rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8004

# **Abstract**

Since the 1990s, India has seen robust economic growth, rising wages, steady fertility decline, increased urbanization, and expanded educational attainment for males and females. But unlike other countries that have undergone similar transitions, urban women’s employment has refused to budge, never crossing the 25 percent mark. This paper fills a critical gap in policy research on women’s employment in India. The discussion is situated in the normative construction of motherhood and the gendered nature of caregiving in India. The analysis uses pooled data from six rounds of the National Sample Surveys to examine the effects of having a young child on mothers’ employment in urban India over 1983–2011. The analysis also looks at household structure, and analyzes the effects of other household members on women’s labor supply. The results show that although the onus of childbearing may have reduced, that of caregiving has increased. Having a young child in the home depresses mothers’ employment, an inverse relationship that has intensified over time. Further, living in a household with older children and women over the age of 50 is positively associated with women’s employment. These results show that the care of young children is an increasingly important issue in women’s employment decisions, in a context where formal childcare is practically nonexistent. These results have significant implications for policy to raise women’s labor force participation in India.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mdas@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
exclusive concentration in the garment sector.

**Table 4: Employment contribution of EPZs**

||**EPZ employment (2008)**|**EPZ employment as % of national**<br>**industrial sector employment**|
|---|---|---|
|**Bangladesh**|218,299|3%|
|**DR**|124,517|30%|
|**Honduras**|130,000|30%|
|**Vietnam**|1,172,000|19%|
|**Ghana(Tema)**|2,025|35%|
|**Ghana(single units)**|26,534|.|
|**Kenya (EPZs)**|15,127|15%|
|**Kenya (single units)**|15,551||
|**Lesotho**|45,130|>80%|
|**Nigeria**<br>**(Calabar‐ est)**|1,156|<1%|
|**Nigeria**<br>**(Onne‐ oil &gas)**|20,000|N/A|
|**Tanzania**|7,500|2.5%|

Source: EPZ employment based on data from individual country EPZ authorities; national industrial employment from various sources Again the bigger concern about the African zones is not so much the scale of their programs at this stage, but the evidence that their growth may already be slowing. Vietnam and Bangladesh have managed to shift to exponential job growth over the last decade. But for African zone programs, the story of jobs is similar to that of exports – rapid growth in the first half of the decade followed by stagnation. Indeed, for African zones dependent on the garment sector (Kenya and Lesotho) it is not a question simply of failure to shift to an exponential growth path, or even just of slowing growth, but of absolute and relatively acute decline in employment. Employment in Lesotho’s export garment sector is down 15% from its 2004 peak. In Kenya’s EPZ program, the decline is more than 20% from the 2003 peak. Even in Ghana, where exports have risen rapidly under the single factory free zone program, job growth was weak (only 4.5%) since 2004; at the end of 2008 free zone employment in Ghana stood at virtually the same level as in 2005, despite reported exports from the program growing by 2.5 times.

# <u>Adjustment</u>

A final measure of economic performance of zones is the degree to which they have played a role in supporting industrialization and/or diversification of their host country’s exports. This can be explored through sectoral patterns of exports as well as by analyzing the inter‐ and intra‐sectoral adjustments within the zone programs over time.

An assessment of the broad structure of national exports in each of the ten countries under study over a period of several decades show quite dramatic structural changes in export patterns towards in the four non‐African countries, toward greater shares of manufacturing and declining primary exports. In the 20
|n Nominal Wages by Type (2000-2010)<br>Blue-collar Nominal Wages<br>White-collar Nominal Wages<br>(3)<br>(4)<br>(5)<br>(6)<br>0.003<br>-0.002<br>0.001<br>-0.0002<br>(0.002)<br>(0.002)<br>(0.004)<br>(0.003)<br>0.765<br>0.764<br>0.825<br>0.825<br>79,652<br>79,633<br>78,619<br>78,628<br>0.001<br>0.002<br>0.002<br>0.006<br>(0.002)<br>(0.002)<br>(0.004)<br>(0.004)<br>0.765<br>0.765<br>0.825<br>0.825<br>79,656<br>79,637<br>78,623<br>78,632<br>0.009<br>-0.006<br>0.003<br>-0.0005<br>(0.008)<br>(0.006)<br>(0.013)<br>(0.009)<br>79,652<br>79,633<br>78,619<br>78,628<br>**ariable: Share of IDP (% Working Age Pop)**<br>0.288***<br>0.328***<br>0.288***<br>0.328***<br>(0.023)<br>(0.025)<br>(0.023)<br>(0.025)<br>151.57<br>446.21<br>151.57<br>446.21<br>82,738<br>82,719<br>82,738<br>82,719<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>No<br>Yes<br>No<br>Yes<br>include interactions of year dummies and i) armed attacks by illegal armed groups<br>penditures in 1995, v) central government transfers to health, education, and other<br>f tax collection offces in 1995, viii) 2000 GDP share in agriculture, services, and<br>ustered standard errors at the frm level are reported in parentheses. _∗∗∗_signifcant|
|---|
|P Infows i<br>l Wages<br>(2)<br>-0.002<br>(0.002)<br>0.862<br>82,228<br>0.002<br>(0.002)<br>0.862<br>82,232<br>-0.006<br>(0.006)<br>82,228<br>**Dep. V**<br>0.328***<br>(0.025)<br>446.21<br>82,719<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>ional controls<br>, iv) public ex<br>vii) number o<br>y in 1995. Cl|
|Impacts of ID<br>Nomina<br>(1)<br>0.001<br>(0.002)<br>0.862<br>82,247<br>0.0003<br>(0.002)<br>0.862<br>82,251<br>0.003<br>(0.008)<br>82,247<br>0.288***<br>(0.023)<br>151.57<br>82,738<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>ression. Addit<br>cide rates 1995<br>tions in 1995,<br>ght light densit<br>e 10%.|
|**Table (V)**<br>**Dependent Variables (in logs)**<br>**Panel A. Reduced Form**<br>Predicted Infows<br>R-squared<br>Observations<br>**Panel B. OLS**<br>Share of IDP (% Working Age Pop.)<br>R-squared<br>Observations<br>**Panel C. 2SLS**<br>Share of IDP (% Working Age Pop.)<br>Observations<br>**Panel D. First Stage**<br>Predicted Infows<br>First Stage F-statistic<br>Observations<br>**Controls (for all Panels)**<br>Year FE<br>Firm FE<br>Year_×_Department FE<br>Homicides Rates<br>Confict Controls<br>Additional Controls<br>_Notes:_ Each coeffcient corresponds to a separate reg<br>in 1995, ii) fatal victims of attacks in 1995, iii) homi<br>expenditures in 1995, vi) number of fnancial institu<br>industry, ix) municipal tax income in 1995, and x) ni<br>at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at th|

36
Table 1. Price Volatility by Harmonized System Groups

|HS|Description|Number of<br>goods|Mean (standard<br>deviation)|Minimum (standard<br>deviation)|Maximum (standard<br>deviation)|
|---|---|---|---|---|---|
|01-05|Animal & Animal Products|505|0.223|0.023|1.499|
|06-15|Vegetable Products|592|0.271|0.027|1.736|
|16-24|Foodstuffs|662|0.219|0.013|1.131|
|25-27|Mineral Products|201|0.376|0.033|1.435|
|28-38|Chemicals & Allied Industries|1564|0.425|0.038|2.543|
|39-40|Plastics / Rubbers|420|0.280|0.026|1.551|
|41-43|Raw Hides, Skins, Leather, & Furs|220|0.444|0.071|1.528|
|44-49|Wood & Wood Products|808|0.293|0.028|2.206|
|50-63|Textiles|2630|0.410|0.028|1.583|
|64-67|Footwear / Headgear|341|0.301|0.016|1.163|
|68-71|Stone / Glass|385|0.415|0.019|2.750|
|72-83|Metals|1448|0.271|0.044|1.678|
|84-85|Machinery / Electrical|2021|0.526|0.034|3.310|
|86-89|Transportation|384|0.382|0.028|2.370|
|90-97|Miscellaneous|773|0.502|0.033|2.326|
|98-99|Service|1|0.406|0.406|0.406|
||Total|12955|0.382|0.013|3.310|

Note: Data are from the Foreign Trade Division of the U.S. Census Bureau.

Table 2. Price Volatility using Alternate Goods Classification

|Description|Number ofgoods|Mean<br>(standard<br>deviation)|Minimum<br>(standard<br>deviation)|Maximum (standard<br>deviation)|
|---|---|---|---|---|
|Primary commodities|110|0.257|0.031|1.736|
|Manufactured goods|12006|0.387|0.013|3.310|
|Others|839|0.316|0.023|1.897|
|Total|12955|0.382|0.013|3.310|

Note: Data are from the Foreign Trade Division of the U.S. Census Bureau.

24
**Figure 9: Domestic credit and housing supply**

Data Source: Authors’ calculation based on WDI data

# **3.2 Drivers of housing investment – estimation framework**

We build on the descritive summaries presented in previous sections and use a panel of over 91 countries to examine the drivers of housing investment. On the demand side, we consider the following relationship: ) (1) DD tt tt tt tt tt HH = ff(uu , aa , xx , yy where, represents demand for housing, is urban growth. It captures natural growth of urban DD population, re-classification of rural settlements, and growth due to rural-urban migration. tt tt HH uu represents agricultural push, is the real interest rate in the economy, and is aggregate tt aa income. Variation in real interest rate influences investment demand since lower interest rate tt tt, xx yy reduces the prospects of capital gains from saving and induces investment.

On the supply side, we focus on funds from capital and financial markets. A well-developed capital market plays is important role in shaping a country’s investment decisions through international capital mobilization (Sachs, 1981). Capital market development encompasses more than just foreign inflows to emerging markets. It also cultivates local investors’ interest as a means of increasing the available investment sources within an economy (Applegarth, 2004). Levine (1996) points out that flourishing capital and financial markets create fluidity in the economy and accentuate private investment in assets. Countries with liquid capital markets 19
and increased laborers available for fuelwood collection. Likewise, Walekhwa et al. (2009) finds that the probability of a household adopting biogas technology in Central and Eastern Uganda increases with decreasing age of head of household, increasing number of cattle owned, increasing household size, male head of household and increasing cost of traditional fuels. In contrast, the study also finds that likelihood of adoption of biogas decreases with increasing remoteness of household location and increasing household land area. There are also several empirical studies that show use of multiple fuels for cooking in support of fuel stacking model.<sup>9</sup> In general, household cooking fuel choice and adoption of cookstoves are mutually inclusive. In the literature, several studies also focus on identifying wide range of socioeconomic factors that influence the adoption of ICS. For instance, Jan (2012) in rural northwest Pakistan and Pine et al. (2011) in Rural Mexico find that education and household income are the most significant factors that determine a household willingness to adopt improved biomass stoves. Based on duration analysis for urban Ethiopia, Beyene and Koch (2013) finds that product price, and household income and wealth are the important determinants of adoption of clean fuel saving technologies. Assessing relative strength of factors in terms of marginal willingness to pay, Takama et al. (2012) finds product-specific factors such as usage cost, stove price, safety and smokiness, significantly affected stove and fuel choices in Addis Ababa, Ethiopia. Using household level panel data, Alem et al. (2013) finds that the price of electricity and fuelwood, and access to credit are the major determinants of adoption of electric cookstoves and cooking fuel transition in urban Ethiopia. Based on the ICS program in rural Mexico, El Tayeb Muneer and Mohamed (2003) find that the adoption rate of ICS is very slow mainly due to lack of knowledge and the educational level of female heads of households in Khartoum, Sudan. Likewise, Lambe and Atteridge (2012) find that despite households' willingness to purchase ICS, the cost of ICS remains the most important factor in decision making in rural Haryana State, India. Using duration analysis in urban Ethiopia, Beyene et al. (2013) find that price of the cookstoves, household income and wealth influence the adoption of biomass ICS ("Mirte" and "Lakech" cookstoves). The study also finds that the availability of substitute electric ("Mitad") and metal cookstoves tends to hinder the adoption of ICS. A review study by

> 9 See  Leach (1992), Davis (1998), Karekezi and Majoro (2002), Campbell et al. (2003), Heltberg (2004), Brouwer and Falcao (2004), Martins (2005) and Arnold et al. (2006).

14
including on poverty measures, non-parametrically, assuming nothing more than classical measurement errors in survey responses.

It may be noticed that this method is similar in some respects to expectations surveys, in which respondents are asked for their point expectation for some event or variable at some point in the future, which cannot be directly observed at present. Here we are also asking about an unobserved state—the outcome in the absence of the workfare scheme. The difference is that we are asking about a concurrent counterfactual state rather than a future state. Essentially each sampled participant is asked for their expectation of employment and earnings if they did not have the workfare opportunity at the time. While we acknowledge that counterfactual survey questions can be difficult, we found that with care in design, response rates were high and (as we will show) the answers make sense.

We apply this method to a workfare scheme in the Indian state of Bihar. This is one of the poorest states of India (the poorest by some measures), with 55% of its rural population of 90 million living below the official poverty line in 2009/10.<sup>4</sup> At the same time, the rural unemployment rate of 18% (16% for men and 32% for women) is twice the national average (Ministry of Labour and Employment, 2010). The rate of under-employment (workers whose normal status is employed but work less than they want) is likely to be even higher. This is the type of poor labor-surplus economy in which workfare schemes have been seen to have much promise for fighting poverty. With that aim in mind, a large national workfare scheme was introduced in 2005, promising to give up to 100 days of unskilled manual work per year to any rural household that wants it.

The paper asks whether the pure workfare aspect of this ambitious scheme is sufficiently pro-poor to justify it as an efficient means of transferring money to poor people. Could it be that the information constraints are so severe and the unemployment rate so high that the selftargeting mechanism using work requirements tilts the balance in favor of workfare even if the work produces nothing of value? Or are the latent forgone incomes too large even in this poor labor-surplus economy?

An important issue in addressing these questions is the choice of the counterfactual. It would hardly be surprising that one can reduce poverty by spending public money on a large

> 4  This is based on official Planning Commission poverty lines for 2009/10. The state has had one of the lowest longrun trend rates of poverty reduction in India; indeed, there was virtually no long-run trend over 1960-2000 (Datt and Ravallion, 2002).

4
**Table 5. Do You Expect to Live with Your Children When You Can No Longer Care For Yourself?**

||With|Son|**With**D|aughter|WithN|either|Total|
|---|---|---|---|---|---|---|---|
||**Obs**|**%**|**Obs**|**%**|Obs|°||
|Rural||||||||
|Nochildren|10|15.2|12|17.9|75|26.3|97|
|Lessthan3|32|48.5|24|35.8|107|37.5|163|
|Between3 -_5_|18|27.3|22|32.8|78|27.4|118|
|More than5|6|9.1|9|13.4|25|8.8|40|
|**Total**|**66**|**100.0**|**67**|**100.0**|**285**|**100.0**|**418**|
|Urban||||||||
|No children|26|22.2|23|12.2|418|26.9|467|
|Lessthan3|64|54.7|88|46.8|591|38.1|743|
|Between3- 5|26|22.2|65|34.6|467|30.1|558|
|Morethan5|1|0.9|12|6.4|76|4.9|89|
|**Total**|**117**|**100.0**|**188**|**100.0**|**1,552**|**100.0**|**1,860**|

Source: PRIESO

**Table 6. Do You Expect To Receive Care from Your Children When You Can No Longer Care for Yourself?**

||From|**Son**|**FromD **|**aughter**|**From**|**Neither**|**Doesn'**|**tKnow**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
||**Obs**|%|**Obs**|**%**|**Obs**|**%**|Obs|**%**||
|Rural||||||||||
|Nochildren|28|20.9|23|15.3|23|30.7|23|39.0|97|
|Less than3|65|48.5|54|36.0|26|34.7|18|30.5|163|
|Between3-5|32|23.9|56|37.3|19|25.3|11|18.6|118|
|Morethan5|9|6.7|17|11.3|7|9.3|7|11.9|40|
|**Total**|**134**|**100.0**|**150**|**100.0**|**75**|**100.0**|**59**|**100.0**|**418**|
|Urban||||||||||
|Nochildren|68|18.9|92|15.6|184|33.2|124|34.9|468|
|Lessthan3|170|47.2|244|41.3|201|36.3|128|36.1|743|
|Between3-5|106|29.4|218|36.9|147|26.5|89|25.1|560|
|Morethan5|16|4.4|37|6.3|22|4.0|14|3.9|89|
|**Total**|**360**|**100.0**|**591**|**100.0**|**554**|**100.0**|**355**|**100.0**|**1,860**|

Source: PRIESO 45
points; and in all cases, the 2016-2018 estimate of the share deprived is contained in the 95 percent confidence interval for the 2009 estimate.

Given the large sample sizes of each survey, the standard errors are precise and the estimates can rule out any large decline in deprivation at standard levels of significance. At the 5 percent confidence level, the estimates can rule out any poverty reduction greater than 2.4 percentage points, which would still be very low amounts of poverty reduction given the amount of resource-related growth that occurred relative to some of the other settings that have been analyzed (e.g., Savoia and Sen 2021). Furthermore, for access to basic services, the estimates can rule out declines in poor access to essential services greater than a threshold between 0.8 and 7.7 percentage points.

Further illustrating the extremely limited ability of economic growth to benefit the population in PNG, we show how poor this lack of improvement in well-being looks relative to comparison countries and the rest of the world. Although we cannot estimate standard errors given that we do not have access to the microdata in all countries, Figure 7 reports how the average of the share deprived changed for each comparison region. The figure reports the average number of the five indicators in comparison regions that improved by greater than 5 percent, which are likely to be statistically significant improvements given the large sample sizes of nationally representative surveys.

We compare PNG’s performance to several different comparison regions. Specifically, we compare PNG to countries with the most similar share of the population that was multidimensionally poor in the pre-period, capturing the fact that countries with the most deprivations in the world might be improving at a faster rate than other countries; and we also compare PNG to the rest of the countries aside from the those with a similar MPI profile captured in the World Bank’s global database.

Figures 7a-7b illustrate that there was broad improvement in each of these comparison regions across nearly all indicators, including in places that did not have nearly as much economic growth as PNG. The average figures across each comparison region demonstrate an improvement in four out of the five indicators in countries with a similar MPM profile, and an improvement in all five indicators in the rest of the world. The decline in the 15
The distribution channels (for final products) which used to be controlled at the wholesale level by large, formal enterprises with heavy overheads (reflecting comfortably protected margins) now largely depend on aggressive smaller wholesalers of a less tractable nature. These lean, competitive traders (a majority of them Lebanese) are gradually replacing the large colonial trading houses (now left with at most one-third of the total wholesale market). The much maligned Lebanese traders could be considered an economic asset: they serve the consumer well and have probably reduced distribution margins significantly from the prior ones. Nevertheless, it is a less controllable system which does not keep complete accounting records and is prepared to tap into irregular sources of supply when they offer favorable terms -- thus evading the prevailing distortions and rigidities.

This evolution of the distribution system for domestic manufactures and imports is symptomatic of the erosion of a partnership between the state and large enterprises which for two decades extracted considerable consumer surplus (increasing the cost of living) and created an artificially modern system. The central government's tax structure was designed with this modern system in mind and is unlikely to be well-suited to the post-adjustment private enterprise sector.

Small and medium scale enterprises (SMEs) are a particularly interesting subject of study because of the key role their flexibility equips them to play in resource reallocation processes, in pur lit of efficiency. In CBte d'Ivoire, SMIEs fall into two fairly distinct categories which are not separated so much by size as by enterprise culture. At one end of the spectrum are fully documented corporations and unincorporated limited liability enterprises (ULLEs) and at the other are sole proprietorships which keep few records.

The present situation is particularly bleak for the smaller enterprises attempting to comply with all legal requirements. The characteristics of corporate SMEs are reasonably well-known, on the basis of the records submitted by over 1,500 of them to the Government (see table 2). They are a rapidly declining breed'<sup>0</sup> that in 1988 employed 22% fewer people than in 1982 and that, over that six-year period, has seen:

- (i) turnover and operating margins decline (in nominal terms, the value added of the average enterprise dropped by 19% but payroli costs were only 12% lower); and

- (ii) tax burdens increase in both relative (from 16% of value added to 27%) and absolute terms (although SME's value added was 27% lower in 1988 than in 1982, they contributed 19%O more to the public coffers).

10 Between 1982 and 1988 their total number declined by over 10%, with 173 enterprises either dropping out of sight (becoming informal operations) or (most likely) just closing down.

9