rafmacalaba/datause-encoder-data huggingface dataset:
# Heterogeneity by gender equality in access to divorce

The study investigates whether the observed association differs between countries in which women face more legal, financial, customary or religious barriers in access to divorce than men. It uses the OECD 2019 Social Institutions Gender Index (SIGI) which contains a measure of this. The study estimates the main specifications for subsamples defined by whether or not men and women have the same rights to initiate and finalise divorce (see Table S1.2 for details of these samples, as well as comparison with the UK and US as the study referred to studies of these countries earlier).<sup>25,26</sup>

# **3 Results**

# Baseline specification

The study shows estimates of equation (1) for each indicator of IPV, with controls sequentially added (Table 1). The study first presents the unconditional correlation, which is negative for male unemployment and insignificantly small for female unemployment, mirroring the association observed in the raw data in Figure S1.3. The estimates are not sensitive to inclusion of year fixed effects. However, they are sensitive to inclusion of country fixed effects, consistent with time-invariant country-level characteristics being correlated with both IPV and unemployment rates. Once country fixed effects are in, the estimates are not sensitive to covariates.

> 25 Equality in access to divorce is defined in the SIGI as "Women have both the same rights to initiate divorce and the same requirements to finalise divorce or annulment as men, without negative repercussions on their parental authority. This applies to all groups of women. Customary, religious and traditional laws or practices do not discriminate against women’s rights regarding divorce or parental authority after divorce" (SIGI 2019 )

> 26 Variation in access to divorce may also pick up the variation in social acceptability of divorce. The study cross-checked this using data from the World Values Survey (WVS), available for 19 countries in the sample. In the sample with equal access, the average score for social acceptability is 4.28, while in the sample where women have weaker access to divorce, the social acceptability score is 3.53. Note that social acceptability is scored 1 to 10, where 1 indicates that divorce is never justifiable, 10 indicates it is always justifiable and the average across the (19) DHS countries for whom WVS data is available is 3.68 while the average over the UK and the US is 6.03.

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Hasan, Rana, Devashish Mitra, Priya Ranjan, and Reshad N. Ahsan. 2012. “Trade Liberalization and Unemployment: Theory and Evidence from India.” Journal of Development Economics 97(2): 167–518.

- Krueger, Alan B. and Lawrence H. Summers. "Efficiency Wages and The Inter-Industry Wage Structure," Econometrica, 1988, v56(2), 259-294.

- Lopez-Acevedo, Gladys C and Raymond Robertson. 2016. Stitches to riches? : apparel employment, trade, and economic development in South Asia (English). Directions in development; poverty. Washington, D.C.: World Bank Group.

- Lusome, R., Bhagat, R. (2006) Trends and patterns of internal migration in India, 1971-2001. In Paper presented at the ‘Annual Conference of Indian Association for the Study of Population (IASP)’, vol. 7, p. 9.

- McCaig, Brian. 2011. Exporting out of poverty: Provincial poverty in Vietnam and U.S. market access, Journal of International Economics, 85, (1), 102-113

McCaig, Brian, and Nina Pavcnik. 2018. "Export Markets and Labor Allocation in a LowIncome Country." American Economic Review, 108 (7): 1899-1941.

- Marshall, Richard, and Shibaab Rahman. 2013. “Internal Migration in Bangladesh: Character, Drivers and Policy Issues.” UNDP

Melitz, Marc. (2003) “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity” Econometrica, 71(6) November: 1695-1725.

- Robertson, Raymond. 2000. "Wage Shocks and North American Labor-Market Integration." American Economic Review, 90 (4): 742-764.

- Robertson, Raymond, Drusilla Brown, Gaëlle Pierre, and Maria Laura Sanchez-Puerta, eds. 2009. Globalization, Wages, and the Quality of Jobs: Five Country Studies. Washington, DC: World Bank.

- Raymond Robertson, Gladys Lopez-Acevedo & Yevgeniya  Savchenko (2020) Globalisation and the Gender Earnings Gap: Evidence from Sri Lanka and Cambodia, The Journal of Development Studies, 56:2, 295-313, DOI: 10.1080/00220388.2019.1573986

Singh, D. (1998). Internal migration in India: 1961-1991. Demography India, 27: 245–261.

Srivastava, R., McGee, T. (1998) Migration and the labour market in India. Indian Journal of Labour Economics, 41: 583–616.

Srivastava, R. (2012). Internal migrants and social protection in India. Human Development in India. New Delhi, India: UNICEF Country Office.

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domestic work ‐ as mostly done by females. While our sample of migrants is small, our survey data also evidence the shift toward female migration: at the time they first moved for work, almost one‐fourth of respondents experienced household job loss, the majority of which belonged to males (77 percent), and one‐third experienced a death in the family, also the majority of whom were male (67 percent). However, female migrants also move in response to other household shocks: 28 percent of respondents experienced a major crop or livestock loss at the time they first moved away for work.

Education is another important “push factor” linked to female migration, although it operates differently for younger and older women. First, primarily for younger women, dropping out of school due to lack of finances serves as an impetus to move. Many migrants reported dropping out of school because their parents or siblings could no longer afford their school fees. It is often after that, and related to the household’s inability to pay, that women move for work. These women also find that, since they are no longer in school, they are a burden to an already financially strapped household. Second, for older women, education is linked to the purpose of migration, as children’s school fees and related expenses are key motivating push factors and serve as a priority for women’s remittances.

Overall, these “push” factors – death, job loss, crop failures, and education ‐ are the main drivers of female migration in Lesotho. “Pull” factors are not as important, although established personal networks ease and expedite the process, and in rare cases start it. This is discussed in more detail below.

# _Perceptions of migration_

Rural communities generally understand “migration” to apply to those crossing international boundaries; they do not often use the term “migrant” for those who move internally, most likely due to Lesotho’s history of male migration to South African mines. They have different terms for migrants based on the work they do, “Some are called _‘Mapetoria’ (Pretorians_ ), others are just called ‘ _Mamaenara’ (Miners),_ others are called ‘ _Makhomosha’ (vendors_ ) and women are called ‘ _Makitjikitji’ (Kitchen girls)”; “’_ ma weekend’, _Weekenders_ , that is people who come home on a weekly basis”. Despite the different names, the most commonly used term sums up the harsh reality in which communities see these migrants to be living: “Baitsokoli”: strugglers. “We call them that because some will go out and find little jobs to sustain their lives”.

Rural perceptions of migration are overwhelmingly negative: many understand migrants to be those who move away and come home just to die from illness. Older, hardened perceptions most likely stem from males returning from South Africa and dying from HIV, or disabled and very ill. More recent perceptions 18
# **Table A.2. Second stage instrumental variables regressions using migrants’ characteristics**

This table shows marginal effects from the estimations of a probit using instrumental variables. The instruments used are _Average time abroad, Average time abroad_<sup>_2_</sup> and _Kinship relationship._ The dependent variables are dummy variables for whether the household has a deposit account (columns (1) and (4)), received a loan (columns (2) and (5)) and requested a loan (columns (3) and (6)). In columns (1)-(3), the variable _Remittances_ refers to a dummy for whether the household receives remittances from overseas, whereas in columns (4)-(6) _Remittances_ refers to per capita amounts received in remittances from overseas. Department fixed effects are estimated but not reported. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|Variables|Remittances<br>household re|: dummy for w<br>ceives remitta|hether<br>nces|Remittances:<br>remittances|per capita am|ount of|
|---|---|---|---|---|---|---|
||Deposit<br>account|Loan<br>received|Loan<br>requested|Deposit|Loan<br>received|Loan<br>requested|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Remittances|0.197|-0.015|-0.021|0.084|-0.007|-0.011|
||[5.92]***|[-1.10]|[-1.50]|[6.27]***|[-0.79]|[-1.17]|
|Real per capita income|0.013|0.000|0.000|0.013|0.000|0.000|
||[4.48]***|[-0.22]|[0.15]|[4.48]***|[-0.19]|[0.17]|
|House and land ownership|0.035|0.022|0.019|0.033|0.022|0.020|
||[2.08]**|[2.32]**|[1.87]*|[1.95]*|[2.34]**|[1.91]*|
|Adults average education|0.022|0.006|0.006|0.021|0.007|0.006|
||[7.12]***|[3.40]***|[3.07]***|[6.57]***|[3.49]***|[3.18]***|
|Agriculture|-0.026|0.008|0.010|-0.027|0.007|0.009|
||[-1.05]|[0.63]|[0.74]|[-1.09]|[0.57]|[0.68]|
|Electricity|0.099|0.015|0.021|0.092|0.016|0.021|
||[5.95]***|[1.50]|[1.93]*|[5.42]***|[1.50]|[1.96]**|
|Returned migrant|0.073|0.034|0.042|0.078|0.037|0.046|
||[1.69]*|[1.11]|[1.26]|[1.79]*|[1.13]|[1.28]|
|Observations|2122|2097|2103|2098|2073|2079|
|Wald test chi<sup>2</sup>|288.4|53.53|59.03|289|53.08|58.42|
|Wald test p-value|0.00|0.00|0.00|0.00|0.00|0.00|
|Log likelihood|-655.8|-284.4|-322.1|-3291|-2910|-2954|
|Over-identification test chi<sup>2</sup>|0.79|1.66|1.90|0.58|1.64|1.89|
|Over-identification testp-value|0.67|0.44|0.39|0.75|0.44|0.39|

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**29**

**Table 7: Decomposition of the participation effect of sectoral growth with respect to head count poverty into its share and elasticity components**

|_Region_|_# of_<br>_countries_|_GDP s_<br>_Agric_|_hare (%)_<br>_Nonag_|_Estimated_<br>_Agric_|_coefficient_<br>_Nonag_<sup>_1)_</sup>|_Particip_<br>_growth_<br> <br>_Agric_|_ation effect of_<br>_on head count_<br>_poverty_<br>_Non-agric_<sup>_1)_</sup>|
|---|---|---|---|---|---|---|---|
|**Low-income group**||||||||
|SSA|18|31|70|-6.00|**-**|-1.83|**-**|
|South Asia|4|29|71|-6.00|**-**|-1.73|**-**|
|East Asia & Pacific|6|24|76|-6.00|**-**|-1.44|**-**|
|Eastern & Central Europe|4|26|74|-6.00|**-**|-1.57|**-**|
|Latin America & Caribbean|4|19|82|-6.00|**-**|-1.11|**-**|
|Middle East & North Africa|2|15|85|-6.00|**-**|-0.92|**-**|
|**ALL LOW-INCOME**|**38**|**27**|**73**|**-6.00**|**-**|**-1.60**|**-**|
|**Middle-income**<br>||||||||
|SSA<sup>2)</sup>|2|4|97|-21.74|-1.57|-0.76|-1.52|
|South Asia|0|||||||
|East Asia & Pacific|2|13|87|-21.74|-1.57|-2.76|-1.37|
|Eastern & Central Europe|12|10|91|-21.74|-1.57|-2.07|-1.42|
|Latin America & Caribbean|16|10|90|-21.74|-1.57|-2.13|-1.42|
|Middle East % North Africa|5|14|86|-21.74|-1.57|-3.07|-1.35|
|**ALL MIDDLE-INCOME**|**37**|**10**|**90**|**-21.74**|**-1.57**|**-2.24**|**-1.41**|
|**Pooled sample**||||||||
|SSA|20|28|72|-9.35|-0.98|-2.66|-0.70|
|South Asia|4|29|71|-9.35|-0.98|-2.70|-0.70|
|East Asia & Pacific|8|22|78|-9.35|-0.98|-2.03|-0.77|
|Eastern & Central Europe|16|14|86|-9.35|-0.98|-1.27|-0.85|
|Latin America & Caribbean|20|11|89|-9.35|-0.98|-1.03|-0.87|
|Middle East & North Africa|7|14|86|-9.35|-0.98|-1.34|-0.84|
|**ALL POOLED**|**75**|**18**|**82**|**-9.35**|**-0.98**|**-1.72**|**-0.80**|

1) The estimated coefficients on the effect of (share weighted) non-agricultural growth in the low-income countries are not statistically significantly different from zero.

2) The two middle-income countries are Botswana and South Africa Source: Authors’ calculations based on World Bank data (2005c) While an overall focus on fostering agricultural growth in reducing world poverty appears justified from the broad average perspective, the results in Table 7 also underscore the critical need to look beyond the averages and explore the size of the participation effect across regions and countries. First, as discussed above, the elasticity of total poverty reduction with respect to sectoral GDP (i.e. the estimated coefficients) differs substantially between the middle and low-income countries where the effect of agricultural growth on overall poverty was found to be much more important. Second, the larger the share of agriculture in the total economy the more important the participation effect from agriculture. The combined effect of these two forces in the middle-income
<!-- Start of picture text --> Foreign Investment<br>5.0 1.8<br>4.5 FDI 1.6<br>4.0 1.4<br>3.5 Employment<br>1.2<br>3.0<br>Employment in Non-state-owned 1.0<br>2.5 Units<br>0.8<br>2.0<br>0.6<br>1.5<br>1.0 0.4<br>0.5 0.2<br>0.0 0.0<br>(Billion USD)<br>(Million Workers)<br>1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Shenzhen Statistics Bureau, various years.

**Figure 1.7    Productivity of Selected Sectors in Shenzhen (output per worker), 1993–2004**

<!-- Start of picture text --> 2.5<br>Farming, Forestry, Animal<br>Husbandry and Fishery<br>2<br>Transportation, postal, and<br>telecommunication services<br>1.5<br>1<br>0.5<br>0<br>1993-1998 1999-2001 2002-2004<br><!-- End of picture text --> _Source:_ Author’s calculations based on data from the Shenzhen Statistics Bureau, 1994, 1999, 2002, 2005, 2006, 2007.

**_Technology learning, innovation, upgrading, and strong links with the domestic economy._** One of the key strengths of the SEZs is that they have a high concentration of very skilled people, including many R&D personnel, especially in the HIDZs and ETDZs. As a result, they have become centers of knowledge and technology generation, adaptation, diffusion, and innovation. The abundance of FDI provides a good opportunity for technology learning. Governments also put strong emphasis on technology learning and innovation, as well as on technology-intensive industries. For

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# **_Demanding to Be Served: Holding Governments to Account for Improved_**

# **_Access_**

Anwar Shah<sup>1</sup> , World Bank (ashah@worldbank.org)

> 1  An earlier version of this paper was presented at an invited seminar at Kennedy School of Government, Harvard University in April 2007.  The author is grateful to the seminar participants and Dr. Jorrit de Jong and Dr. Gowher Rizvi for comments and to Nahida Ahmadova and Kiran Choudhry for their comments and contributions towards improvement of this paper.  The views expressed in this paper are those of the author alone and should not be attributed to the World Bank Group. Comments on this paper may please be addressed to: ashah@worldbank.org
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# and allowed women to be the lead farmer in a couple.

3.28 But this alone was not enough. PAFSAT realized this and took further steps to redress the male orientation and went on to introduce modifications that recognized women's roles. PAFSAT hired a cadre of women demonstrators and fully integrated them into its demonstration service. It shortened its residential phase of training, it began working with women's groups, and it changed the mission of the women's ptogram from one of being separate to one of being supplemental. The women's program is now an important adjunct that ensures that the technical service reaches all of its intended clientele. Although it is a too early to tell if this new approach will be more successful than previous ones, if these themes have prescriptive power then the answer should be affirmative.

# Information is Crucial for a Sound Strate&W

3.29 A crucial element in the success of MIDENO's strategy in extension or other agricultural services was the availability of high quality baseline data. Making intelligent interventions into the farming systems of small farmers and in creatively responding to women's roles required a thorough understanding of the agricultural and social environment.

3.30 MIDENO was fortunate that the North West Province was well-studied anthropologically and economically. When the initial design team came, they were able to draw on existing studies that pointed out the importance of groups and women's contribution to agriculture and these perspectives were incorporated into the design. Moreover, the design team did survey research of its own to fill in gaps. Thus MIDENO was better integrated into the existing envirornent than most projects. MIDENO also benefitted from a variety of economic (Wertime, 1986), anthropological (Koons, 1988), and agricultural (Eyben, 1984) studies that were conducted during the course of implementation and which influenced the direction of MIDENO's strategy teward women farmers. Information from monitoring the differential impacts of implementation on women and the disadvantaged has also proven valuable in refining interven.tions.

3.31 MIDENO was much less successfudl in providing the necessary postimplementation monitoring information. Due to competing demands for limited resources and the untimely withdraw/transfer of senior staff, monitoring has been sketchy with, in particular, a lack of gender,lisaggregated data.

# An Exglicit Goal Provides Strategic Direction

3.32 It is hard to overestimate the importance that the PAR's stated objectives of reaching small farmers and women farmers had on the course of implementation. The ideas were carried forward in Plan MIDENO and both objectives are repeated time and again in quarterly reports, internal monitoring reviews, and the reports of evaluation teams. It is not a profound insight that articulating goals and objectives helps to focus attention on attaining them. But when small farmers, much less women farmers, are left out of program goals, it is worth remembering how crucial the omission can be.
fiscal policy are included in the vector _Z it_ . The vector _Pit_ includes indicators for electoral rules (whether majoritarian or proportional) and system of government (whether presidential or parliamentary), and an indicator for regularity of elections, in place of more subjective measures of quality of democracy used in the Persson-Tabellini specifications. A time effect,<sup>δ</sup> _t_<sup>, and regional indicator variables,</sup> _Rit_ , are included as well. The specification is therefore as follows: _Yit_ = α⋅ _Z it_ + β⋅ _Pit_ + δ _t_ + _Rit_ + ε _it_ (7) We then augment this model received from the literature to include indicators of coalition government:

> Our model predicts γ < 0 , contrary to models in the received literature. This is the main test we perform to provide empirical support for our model. That is, we use the same cross-country setting, specification, and data used by recent influential contributions in the literature on political influences on fiscal policy, and then augment the model to test how coalition governments correlate with fiscal policy. We do not have a reasonable instrument for coalition government that is convincingly exogenous to fiscal policy. However, we do not think this is a problem for our purpose of showing both theoretically and empirically that instances of coalition government can be associated with lower spending and taxes. This is because possible reverse causality in specification (8) is also consistent with the argument we make. Fiscal shocks, for example, that reduce an incumbent government’s revenue raising capacity are more likely to lead to an incumbent single-party making an offer to form a minimum winning coalition to stay in power.

The more important empirical concern is whether other omitted variables, capturing alternate political explanations, are correlated both with party composition of government and its size, and hence driving the result. We have already included in our basic specification one of the more important alternate political variables determining government size in the literature—electoral rules (Persson et al, 2007). We also consider other prominent variables in the literature such as voter turnout (greater turnout of coresupporters may be positively correlated both with larger government and single-party government); federalism (greater fiscal and political decentralization may lead to smaller size of central budgets, and greater likelihood of coalitions); political ideology (right wing governments may be smaller and in coalitions); and ethno-linguistic fragmentation (greater social fragmentation may lead to smaller government and coalitions). Each of these is discussed in more detail in the next section where we present the empirical results.

Following a large literature with cross-country specifications, we use fiscal data from the IMF’s _Government Finance Statistics_ , economic data from the World Bank’s _World Development Indicators_ , 15
distinction for the “hearts and minds” theory which is considered to be driven as much by a change in perceptions as by actual change.

The follow-up survey used many questions from the baseline survey, but changed the sample for the female individual questionnaire from female focus group participants to wives of male household respondents. Enumerators administering the male household questionnaire were instructed to locate and interview the same households and, whenever possible, the same villagers who participated in the baseline survey. Enumerators were able to successfully locate such respondents in 65 percent of households in which male respondents were interviewed during the baseline survey.<sup>15</sup> Due to deterioration in security conditions, 26 villages (11 treatment and 15 control villages) could not be surveyed during the follow-up survey. Since attrition in the sample was related to security, it cannot be treated as random, despite not being related to treatment status. Thus, although the analysis based on the data from the follow-up survey provides internally valid estimates of the average treatment effect for villages that were secure enough to be surveyed, the results cannot be generalized to villages inaccessible due to security.

# **V.3. Security Incidents**

Data on security incidents come from the ISAF Combined Information Data Network Exchange (CIDNE) database which includes the date, time, location, and type of events as reported by ISAF soldiers and affiliates.<sup>16</sup> The data contain information on security incidents in the 10 districts between March 2003 and March 2010. There were 535 security incidents prior to the start of NSP mobilization in October 2007 and 688 incidents after. Almost all the incidents are caused by Improvised Explosive Devices (IED), with 45 percent of incidents being IED explosions and 53 percent being cases where an IED was found and cleared. Only two percent of incidents were related to mine strikes.

The data was used to construct dummy variables that indicate whether there was at least one security incident starting from October 2007 within a certain radius of a particular village. Given that IEDs are usually placed beside roads rather than in villages, incidents are assigned to villages based on

> 15 The predominant reason for enumerators not being able to interview baseline respondents was that the person was away from home on the day that the survey team visited the village as it was the time of harvest. Differences between treatment and control groups in individual-level attrition are not statistically significant.

> 16 In general, the data contains four categories for ISAF events and thirteen categories for Taliban events, but the data available to us contains information only on Taliban events with Improvised Explosive Devices and mine strikes, which account for most of the security incidents in the sample districts.

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