rafmacalaba/datause-encoder-data huggingface dataset:
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Blau, F. D., Kahn, L. M. and Papps, K. L. (2011). Gender, Source Country Characteristics, and Labor Market Assimilation among Immigrants. _Review of Economics and Statistics_ , 93(1): 4358.

Boudet, A. M. M., Buitrago, P., Leroy de la Briere, B., Newhouse, D., Matulevich, E. R., Scott, K., and Suarez-Becerra, P. (2018). Gender Differences in Poverty and Household Composition through the Life‐cycle: A Global Perspective. _Policy Research Working Paper_ 8360. Washington, DC: World Bank Group.

Brown, C. and van de Walle, D. (2020). Headship and Poverty in Africa. _Center for Global Development: Working Paper 531, April 2020_ . Washington, D.C.: Center for Global Development.

Brück, T. and Schindler, K. (2009). The Impact of Violent Conflicts on Households: What Do We Know and What Should We Know about War Widows? _Oxford Development Studies_ , 37(3): 289-309.

Brück, T. & Stojetz, W. (2021). Gendered Dimensions of Protracted Forced Displacement in Sudan. World Bank and Policy Research Working Paper, forthcoming. World Bank Group.

Buchanan, E. (2019). ‘Born to be Married’: Addressing child, early and forced marriage in Nyal, South Sudan. Retrieved from https://www.oxfamamerica.org/explore/researchpublications/born-be-married/ <mark>Buvinić, M., & Gupta, G. R. (1997). Female-headed households and female-maintained families: are they worth targeting to reduce poverty in developing countries?.</mark> _<mark>Economic development and cultural change</mark>_ <mark>,</mark> _<mark>45</mark>_ <mark>(2), 259-280.</mark> Buvinic, M., Das Gupta, M., Casabonne, U., and Verwimp, P. (2013). Violent Conflict and Gender Inequality: An Overview. _The World Bank Research Observer,_ 28(1): 110-138.

Chaaban, J. M., Seyfert, K., Salti, N. I., El Makkaoui, G. S. (2013). Poverty and Livelihoods Among UNHCR Registered Refugees in Lebanon. _Refugee Survey Quarterly_ , 32(1): 24-49.

Espinoza-Delgado, J. and Klasen, S. (2018) ‘Gender and multidimensional poverty in Nicaragua: An individual based approach’, World Development, 110: 466-491 Evans, M., Nogales, R., and Robson, M. (2020). Monetary and Multidimensional Poverty: Correlations, Mismatches, and Joint Distributions. _OPHI Working Paper 133_ . Oxford: University of Oxford.

Hanmer, L., Rubiano, E., Santamaria, J., and Arango, D. J. (2020). How Does Poverty Differ among Refugees? Taking a Gender Lens to the Data on Syrian Refugees in Jordan. _Middle East Development Journal_ , 12(2): 208-242.

30
time and the floodplain dummy are time-invariant, their additive effects are taken care of by upazila fixed effects. We take a conservative approach, and allow for time varying effects of these variables, and include interactions of these variables with the time trend in the empirical model. As additional control for time varying heterogeneity across upazilas, we include proportion of household with electricity as a control variable. To capture changes in labor endowment, we control for upazila population, total active labor force, and proportion of active labor force with secondary or above education (human capital). This makes the empirical model consistent with the theoretical assumption of a fixed labor endowment (i.e., insignificant labor supply response to productivity shock in agriculture), and enables us to focus on structural change in employment through labor reallocation.<sup>12</sup> To account for any agglomeration externalities that may affect firm location, we control for upazila population in 1991 interacted with time trend (initial condition) and the share of urban households in total households in the upazila as an indicator of agglomeration economy. 13 A final issue for the empirical specification is that rainfall is expected to have a significant effect on rice yield only if a upazila has significant agricultural production (i.e., _A_ is not too small in our model). Since we exclude upazilas located in two main metropolitan areas from our sample, this is not an issue with our samples. An important insight from the theoretical analysis is that the effects of rainfall on wage is likely to be stronger when the share of agriculture is higher in the upazila economy. Accordingly we carry out empirical analysis for two different samples: a “rural sample” and a “small town sample” which are defined in the data section.

# **4. Data**

To test the hypotheses regarding the effects of agricultural productivity growth on wage and the structure of employment, we combine three different data sources to construct an upazila (subdistrict) level panel data set covering the period 2000 to 2009/2010.

A primary source of employment data in Bangladesh is the population censuses (1990, 2000 and 2010).

> 12In an economy with a growing labor force, a second source of structural change is allocation of new labor to the nonfarm activities, which is not the focus of this paper.

> 13Our empirical results are robust is we instead use finer controls of agglomeration economies such as area share in total industry employment in 1991.

17
-36 -

by Landis and FlegaP indicate that it is not unreasonable to apply the blood lead-blood pressure relationship to all males, age 20 to 70.

The probability of hypertension as a funcion of log of blood lead is expressed by: Change in H = (1 + exp (2.744 - 0.793 Qn PbBj)) - (1 + exp (2.744 - 0.793 Qn PbB,))r where: The standard error of the estimate of 0.793 is 0.25.

H = the probability of hypertension PbBT = init blood lI level (gdl) PbB2 new blood lead level (Wdl) Since the risk of all-cause mortality and of heart attacs was provided as a function of diastolic blood pressure, the association between blood lead and diastolic blood pressure is needed. Based on Pickde a alt. and U.S. EPA," we use the following relationship: change in DBP =2.74 n PbB1- In PbB) wher DBP = diastolic blood pressure Using the sample means of the independent variables in the Pooling Ptoect, we can express the relationship between the change in blood pressure and the change in the probability of a C(D event (nofatal myocardial intion) in the following ten years as: Change in Pr(CHD) = (1 + exp (5.0 - 0.030 (DBPJ))r - (1 + ep (5.0 - 0.030 (DBP2 )Y' where PR(CHD) = the 10-year probability of a cornary heart disease event; DBP; initial and new diasolic blood pressure levels.
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FEMA 203
2015); both in the pooled sample and the gender-disaggregated independent samples. Urban residence increases the odds of employment in all samples (Contreras et al., 2011; Naudé & Serumaga-Zake, 2010) although opposite effects have been observed in the literature (Fadayomi et al., 2014). Similarly, residence in the Ashanti, rather than the northern region, is associated with higher odds of employment for the male sample. A closer look at the genderdisaggregated results indicates that men who have at least high school education, and reside in male-headed households are more likely to be employed, which is not the case in the female sample.

With respect to the second stage decision of choosing between self- vs. wageemployment, the probability of the latter are higher among men, compared to women (pooled sample column of Table 7). Other factors that are associated with higher probability of wage employment among both men and women include education and urban residence, while being Muslim (H’madoun, 2010) and residing in a large household lowers the probability in both groups. Residing in a male-headed household increases the likelihood of wage employment among men, while having a spouse present in the household has the reverse effect.

# **5.2. MNL regression results of labor market outcomes and traditionalism, personality and other controls**

The _pooled sample_ results of the effects of gender role attitudes and personality traits on labor market participation decisions are presented in Table 8. The naïve regression of traditionalism on employment indicate a negative correlation (specification 1) that is retained even after controlling for other individual, household and geographic characteristics (specification 2). Results from the interactions between traditionalism and personality traits indicate that among less traditional individuals, traits such as work centrality (5) and high achievement (9) are positively associated with being employed, while high internal locus of control (8) has a negative significant effect on employment. The additional effect of being more traditional, however, masks the significant effects of work centrality and high achievement, and reverses the effects of high locus of control on employment. Although individual personality traits such as power motivation (10) and optimism (12) have no statistically significant effect on a less traditional person being employed, these traits appear to be negatively and positively associated with employment, respectively, among more traditional individuals. In the presence of personality traits and gender role attitudes, the results show 14
# Knowledge products per year

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laboratory-based values represent considerable reductions with potential health gains (Burnett et al., 2014).

We build on an initial one-year impact evaluation of _Mirt_ dissemination that showed the stoves saved wood when cooking standardized _injera_ batches and users highly valued the benefits provided by the stove (Gebreegziabher et al., 2018). We also found they were adopted and used by over 90 percent of treatment households in the 12 months after their distribution (Beyene et al., 2015). This rate is significantly higher than a number of high-profile prior trials involving improved stoves (e.g. Hanna et al., 2016; Mobarak et al., 2012; WHO, 2006a), and in line with more recent work studying low-cost stoves particularly well-suited to the local environment and cooking practices (e.g. Bensch and Peters, 2015; Rosa et al., 2014).

In the present study, we pay particular attention to secondary health outcomes that may benefit from several years of reduced indoor air pollution, including child growth, a marker of inflammation and early-life health sensitivity during the first years of life that is a powerful predictor of well-being in adulthood (Hoddinott et al., 2013), as well as symptoms of respiratory disease and measures of physical function.<sup>1</sup> Comparing across those randomly offered the _Mirt_ stove with those that were not, our findings suggest the _Mirt_ treatment increased height-for-age for children exposed to this technology before the age of three by approximately 0.3 to 0.4 standard deviation, while the respiratory symptoms and physical functioning of adult cooks and older children did not improve. These patterns are consistent with a critical early period in life when health interventions see their largest positive impacts (e.g. Cusick and Georgieff, 2016; Walker et al., 2007) and the effects of accumulated exposure causing lasting damage in older individuals. The comparison across treatment and control households is supported by within household estimates that focusing on those exposed during the sensitive earlylife period to isolate the _Mirt_ treatment effect from confounding factors.

The observed effect on child growth is both precisely estimated and meaningful in magnitude. A wide body of evidence links early-life health to adult health, educational attainment, cognitive function, and earnings (e.g. Adair et al., 2013; Almond et al., 2018; Fogel, 2004; Glewwe and Miguel, 2008; Grimard and Laszlo, 2013; Groppo and Kraehnert, 2016; Hoddinott et al., 2008; LaFave and Thomas, 2017; Strauss and Thomas 1998; Wisniewski, 2016). For example, analysis of the Ethiopian

> 1 While child growth is often used as a marker of nutrition, a significant and growing body of work notes physiological associations between household air pollution from biomass cookstoves and reduced height-for-age (e.g. Balietti and Datta, 2018; Edwards and Langpap, 2012; Ghosh et al., 2011; Kim et al., 2016; Kyu et al., 2009; Mishra and Retherford, 2007; Pongyo et al., 2006).

3
As indicated by previous studies, the effect of a woman’s intrahousehold economic status on violence seems to be theoretically ambiguous. Although an increase in household economic resources attributable to women may reduce economic stress and spousal violence, it may also introduce additional tension and conflict. In an effort to maintain the status quo, a woman’s increased economic strength may be countered by an increase in violence. Consistent with this theoretical ambiguity, the existing empirical evidence on the link between women’s involvement in income-generating activities and violence is inconclusive.

In a recent survey of developing countries, Vyas and Watts (2008) find that women’s involvement in income-generating activities is generally associated with a higher lifetime history of physical violence. The National Family Health Survey 1989– 99 for India reveals that women face greater domestic violence, and women who work away from home face even more violence (Eswaran and Malhotra 2009). The authors argue that this increase in violence occurs because these husbands perceive a greater danger for their women to have contact with other men, which triggers spousal jealousy and violence as a response.

In contrast, Panda and Agarwal (2005) find that in Kerala (India), women with regular employment are far less likely than unemployed women to have ever experienced violence. Beyond employment status, Panda and Agarwal’s innovative study (2005) uses women’s ownership of property (land and house) to capture economic status. They find that women’s ownership of property is associated with a sharp reduction in domestic violence. Aizer (2010) uses evidence for the United States for 1990–2003 and finds that decreases in the male–female wage gap reduce violence against women. This evidence suggests that improving the employment and earning opportunities of women relative to men reduces violence and its associated costs. Note that this study is based on administrative data on female hospitalization for assault. Therefore, its conclusions are unaffected by the important degree of nonrandom underreporting that affects other studies based on self-reported measures of domestic violence.

Microfinance, both credit and savings, has also been proposed as a potential tool for empowering poor women in developing countries. However, these programs have a varied effect on men’s violence against women (Goetz and Gupta 1996; Kabeer 2000; 13
# **4. Findings: PTR variation and correlates**

In this section we present descriptive statistics of the extent of variation in PTR, and decompose the incidence of variation at district, zone, and school level. In order to test the proposition that the persistence of inequities in PTR reflects political capture of the teacher allocation system, we assess the impact of key teacher preferences in school choice on PTR variation, as compared with other relevant factors. Finally, we present the results of a political economy network mapping exercise in order to more clearly illustrate the local-level dynamics of teacher allocation.

## **4.1. Extent and patterns of PTR variation**

The finalized database revealed considerable disparities in PTR, not only along traditionally understood demarcations of rural and urban or across district boundaries, but between schools within the same small geographical area.

PTR varies significantly between districts, consistent with findings from Mulkeen et al. (2010). The district with the best-staffed schools, the town of Zomba, has an average school-level PTR of 52, while the most understaffed rural district, Machinga, has an average school PTR of 117. Twelve districts have a median school PTR of 80 or more.

In addition to between-district PTR disparities, the finalized database revealed large inequities within districts **[Figure 2]** . Within districts, the interquartile range (IQR) is quite high for a large majority of districts, with the highest spread in Lilongwe Rural West and Dedza district. This shows that variation in PTRs between sub-district units is substantial.

Malawi’s education districts are divided into 427 zones, with an average of 12 zones per district and 13 schools per zone. Per-zone average PTRs vary substantially within a single district: in Machinga district, for example, the lowest zonal PTR is 64, in peri-urban St. Therese zone, and the highest is 264, in remote Nampeya zone.

Furthermore, even within one zone, equivalent to an area of a few square kilometers, PTRs tend to vary significantly. For example, within the Khombwe Zone of Blantyre Rural, between 3-6 km West of Blantyre city, the zone-level PTR is 80, but due to the uneven distribution of teachers it spans from 27 to 130.<sup>9</sup> The largest schools, Chimembe and Khombwe, with over 2,800 students between them, have only 22 teachers in total, while a smaller school 3 km closer to the city, Maliya, with 853 students, has 12 teachers to itself.

We employ variance component analysis to examine how much of the total variation in schoollevel PTRs is attributable to the variability in PTRs (1) between districts; and (2) between zones within districts; and (3) between schools within zones. If teacher preferences for school choice are a key driver of PTR variation, we would expect to see most PTR variation to exist at school level, rather than between larger administrative units such as districts.

> 9 See **<u>Table 12</u>** , in Annex 1 (available online: see page 40).

16
ates measured in the ENLACE context questionnaire, which allows us to also control for parental education and occupation. Specifically, the model includes a set of dummy variables equal to one when the mother (or father) completed lower secondary and a dummy capturing if she (or he) is a white-collar worker. As before, we first estimate the simple equation to verify that any change in the coefficients of interest is due to the inclusion of covariates and not due to sample variation. Table 5 (columns 1 to 4) reports the results without controls for parental characteristics but restricted to the sample included in the ENLACE context questionnaire, which are similar to those already reported. Columns 5-8 in the same table report the results from the regressions that include family characteristics. As expected, ENLACE test scores at Grade 6 are strong predictors of on-time graduation and test scores at Grades 9 and 12, even after including in the model variables that directly control for family background at the individual level. The inclusion of these covariates reduces the point-estimator of the association between Grade 6 test scores and the probability of on-time graduation in Grade 9 and 12 by 22 percent and 13 percent, respectively; and by 3 percent for test scores for both Grades 9 and 12, respectively. This reduction in the estimated parameter is considerably smaller than the reduction observed by the inclusion of twin fixed effects, which shows the importance of unobserved family characteristics, orthogonal to the conventional parental control, that are important in determining future education outcomes.

The specification with controls for family background shows that there are significant gaps in outcomes by parental background. Students with more educated parents are more likely to finish lower- and upper-secondary on time, and conditional on this, have higher test scores. Having a mother who at least completed lower secondary education is associated with an increase of 8 percentage points in the probability of on-time graduation from lower- and upper-secondary (columns 5 -6 in Table 3) and with 0.05 and 0.10 SD higher test scores, respectively (columns 7-8 in Table 3). In three of the four cases, results are statistically significant at conventional levels. The point estimates for the conditional correlation between paternal education and on-time graduation also have high statistical significance, although their magnitude is smaller (about 3.5-6.2 percentage points, as 18
In practice the large number of informal firms with at most a couple of workers are likely to cover a mix of different types of firms, including subsistence enterprises and individuals who would rather be wage workers, but also potential entrepreneurs with the desire and sometimes ability to grow their businesses over time. De Mel et al. (2010) classify the self-employed in Sri Lanka using statistical methods designed for species classification in biology, and find that between one-quarter and one-third of the self-employed appear to share characteristics that give them the potential to be owners of somewhat larger firms, while the remaining two-thirds to three-quarters look much more like wage workers. Our focus is on the role of business regulations on the formalization decisions of this overall group of small scale enterprises, before discussing the extent to which policy can differentiate among them. We note also that while our focus is on regulatory policy, governments have a number of other policy tools such as credit and labor market policies that will also affect the decision of individuals to operate firms at all, and if so, whether to do so formally or informally.

# **DISCUSSION OF KEY FINDINGS**

We divide our discussion of key findings into two subsections. The first considers the extent to which business regulations appear to be barriers to firms _becoming formal_ , and whether efforts to make it easier for firms to formalize or tougher to remain informal have succeeded in bringing low-scale enterprises into the formal sector. The second subsection then examines the extent to which _being formal_ benefits these types of firms. Attempts to answer both sets of questions must deal with the fact that places that change regulations may differ systematically from those which do not, and that firms which choose to become formal are likely to differ in many unobservable ways from those who choose to remain informal.

# **_Measuring the impacts of formalization efforts and of becoming formal_**

Simple comparisons of formal and informal firms usually reveal that formal firms are more productive and profitable. A series of high-profile sector studies by the McKinsey Global Institute comparing the operation of formal and informal firms in a number of countries around the world concluded that informality has a very negative impact on productivity, accounting for nearly 50 percent of the overall productivity gap between countries like Portugal and Turkey and the United States (Farrell, 2004). La Porta and Shleifer (2008) use World Bank firm surveys to compare the productivity of informal and formal firms, and find the formal firms to have substantially higher productivity levels.

3