rafmacalaba/datause-encoder-data huggingface dataset:
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# The Structure of Social Disparities in Education: Gender and Wealth

Deon Filmer The World Bank Dfilmer@worldbank.org Paper prepared as background to, and with support from, a World Bank Policy Research Report on Gender and Development (http://www.worldbank.org/gcnder/prr). This paper, and other background papers to the report can be downloaded from http://www.worIlbank.ornig/clder/ piT/workingp.hti .
over and above providing hedging facilities. The amount of resources devoted to insurance activities, therefore, might also constitute beneficial savings, and be channeled to profitable investments, at home or abroad. Devereux and Smith (1994) also notice, among other authors, that integration may lead to a reallocation of savings to investment projects with higher risks and returns.

# **6. Concluding remarks and extensions**

The focus of this paper has been that of exploring and gauging the extent of insurance vis à vis idiosyncratic risks enjoyed by LAC countries. This has been done by running consumption insurance tests on most countries in the LAC region over the horizon 1972-2003, to estimate the crucial parameter of insurance. The analysis has been preliminarily performed over a much larger set of countries, and then has focused on LAC economies. This has shown that LAC economies share the features of less mature and less developed or emerging economies with respect to insurability of idiosyncratic risks. Apparently, there is no clear cut relationship between insurability and per capita real income, unlike in the case of more developed, high income economies. A sort of threshold effect seems to be at work. Some interesting insights could be obtained by the more detailed consumption insurance analysis on Latin American countries: firstly, it has been shown that LAC economies are fairly much hit by non diversifiable income shocks, which get therefore transmitted to consumption, and thus to welfare. An original methodology has been employed to show that idiosyncratic risk is relatively more important than aggregate risk, and that some countries do appear to enjoy a certain amount of international risk diversification (though seemingly not to a large extent). More importantly, our analysis suggests that most LAC countries appear to pool some fraction of their risk within the region, rather than outside (for example, within the OECD set of economies).

By replicating our consumption insurance test several times for each country, we have built up a dataset of insurance parameters, which reveals some interesting dynamics, especially in view of recent results by Kose et al. (2006), which seemed to suggest that correlations of growth rates of output and consumption with their corresponding world aggregates have not changed substantially during the period of globalization for emerging market economies. Our dataset has then been enriched with a host of macroeconomic variables for the same countries and for the period 19922003, and has been used to test the potential impact of some macroeconomic variables on insurance opportunities available to LAC countries. Some preliminary results show that the financial development of an economy is crucial in determining the amount of risk sharing opportunities, as well as public expenditure, and that trade openness and shocks to terms of trade also play an 21
Karnataka and West Bengal reported that services such as registration of land or property, or issuance of driving license were too expensive. Most respondents reported that goods such as schooling and health care were reasonably priced.

# **_<u>5.4 Travel Times</u>_**

On average, it takes 40 minutes to travel to an office to file an application for a government provided good. It takes 70 minutes for a service. Furthermore, a good requires two or less visits, while a service requires two or more. There has been little change or improvement in this area over the last five years. Travel times vary widely across states. People seeking to register land or get a driver’s license in Chhattisgarh, Himachal Pradesh, Rajasthan, Uttar Pradesh and Andhra Pradesh must travel more than an hour each time they visit the application center or a government office. Overall, travel times are the highest for issuance of driver’s licenses and lowest for Aadhaar cards (Table 7).

**Table 7: Travel Time to Apply for a Good or a Service**

Percent of adults that “Needed and Applied” for a goods or service in the past five years

|**Type of Good or Service**|**Average Total Travel Time**<br>**in Minutes**|**Number of Visits**|
|---|---|---|
|Voter ID|87|1.90|
|Aadhaar Card|66|1.64|
|DrivingLicense|166|2.39|
|Register Land or Property|137|2.33|
|Public Utility|124|2.28|
|Public School|88|1.86|
|Health care|91|1.94|

One way to reduce travel time would be to offer more avenues for submitting applications for a government provided good or service. For example, about 20 percent of Aadhaar card applications are submitted somewhere other than a government office, such as through an agent or at a post office. As a result, the average travel time to make an application for an Aadhaar card was the 37
4 and 5. In 4, the estimated coefficient corresponds to the excluded region, which is South Asia. Upon closer inspection of the other regional dummy variables times the barrier index, only the one for Africa is significant and negative. The same is true for the model under column 5, which corresponds to the specification where the proxy for barriers to entry is the number of days to clear customs. This implies that for most regions, there is weaker evidence in favor of the market-failure hypothesis when the dependent variable is the total number of export discoveries at the national level, as the relevant estimated coefficient tends to be smaller and less significant than in the models that estimate this relationship within country-industry pairs. Hence we are tempted to conclude that there might be inter-industry spillovers in export discoveries and thus barriers to entry tend to decrease the number of discoveries by country compared to the counter-factual of only within country-industry spillovers.

# **4. Conclusions and Policy Implications**

Recent research has highlighted the importance of diversification for developing countries: a process that may be hindered by spillovers in the discovery of new products for export. After extending this finding from domestic production data to export data, we use disaggregated export data to identify export discoveries by countries and by countryindustry pairs, and characterize its relationship with patent counts, export diversification, and development. We then use this discovery metric to test the market-failure hypothesis.

According to the data, in countries where first movers expect fewer of the benefits of their discoveries due to the threat of imitation, the rate of discovery of new export sectors is lower. Even after controlling for the quadratic relationship with GDP per 20
To what extent are such relations significant? In the next section we apply our model to determine the demand for energy consumption at the household level, controlling for income levels but also for different geographical and socioeconomic characteristics of the households.

# **_3.3 Model_**

Here we want to know the determinants of energy consumption (in KgOE) in households and, especially, the variation in consumption according to the income level. Following Barnes et al (2011) and other authors (i.e. Jamasb and Meier, 2010; Navajas, 2007 among several others), we set our model as:

# Eij= α + β 1Xj+ β 2Hj+ β 3Ij+ ε ij

where: E is consumption units in KgOE of energy type i for household j X is a vector of sociodemographic characteristics of the household and its head H is the physical characteristics of the house.

I is income decile of the household j This model could be implemented in logs with OLS; however, in all cases for different energy consumption, there are several households that do not consume such specific energy sources, and therefore having zeros in the variables and positive values for those that spend on the energy source is appropriate.

In this case, using OLS does not give consistent parameters as it is a censored sample and not representative of the population. For this reason, we implement the model using a Tobit specification, censored from below with zeros in the dependent variable (Cameron and Trivedi, 2005), where, simplifying all dependent variables into x: 17
Policy Research Working Paper 9678

# **Abstract**

Robots are rapidly becoming a key part of manufacturing in developed and emerging economies. This paper examines a new channel for how automation can affect international trade: quality upgrading. Automation can reduce production errors, particularly of repetitive processes, leading to higher quality products. The effects of robot use on export quality are estimated, by combining cross-country and cross-industry data on industrial robots with detailed Harmonized System 10-digit trade data. Robot diffusion in (preexisting) foreign customers is used as an instrumental variable to predict robot adoption in the home country-industry. The findings show that robot diffusion leads to increases in the quality of exported products. Quality improvements are predominantly driven by the upgrading of developing country exports; and within countries, quality improvements are driven by upgrading of (initially) lower-quality exports of developed and developing countries. The paper also finds some differences in the type of robots—sophisticated or more basic—associated with quality gains in developing and developed economies.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific 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 tdestefano@hbs.edu and jtimmis@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
# **Table 5.   Topics with decreasing focus**

|||**1999-2001**|**2002-2004**|**2005-2007**|**Score**<sup>**8**</sup>|
|---|---|---|---|---|---|
|1|_Capital Markets - Market competition_|0.5|0.1|0.0|177.4|
|2|_Dealer and Brokers - Capital Markets - Regulation_|0.4|0.1|0.0|169.9|
|3|_Pensions - Crisis Management_|0.1|0.1|0.0|154.8|
|4|_Microfinance - Consumer Protection_|0.2|0.1|0.0|154.8|
|5|_Other NBFIs - Crisis Management_|0.2|0.2|0.0|132.2|
|6|_Closure – Development Banks_|0.2|0.2|0.0|132.2|
|7|_Coordinated Oversight - Global Oversight - Regulation_|0.4|0.3|0.0|109.7|
|8|_Insurance - Consumer Protection_|0.1|0.1|0.0|109.7|
|9|_Check clearing and settlement – Payments System_|1.3|0.4|0.3|103.1|
|10|Introduction - Deposit Insurance|0.4|0.2|0.1|97.1|
|11|_Investment Instruments - Capital Markets - Regulation_|1.1|0.8|0.3|87.7|
|12|_Capital Markets - Regulation_|2.2|1.4|0.7|85.2|
|13|_Enforcement - Judiciary_|0.4|0.3|0.1|81.2|
|14|Oversight - Corporate Governance - Corporate Governance|0.4|0.3|0.1|81.2|
|15|_Capacity Building - Judiciary - Contractual_|1.3|0.9|0.5|78.7|
|16|_Strengthening – Security Registries_|0.2|0.2|0.1|75.3|
|17|_Insurance - Crisis Management_|0.2|0.2|0.1|75.3|
|18|_RTGS systems- Payments_|2.3|1.3|0.9|75.2|
|19|_Gross Payments_|2.0|0.9|0.7|74.5|
|20|_Movable Collateral - Creditor Rights - Contractual_|0.5|0.2|0.2|69.5|
|21|_Banking - Tax Management_|1.1|0.6|0.5|68.6|
|22|_FX Market Development - Operations Monetary and FX Mgmt_|1.1|0.4|0.4|67.3|
|23|Fiscal and public sector management|0.8|0.5|0.4|66.0|
|24|_Transparency - Policy Monetary and FX Mgmt_|1.1|0.6|0.5|54.4|
|25|_Corrections - Banking - Crisis Management_|0.9|0.9|0.5|53.8|
|26|_Licensing_|0.4|0.3|0.2|52.8|
|27|_Regulatory framework - AML_|0.4|0.3|0.2|52.8|
|28|_General Sector Development - Pro-market Activism_|0.4|0.3|0.2|52.8|
|29|_Money Market Development - Operations Monetary and FX Mgmt_|1.2|0.8|0.6|52.6|
|30|_Deposit insurance - Banking - Crisis Management_|2.0|1.4|1.1|52.1|
|31|_Capital Markets - Supervision_|1.2|0.7|0.6|51.4|
|32|_Institution based risk management - Banking Regulation_|0.8|0.7|0.5|48.7|
|33|_Resolution framework - Banking - Crisis Management_|2.7|2.6|1.4|48.0|
|34|Modification - Deposit Insurance|0.9|0.7|0.5|47.8|
|35|_Retail Payments_|1.9|1.2|1.1|44.8|
|36|_Banking - Crisis Management_|6.7|5.9|4.0|44.2|
|37|_Operations - Monetary and FX Mgmt_|6.2|4.2|3.7|44.1|
|38|Crisis management|7.2|6.5|4.3|43.8|
|39|_Open-market Operations - Operations Monetary and FX Mgmt_|0.9|0.9|0.5|43.8|
|40|_Restructuring - Retail Banking_|0.6|0.5|0.4|42.8|

8 The score is the sum of the percentage decreases from each column to the next, where the percent is calculated with respect to the earlier period.

12
Table 10: Estimates of the correlation between treatment and measurement error (a) Constrained, with block fixed effects (Equation 2)

|||_y_<sup>_s _</sup>_−y_<sup>_v_</sup>,<br>|Sensitive be<br>|Dependent v<br>haviors<br>|ariable (N=239<br>|)<br>_y_<sup>_s _</sup>_−_<br>|_y_<sup>_v_</sup>, Expendi<br>|tures<br>|
|---|---|---|---|---|---|---|---|---|
|Covariate|Stealing|Marijuana|Gambling|Homeless|All (0–4)|Video|Phone|All (0–2)|
|||||||Club|Charging||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_βo_ (Constant)|0.087|0.555|-0.638|-0.096|-0.084|-1.330|-0.667|-1.987|
||[.124]|[.395]|[.363]*|[.098]|[.803]|[.137]***|[.321]**|[.384]***|
|_β_1|||||||||
|Therapy|-0.005|-0.012|0.122|0.056|0.158|0.276|0.240|0.504|
||[.092]|[.064]|[.111]|[.105]|[.209]|[.112]**|[.089]***|[.145]***|
|Cash|0.045|-0.025|-0.076|0.042|-0.002|0.051|0.142|0.182|
||[.102]|[.067]|[.093]|[.079]|[.204]|[.116]|[.083]*|[.138]|
|Both|0.058|-0.011|0.185|0.159|0.383|0.222|0.139|0.346|
||[.094]|[.081]|[.107]*|[.100]|[.228]*|[.116]*|[.089]|[.127]***|

(b) Unconstrained, with block fixed effects (Equation 3)

|||||Dependent v|ariable (N=239|)|||
|---|---|---|---|---|---|---|---|---|
|||_y_<sup>_s_</sup>, S|ensitive beha|viors||_y_<sup>_s_</sup>|, Expenditur|es|
|Covariate|Stealing|Marijuana|Gambling|Homeless|All (0–4)|Video|Phone|All (0-2)|
|||||||Club|Charging||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
| ̃_β_0 (Constant)|0.073|0.613|-0.179|-0.096|0.157|-0.472|-0.162|-0.788|
||[.088]|[.381]|[.091]*|[.087]|[.599]|[.160]***|[.290]|[.396]**|
| ̃_β_1|||||||||
|Therapy|-0.050|-0.092|0.066|0.067|0.215|-0.061|0.112|0.113|
||[.076]|[.082]|[.083]|[.093]|[.235]|[.131]|[.076]|[.207]|
|Cash|0.008|0.059|-0.068|-0.012|0.123|-0.044|0.047|0.163|
||[.072]|[.095]|[.072]|[.069]|[.219]|[.148]|[.083]|[.225]|
|Both|-0.009|-0.044|0.083|0.095|0.455|0.032|0.032|-0.121|
||[.077]|[.104]|[.078]|[.089]|[.274]*|[.147]|[.080]|[.216]|
| ̃_β_2(_y_<sup>_v_</sup>)|0.376|0.667|0.249|0.382|0.583|0.097|0.488|0.333|
||[.152]**|[.110]***|[.107]**|[.155]**|[.113]***|[.125]|[.098]***|[.131]**|
| ̃_β_3|||||||||
|Therapy_×y_<sup>_v_</sup>|-0.091|0.115|-0.097|-0.094|-0.169|0.462|0.194|0.318|
||[.221]|[.134]|[.161]|[.221]|[.135]|[.170]***|[.148]|[.173]*|
|Cash_×y_<sup>_v_</sup>|-0.155|-0.108|0.026|0.211|-0.115|0.097|0.166|-0.031|
||[.204]|[.144]|[.159]|[.198]|[.152]|[.181]|[.156]|[.177]|
|Both_×y_<sup>_v_</sup>|-0.098|-0.029|-0.246|-0.012|-0.318|0.385|0.232|0.473|
||[.203]|[.147]|[.177]|[.234]|[.155]**|[.180]**|[.142]|[.174]***|

_Notes_ : The table reports the degree and direction of bias in our treatment effects. In Figure A, we assume that our measurement error does not vary by whether or not the individual engages in the behavior, which allows for a simple way to use _β_ 2 to adjust our ITT estimates. In Figure B, we relax this assumption and let the measurement error vary by behavior and treatment arm at the cost of reduced statistical power.

37
The results display large variation within sectors. The estimated elasticity in the digital sector are large and significant for micro-output (estimated on the basis of firm- or household-level observations), between 0.05 and 0.06, but about three times smaller for macro output. Note that this is consistent with the positive and significant coefficient found for firm-level data in Table A.9 to A.11. For micro- and macro-elasticities energy, are between 0.04 and 0.05 and but from zero comparable, significant, indistinguishable when looking at labor market outcomes. Finally, for transport, elasticities are systematically significant, being the largest for trade (0.9), followed by labor outcomes (0.16), and micro-output (0.08), and smaller for macro-output (0.03), and null for population. It thus appears that across sectors, elasticities are larger when based on studies covering micro-, and labor market data than more aggregated data.

Next, results by level of country development are of specific interest for drawing policy implications. Theoretically, it could be that in contexts where there is greater scarcity of capital, the marginal product of infrastructure investment is higher, and that decreasing returns kick in and the marginal product decreases as countries accumulate larger stocks of capital. However, there could be threshold or network effects, so that returns are very different across countries at different levels of development or of coverage. Finally, marginal returns may be high in case of last-mile connections only.

In Figure 4, we represent sector-level (panel A) and sector-outcome results (panel B) including rigorous LASSO-selected controls, splitting the sample between developed and developing countries.<sup>17</sup> The results are somewhat surprising, as elasticities are higher for developed countries in 2 out of 4 sub-samples, namely digital and energy studies, and only larger for developing countries in the case of transport and cross-sectoral studies. This may argue for the existence of network effects, whereby high returns require a minimum level of quality coverage. They can also be related to studies showing low returns to investment in some developing countries contexts, for example in the case of rural electrification (Lee et al., 2020). On the other hand, the results for transport are consistent with some qualitative evidence for large deficits and high returns in this sector (Foster et al.,

> 17The corresponding Tables are in Appendix D, Table A.12 and A.13

18
|**Table 5 : OLS and FE Regre**|**ssions with VAT Re**<br>**EU11 group &**|**venue as share of**<br>**non-EU11**<br>Depend|**GDP as dependent v**<br>ent variable|**ariable,**|
|---|---|---|---|---|
||8|VAT R<br>9|evenue/GDP<br>10|11|
|Independent variable|**FE**<sup>**1/ 2/**</sup>|**FE**<sup>**1/ 3/**</sup>|**GMM one-step**<sup>**4/**</sup>|**GMM two-step**<sup>**4/**</sup>|
|L[VAT Revenue/GDP]|0.52***|0.21**|-0.02|0.03|
||(0.06)|(0.09)|(0.12)|(0.25)|
|Net Exports/GDP|-0.04***|-0.09***|-0.06***|-0.05***|
||(0.01)|(0.02)|(0.01)|(0.01)|
|GDP Growth|-0.03**|0.05***|0.02*|0.02|
||(0.02)|(0.02)|(0.01)|(0.02)|
|Terms of trade, index|0.00|-0.01|||
||(0.01)|(0.01)|||
|Output gap/GDP|2.86|-14.59***|-6.16***|-5.62*|
||(2.10)|(2.41)|(1.30)|(3.17)|
|(Net Exports/GDP)*(Output Gap/GDP)|-0.08|-0.60***|-0.26***|-0.29|
||(0.08)|(0.14)|(0.09)|(0.21)|
|Log of GDP per capita|0.56|0.64|1.02***|0.86***|
||(0.52)|(0.57)|(0.26)|(0.25)|
|Constant Term|-0.69|0.95|-0.99|-0.15|
||(3.51)|(4.12)|(1.83)|(1.77)|
|R-sq: within|0.66|0.64|||
|# of observations<br>_Notes_:|141|113|250|250|

This table reports beta coefficients and standard errors from OLS and FE regressions. All regressions exclude oil exporters.

L is the lag operator and L (Xt) = Xt-1.

***, ** and * denote significance at 1%, 5% and 10% levels respectively.

1/ Country and time fixed effects.

2/ Regression excludes EU11 countries.

3/ Regression include EU11 countries only.

4/ Standard and GMM-type instruments are used for differenced equation.

18
**Figure 5: Share of Filipino firms rating each skill as one of the top skill priorities, by export orientation**

<!-- Start of picture text --> independent work<br>problem solving<br>communications<br>team work<br>creativity<br>leadership<br>literacy<br>exporter<br>time management<br>negotiation non-exporter<br>initiative<br>math<br>writing<br>computer<br>language<br>0 2 4 6 8 10 12 14 16<br><!-- End of picture text --> Question: rank the five most important generic skills and job-specific Rank the three most important generic skills and job-specific skills for which gaps were most noticeable _Source: Adapted from Figure 2.5, page 86 in diGropello 2010._

**Figure 6: Skills Lebanese employers want in their managers and in their employees**

<!-- Start of picture text --> non-computer office equipment skills<br>foreign language<br>computer skills<br>numerical skills<br>writing (Arabic)<br>reading (Arabic)<br>leadership<br>managers<br>problem solving<br>creativity/innovation employees<br>adaptability<br>independent work<br>time management<br>communication skills<br>team work<br>0 10 20 30 40 50 60 70 80 90<br><!-- End of picture text --> _Source: Adapted from World Bank (2012), page 36 Figure 2.8._ 44
households to purchase more medical and nutritional inputs since health is a normal good. Secondly, the income from migration may relax liquidity constraints that prevent parents from investing in child health in the current period in order to reap the returns in the future. Moreover, the migration experience developed by parents will make it easier for their children to migrate; raising the expected adult wages of children and hence returns to investment in their health.

# **6.1 Migration and Health Knowledge**

Migration may also impact on health outcomes through non-monetary channels. One such channel is the transfer of health knowledge information. Grossman (1972) allows for education to change the efficiency of the health production process, allowing for better health production from a given input set. Glewwe (1999) studies the mechanisms through which mother’s education raises child health in Morocco and concludes that mother’s health knowledge rather than their level of schooling per se is the crucial skill for improving child health. Migrants may gain information about basic health practices while abroad and share this with family members. Such information may include better understanding of contraceptive practices, the importance of sanitation, and knowledge about diet, exercise and other lifestyle behaviors. Menjívar (2002) provides evidence that social networks among Guatemalan immigrants in the U.S. engage in regular transmission of medical knowledge, helping one another with information about treatments and health advice.

As discussed in the Appendix, we measure health knowledge by the first principal component of a set of questions asking mothers whether they can name on their own or with help ten different contraceptive methods. This provides a measure of health knowledge which clearly represents fertility knowledge, and may be broadly associated with more general health knowledge on the part of the mother. For example, using data from the Mexican Health and Migration Survey (HMS) taken in eleven communities in the state of San Luis Potosi, we find a strong and significant correlation between our measure of health knowledge and whether the mother has a good explanation for the causes of diarrhea.<sup>14</sup> Table 8 examines the impact of migration on the health knowledge of mothers. Columns 1 and 2 show that mothers in migrant households are found to have more health knowledge than mothers in non-migrant households. After

> 14 See <u>www.mexmah.com</u> for further description of this survey.

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