rafmacalaba/data-use-mentions-extended huggingface dataset:
This paper investigates the patterns of female entrepreneurship, labor force participation and gender-based segmentation across Indian manufacturing and services sectors. The paper attempts to link the broad changes in these patterns over the last two decades to change in pro-competitive reforms. While some of the imbalances observed are undoubtedly due to discrimination, we use the term “segmentation” because perfect gender balance is not efficient either. Gender-based comparative advantage in certain occupations and group-based specialization imply that workforces will never be perfectly balanced at the plant or industry level. India's enormous imbalances, however, suggest that it is far from the efficient level.

Our paper makes two important contributions to the literature. One, the limited research that does study the ‘gendered’ impact of competition in developing countries mainly investigates the effect on female labor force participation (FLFP) rates, sectoral employment patterns and wage inequality. The focus of our work is to additionally highlight the impact of such reforms on gender segmentation. Specifically, India's work force is highly segmented by gender. For instance, an earlier study on India’s informal manufacturing reveals that 92% of employed women are in female-owned businesses while 97% of working men are employed in male-owned enterprises (Ghani, Kerr and O'Connell, 2014). Said differently, our work describes the extreme inclination of female-led establishments to hire female employees and likewise the tendency of male-owned plants to have a primarily male-dominated workforce. Furthermore, we use a novel index that measures segmentation based on the perspective of an average employee, to additionally offer insights on the role of pro-competitive forces in mitigating segmentation.

Two, most studies on developing countries have employed trade liberalization as a competitive force to study the changes in gender discrimination thereafter. A few studies in the context of developed countries have also focused on domestic banking reforms. Using establishment level data from India, we study the impact on gender-based outcomes and segmentation in both manufacturing and services emanating from a range of pro-competitive reforms including not only India’s mostly exogenous trade liberalization episode,
Policy Research Working Paper 6166

# **Abstract**

This paper describes stylized facts about internal migration and the labor force in Egypt, and shows how internal migration in the country is low compared with international standards. Using aggregate labor force survey data, the paper shows how individuals migrate to governorates with higher wages. With a Mincerian equation, the analysis finds that migrants earn premiums with respect to non-migrants, except for those migrants with low education levels. The aggregate labor statistics reveal lower unemployment rates among migrants, a phenomenon that is verified by an employment equation. According to the econometric results, migrants are more likely to be employed, even after controlling for other observable individual characteristics. Finally, the paper estimates a Probit model for the decision to migrate, finding that more educated individuals are more likely to migrate, agricultural workers have a lower probability of migrating, and individuals from governorates in which food production for own consumption is higher are less likely to migrate. These results suggest that low educational attainment and the “food problem”, which ties resources to food production to meet subsistence requirements, are at the root of low migration in Egypt.

This paper is a product of the Poverty Reduction and Economic Management Unit, Middle East and North 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://econ.worldbank. org. The authors may be contacted at sherrera@worldbank.org and kmohamed@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
strument using teacher and parent reports (Brinkman et al., 2017). Data collected on a cohort of 4 year-olds also shows that as children get older their performance on each of the domains goes up and their vulnerability declines (Hasan et al., 2013).

Figure 5 provides a visual depiction of the association between maternal education and child development as reported by teachers and caregivers. Across the board we see a positive relationship between the two variables. Language and cognitive development, communication skills and general knowledge and social competence are dimensions with strongest relationship. Literate mothers have kids with raw social development scores 0.30 s.d. above those of kids whose moms do not read. Language and cognitive development scores are 0.43 s.d. higher, and communication skills and general knowledge scores are 0.26 s.d. higher, respectively.<sup>9</sup> Once again, Appendix Table A5 reports the descriptive statistics regarding all dimensions (Panel C).

# **2.3 Parenting, child activities and human capital investments**

## **2.3.1 Parenting**

Given the association between parenting styles and child outcomes posed by the broader developmental literature, we make full use of our survey instruments to take a unique approach and investigate elements of parenting that may influence child development. In particular, we presume some of these can be influenced by the amount of formal education caregivers acquire.<sup>10</sup> The focus is on how families influence their children’s development through their relationships, such as the level of warmth or anger that mothers feel for their children and the kind of discipline that mothers tend to use when children misbehave. These practices are important because children living in an environment with higher-quality parenting are believed to be more likely to have higher pre-academic skills, better language skills, more social skills, and fewer behavior problems than children who received lower quality parenting (NICHD, 2002).

In our
higher education of a father lowers the conditional variance of schooling for both sons and daughters, but the magnitude of the impact is substantially larger for sons (-0.48 for sons, and -0.36 for daughters). The evidence is dierent in Indonesia: there is no signicant dierence across gender.<sup>16</sup> In the online appendix, we discuss the estimates for four subsamples dened by gender and rural/urban location of a child (see Table A.1 in the online appendix section OA.1). The evidence on India suggests that the rural daughters face very dierent educational prospects: the impact of father's education is positive and numerically large for this subgroup, while the eect is negative in the other three subgroups. The nding that the rural daughters are qualitatively dierent from the other three groups also holds in China: there is no signicant impact on conditional variance of rural daughter's schooling, while the eect is negative and signicant in the other three subgroups.

# (4.2) The Evolution of Conditional Variance: Evidence from Decade-wise Birth Cohorts

Table 4 reports the estimates for equations (1) and (2) for decade-wise birth cohorts: 1950-1959, 1960-1969, 1970-1979, 1980-1989. The evidence shows interesting pattern in the evolution of the inuence of family background on conditional variance of children's schooling. If we focus only on the mean eect as is done in the existing literature, the evidence suggests that relative mobility has improved in India and Indonesia over time, while it has worsened in China. However, the impacts on the conditional variance shows a much stronger role of the family background in the recent decades which counteracts the improvements in the mean eects. In all three countries, the inuence of father's education on the conditional variance is negative and substantial in magnitude in the 1980s, suggesting that the children born
# **2. Wage subsidy program**

The Mexican government announced the wage subsidy program on January 7, 2009 and it was officially launched on January 30, 2009. The program was operated by the Ministry of Economy ( _Secretaría de Economía_ ) as Part B of the Program for the Development of High Technology Industries ( _Programa para el Desarrollo de las Industrias de Alta Tecnología_ ‐ PRODIAT) and lasted until August 2009.<sup>2</sup> It provided subsidies to firms in eligible industries who kept employees on their payroll and agreed on reduced work schedules with them instead of firing these employees. Under this arrangement, workers would temporarily receive less salary, but they would keep their job. At the same time, firms had to pay lower salaries and they later received a payment from the government corresponding to a proportion of these salaries (described in more detail below).

To receive a subsidy, firms were not allowed to fire more than one‐third of their workforce as a percentage of the drop in sales they had experienced during the economic crisis, e.g. if sales dropped by 30%, they could not fire more than 10% of their workforce. The program applied to permanent employees who were registered with IMSS. The Ministry of Economy used data from IMSS, together with audited statements of sales, to verify that firms met the restrictions on firing. The operating rules stated that payments would only be made after the necessary information on employees and sales for the subsidy period was available, i.e. after the end of this period. Firms also had to be current on their federal tax and social security payments to be eligible for the subsidies.

Firms applied for the subsidies for one or more two‐month periods (e.g. January, February 2009 or February, March 2009). The original operating rules specified that applications had to be submitted by the end of the second month, e.g. February 28, 2009 for the January, February
the NGO “Business Against Corruption” from entrepreneurs claiming to have faced unlawful criminal prosecution ( _c.f._ <u>http://www.nocorruption.biz/?cat=6</u> _)_ . The data from the registry include information about the initiation of criminal cases, the types of business activities carried out by the business issuing the complaint, and the region in which it is located. We discuss both of these measures in greater depth below.

Further regional controls for our regressions were collected from data published by the Federal State Statistics Service of Russian Federation and by the Ministry of Regional Development of Russian Federation.

# **B. Hypotheses**

Drawing on the literature reviewed above, this paper aims to distinguish between the effect of regulatory burdens and regulatory uncertainty, on the one hand, and of different types of regulatory uncertainty, on the other. With respect to regulatory burden, the literature noted above generates a straightforward prediction: greater regulatory burdens are associated with greater transaction costs, which decrease incentives to invest. This suggests:

# **<u>Hypothesis 1: The greater the regulatory burden on firms, the less likely they are to invest.</u>**

Typically, the overall level of regulation in Russia has been measured using indicators such as the World Bank’s Doing Business indicator, which measures how difficult regulations make certain business activities ( _c.f._ Eiffert 2009). This measure was constructed using data from experts on statutory regulations; however, making it inappropriate for a study such as ours that is concerned primarily with practices related to _informal regulatory enforcement_ . Instead, here we concern ourselves with the level of corruption, which we argue likely represents an excessive, informal regulatory burden on firms. Such a burden can emerge either due to excessive regulatory 11
presence, we aggregate the dummies over the period 2000 to 2004 to obtain a measure of total length of sector targeting in the early 2000s. The sum can range from 0 (no targeting over this period) to 5 (continuous targeting over this period). To control for non-linearities, we use the measure in natural logarithms, i.e. ln(total sector targeting + 1). We believe that total sector targeting is wellsuited as an instrument, as it is correlated with the foreign output share variable, but not with domestic firm productivity – especially since there is a time lag between these two measures.

Finally, following Keller and Yeaple (2009), we include sectoral tariffs obtained from the World Bank WITS Database. The data are based on the WTO NAMA method (effectively applied rates).

> 13 We thank Minh Cong Nguyen for making an older version of the Enterprise Surveys Indicator Database available to us.

> 14 The authors are grateful to Torfinn Harding, Beata Javorcik and Daniel Lederman for making this dataset available to us. For more information on the dataset, see Harding and Javorcik (2011).

15
(4) firm productivity, _φit_ , which is unobserved by researchers but known to the firm when making import and investment decisions. The error term, _εit_ , captures measurement errors or unexpected productivity shocks.

As in OP, given the assumption that productivity evolves according to a Markov process, _φit_ is therefore monotonically increasing in investment when investment is larger than zero, allowing us to invert (10) to construct a control function for _φit_ : which can be substituted into (12) to arrive at the following specification: with where _φ_ ( _Iit_<sup>_W, Eit, K_</sup> _it_<sup>_W_) can be proxied semiparametrically using a higher-order polynomial function in</sup> the regressions. Although we cannot separately identify _D_ ( _Eit, Kit_<sup>_W_) and</sup><sup>_φit_, least squares estimation</sup> of (14) will still yield a consistent estimate of _κ_ . In practice, we estimate (14) for both processing and non-processing firms using Chinese customs transaction data from 2000 to 2016. For processing firms, we expect the import coefficient to be close to one, while for non-processing firms, we anticipate the coefficient to be significantly less than one.

# **3.3 Results of DVAR Estimation**

Equation (14) serves as the foundation for our industry-level estimations, where we allow _κ_ to vary between processing and non-processing firms within the same industry. We test the hypothesis that _κ_ for processing firms is significantly higher than for non-processing firms and, moreover, that _κ_ for processing firms is not statistically different from one. Table 1 presents the pooled regression results based on (14). Column (1) shows the least squares estimation with only firm and year fixedeffects, yielding an estimated _κ_ of 0.756. In Columns (2) and (3), we separate ordinary exporters from processing exporters. The import share for ordinary exporters is estimated at 0.352, while for processing
the data. Our within-week models place less weight on periodicities, and more weight on the fraction of duration spoken during the workday or late at night, the distance traveled, variation in usage, and correlations between calls and SMS. The OLS fixed effect model is also simpler than the logistic model, suggesting the fixed effect approach penalizes model complexity more.

# **_Performance_**

# _Within Time_

We measure how the method performs out of sample using cross validation. Following common practice in supervised machine learning, the sample is divided into _R_ randomly selected folds. The algorithm cycles through each fold, estimating (training) the model on _R-1_ folds, and reporting predictive performance on the _R_ th omitted fold (testing). The results are averaged across each fold, and over multiple fold draws. Larger values of _R_ exploit more of the sample for training, which tends to improve predictive performance, but increase the computational burden because the model must be estimated _R_ times. The main results report _R_ =5, which is commonly used in the machine learning literature (Table S2 reports results for R=10).

As a first check, consider how well models separate low and high risk borrowers. Results are reported from the most conservative model using our method, the random forest weekly ensemble, and the least conservative bureau model. These models generate continuous scores, so performance can be assessed by comparing a few example acceptance thresholds. In our most conservative model, individuals with the highest quintile of risk scores are 2.8 times more likely to default than those with the lowest quintile. Among those with credit histories, if credit were extended to the 50% lowest risk prospects according to the credit bureau score the default rate would be 9.7%, whereas it would be only 8.3% based on our scoring using phone records. Moreover, if credit were extended to those without credit histories whose predicted risk of default would place them in the top 50% of risk-prospects for those with credit records
|United States|Since 1980|Tax-reduction and tax-rebate: 0.13|Feldstein (2009)|
|United States|1950-2006|1% within a year, and up to 3% after three years|Romer and Romer (2010)|
|G7|Since mid-1970s|High set: 0.6 for revenue, 1.8 for capital spending, 1.0<br>for other spending<br>Positive spending shock: 1.22 (negative output gap);<br>0.72 (positive output gap)|IMF Fiscal Monitor (April 2012)|
|G20|2008 global crisis|Low set: 0.3 for revenue, 0.5 for capital spending, 0.3<br>for other spending|Spilimbergo, Symansky, and<br>Schindler (2009)|
|Developing||||
|countries|1970-2010|Government spending 0.4|Kraay (2012)|

_Source_ : Authors’ compilation.

Whether developing or advanced economies have larger multipliers depends on several factors. On the one hand, multipliers may be larger in developed countries because they are better able than developing countries to maintain increased government consumption. Ilzetzki, Mendoza, and Vegh (2011) examined quarterly data on 44 countries (20 high-income and 24 developing 9
.203|0.225|
|mortgage value (US $)|36,311|26,874|48,508|41,617|
|<br>Plots reg. by males & female jointly|<br>0.185|<br>0.016|<br>0.192|<br>0.411|
|<br>mortgage value (US $)|41,104|32,748|44,619|40,838|
|Plots reg. by companies|0.079|0.083|0.084|0.073|
|mortgage value (US $)|169,153|158,281|263,473|139,489|
|Days for registration process|78|137|29|19|
|<br>Days to get ministerial consent|98|98|||

_Source:_ LAA, land administration database and digitized records, 2020. aNote that the number of observations for purchase price and perice per unit of land slightly different due to missing plot area, and hence unit price cannot be computed from the mean values.

21
Table 4: IV Estimates of the Effect of Disruptiveness on the Probability and Elasticity of Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded<br>(1)|Last Funded<br>(2)|Ever Funded<br>(3)|Last Funded<br>(4)|
||Pa|nel a. Probabil|ity of Being Fund|ed|
|Disruptive|0.070**|0.076***|0.061*|0.066**|
||(0.030)|(0.024)|(0.035)|(0.029)|
|_R_<sup>2</sup>|0.144|0.123|0.145|0.125|
|Observations|2428|2428|2428|2428|
|Countries|9|9|9|9|
|||Panel b. Elast|icity of Funding||
|Disruptive|1.049**|1.091***|0.922**|0.965***|
||(0.412)|(0.301)|(0.454)|(0.337)|
|_R_<sup>2</sup>|0.163|0.142|0.164|0.144|
|Observations|2428|2428|2428|2428|
|Countries|9|9|9|9|
||Panel c. Elasti|city of Fundin|g Conditional on|Being Funded|
|Disruptive|0.422*|0.343|0.278|0.216|
||(0.231)|(0.288)|
31 go a long way toward creating new opportunities for employment and sustained growth in Africa.<sup>25</sup> Clearly, the potential opportunities for Africa‘s labor-intensive economies, which today are exporting mostly minerals are enormous.

The story for low-income countries elsewhere in the world is similar. In 2009, with a total population of 846 million and 13 percent of their GDP coming from manufacturing, their employment in the sector likely amounted to no more than 10 million. Thus, just as for African countries, China‘s industrial upgrading would provide them a golden opportunity for dynamic manufacturing-led growth. But for developing countries everywhere, the ability to benefit from the opportunities depends on their quickly formulating and implementing credible economic development strategies that are consistent with their comparative advantage and the flying-geese paradigm.

# **_A Road Map for Seizing the Moment: The Growth Identification and Facilitation Framework_**

The coming graduation of China and other middle-income growth poles from low-skilled manufacturing jobs is timely for low-income countries. But it is especially so for those in SubSaharan Africa. Despite the region‘s grim long-run performance and the potentially heavy economic and human cost of the recent global crisis, there is renewed optimism about its economic prospects: since the mid-1990s Africa has embarked on a new and higher growth trajectory. The main challenge facing African leaders is to avoid the policy mistakes of the past and instead implement the winning strategy.

Africa may be on the verge of an economic takeoff, recent empirical work suggests. Young (2010) sees an ―African growth miracle‖ in his analysis of such measures as real consumption, housing quality, and health and education. His results show that for the past two decades living standards in Sub-Saharan Africa have been rising by more than 3 percent a year—more than three times the rate indicated in international data sets. Using a new methodology to estimate income distribution, poverty rates, and inequality and welfare indexes for African countries in 1970–2006, Pinkovskiy and Sala-i-Martin (2010) conclude that
Policy Research Working Paper 9107

# **Abstract**

Uzbekistan has one of the lowest rates of internal migration in the world, leading to persistent economic imbalances. Drawing from a unique monthly panel survey called Listening to the Citizens of Uzbekistan and a survey experiment, this paper focuses on two factors that prevent domestic mobility: (i) restrictive _propiska_ registration policies, and (ii) the exceptionally high cost of urban housing. Registration rules prohibit migration to urban centers, and urban housing costs push up the cost of living to as much as 550 percent of the national average, levels severely unaffordable for almost all rural residents. But the proposed government reforms in 2019 to address these challenges are very popular.

The results show that about 90 percent of people support lifting all registration restrictions and over 80 percent favor increasing urban housing construction. The results of the experiment show that reform popularity increases when _propiska_ rules and housing costs are referenced in randomly assigned vignettes. However, views may also be sensitive to perceptions of fairness. Recent high-profile involuntary demolitions coincided with a doubling of the share responding that policies are unfair. The increase was further associated with declining optimism and lower support for the wider government national development program, beyond urbanization issues.

This paper is a product of the Poverty and Equity Global Practice. 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 author may be contacted at wseitz@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