rafmacalaba/datause-encoder-data huggingface dataset:
workers (0-4 scale) as an obstacle for firms’ operations ( _Skills obstacle_ ); and severity level of the labor laws (0-4 scale) as an obstacle for firms’ operations ( _Labor laws obstacle_ ).

For macro-level controls, we begin with GDP per capita. Overall economic development as reflected in the level of GDP per capita may lead to improved outcomes for women’s labor market participation through several channels such as better education and health for women; better child care facilities; etc. If maternity leave also varies between rich vs. poor countries, our main results could suffer from an omitted variable bias problem. We account for this potential problem by controlling for the (log of) GDP per capita (PPP adjusted and at constant 2011 international $). Value of GDP per capita used is the average value over the five-year period prior to the year the ES was conducted. The data source is WDI, World Bank.

Employment of men and women may also be affected by the current level of labor demand. If the current level of demand varies systematically with the maternity leave, our main results could be spuriously affected despite controlling for GDP per capita and related variables. Thus, following Wamboye and Seguino (2015), we control for the current state of labor markets by capturing aggregate demand through the growth rate of GDP per capita ( _Growth rate_ ). The data source for the variable is WDI, World Bank. Average values of the variable over the five years prior to the year the ES was conducted are used.

A larger proportion of females in the total (male plus female) adult population may positively impact the proportion of workers in the country that are females. Thus, demographic factors that influence female and male population in the country may cause our main results to suffer from an omitted variable bias problem if they vary systematically with the level of maternity leave. We guard against such possibility by controlling for the share of females in the total adult population in the country ( _Female population_ ). We use average values of the variable taken over 12
elasticity however being small. However, since the price of children is time, children are more expensive for women with higher incomes who have a higher opportunity value of time, which would result in a negative relation between income and fertility. Empirically, the relationship between income and fertility is negative for most countries at most times (Jones at al., 2008). Given that income is largely a function of labor market participation, and labor market participation is jointly determined with fertility, income is endogenous in the study of fertility. As a result, women’s economic conditions should be measured by variables that are not a function of women’s labor force participation, such as inherited nonhuman capital assets or exogenous transfers (Benefo and Schultz, 1996).

The availability of family planning services may also affect fertility levels. In the US, introduction of family planning programs have been shown to drive down fertility rates (see for instance Anderson and Cope, 1987; Mellor, 1998). In the developing world, declines in fertility rates have also been linked to the roll-out of family planning programs (Bongaerts et al., 1990; Angeles et al., 2005). Family planning can however not be treated as exogenous, as women who wish to limit their fertility are more likely to actively seek family planning services, introducing a spurious negative relationship between demand for family planning services and fertility. In Canning and Schultz’s (2012) study of the Matlab experiment however, random introduction of family planning programs did significantly lower fertility.

A main hypothesis in the demographic literature is that high child mortality induces parents to have many children in order to be left with the desired number of children. In this fashion, a decrease in child mortality would contribute to parents wanting fewer children, driving down fertility, and vice versa. The empirical specification of the relationship between child mortality and fertility is however complicated by the two-way causality: While high child mortality may increase fertility due to parents’ anticipation of child deaths, high levels of fertility may increase child mortality due to the strain on household resources and the biological capacity of the woman to bear healthy children (Benefo and 6
the end product to avoid simple yes/no questions that tend to be more susceptible to response bias (cp. Online Appendix S2). For inorganic fertilizers, enumerators used pictures to identify the type of fertilizer used by the farmer and in addition checked the labels on fertilizer bags.<sup>9</sup> For lime, they also inspected the respective bags. In order to verify the use of improved seeds, the survey contained detailed questions on the original source of the seed and for how long it had been reused. If seeds had been reused for more than four seasons, they are not considered improved seeds in the analysis, because improved traits get lost over generations.<sup>10</sup> The endline survey further comprised questions on the participation in ISFM+ extension activities as well as a detailed knowledge test on ISFM technologies. Enumerators first assessed farmers’ awareness by asking them which ISFM components they actively remembered, and in a second step, asked them which practices they recognized when read to them off a list by name.<sup>11</sup> Subsequently, questions on their underlying principles and purpose (‘principles knowledge’) as well as their mode of implementation (‘how-to knowledge’) were posed. The survey combined different types of knowledge questions, including open-ended and multiplechoice questions, to minimize fatigue effects (for details of the knowledge exam, see Online Appendix S3).

For the survey, a team of 34 enumerators was recruited and intensively trained during a tenday training period. Questionnaire contents were carefully translated into the three local languages and pre-tested in several rounds. In addition to the farm household survey, the survey team administered two community level questionnaires to key informants at the Woreda and mws levels, in order to collect data on infrastructure, availability of extension services, rainfall and temperature, as well as other contextual characteristics.

> 9 Even if bags are empty, farmers usually keep them for other purposes, e.g., to sit on them or to store other things.

> 10 Please note that visual inspections in the field were not possible for the practices improved seeds and line seeding, because at the time of the survey, packaging material of seeds was usually not available anymore and fields had already been harvested. 11 Inspired by (Kondylis, Mueller, and Zhu 2015), this list included a placebo practice (“seeding in circles”) to get a sense for possible response bias, which does not appear to threaten the results since yes-answers regarding this practice are close to zero.

11
Internal Displacement Monitoring center. 2010. Internal Displacement: Global Overview of Trends and ‐ Developments in 2009 (May 2010), accessed at <u>http://www.internal</u> <u>displacement.org/publications/2010/internal‐displacement‐global‐overview‐of‐trends‐and‐ developments‐in‐2009.</u> Internal Displacement Monitoring center. 2009. Internal Displacement: Global Overview of Trends and ‐ Developments in 2008 (Aptil 2009), accessed at http://www.internal <u>displacement.org/publications/2009/internal‐displacement‐global‐overview‐of‐trends‐and‐ developments‐in‐2008.</u> Internal Displacement Monitoring center. 2008. Internal Displacement: Global Overview of Trends and ‐ Developments in 2007 (April 2008), accessed at http://www.internal <u>displacement.org/publications/2008/internal‐displacement‐global‐overview‐of‐trends‐and‐ developments‐in‐2007.</u> Internal Displacement Monitoring center. 2007. Internal Displacement: Global Overview of Trends and ‐ Developments in 2006 (April 2007), accessed at http://www.internal <u>displacement.org/publications/2007/internal‐displacement‐global‐overview‐of‐trends‐and‐ developments‐in‐2006;</u> Internal Displacement Monitoring center. 2006. Internal Displacement: Global Overview of Trends and ‐ Developments in 2005 (March 2006), accessed at <u>http://www.internal</u> <u>displacement.org/publications/2006/internal‐displacement‐a‐global‐overview‐of‐trends‐and‐</u> <u>developments‐in‐2005;Internal Displacement Monitoring center. 2005. Internal Displacement: Global</u> ‐ Overview of Trends and Developments in 2004 (March 2005), , accessed at <u>http://www.internal</u> <u>displacement.org/publications/2005/internal‐displacement‐a‐global‐overview‐of‐trends‐and‐ developments‐in‐2004.</u> Internal Displacement Monitoring center. 2004. Internal Displacement: A Global Overview of Trends and ‐ Developments in 2003 (Feb. 2004), accessed at http://www.internal <u>displacement.org/publications/2004/internal‐displacement‐a‐global‐overview‐of‐trends‐and‐ developments‐in‐2003.</u> International Rescue Committee 2014, Emgency Economies: the impact of cash assistance in Lebanon, Beirut, August, 43p Jacobsen, K. 2000. A framework for exploring the political and security context of refugee populated areas. _Refugee Survey Quarterly_ 19(1): 3‐22.

35
# **3. Variable Description**

Table A3: Variable description

|**Variables**|**Definition**|**Variable name (original)**|
|---|---|---|
||**DHS Variables**||
|_Employment_|1 if having worked in the past 7 days, including<br>women who did not work in the past 7 days but<br>who are regularly employed and were absent from<br>work for leave, illness, vacation, or any other such<br>reason. 0 otherwise|V714|
|_Education_|Education in singleyears|V133|
|_Marriage age_|Age at first cohabitation (for women aged above 24<br>years)|V511|
|_Age at first birth_|Age of the respondent at first birth (for women<br>aged above 24 years)|V212|
|_Son preference_|1 if respondents’ ideal number of sons > ideal<br>number of daughters, 0 otherwise. (Ideal number<br>of each gender that the respondent would have<br>liked to have in her whole life irrespective of the<br>number she alreadyhas).|V627(ideal number of sons)<br>V628 (ideal number of<br>daughters)|
|_Agency_|1 if a woman has a say in at least one of the three<br>decisions within the household|(i) V743b (large household<br>purchases), (ii) V743a<br>(woman’s health care), and<br>(iii) V743d (visit to relative<br>and friends).|
|_Spousal difference in_<br>_age _|Husband/partner’s age minus woman’s age (both<br>ages are reported bythe wife)|V730 (Husband’s/partner’s<br>age)|
|_Spousal difference in_<br>_education_|Most recent husband or partner's education in<br>single years minus woman’s education in single<br>years(bothyears reported bythe wife)|V715 (recent husband or<br>partner's education in single<br>years)|
|_Fertility outcome_|Total number of living children minus ideal number<br>of children. This variable is calculated only for<br>women above 40 years|V218 (Total number of living<br>children)<br>V613 (Ideal number of<br>children)|
|_IPV justification_|1 if a woman responds ‘yes’ to at least one of the<br>five circumstances that justifies wife beating and 0<br>if she responds ‘no’ to all circumstances or missing<br>for all circumstances|(i) V744a (She goes out<br>without tellinghim)<br>(ii) V744b (She neglects the<br>children)<br>(iii) V744c She argues with<br>him<br>(iv) V744d (She refuses to<br>have sex with him)<br>(v) V744e (She burns the<br>food)|

52
<!-- Start of picture text --> 400 $400<br>350 $350<br>$ 300 $300 &<br>e 3<br>$5 250 $250 &<br>n fo}<br>oO oa<br>3 [S}<br>© 200 $200 €<br>S fe)<br>° <<br>@ 150 $150 9<br>< Ww<br>2 100 $100 §<br>. i i | ” ke<br>0 10)<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>mmm Total Economic Losses ($B) === Number of Catastrophes<br><!-- End of picture text -->
20. Arslan, A., Floress, K., Lamanna, C., Lipper, L. & Rosenstock, T. S. A meta-analysis of the adoption of agricultural technology in Sub-Saharan Africa. _PLOS Sustain. Transform._ **1** , e0000018 (2022).

21. Hallegatte, S. & Rozenberg, J. Climate change through a poverty lens. _Nat. Clim. Change_ **7** , 250–256 (2017).

22. Hallegatte, S., Vogt-Schilb, A., Rozenberg, J., Bangalore, M. & Beaudet, C. From Poverty to Disaster and Back: a Review of the Literature. _Econ. Disasters Clim. Change_ **4** , 223–247 (2020).

23. Markhvida, M., Walsh, B., Hallegatte, S. & Baker, J. Quantification of disaster impacts through household well-being losses. _Nat. Sustain._ **3** , 538–547 (2020).

24. Centre for Research on the Epidemiology of Disasters, United States Agency for International Development & Université Catholique de Louvain. _2021 Disasters in numbers - World | ReliefWeb_ . https://reliefweb.int/report/world/2021-disasters-numbers (2022).

25. Song, Z., Hochman, G. & Timilsina, G. R. _Natural Disaster, Infrastructure, and Income Distribution : Empirical Evidence from Global Data_ . https://documents.worldbank.org/en/publication/documentsreports/documentdetail/099312406272326097/IDU018bdbef90c42104d730a46700d5b8c4c1c83 (2023).

26. Hallegatte, S., Vogt-Schilb, A., Bangalore, M. & Rozenberg, J. _Unbreakable: building the resilience of the poor in the face of natural disasters_ . (World Bank Publications, 2016).

27. FAO. _The status of women in agrifood systems_ . (Food and Agriculture Organization of the United Nations, 2023). doi:10.4060/cc5343en.

28. Philippon, N., Mougin, E., Jarlan, L. & Frison, P.-L. Analysis of the linkages between rainfall and land surface conditions in the West African monsoon through CMAP, ERS-WSC, and NOAA-AVHRR data.

- _J. Geophys. Res. Atmospheres_ **110** , (2005).

29. EM-DAT Guidelines. https://public.emdat.be/about.

18
number of types and a short time-series. Therefore, we consider two separate regressions for the two data-sets we have in hand. In the next section, when we run counterfactual simulations, we demonstrate how we use results from both regressions in the same simulation.

# **Estimation with CPS**

Since we do not observe sectoral history, neither unobserved heterogeneity nor sectoral experience is included. This means that we have to impose two restrictions in order to make the model identifiable with the CPS data: _C_<sup>2</sup> = 0 and _C_<sup>3</sup> = 0. Henceforth, we call this version of the model the “constrained model.” The restrictions imply that workers’ sectoral experience and unobserved type have no affect on mobility costs. For the first step regression (the flow equation), we correct the mobility rates reported in CPS to annual rates. Then, we run Poisson pseudo maximum likelihood regression using (18). For the second step (the Bellman equation), we use time lagged variables as instruments and run IV regression using (20). We normalize the average wage in the economy to one. Since we can not identify the discount parameter, _β_ , we assume that _β_ = 0 _._ 95. The estimation procedure is explained in detail in Artuc (2012).

The regression results are reported in Table 3. We find that the moving cost parameter, _C_<sup>1</sup><sup>_,s_</sup> _/ν_ , is increasing with age and is between 2 _._ 62 and 4 _._ 5, significant at the 99% level for all types. The moving cost does not seem to vary much across education groups. The variance parameter, 1 _/ν_ , is about 1 _._ 67 and also significant at the 99% level. We find that the mean of the fixed utility parameter for the metal sector, _η_ ̄<sup>_i,s_</sup> , is negative and significant at the 99% level for unskilled workers. Also, the mean fixed utility parameter for the trade sector, _η_ ̄<sup>_i,s_</sup> , is positive and significant at the 95% level for skilled workers.

ACM report that the common moving cost parameter for all types, _C_<sup>1</sup><sup>_,s_</sup> , is equal to 6 _._ 56 when _β_ = 0 _._ 97 and 4 _._ 7 when _β_ = 0 _._ 90, relative to the annual average wages normalized to one. They find that the variance parameter, _ν_ , is equal to 1.88 and 1.22 respectively. Differently, we use _β_ = 0 _._ 95 and report parameters divided by the scale parameter _v_ . If we convert our results to make them consistent with ACM, the estimated moving cost is 19
Table 4: Heterogeneity analysis using subsamples

||_Homi_|_cides_|_Hospita_|_liza tion s_|_Rep_|_orts_|
|---|---|---|---|---|---|---|
|_a. Human development index_<br>Below median|||||||
|Leave-out F-M wage ratio|−2.3**|−2.4**|−1.2|−.84|.39|.99|
||(1.1)|(1.1)|(1.3)|(1.2)|(.89)|(.85)|
|Mean of dependent variable (natural log)|0.16|0.16|−0.40|−0.40|1.51|1.51|
|Mean Rate (per 100,000 women)|8.59|8.59|9.76|9.76|76.14|76.14|
|Number of municipalities|421|421|421|421|421|421|
|Above Median|||||||
|Leave-out F-M wage ratio|−.19|.16|1.1|1.9|−2.3|−2.5|
||(1.2)|(1.3)|(1.6)|(1.4)|(3.3)|(2.4)|
|Mean of dependent variable (natural log)|1.13|1.13|1.19|1.19|3.77|3.77|
|Mean Rate (per 100,000 women)|6.05|6.05|11.44|11.44|89.65|89.65|
|Number of municipalities|420|420|420|420|420|420|
|_b. Women’s police station_|||||||
|No women’s police station|||||||
|Leave-out F-M wage ratio|−1.6|−1.8|−.37|.051|1.5|2**|
||(1.3)|(1.2)|(1.3)|(1.3)|(.92)|(.92)|
|Mean of dependent variable (natural log)|−0.76|−0.76|−1.17|−1.17|0.97|0.97|
|Mean rate (per 100,000 women)|7.18|7.18|9.43|9.43|88.96|88.96|
|Number of municipalities|517|517|517|517|517|517|
|Women’s police station|||||||
|Leave-out F-M wage ratio|−1|−.9|−1.4|−1.3|−3.3|−3.5*|
||(.95)|(1)|(1.6)|(1.6)|(2.8)|(2.1)|
|Mean of dependent variable (natural log)|1.43|1.43|1.43|1.43|3.92|3.92|
|Mean rate (per 100,000 women)|6.57|6.57|11.53|11.53|85.05|85.05|
|Number of municipalities|324|324|324|324|324|324|

_Note:_ The outcome variables are in natural logs; 0.01 is added to each value. All specifications include year and municipal fixed effects. The control variables are the natural log of the population of women ages 15–49, the natural logs of male homicides, hospitalizations and reports, the number of households receiving cash transfers, divided by the population of women ages 15–49, the municipal per capita value of cash transfers, the ratio of females to males in secondary education completion, the share of the population ages 15–49 that is female, the per capita number of health clinics, and the per capita number of hospital beds. Regressions are weighted by the average number of women ages 15–49 in 2011–16. F-M = female to male.

Significance levels are reported as * p < .10 ** p < .05 *** p < .01.

32
**Table 4: Productivity Convergence and Regulation: sector level analysis for 2003-08 period** (robust standard errors in parenthesis)

||1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|∆lnLPit<br>leader|0.0257***|0.0334***|0.0276***|0.0309***|0.0302***|0.0246***|0.0326***|0.0278***|0.0300***|0.0298***|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|prodgapijt−1|-0.0548***|-0.0568***|-0.0946***|-0.0483***|-0.0217|-0.0556***|-0.0576***|-0.0947***|-0.0489***|-0.0217|
||(0.007)|(0.014)|(0.022)|(0.013)|(0.017)|(0.007)|(0.014)|(0.022)|(0.013)|(0.017)|
|humanKit−1|0.0017|0.001|0.0004|0.0011|0.0015|0.0015|0.001|0.0003|0.0011|0.0013|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Open_tradeit−1|0.0005|0.0004|-0.0003|0.0005|0.000||||||
||(0.001)|(0.001)|(0.001)|(0.001)|(0.000)||||||
|Open_tradeEUit−1||||||0.0003|0.0005|-0.0007|0.0005|-0.0003|
|||||||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Regit|0.0515*|||||0.0439*|||||
||(0.0340)|||||(0.025)|||||
|(Reg∗prodgap)it−1|-0.0110**|||||-0.0104**|||||
||(0.004)|||||(0.004)|||||
|All_DBit||0.0047***|||||0.0047***||||
|||(0.002)|||||(0.002)||||
|(All_DB∗prodgap)ijt−1||-0.0002**|||||-0.0001**||||
|||(0.000)|||||(0.000)||||
|Start−up_DBit|||0.0081***|||||0.0081***|||
||||(0.002)|||||(0.002)|||
|(Start−up_DB∗prodgap)ijt−1|||0.0003|||||0.0003|||
||||(0.000)|||||(0.000)|||
|Bus. Operations_DBit||||0.0025**|||||0.0024*||
|||||(0.001)|||||(0.001)||
|(BusOperations_DB∗prodgap)ijt−1||||-0.0003|||||-0.0002||
|||||(0.000)|||||(0.000)||
|Inst−Env_DBit|||||0.002*|||||0.0019*|
||||||(0.001)|||||(0.001)|
|(Inst−Env_DB∗prodgap)ijt−1|||||-0.0007***|||||-0.0007***|
||||||(0.000)|||||(0.000)|
|_cons|-0.5913***|-0.7969***|-0.9979***|-0.6575***|<br>-0.5876**|-0.5314***|-0.7765***|0.000|-0.6335***|<br>-0.5541**|
||(0.157)|(0.179)|(0.221)|(0.171)|(0.229)|(0.154)|(0.184)|(0.000)|(0.175)|(0.234)|
|R-squared (within)|0.1348|0.1312|0.1247|0.1334|0.1379|0.136|0.1322|0.1239|0.1347|0.1379|
|N. obs|5129|5129|5129|5129|5129|5129|5129|5129|5129|5129|

_Note:_ Country, sector and time dummies were included but not reported. _* significant at 10%; ** significant at 5%; *** significant at 1%_ 12
# **Figure 7 Staff resources, 2005**

<!-- Start of picture text --> 800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Physicians per 100,000 Nurses per 100,000<br>Albania Serbia Croatia Slovenia EU EU-15 EU-12<br>FYR of<br>Bosnia and Herzegovina Montenegro Macedonia,<br><!-- End of picture text --> Source: WHO HFA-DB, 2007 Note: Data are for 2005, except for the EU-15 and Slovenia where data are for 2004 There is an expectation that there will, in future, be a demand for higher salaries, especially among doctors. Wage compression across different medical professions (e.g. doctors, nurses, specialists) is typical in the Western Balkans and means that doctors’ salaries are among the lowest in Europe, which fuels wage demands and creates an incentive for doctors to emigrate. Thus, while expenditure on wages, as a percentage of public spending on health, is already close to EU levels, future upward pressure on the wage bill is expected. The example of the EU-8 countries may be instructive: between 1999 and 2003, the average monthly salary in the Czech Republic, Slovakia and Hungary increased by more than the average monthly salary of occupational groups with equivalent levels of education and experience (Chawla 2005). Moreover, the ratio of physician salaries to national average for these occupational groups in the Western Balkans is well below the average ratio of 2 that is observed in EU-15 countries.

Another source of upward pressure on the wage bill is a staffing structure that exhibits large nonmedical (i.e. ancillary) staff to medical staff ratios. In Macedonia, for example, non-medical staff account for almost 30% of total staff numbers; in Bosnia and Herzegovina, the figure is 33%. The share is slightly lower at around 28% in Serbia and Montenegro, and is being further reduced under current reform programs, but is still high compared to the ratios in the EU and comparator countries. In the United Kingdom, for example, the figure is only 13%. However, since the functions of many ancillary staff working in the United Kingdom and other European health systems tend to be contracted out, this figure is likely to substantially underestimate the number of ancillary workers in the public healthcare sector in other countries, thus making it very difficult to accurately gauge to what extent the non-medical to medical ratio in the Western Balkans really exceeds that of the EU.

Improving the efficiency of the health sector will necessarily involve the rationalization of the number and type of workers in the health sector, as well as their functions. Serbia, for example, has already taken steps to reduce the number of medical and non-medical employees, as part of a wider public sector rationalization program 26