rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9431

# **Abstract**

This paper investigates whether the relative self-esteem level of spouses can lead to within- household competition for inputs and affect economic gender inequality in the home. Using data on smallholder farmer couples in Côte d’Ivoire, the paper examines the relationship between spouses’ self-esteem and income-earning in agriculture. Although the link between own self-esteem and crop income earning is positive, there is a “battle of the sexes” in which one spouse’s self-esteem is negatively related to the other’s income earning, particularly income earning in higher-value, export-oriented agriculture. Women’s outcomes are more sensitive to their own self-esteem (positively) and to their partners’ (negatively) than men’s. This negative relationship is driven by individuals during middle age, when self-esteem is considered most stable. A key channel through which self-esteem appears to matter is by increasing control over household land, which is a scarce but crucial input to agricultural production. In addition to confirming the importance of noncognitive skills for poverty reduction in rural settings, the findings highlight the importance of their impact on intra- and inter-household inequality, especially in the presence of interlocking market failures constraining the supply of inputs to production.

This paper is a product of the Gender Innovation Lab, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at adonald@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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Thus human capital variables, such as age education and experience and household variables such as income and household size effects an individuals participation decision.

# **3. The Absorption of Labor**

## **3.1 Sectoral Employment**

The growing labor force was absorbed in the different segments of Ghana's labor market. In this section we analyze the distribution of formal and informal workers, the public-private composition of wage workers-especially the formal workers. The questions one would like to ask is: Is public sector the main provider of wage jobs? Does female employment distribution differ from that of male employment distributions? What is the main source of wage employment for females? Does public employment depend on educational level? Are most of the wage jobs in the rural or urban areas? Is agriculture still the main source of employment in the rural areas or has there been a decline in the agricultural sector employment. The objective here is to identify the important sectors of employment and changes -if any -in the relative importance of sectors during this period. A related issue will be to find out to what extent are the employment differentials - occupational or industrial- justified? Another broad distinction is the difference between private and public wage employment. The GLSS data set allows us to make this distinction within the formal labor<sup>4</sup> market.

Looking at the broad employment distribution we find that agriculture is still the main form of employment for the labor force as a whole. In terms of formal and informal employment, in 1992, 21% of the workers were wage workers, of which 13% worked in the public sector; in 1987, 24% of the workers were wage workers, of which 14% were formal sector workers. Over 74% of the workers were self employed, of which 48% were engaged in agriculture (a decline from 49% in 1987). Among the wage workers, 62% were formal sector workers and 38% were informal sector workers. In terms of male and female workers, over 49% of the male workers and 47% of the female workers were engaged in agriculture (Table 6). These figures indicate that formal sector is still the main provider of wage jobs although its share has declined during this period. Interestingly, among the formal wage workers, 71% were males and the only 29% were females. Similar pattern was observed among the informal sector wage workers. Thus most of the wage jobs are taken up by males. Only in the self employed category were the proportion of females greater than those of the males. During 1992, in the urban areas, 43% were self employed doing business and 24% were public sector workers, while in the rural areas 64% were engaged in agriculture. Over 39% of the urban workers were wage workers while only 14% of the rural workers were wage workers, perhaps an indication that most of the wage jobs exist in the urban areas (Table 5).

> 4 A person is classifed as employed in the formal sector if he is a wage worker, is not self-employed and gets fringe benefits such as health insurance, leave, pension etc.

14
responsible for their loss in SWB and think that under different leadership they would have been better off. Since the Gallup World Poll does not include a voting variable and some of the demographic variables of interest to us, we again use the data from the Brazilian Electoral Study and a logistic regression to link the likelihood to vote for Bolsonaro to a similar set of variables as those included in the analysis of SWB change in section 4.4.

**Table 8: Factors Associated with the Bolsonaro Vote – Logistic Regression**

|**Panel A**|**Dependent**<br>**variable:**<br>Voted for|**Dependent**<br>**variable**:<br>Voted for|**Dependent**<br>**variable:**<br>Voted for|
|---|---|---|---|
||Bolsonaro in<br>Second Round of<br>Presidential<br>|Bolsonaro in<br>Second Round of<br>Presidential<br>|Bolsonaro in<br>Second Round of<br>Presidential<br>|
||Elections|Elections|Elections|
|Economic situation country||||
|Improved|1.09||1.08|
|Same|Ref.||Ref.|
|Worsened|0.59**||0.70#|
|Personal economic situation||||
|Improved (Upward)|0.62**||0.78|
|<br>Same<br>|Ref.||Ref.|
|Worsened (Downward)|0.84||0.91|
|Perceived corruption|1.17#||0.95|
|<br>Against income redistribution|1.07||1.13#|
|<br>Immigrants bad for economy<br>|1.14**<br>||1.12*<br>|
|Satisfied with crime control|0.99||1.01|
|Satisfied with public services provision|1.01||1.01|
|Conservatism|1.14**||1.15**|
|Nativism|1.16#||1.13|
|Confidence in the military||1.51**|1.53**|
|Satisfied with democracy||1.17*|1.13|
|Trust in political parties<br>Antipetismo||0.98<br>1.54**|1.03<br>1.58**|
|<br>Bolsa familia beneficiary||0.72#|0.75|
|Socio-demographic controls|YES|YES|YES|
|<br>N|1,566|1,566|1,461|
|**Panel B**|**Dependent**|**Dependent**|**Dependent**|
||**variable:**|**variable:**|**variable:**|
||Switched from<br>|Switched from<br>|Switched from<br>|
||voting Dilma in<br>|voting Dilma in<br>|voting Dilma in<br>|
||2014 to Bolsonaro|2014 to Bolsonaro|2014 to Bolsonaro|
||in 2018|in 2018|in 2018|
|Economic situation country||||
|Improved|0.92||0.97|
|Same|Ref.||Ref.|
|Worsened|1.36**||1.51**|
|Personal economic situation||||
|Improved (Upward)|1.20||1.27|
|Same|Ref.||Ref.|
|Worsened (Downward)|1.08||1.24|
|Perceived corruption|1.11||1.06|
|<br>Against income redistribution|0.91||0.92|
|<br>Immigrants bad for economy|1.02||1.00|
|<br>Satisfied with crime control|1.00||0.99|
|Satisfied with public services provision|1.00||0.99|
|Conservatism|1.07||1.07|
|Nativism|115||112|
|<br>Confidence in the military|.|1.16*|.<br>1.17*|
|Satisfied with democracy||1.00|1.01|
|Trust in political parties||1.05|1.07|
|Antipetismo||1.12**|1.12**|
|<br>Bolsa Familia beneficiary||1.01|1.09|
|Socio-demographic controls|YES|YES|YES|
|N|1,566|1,566|1,461|

Notes: #p<0.10, *p<0.05, **p<0.01. All presented values are odd’s ratios. All models are estimated using robust standard errors. Ref.=Reference category.

32
been constructed while Phase-II will construct a four lane freight road along the corridor to service its upcoming container and general cargo terminal.

While urban features like paved roads and street lighting are not present, there is considerable activity in residential construction, with relatively elaborate buildings becoming increasingly common. A number of people from outside the settlement are taking up residence in Erein and there is evidence of land development and renting. The area has a number of rice mills, which are located outside the municipal boundary. There is also an FCI procurement godown nearby. These mills provide casual employment for unskilled labor. In addition, there is also a transport industry, including vehicle repairing, etc. In Figure 1, Erein is plotted with respect to the composite index of development and population size. As can be seen, Erein is quite ‘average’ for Orissa.

**Figure 1. Erein in relation to other urban settlements in Odisha**

<!-- Start of picture text --> 4<br>3 Other CTs Shortlisted CTs STs<br>2<br>1<br>Erei (CT)<br>0<br>-1<br>-2<br>-3 Population<br>-4<br>0 10,000 20,000 30,000 40,000 50,000<br>Standardised Score<br><!-- End of picture text --> In Jharkhand, the Census Towns of Cherra (pop: 5,279) and Bishnugarh (pop: 4,847), which form a settlement agglomeration, were selected. Not only are they both at the boundary of classification as an urban area (Bishnugarh actually does not meet the three-fold test of urbanization, even though it has been declared as a census town<sup>2</sup> ), which will permit investigation into the nature of classification, their proximity to each other permits a closer look at the regulatory issues relating to rural administration. While both Bishnugarh and Cherra belong to Bishnugarh Panchayat Samiti, they are in different Gram panchayats – Bishnugarh is in Bishnugarh Gram panchayat with two other villages, while Cherra is in Cherra Gram Panchayat, with one other village. We plot these two CTs in a manner similar to Erein. As seen in Figure 2, both Bishnugarh and Cherra are ‘average’ in terms of their development index. It should also be noted that Jharkhand has a number

> 2 This is because there is a predictive component to the classification of rural and urban areas. Since CTs are identified prior to the census operation, information from the last census is used for such identification. For CTs in 2011, all villages having a population of 4,000 or more at the 2001 census are taken up for examination with the assumption that such settlements are expected to cross 5,000 population by the time of the 2011 census. For such units the density and the male main workforce composition of the population are examined to see whether these qualify to be categorized as CT.

8
10

# 4. Female-Male Pay Differentials

Table 3 shows the average earnings of women and men in the 15 countries for which we have household-level surveys.<sup>20</sup> In all countries female workers are paid less than male workers and in some countries (such as Argentina, Bolivia, Chile, Ecuador and Jamaica) women are paid substantially less than men (female pay less than 30 percent of female pay). Though some variation is due to the fact that the time period to which earnings refer is different across countries, this variation is not great: the standard deviation of the ratio of female-to-male pay is 11 percentage points compared with a regional mean of 73 percent. This may be taken as evidence that women, as a factor of production, are again treated in a rather uniform way across the region with respect to pay as was, indeed, the case of participation.

Table 3 Mean Earnings by Sex

||||E|arnings|Female/Male<br>Earnings Ratio|
|---|---|---|---|---|---|
|Country|Year|Currency|Female|Male|(percent)|
|Argentina|1985|Australes/month|63559.0|98483.5|64.5|
|Bolivia|1989|Bolivianos/week|68.9|110.5|62.3|
|Brazil|1989|Cruzados/hour|7.7|10.9|70.3|
|Chile|1987|Pesos/month|10912.0|23166.0|47.1|
|Colombia|1989|Pesos/week|9078.0|10727.0|84.6|
|CostaRica|1989|Colones/month|14910.0|18459.0|80.8|
|Ecuador|1987|Sucres/month|22327.0|35077.0|63.7|
|Guatemala|1989|Quetzals/month|183.5|238.8|76.8|
|Honduras|1989|Lempiras/week|73.5|90.4|81.3|
|Jamaica|1989|Jam$/week|33.1|57.4|57.7|
|Mexico|1984|Pesos/week|5643.7|6590.9|85.6|
|Panama|1989|Balboas/hour|1.7|2.0|84.8|
|Peru|1990|Intis/hour|46.1|56.0|82.3|
|Uruguay|1987|Pesos/hour|730.0|980.0|74.5|
|Venezuela|1989|Bolivares/week|1179.9|1518.2|77.7|

Source: Psacharopoulos and Tzannatos (1991) and references therein.

2 For reasons of comparison, in Table 3 earnings are presented in the same "form" (that is, per hour, week or month) as they were used in the earnings functions that follow.
the paucity of better quality data.

The data set of OPSW plays a key role in our imputation strategy. While not providing data disaggregated by skill level and additionally comprising migrants of all ages (as opposed to only those aged 25 and above), OPSW embodies significant informational content about the composition of overall bilateral migration stocks globally; arguably far more than any estimation procedure alone could be reasonably expected to capture. These data, which span 1960-2000, prove useful on two counts. First, they provide information on past (pre1990) migrant stocks, i.e. migrant networks, that we use in our estimations. Second, since the data that we are imputing are a sub-sample of these overall migrant stocks, the OPSW data importantly provide upper-bounds to our estimates that can then be subsequently disaggregated according to this paper’s main focus, migrants’ level of human capital.

In the next section, we first outline the pseudo-gravity model, a suitable econometric specification for our purposes and further highlight the need for our three-step econometric procedure. We then discuss a number of estimation issues that further need be considered before continuing to an analysis of the accuracy of our results.

# **3.1 The Three Step Econometric Model**

The econometric model that we use to construct our out-of-sample predictions is an extension of the recent developments in the literature. Our theoretical foundation is the random utility model of migration that has been used extensively in the literature (Anderson, 2011; Beine et al., 2011; Grogger and Hanson, 2011; Ortega and Peri, 2012; Beine and Salomone, 15
<!-- Start of picture text --> Households without access to an improved water source (%)<br>100% 2000 2008<br>80%<br>60%<br>40% 30%<br>20% 13% 10%<br>1% 1% "4% 1% 0% i0 2% 8% 4,% % Sh24<br>O%<br>Urban Rural Urban LowerEgypt | Upper Egypt Frontier TOTAL<br>Governorates Governorates<br>Urban/Rural Location Region TOTAL<br><!-- End of picture text -->
Narrow tax bases, widespread evasion and poor tax administration translate into low revenue collection _._ Tax revenues in South Asia are lower than in other countries at similar development levels (Figure 6). Low revenue constrains government spending including on infrastructure, education and other essential services. South Asian countries have been spending less than emerging economies or developing countries in other regions at comparable levels of per capita income.<sup>59</sup> Low revenues also contribute to persistent fiscal deficits, high government debts and macroeconomic instability, which hurt the investment climate and economic growth more broadly.

**Figure 6. Tax revenue in South Asia is lower than in other countries at comparable income level**

<!-- Start of picture text --> 40<br>Macao (China)<br>Algeria<br>Denmark<br>30<br>Georgia<br>20 Senegal<br>10<br>India Sri Lanka<br>Bangladesh Pakistan,<br>Oman<br>Nigeria Bahrain UAE Kuwait<br>0<br>6.0 7.0 8.0 9.0 10.0 11.0 12.0<br>Log of GDP per capita, PPP (constant 2011 international $)<br>Tax revenue (% of GDP, 2011)<br><!-- End of picture text --> Source: World Bank. World Development Indicators

# **V. Conclusions and the Way Forward**

Tax policy is one of the most important aspects of a country’s business environment. It creates an incentive framework for private sector development and generates the revenue needed to fund essential public goods. It is also a political battleground as governments strive to balance the objectives of maximizing revenue, encouraging business growth, ensuring equity and winning electoral support. In South Asia, governments have systematically used tax incentives to promote development of certain industries, types of firms or locations. The effectiveness of tax incentives in achieving these objectives in the region has not been evaluated. While the benefits of existing tax policies are not clear, the negative impacts are obvious ‐ distortions to firms investment decisions, increased tax administration and compliance costs as well as public revenue losses.

Tax policies in South Asia distort investment and consumption decisions as firms are given incentives to specialize in activities and products based on tax schemes rather than economic rational. There is little evidence that tax incentives in South Asia address market failures. They are granted to less productive businesses (small firms and those involved in agriculture and traditional industries), high tech industries

> 59 World Bank. 2012. Creating Fiscal Space through Revenue Mobilization, Washington DC

23
Building on these findings, this study explores the drivers of vocational training choices in two high-paying sectors: energy and information and communications technology (EICT). Both sectors were identified as male-dominated and high-paying sectors using a nationally representative dataset, the ENV 2015 (Institut National de la Statistique, 2015). Our empirical strategy uses linear regressions with cohort and city fixed effects to examine the correlates of training choices. The analysis is based on a sample of 1128 women and 1400 men who applied for job training in urban areas of Cˆote d’Ivoire (Abidjan and Grand-Bassam). We use data collected as part of the PRO-Jeunes program (or _PRO-Youth_ in English). PRO-Jeunes was designed to provide (self-)employment support services that are particularly flexible to meet a wide range of beneficiaries’ needs. It targets youth aged 15 to 30 in rural and urban areas. During the application process, each candidate could choose between two main tracks: an employment track, which provided support for finding employment, or a self-employment track, which offered either a generalist path or a vocational training path. The generalist path was designed for candidates who were already working or engaged in an incomegenerating activity within a specific sector, or who had a particular sector in mind. For those opting for the vocational training path provided as part of the program, candidates could choose to enhance their skills in high-paying sectors such as energy or information and communications technology (ICT), or in retail, specifically in the sales of Lipton products.

This study identifies several key factors influencing individuals’ decisions to pursue training in the EICT sectors. A significant portion of the sample, 72% of men and 51% of women, chose this path. Education and training are pivotal: an additional year of education increases the likelihood of seeking EICT training by 3.2 percentage points for women and 4.4 percentage points for men. This is partly due to the stringent educational entry requirements of vocational training programs. For women, prior training in EICT sectors markedly boosts the likelihood of pursuing further training in these high-paying fields, indicating path dependency. Additionally, a larger professional network increases the probability of choosing EICT training for both men and women by 3 percentage points.

Gender-specific factors also play a role. Women benefit significantly from role models, particularly male role models, while men do not experience the same effect. Women with greater agency are more likely to opt for EICT training. However, traditional gender role attitudes can hinder women’s participation in EICT training. Women who prioritize household responsibilities are significantly less likely to choose EICT training.

The main contribution of this paper is to demonstrate that different factors motivate men and women to train in high-paying, often male-dominated sectors. This reflects broader gender disparities in society. Our findings highlight the need for targeted interventions to address women’s 3
Table A8: Impact on economic outcomes

||Engaged in IGA<br>(last 6 months)|Reported<br>profts|Monthly<br>profts|Engaged in<br>non-Ag IGA<br>(last 6 months)|Number of<br>IGAs|Number of<br>own<br>initiative<br>IGAs|
|---|---|---|---|---|---|---|
|T1 (safe spaces)|0.093*|0.014|0.126|-0.036|0.137*|0.013|
||(0.053)|(0.056)|(0.546)|(0.038)|(0.079)|(0.055)|
|T1 + Leaders|0.044|0.041|0.347|-0.017|0.105|-0.016|
||(0.064)|(0.048)|(0.459)|(0.047)|(0.100)|(0.059)|
|T2 (safe spaces + IGA)|0.096*|0.109**|1.086**|0.013|0.230**|0.144**|
||(0.056)|(0.053)|(0.527)|(0.039)|(0.098)|(0.057)|
|T2 + Leaders|0.105*|0.113***|1.038**|0.034|0.229**|0.156***|
||(0.053)|(0.042)|(0.418)|(0.044)|(0.094)|(0.055)|
|T3 (safe spaces + husbands’ clubs)|0.139***|0.041|0.406|0.151***|0.210**|0.198***|
||(0.049)|(0.043)|(0.443)|(0.049)|(0.084)|(0.065)|
|T3 + Leaders|0.189***|0.085|0.858|0.050|0.257***|0.178**|
||(0.061)|(0.057)|(0.570)|(0.050)|(0.081)|(0.070)|
|T4 (control spillover)|0.050|0.013|0.121|0.029|0.097|0.041|
||(0.047)|(0.032)|(0.331)|(0.034)|(0.074)|(0.051)|
|T1 = T1 + L|.468|.69|.739|.718|.758|.654|
|T2 = T2 + L|.881|.951|.938|.684|.996|.846|
|T3 = T3 + L|.421|.51|.509|.124|.623|.811|
|T1 = T2|.964|.19|.178|.308|.38|.0562|
|T2 = T3|.444|.285|.289|.0178|.856|.447|
|T1 = T3|.419|.674|.661|.00129|.448|.0177|
|T1 + L = T2 + L|.393|.225|.222|.387|.296|.0101|
|T2 + L = T3 + L|.203|.681|.785|.797|.785|.781|
|T1 + L = T3 + L|.0575|.541|.465|.308|.175|.0164|
|N|3,003|3,003|3,003|3,003|3,003|3,003|
|Control mean|.529|.235|2.38|.266|.652|.371|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; nonag = nonagricultural. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p-values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.