rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Direct estimates of the bias in the dependent Variable due to observable characteristics** <u>[Probit regression]</u>

|_Dependent Variable: a member of the household_|**_dF/dx_**|**_Coeff._**|**_Standard_**|**_z -_**|
|---|---|---|---|---|
|_participates in aproductive association_|||**_Error _**|**_Statistic_**|
|**Characteristics of the household**|||||
|Log. of Household size|0.01***|0.27***|0.06|0.00|
|Head is indigenous|-0.02|-0.12|0.10|0.22|
|Household is poor|-0.02|-0.12|0.12|0.32|
|Head is male|0.01|0.12|0.09|0.15|
|Age of the head|0.00|-0.01***|0.00|0.01|
|Age of the spouse|0.0009**|0.01**|0.00|0.03|
|Main way is accessible all the time|-0.02***|-0.18***|0.06|0.00|
|Head is widowed|0.06***|0.37***|0.10|0.00|
|Head is living in couple|-0.02|-0.14|0.15|0.35|
|Household owns house|0.01|0.20***|0.07|0.00|
|H. participates in a non-productive organization|0.10***|0.59***|0.06|0.00|
|**Socio Economic Condition**|||||
|Quintile 2|0.02|0.13|0.08|0.12|
|Quintile 3|0.03*|0.22*|0.12|0.06|
|Quintile 4|0.05**|0.34**|0.14|0.02|
|Quintile 5|0.07***|0.49***|0.15|0.00|
|**Age Dummies**|||||
|Head have 16-23 years|-0.06***|-1.02***|0.30|0.00|
|Head have 24-33 years|-0.03***|-0.20**|0.08|0.01|
|**Occupation of the Head**|||||
|Head is self-employed|0.03***|0.22***|0.05|0.00|
|Head works in Commerce|0.00|-0.04|0.08|0.61|
|Head works in Finance|0.08***|0.45***|0.15|0.00|
|Head works for the government|0.01|0.09|0.17|0.60|
|Head works in Agriculture or Mining|0.03***|0.25***|0.07|0.00|
|Spouse is professional|0.03|0.23|0.22|0.31|
|**Education of the Head**|||||
|Head finished primary school|0.04***|0.27***|0.06|0.00|
|Head finished secondary school|0.05***|0.34***|0.09|0.00|
|Head finished technical|0.11***|0.57***|0.14|0.00|
|Head finished tertiary|0.09***|0.51***|0.12|0.00|
|**Regions**|||||
|<br>Region: Managua|0.00|-0.02|0.11|0.82|
|Region: Pacific|0.01|0.07|0.07|0.34|
|Region: Central|0.04***|0.30***|0.06|0.00|

Source: Authors using the 2005 Nicaragua EMNV. Absolute value of z statistics in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. Reference categories: education of the head, no education or preschool; socio-economic group, poorest quintile; geographic location, Atlantic.
Table 4. State-level bottom and top Gini and growth at different percentiles of state income distribution (dependent variable = per capita income growth at a given percentile of state income distribution; 1960-2010; five 10-year periods)

||dlny5|dlny10|dlny25|dlny50|dlny75|dlny90|dlny95|dlny99|
|---|---|---|---|---|---|---|---|---|
|gini_b40|-0.379***|-0.183**|-0.0309|0.0248|0.0488*|0.0601**|0.0745***|0.0651**|
||(-2.91)|(-2.34)|(-0.68)|(0.74)|(1.75)|(2.43)|(3.06)|(2.00)|
|gini_t40|-0.118|-0.163**|-0.143**|-0.0823*|-0.0123|0.00320|-0.000675|0.0187|
||(-1.25)|(-2.04)|(-2.35)|(-1.73)|(-0.31)|(0.09)|(-0.02)|(0.34)|
|edu_ms_age2139|0.0936***|0.0840***|0.0821***|0.0888***|0.0818***|0.0791***|0.0680***|0.0760***|
||(3.09)|(3.37)|(4.21)|(5.27)|(5.08)|(5.17)|(4.35)|(3.34)|
|edushort1518|-0.0349***|-0.0272***|-0.0137**|-0.0115**|-0.0121***|-0.0115***|-0.00932**|-0.00453|
||(-3.19)|(-3.15)|(-2.04)|(-2.21)|(-2.74)|(-2.84)|(-2.38)|(-0.85)|
|age015|-0.240***|-0.207***|-0.145***|-0.159***|-0.143***|-0.141***|-0.121***|-0.132***|
||(-2.73)|(-3.10)|(-2.80)|(-3.47)|(-3.60)|(-4.09)|(-3.63)|(-3.01)|
|age65|-0.159*|-0.119**|-0.0776*|-0.0988**|-0.0954***|-0.0941***|-0.0745**|-0.0515|
||(-1.96)|(-2.04)|(-1.77)|(-2.54)|(-2.77)|(-2.98)|(-2.08)|(-1.11)|
|olf_female|-0.0798***|-0.0724***|-0.0538***|-0.0471***|-0.0380***|-0.0311***|-0.0315***|-0.0219|
||(-3.19)|(-3.72)|(-3.52)|(-3.74)|(-3.36)|(-2.76)|(-2.87)|(-1.35)|
|Mid-West|0.00258|0.00177|0.000862|0.000218|-0.000640|-0.00125|-0.00224*|-0.00259*|
||(0.92)|(0.76)|(0.49)|(0.15)|(-0.48)|(-1.00)|(-1.88)|(-1.78)|
|South|0.00118|0.000494|0.000158|-0.00000181|-0.0000911|-0.0000836|-0.000875|-0.000708|
||(0.36)|(0.19)|(0.08)|(-0.00)|(-0.06)|(-0.06)|(-0.59)|(-0.41)|
|West|0.00267|0.000383|-0.000848|-0.000733|-0.000249|0.0000733|-0.000744|-0.000629|
||(0.83)|(0.15)|(-0.43)|(-0.46)|(-0.17)|(0.05)|(-0.52)|(-0.37)|
|lnyxx|-0.0828***|-0.0728***|-0.0586***|-0.0552***|-0.0464***|-0.0404***|-0.0319***|-0.0348***|
||(-7.65)|(-9.03)|(-9.47)|(-9.61)|(-8.91)|(-8.88)|(-6.99)|(-5.38)|
|constant|0.951***|0.844***|0.673***|0.641***|0.549***|0.495***|0.408***|0.446***|
||(6.84)|(8.07)|(8.59)|(9.03)|(8.80)|(9.22)|(7.73)|(6.17)|
|N|245|245|245|245|245|245|245|245|
|adj. R-sq|0.733|0.773|0.793|0.770|0.743|0.758|0.813|0.882|

Note: All right-hand side variables estimated at time=0. Income is household per capita. Gini calculated across individuals ranked by their household per capita income. _t_ statistics in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Time-period dummies are included but not reported.

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TABLE 2: summary statistics

||Observations|Mean (st. dev)|Min|Max|
|---|---|---|---|---|
|Outcome Multiple|1912|4.127 (1.207)|1|6|
|Outcome Binary|1912|0.754 (0.430)|0|1|
|Borrower Multiple|1912|4.140 (1.354)|1|6|
|Borrower Binary|1912|0.722 (0.447)|0|1|
|Bank Multiple|1912|4.296 (1.282)|1|6|
|Bank Binary|1912|0.769 (0.421)|0|1|
|Size|1912|1 (1.209) [104.088]*|0|8.988|
|GDP p.c.|1891|4177.30 (3678.40)|351.03|21211.91|
|Volatility|1866|602.14 (726.80)|0|4948.75|
|Growth|1872|0.247 (0.314)|-1|2.353|
|Infrastructure|1891|0.053 (0.063)|0.001|0.321|
|Bureaucracy|1742|1.944 (0.730)|0|4|
|Corruption|1742|2.568 (0.739)|0.5|5|
|Accountability|1742|3.439 (1.333)|0|6|
|Stability|1742|8.179 (1.607)|2.5|11.5|
|Ethnic|1742|3.744 (1.202)|0|6|
|Ext. conflict|1742|9.942 (1.391)|3.5|12|

Note: the asterisk indicates value reported in square brackets, which is the mean size of all projects available in our database, reported in 2005 constant international dollars.

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# **Table 9: Restricting the comparison group to Faisalabad District**

||**Housework**|**Paid Work**|**Children's Needs**|**Sleep**|
|---|---|---|---|---|
|Treatment District|||||
|Round 3|-162.359***|-19.493|106.590***|24.322|
||(49.709)|(19.982)|(37.362)|(31.391)|
|Round 4|-135.845***|-24.580|94.908***|44.371*|
||(41.746)|(17.373)|(25.434)|(25.866)|
|Treatment District * Round 3|134.055***|-15.927|-102.571***|-11.613|
||(37.017)|(15.205)|(26.502)|(24.428)|
|Treatment District * Round 4|126.243***|-6.079|-106.384***|-2.996|
||(37.154)|(21.024)|(26.474)|(26.549)|
|Constant|888.179***|-28.246|-11.047|549.320***|
||(156.025)|(100.604)|(111.163)|(108.840)|
|Number of observations|817|817|802|793|
|Number of mothers|287|287|282|279|
|R2 within|0.154|0.145|0.210|0.173|
|Controls|Full|Full|Full|Full|
|Mean of dependent variable at baseline|522|44|166|596|
|p-value of F-test ofequality ofcoefficients|0.8175|0.4939|0.8810|0.7062|

Note: *** p<0.01, ** p<0.05, * p<0.1. Dependent variables are measured as minutes per typical school day. Heteroskedasticity-robust standard errors adjusted for clustering at the household level in parentheses. Full set of controls: household composition decomposes the size of the household into number of adult men, adult women, teen boys, teen girls, preteen boys, male infants, female infants. The number of preteen girls is entered as a series of indicator variables for up to 5 preteen girls. The omitted category is no preteen girls. Two measures of relative wealth are asked in each round of the survey and respondents rated themselves as much better off, better off, about the same, worse off or much worse off. Head of household occupation indicators included indicators for livestock rearing, salaried job, currently not working, self-employed trader, employed skilled laborer, employed unskilled laborer. The excluded category is farming.

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7

# Econometric Estimates of the Determinants of Wages

In this section we use individual worker data from the 17-firm sample to explain wage differentials in terms of productivityrelated and other characteristics of individuals. Because the cross tabulations in Chapter 4 indicate that earnings differentials widen when viewed in terms of an individual's total compensation as opposed to base salary, we estimate the earnings functions using both total compensation and base salary as the dependent variables.

The results from this exercise are presented in Table 7.1. They indicate that the male-female earnings differential is significant and widens when total compensation is considered, in comparison to the base salary. Controlling for differences in education, experience, nationality and other explanatory variables, males earn on average 17.8 percent more than females in base salaq and 19.2 percent in total compensation.

Earnings differentials are enormoujs between the non-African and African groups: After controlling for differences in education, experience, gender, and other explanatory variables, non-Africans are on average found to earn from 123 percent to 142 percent more than Africans.<sup>1</sup> In contrast, there is no noticeable earnings differential between similar other Africans and Senegalese, which is consistent with the information gathered from our interviews with the managers. Other Africans tend to be employed in the

1. The differential between non-Africans and Africans does not increase when total compensation is considered. This may be accurate as we understand that the extra pay of expatriates comes in the form of both extrm base pay and supplements. Unfortunately, some of the extra supplements come in benerits 'in kind,' such as housing, for which we could not gather data and which would probably increase the relatve total earnings differentiaL
Table 2: Participation in GVCs (°%o gross exports) and Size Distribution of Urban Agglomerations

|DV: SpatialDispersion (Absolute<br>valueofZipf’scoefficient) in|Re-tmportedDVA|DVA ininterm.<br>=Exports|DVA embodied in<br>foreign exports|Ratio offorwardto<br>backward linkages|
|---|---|---|---|---|
|Aggregate|QQ)|(2)|G)|(4)|
|Participation inGVCs|-0.033++|0.09344|-0.087+-4+|-0.165+4+|
||(0.016)|(0.033)|(0.027)|(0.051)|
|R-squared|0.606|0.629|0.641|0.626|
|AdjustedR-squared|0.545|0.572|0.586|0.569|

Notes: The table shows the impact of participation in GVCs on size distribution of urban agglomerations in a panel of countries from 1995, 2000, 2005, 2008-2011. The base model corresponds to 91 country-year patrs for 37 countries. All explanatory variables are in unit standard deviation format. The measure of spatial dispersion, that is, the absolute value of Zipf coefficient, is winsorized at the bottom 2% and top 98% percentile level. All estimations include an year fixed effect and control for the following country characteristics GDP per capita, squared GDP per capita, population density, an indicator of whether the country is in the OECD group. and Government expenditure (% GDP). Estimations report standard errors in parantheses. +, ++, and +++ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
- 15 -

||Year|**Tabl**|**e 3. Samp**|**le covera**|**ge by mon**|**th**|||
|---|---|---|---|---|---|---|---|---|
||200|2|200|3|200|4|200|5|
|Month|Obs.|%|Obs.|%|Obs.|%|Obs.|%|
|4|29|4.5|||||||
|5|324|50.5|569|48.2|648|48.7|1,058|65.8|
|6|244|38.0|594|50.3|668|50.2|538|33.5|
|7|45|7.0|17|1.4|16|1.2|12|0.8|
|Total|642|100.0|1,180|100.0|1,332|100.0|1,608|100.0|

Source: Albania LSMS data.

In Albania the climate differs from year to year and depends on location. This variation is, of course, of particular importance to identify the climatic impacts on the electricity demand in our empirical model. In the sample period, the month of June in 2003 was hotter than usual. In that year, air conditioners and other seasonal home appliances may have contributed to increasing the residential electricity demand. In addition, climate conditions vary significantly across region in the country. The northern and eastern mountainous regions are relatively cool in Albania. By contrast, the coastal and southern areas are hotter (Figure 5). In 2003, temperature was high, exceeding 30°C almost all over the country. Summer 2004 was relatively cool. Correspondingly, more precipitation fell over most of the country in 2004 (Figure 6). Summer 2002 was driest among the sample years.

<!-- Start of picture text --> Figure 5. Monthly average maximum temperature by district<br>June 2002 June 2003 June 2004<br>Monthly average Monthly average Monthly average<br>maximum maximum maximum<br>temperature temperature temperature<br>23.9 - 25.0 25.5 22.2 - 25.0<br>25.1 - 27.0 25.6 - 27.0 25.1 - 27.0<br>27.1 - 29.0 27.1 - 29.0 27.1 - 29.0<br>29.1 - 31.0 29.1 - 31.0 29.1 - 29.5<br>31.1 - 33.0 31.1 - 35.3 29.6 - 29.9<br>. . .<br>Source: Author’s drawing based on weather data from IEWE, Albania.<br><!-- End of picture text -->
# **1. Introduction**

The existence of large informal labor markets in developing countries has attracted renewed attention because of its connection to poverty, low productivity and lack of social protection. One issue of concern is whether non-contributory programs introduced to address the lack of social safety nets for those not in the formal sector are providing perverse incentives for formal sector workers (Levy, 2008).<sup>2</sup> Formal sector workers could now avoid having to contribute to the formal social security program (SS) by moving to the informal sector while receiving (at least some) services under the new program. We propose to test this hypothesis by looking at the effect of rolling out _Seguro Popular_ ( _SP_ ), a non-contributory health program for informal households in Mexico from 2000(Q2) - 2009(Q2), using longitudinal data constructed with rotating panels of households.

This issue is of particular relevance in Mexico as other work has shown a relatively high degree of mobility of workers in Mexico between the formal and informal sectors. This feature has often been raised as evidence that formal and informal sectors are not segmented. Maloney and Bosch (2006) show high labor mobility and that entry and separation rates are not significantly different across the two sectors. In addition, the probability of movement between sectors does not depend on the age of the worker, which seemingly contradicts the hypothesis that informal workers queue to get formal jobs. Maloney (1999) finds that wages sometimes increase and sometimes decrease as a worker moves from one sector into the other, and that the substantial flows in both directions persist even in periods of economic expansion. Madrigal and Pagés (2008) and Maloney (2004) find that workers value features of informal work such that it should not necessarily be taken as an inferior or residual employment option. What remains a question is whether the existence of _SP_ has motivated some households to stop paying into SS to be able to qualify for free health insurance. Or, alternatively, if the availability of _SP_ discouraged workers in the informal sector from seeking formal sector jobs covered by SS.

> 2 In this paper, we consider  ̳formal sector workers‘ or  ̳formal workers‘ those individuals who are registered and contributing to Social Security, regardless of the registration or tax status of the enterprise they work for.  Thus, workers who are working for a registered firm but who are not themselves contributing to SS, are not considered to be formal sector workers.

2
18

# **V.C Consumers**

The _Nn_ agents living in region _n_ earn income and consume. Mobile workers inelastically supply one unit of labor to local firms and are perfectly mobile across sectors, thus earning the local mobile wage _wn_<sup>_m_independentlyofthesectorinwhichtheywork.47Estateworkersalsosupply</sup> one unit of labor inelastically, but they can only work in tea agriculture, earning the local estate wage _wn_<sup>_e_.Farmers, who own land, rent out this land for agricultural production, earning land rents.</sup> Finally, there is a measure-zero set of middlemen who own Sri Lanka’s fertilizer endowment and the fertilizer import licenses issued by the government (if any), thus deriving their income from fertilizer sales and license rents.

## **V.C.1 Land Ownership**

We allow for inequality in land ownership. Denoting the total extension of land owned by farmer _h_ in region _n_ as _L_<sup>_h_</sup> _n_<sup>, we assume that variable</sup><sup>_Lh_</sup> _n_<sup>follows a log-normal distribution with parameters</sup> _μn_ and _σLn_<sup>2.48Wealsoassumethat,conditionalonhertotallandsize</sup><sup>_Lh_</sup> _n_<sup>,eachfarmerreceivesa</sup> random sample of plots. These assumptions imply the land rent _Rn_<sup>_h_received by a farmer</sup><sup>_h_in region</sup> _n_ is proportional to her land size: where _Ln_ =<sup>�</sup> _k_<sup>_Lnk_is total arable land in region</sup><sup>_n_, and</sup><sup>_Rn_is the aggregate land rent paid to all</sup> land in region _n_ . Given the log-normal distribution of land size, it follows that land rent in region _n_ also has a log-normal distribution, as follows:

## **V.C.2 Consumption Across Sectors**

Preferences for agricultural versus manufacturing goods are non-homothetic.<sup>49</sup> For all agents except middlemen, we represent this non-homotheticity using PIGL preferences (Eckert and Peters, 2023), in which an agent’s indirect utility function is given by:<sup>50</sup>

> 47We relax the perfect mobility assumption in Appendix H by introducing frictions to cross-sector mobility.

> 48This assumption is consistent with the empirical distribution of landholdings observed in Sri Lanka’s household survey data (see Appendix C.C).

> 49This assumption is strongly supported by Sri Lanka’s household survey data, in which the expenditure share of agricultural goods decreases with household income (see Appendix Figure A10).

> 50Similarly to Eckert and Peters (2023), we choose PIGL over other classes of non-homothetic preferences (e.g. Stone-Geary) due to its convenient aggregation properties.
category (other environmental management) has no direct analog, so we use the mean value of the five environmental indicators for our matching exercise.

Perhaps the most striking feature of the Bank's environmental lending is the stability of its thematic allocation over time.<sup>7</sup> As Figure 4.1 shows, annual environmental lending declined from around $3.5 billion in FY 1993 to $1.0 billion in FY 2003.

Despite this sharp change in aggregate lending, the regression results in Table 4.1 suggest that thematic shares remained stable: None exhibits a significant time trend since 1993.

**Figure 4.1  World Bank Environmental Lending, 1993-2003 ($US Million)**

<!-- Start of picture text --> 4,000<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>1993 1995 1997 1999 2001 2003<br>Fiscal Year<br>Total Environmental Lending<br><!-- End of picture text -->

> 7  We have not analyzed thematic trends for AAA, since the available time interval is much shorter.

10
<!-- Start of picture text -->
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**Figure 4 Quarterly Ratio of Vacancies to Job Seekers in China, 2001–09**

<!-- Start of picture text -->
1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>2001.1 2001.3 2002.1 2002.3 2003.1 2003.3 2004.1 2004.3 2005.1 2005.3 2006.1 2006.3 2007.1 2007.3 2008.1 2008.3 2009.1 2009.3<br><!-- End of picture text -->

_Source_ : China Labor Market Information Center (2010) **.** Note that the circle highlights the period of the global financial crisis.

22