>1.001|<br>0.970|1.020|1.080|1.037|1.043|1.076|
|Panama|3.261|1.852|0.147|0.279|0.712|0.814|0.148|0.371|

**Table A5: Counterfactual change in women’s LFPR: Varying elasticity** Labor market σσ

||Labor<br>on|market<br>ly|Human<br>o|capital<br>nly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995<br>(1)<br>|2005<br>(2)<br>|1995<br>(3)<br>|2005<br>(4)<br>|1995<br>(5)<br>|2005<br>(6)<br>|1995<br>(7)<br>|2005<br>(8)|
|**Panel A: Labor for**<br>|**ce participati**|**on rate, diff**|**erence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(σ|= 2)||
|Costa Rica|12.1|1.3|4.8|3.3|-21.1|<br>-10.7|<br>-10.6|-10.6|
|El Salvador|-6.4|-1.2|-0.2|-0.3|37.7|<br>32.5|<br>33.8|30.4|
|Panama<br>|13.5<br>|4.9<br>|19.8<br>|15.8<br>|6.4<br>|10.0<br>|30.0<br>|22.6|
|**Panel B: Labor for**|**ce participati**|**on rate, diff**|**erence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(σ=|4)||
|Costa Rica|17.6|3.4|3.3|1.3|-24.9|<br>-12.9|<br>-19.2|-16.4|
|El Salvador|-6.6|-1.3|-0.4|-0.2|39.5|<br>34.9|<br>36.1|33.3|
|Panama|24.6|10.0|8.4|6.1|5.3|9.7|11.0|13.8|

47
labor force can foster the idea that girls are equally capable of being economically productive. This in turn can change young girls’ aspirations and perceptions,<sup>19</sup> and encourage parents to invest in their daughters (similar to the role model effect described above). Schooling and employment opportunities can also bring about ideational change by exposing young individuals to gender egalitarian ideas (see Thornton’s (2001) literature review/commentary on developmental idealism).

Note that women with higher education and/or employment status do not always display a lower tendency to sex select. In fact, there are various studies that suggest that they are more likely to sex select (DasGupta 1987; Amin 1990). The results from the studies cited in this paragraph and the one above are not necessarily contradictory if one accounts for the non-linearity between maternal education (or other markers of socioeconomic development) and son preference (this argument is expanded upon in Section 5).

**Table 4: Overview of studies that illustrate the link between female education/employment/access to financial resources, and female child wellbeing**

|**Author(s)**<br>**(Date)**|**Research question**<br>|**Data**|**Empirical strategy**|**Key findings**|
|---|---|---|---|---|
|Murthi et. al<br>(1995)|What covariates are<br>associated with<br>gender-specific<br>child mortality<br>rates?|District-level data<br>from 1981 Census|Multi-variate<br>regression<br>(associative study)|Higher female<br>literacy in a district<br>is associated with<br>lower odds of a<br>gender gap in child<br>mortality. These<br>results hold across<br>all regions.|
|Ren (1995)|What covariates are<br>associated with<br>changes in the<br>gender-gap in infant<br>and child mortality<br>rates?|China In-depth<br>Fertility Survey<br>(1985 and 1987)|Multi-variate logit<br>regression<br>(associative study)|When mothers are<br>educated, girls had<br>relatively higher<br>odds of neo-natal<br>survival than boys|
|Pande and<br>Astone (2007)|What covariates are<br>associated with<br>lower levels of son<br>preference?|1992-93 NFHS|Ordered logistic<br>regression<br>(dependent variable<br>is son preference<br>measured in an<br>ordinal scale)<br>(associative study)|Female education,<br>particularly at<br>secondary plus<br>levels, was<br>associated with<br>lower son preference<br>regardless of family<br>size (or other<br>controls)|

> 19 See Stout et al.’s (2011) experimental study on how random exposure to successful women in science, technology, engineering, or mathematics (STEM) changed young girlsgirls’ perceptions about the ability of females to succeed in STEM careers. Also see Marx and Roman’s (2002) experimental study on how women’s math test scores improved in the presence of women who represented stereotype defying information about women’s mathematical abilities in general.

34
<!-- Start of picture text --> 4 (a) Full Plot Calibrated a“ 4 (b) Crop Cut Calibrated 4 (c) Uncalibrated (SCYM) “<br>R? 20.55 R? 0.26 R? = 0.54 Lo<br>3 3 3 “ r<br>= _ _ Fa ao<br>5 ; 5 z Soe<br>= . > va a<br>=a 2 =pe 2 oe. *% =a2 “4 a“<br>3o$ ws° . : oO2S .“e"° °“ a 3a4 aZ“fe7 ae<br>o Ly o . y “ See<br>1 “n° 8 1 1 a“ foe<br>P ‘ o , ae “LO a as ,<br>ae* J&gale ‘as “4 a“eeey<br>o | ‘. ol as ol vats<br>0 1 2 3 4 0 1 2 3 4 0 1 2 3 4<br>RS_CAL yield (Mgiha) RS_CAL_CC yield (Mgiha) RS_SCYM yield (Mg/ha)<br><!-- End of picture text -->
# **Fig 6 Thailand, 2008 (attending public school, ages 7-18)**

**Fig 6a                                                         Fig 6b**

<!-- Start of picture text --> Unitary expenditure on education Unitary expenditure on education<br>by quintiles of consumption (2008) by quintiles of probability (2008)<br>658 559<br>515 572 533 510 500 548<br>484<br>450<br>387 406 421 460 487 473 441 419 403 406<br>-62 -78 -94 -112 -171 -86 -91 -91 -96 -141<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>600<br>600<br>400<br>400<br>US$ 200 US$ 200<br>0 0<br>-200 -200<br><!-- End of picture text --> Source: Author’s estimates from Thailand MOE LAO Survey, CGD data, SES (2008) and Madrigal and Cuesta (forthcoming) For all countries except Paraguay and Thailand, public spending on education was not disaggregated at the regional level––Tajikistan had regional-specific information, but not level specific. Thai data allowed an analysis of primary and secondary education across the main regions of the country, but not at a provincial level (the second subnational level in the country).<sup>24</sup> So, it was only for Paraguay that public spending was specific to both levels of education and each of the departments of the country. Taking advantage of this disaggregation, the following analysis disaggregates the BIA and OIA for each level of education for Paraguay, with benefits specific to each department.

Disaggregated results for Paraguay, reported in figures 7 and 8, show different distributive patterns for elemental (preschool and primary) and secondary education. Both the BIA and OIA portrayed a progressive distribution of net public spending on elemental education, which results from both progressive gross public benefits and private contributions patterns. That is, as welfare levels increase and sets of circumstances become more favorable to attend school in Paraguay, average unitary transfers decrease and private contributions increase (figures 7a and 8a). In contrast, net unitary benefits for secondary education do not follow a clear nonlinear pattern—although the poorest and those with the set of circumstances least favorable have higher net benefits than the richest and those with most favorable circumstances. This is the result of a combination of a regressive pattern of average gross unitary benefits and a progressive pattern of average household contributions toward secondary education.

> 24 Furthermore, some parts of the decentralized budget were not clearly traceable in Thailand (Cuesta and Madrigal forthcoming).

19
(RWSS) sector policy and the preparation of a National RWSS program. To this end, the Project would support a decentralized and demand responsive delivery mechanism and help build the institutional foundation for implementing the National RWSS Program both at the central and local governments levels.

# And:

The specific objective of this project is to support programs designed to halt transmission of HIV/AIDS among vulnerable populations (PLWHA, IDUs, CSWs, and their clients and sexual partners) and between these vulnerable populations and the general population. Key outcome indicators include: Percent of vulnerable groups in participating provinces reporting safer injection practices (from an estimated 20% at baseline to 70% at the project end ); Percent of vulnerable groups in participating provinces reporting condom use in sexual intercourse (from an estimated 40% at baseline to 80% at project end).

Finally, we seek to identify relationships between the contextualization variable and project preparation and supervision characteristics. These characteristics comprise project region, recipient income levels, fragility, and institutional strength; the time and cost of project preparation; the location (headquarters or country office) and experience of management; whether or not analytical work in the sector and country was conducted in the years prior to the project; and various characteristics of the project manager, including education level (PhD or not), age, experience, and prior work in the sector, analytical or lending. Since we have embeddings for all historical World Bank projects, we are able to probe for these relationships across a somewhat larger dataset ( _N_ = 4 _,_ 260).

# _3.5. Non-Linear Models_

Having constructed the residual outcomes and text embeddings, we set two prediction tasks:

1. Binary classification. For each project, the target prediction is whether the residual outcome in the project sector is positive five years after project completion.

2. Regression. For each project, the target prediction is the precise residual outcome in the project sector five years after project completion.

The lag-time for prediction follows the replicated studies and is used because projects may not target the specific sector outcome during the project period itself, or may take place in only a few districts at a time. However, as discussed in section 2.2, this is justified in that almost all development projects aim for systemic effects, whether via demonstration, capacity building or other channels.

As inputs to the non-linear models used for each prediction we construct vectors for each project _p_ consisting of: 10