rafmacalaba/data-use-mentions-extended huggingface dataset:
framework<sup>26</sup> . However, that is only a solution for the first few years and the problem remains not only how to build in the necessary flexibility but also how to exit the constraints after the initial period.

Although the Prayas Report demonstrates clearly that there is a very long way to go before ERCs meet the standards of good regulatory governance, they do not argue that the effort has been a waste of resources. Indeed, they are (cautiously) optimistic about the potential development of the ERCs as effective regulatory agencies.

Given the starting circumstances, it is quite possible that the developments to date are a significant improvement on the previous implicit regulation by State Government agencies. (The Prayas Report dos not assess this.) They certainly seem to have provided some openness, division of powers, pressure for commercialization and open debate. Indian ERC development to date may be all that could reasonably have been expected. However, the key question unfortunately remains completely open. Is the glass half-empty or half-full? What are reasonable expectations in this area – for SubSaharan Africa, Russia and other CIS countries as well as for India and other Asian economies?

The fact that the Prayas Report provides the evidence with which to conduct such a discussion again shows the potential for using structured case studies to examine these issues. To assess the impact of regulatory arrangements on outcomes of course requires marrying data on investment, consumption, etc with regulatory data – but the Prayas Report confirms that this regulatory data must go a lot further than scrutiny of legal and related documents.

It is only unfortunate that the Prayas report did not code its findings (a) on regulatory actions and (b) on the formal requirements along the lines of the 1998 NERA report (and Stern-Holder). A sound and robust coding framework is the essential next step in further work of this kind.

# **3.3 The EBRD Study of Power Sector Regulatory Reform in Transition Economies and Similar Studies**

# **3.
, and revised the national accounts for the period 2006–15. The adoption of SNA 1993 led to more accurate estimates of national accounts data; for instance, nominal GDP was revised upward by 40 percent.<sup>6</sup> The data show that structural change has made a positive contribution to productivity growth in Guinea during the period under investigation.<sup>7</sup> This suggests that labor has flowed from the lower productivity agriculture sector. Labor has moved from agriculture, but not to the highest productive

> 6 For further details, see IMF Country Report No. 17/387.

> 7 Such a result is consistent with the one found by McMillan _et al_ . (2014) for African countries in the post-2000. Their Africa sample includes Ghana, Ethiopia, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, and Zambia.

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Campos, F. and Gassier, M. (2017) “Gender and Enterprise Development in Sub-Saharan Africa: A Review of Constraints and Effective Interventions” Policy Research Working Paper No. 8239. World Bank, Washington DC.

Chakravarty, S., Das, S. and Vaillant, J. (2017) “Gender and Youth Employment in Sub-Saharan Africa: A Review of Constraints and Effective Interventions” Policy Research Working Paper No. 8245. Washington DC.

Duflo, E. (2012) “Women Empowerment and Economic Development” _Journal of Economic Literature_ 50(4): 1051-1079.

EBRD (2016) _Strategy for the Promotion of Gender Equality 2016-2020_ , Document of the European Bank for Reconstruction and Development.

GFLEC (2016) S&P Global Finlit Survey. Global Financial Literacy Excellence Center, George Washington University. http://gflec.org///sp-global-finlit-survey-methodology/.

Guven, D. and Rodrik, D. (2016) _Turkey’s Growth Puzzle_ . Harvard University, Cambridge MA.

Guriev, S., Leipziger D., and Ostry, J. (2017) _Making Globalization More Inclusive: A Way Forward_ , VOXEu.org, October.

Guriev, S., Leipziger D., and Ostry, J. (2018) Inclusive Growth or Else, Project Syndicate, February.

Hausmann, R., Rodrik, D. and Velasco, A. (2005) Growth Diagnostics, mimeo.

Ianchovichina, E. (2018) _Eruptions of Popular Anger: The Economics of The Arab Spring and Its Aftermath_ , MENA Development Report, World Bank Group, Washington DC.

Ianchovichina, E. and Lundstrom, S. (2009) “Inclusive Growth Analytics: Framework and Application” Policy Research Working Paper No. 4851, World Bank, Washington DC.

Ianchovichina, E. and Lundstrom-Gable, S. (2012) “What is Inclusive Growth?” in _Commodity Price Volatility and Inclusive Growth in Low Income Countries_ , Arezki, Pattillo, Quintyn, and Zhi (eds
market; as captured in the EU-LFS, it represents a main barrier for only 6 percent of working-age migrants. Moreover, when we compare the coefficients for different world regions (tables 1-3, columns 5- 6), we do not see much of a change either. This indicates that barriers for certifying foreign credentials, although relevant for some migrants, are not one of the main factors that would explain the higher economic risk exposure of migrants from lower-income countries to the EU15.

6.3. Immigrant Network Effects Even after we control for personal characteristics such as education level and fluency in the local language, there are still unexplained differences between migrants from different regions of the world, in terms of their occupations and the resulting income and health risks to which they are exposed. Results (tables 1-3, columns 5-6) show that migrants from NMS13 countries are still 9.5 percentage points less likely than native workers to be in safe jobs. The gap is 13.5 percent for migrants from other eastern European countries, 6.0 percent for those from Sub-Saharan Africa, 6.6 percent for those from Asia, and 10.5 percent for those from Latin America (Table 1, Column 5).

Another pattern found in the literature for the clustering of migrants into specific occupations is the role of migrant networks. Existing social and cultural networks provide numerous services, especially to newly arriving migrants. In addition to helping them with housing, schools and deciphering the cultural norms of the destination country, migrant networks help the new migrants 18
## Labor force survey

The estimation of the parameters of the workers' problem requires panel data on workers' sector of employment and wages or non-employment status. We use the panel sample of the Encuesta Permanente de Hogares (EPH, Permanent Household Survey). The database contains information on individual wages, employment sector, non-employment and other standard variables in labor force surveys. Part of the EPH is a panel and we can use it to track employment decisions across sector pairs, across employment and non-employment, and average sector wages. We use the available panel from 1996 to 2006.

# A.2 Estimation of the technology parameters

## Production function parameters

We postulate a production function which is Leontief in materials _Mijt_ and a Cobb-Douglas index of capital _Kijt_ and labor _Lijt_ . To estimate the Cobb-Douglas production function coecients on labor and capital, we follow the regression approach of Ackerberg, Caves and Frazer (2015). It is important to note that the structural assumptions of the estimation method are compatible with our own structural model. We use value of production ( _Yijt_ ) on the left-hand side, capital ( _Kijt_ ) and labor ( _Lijt_ ) on the right-hand side, and following the Leontief specication of the production function we exclude materials from the regression at this stage. The regression equation for this stage takes the form where _ε_ is a combination of productivity shocks and measurement error in output. The equation reects the contribution of labor and capital to output and is therefore conceptually a value-added production function (given the Leontief specication), even though total value of production is on the left-hand side. See Ackerberg, Caves and Frazier (2015). We use the predetermined variables _Kijt_ , _Kijt−_ 1 and _Lijt−_ 1 as instruments. Standard errors are computed from 500 bootstrap replications. The labor coecient _αL_ is 0.619 and the capital coecient _αK_ is 0.283. Both are statistically signicant. These results are comparable to those obtained by Pavcnik (2002) for Chile.
aggregate is then adjusted for spatial differences in cost of living and transformed using an adult equivalence scale.

**_Establish a proxy non-housing budget standard_ .** Given that most developing countries do not have an official budget standard, it is necessary to establish proxy living standards for non-housing expenses in order to use the RIM (or REM). It may be possible to use a single non-housing standard across all income groups based on proxies from existing poverty measurements. For example, based on LSMS, HIES or their equivalent, a minimum living standard for food and non-food items (including housing) can be estimated. It may be possible to extrapolate the non-housing expenditure per capita – and accordingly the non-housing budget standard per capita – from this data. This non-housing budget standard per capita can then be used to extrapolate non-housing budget standards for households of different types (e.g., single person household, couple, couple with 1 dependent, couple with 2 dependents etc.) using equivalence scales.

Ideally, it would be better to develop two non-housing budget standards: a low-cost standard for individuals in the bottom income quintiles, and a moderate-cost standard for those in higher-income quintiles. This is because expenditure decisions of low-income persons and households are likely to be different from middle-income groups with regard to what is considered a basic acceptable quality of, for example, education or health care.

# 4.3 Adopt Suitable Equivalence Scales

While there has been considerable research by statistical and other agencies trying to estimate appropriate values for equivalence scales, there is no single accepted global standard. “The choice of a 14
condition on them in the models.

xvii We categorized households as food secure based on the Household Food Insecurity Access Scale (HFIAS), consisting of nine items and four frequency responses, using a 30-day recall period with reference to children under age six in the household (Coates et al. 2007). The widely used and validated scale was developed to reflect three universal domains of the experience of inadequate household-level food access: (1) anxiety about household food supply, (2) insufficient quality, which includes variety and preferences, and (3) insufficient quantity of food supply, the amount consumed, and the physical consequences of insufficiency.

xviii This subsection draws from Saenz de Tejada (2009).

xix This is particularly relevant in the period just after migration, to pay off debt incurred to undertake the journey (usually about $5,000), taking a year or more and likely an inflexible portion of household spending.

xx The left-side panels of Figures 2 and 3a differ slightly, as the latter is based only on the balanced panel sample of children.

xxi Controlling for age, however, the difference in 2008 is 0.30 (P-value=0.035).
Financial Regulation and Development depositors’ reactions. Finally, as our model shows, safe asset regulation has a stronger effect on depositors in riskier/infant financial systems, with few countries presenting the simultaneous strong variation in the size of regulation combined with an underdeveloped banking industry.

For this reason, we investigate the following two case studies that uniquely address the previous concerns, as they present some of the lowest levels of financial penetration, combined with policy changes that caused substantial balance sheet reallocations toward safe asset.

1. Ethiopia (2011). In mid-March 2011, the NBE announced a policy change in financial regulation forcing all private banks to start purchasing 0.27 government bonds for every unit of private sector lending by April 2011.

2. Antebellum USA (1800–1861). Between 1800 and 1850, several states non-simultaneously introduced taxes (on capital or profits) on local banks in response to a loss of seigniorage revenue.

In both case studies, the identification of the policy effects are achieved by combining the time-series shocks (one policy event in Ethiopia, and a tax “phase out” across US states) with cross-sectional variation in bank sizes (big versus small banks), because heterogeneous effect of the treatment is prescribed by the theoretical model.

In order to address the data limitations previously mentioned for the Ethiopian case study, we have constructed a variety of unique databases through which we can track the whole financial system. Through confidential contacts with the NBE, we had access to the regulation documents and could interview senior executives for all private sector banks, which provided substantial insights on how this regulation affected their business. Regarding the available datasets, we track three key indicators of bank behavior: bank balance sheets with monthly frequency; a new map covering over 90% of bank branches opened in Ethiopia between 2000 and 2015; and digitization of all annual reports in the five years around the policy change. First, we obtained access to confidential data on the monthly composition of bank balance sheets, collected and confidentially released by
sampling frame does not necessarily substitute the existing sampling frames but could complement them, particularly the national census frame, and can be used as an alternative.

Finally, this approach creates demand for public datasets like OpenStreetMap. The existence of such highquality, public, geospatial data can return tens to hundreds of millions of dollars in societal value, even without counting their indirect benefits (Hansen and Schrøder, 2019).

# **2 Existing and Accessible Sampling Frames**

The household survey is one of the most popular methods for collecting population and socioeconomic indicators. An accurate sampling frame is essential to carry out household and other surveys at the national level. These frames are frequently created and updated during the national housing and population census. Some countries may not have a digital national sampling frame because their census was not digitized and more seriously some might not even have a physical map of the census frame, or it might be not accessible. Additionally, the frames might be outdated because demographic factors change quickly, and most censuses are conducted every ten years. In Armenia, there is no workable and accessible map or cartographic information that can be used for a national sampling frame. It is a significant obstacle to conducting nationally representative socioeconomic surveys. There is also no up-to-date digitalized national sample frame in the country.

The last census completed in Armenia was conducted in 2011 (ArmStat, 2024), and there are currently no up-to-date enumeration areas that can be employed as national sampling frames for representative socioeconomic surveys. The spatial resolution of the census results in the current use is restricted to the provincial or district level (2<sup>nd</sup> and 3<sup>rd</sup> administrative units), making it difficult to understand how people are distributed at the facility, sub-district, or neighborhood levels—the levels where policy interventions are typically provided. In developing countries, creating a sampling frame for surveys using representative community samples usually involves manually delineating small geographic areas (or enumeration areas) on high-resolution
amount to about $US 12 billion. of which approximately $US 2.2 billion accrued to consumers (at 12% discount rate). The results indicate that, although consumer surplus showed signs of growth, companies were allowed to retain most of the achieved gains. These findings are in line with a shortcoming of the reform design: tariff restructuring and privatisation occurred before a regulatory agency was established.

Estache, Guasch, and Trujillo (2003) argue that price-cap regulation in privatised sectors in Latin America has generally not been very successful. The paper argues that firms have improved efficiency under price cap regime, but price-caps have tended to increase the cost of capital, and hence, tariffs. At the same time, due to regulatory weakness and lack of government commitment, as is the case of electricity distribution utilities in Argentina, much of the gains have either remained with companies or have been captured by the government.

Low rates of public approval for reform may be linked to an inequitable distribution and low level of customer gain. Lora and Panizza (2002) refer to surveys of public opinion in 17 countries in the region that show a growing disapproval of the reforms, in particular among the middle classes. Without visible benefits and strong support from public opinion the sustainability of the reform process can easily be undermined. At the same time, it may be argued that some mistakes and problems are inevitable during the early years of reform.

For example, with regard to incentive regulation of distribution networks, the initial price-caps appear to have underestimated the cost saving potential of the utilities and/or 34
6 whereas the Mozambique preschools and other similar models typically focus on children aged 3 to 5. Almost 60% of women in our sample had at least one child aged between 0 to 2. Given that the take-up rates and treatment effects for children in this age group were similar to those for the older age-group, the added costs of caring for younger children appear to be driving the decreased cost-effectiveness. Thus, the program’s expansive scope of serving the youngest children (typically excluded from childcare programs) comes with the trade-off of increasing costs, which is an important factor to consider in the prioritization of program objectives.<sup>19</sup>

> 19This cost-effectiveness comparison comes with the caveat that attendance at the mobile childcare centers was irregular for many children. We unfortunately cannot link individual attendance records to survey data for children to adjust our cost-effectiveness estimates.

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# **Table 2.  Intermediate Indicators of Empowerment: State Domain**

||**Sub-domain: Justice**||
|---|---|---|
|**Presence of rules (institutions)**|**Enactment of rules (institutions)**|**Existing data**<br>**sources/indices**|
|_Formal_<br>•Number of international instruments<br>and conventions on civil and political<br>rights ratified (6)<br>•Index of Civil Liberties (1)<br>(independent judiciary; civil and<br>criminal rule of law)<br>•Press Freedom Index (1) (legislation<br>protecting press freedom)<br>•Civil Society Index (3) (civil and<br>criminal rule of law)<br>•Number of laws/acts providing<br>protection from political oppression<br>•Number of laws/acts providing<br>protection from social oppression<br>•Number of laws/acts providing<br>_protection_from domestic violence<br>•Number of anti-corruption laws/acts<br>(2, 4)<br>•Number of statutory rights conferred<br>by a national framework of criminal,<br>commercial, and international law|_Formal_<br>•Number of reported incidents of government<br>interference in police force per year<br>•Corruption Perception Index (5)<br>(transparency/accountability/freedom from<br>corruption to ensure accessible justice with respect to<br>public officials and professional groups (including<br>investment in capacity building and in HR education/<br>training)<br>•Number of corruption cases tried per year<br>•Number of constitutional courts and national legal<br>mechanisms protecting national constitutional rights<br>(e.
# **A Event Study Approach Using Gujarat and Its Bordering States Only**

Figure A.1. Women’s Marriage Age Post the Gujarat Riots of 2002 <!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>.5<br>0<br>-.5<br>Diff-in-diff Coefficients<br>-1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. The outcome variable is women’s age at marriage. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level.

31
is roughly the same size as it was at birth (a start-up size of around 7 employees), an indication that only some firms are growing and doing so substantially.

A number of caveats about the data should be kept in mind. First, the sample of the ES survey, while designed to be nationally representative of the universe of formal private sector firms working in manufacturing and services, is subject to the quality of sampling frames, and information on aggregate figures are subject to issues of nonresponse. The ES relied on several sources to build a sampling frame: the firm registry, the list of firms paying VAT, and the list of large taxpayers maintained by GSS were complemented with additional lists of firms from the Ghana Chamber of Commerce and Industry and Business Associations. Nonetheless, the sampling frame proved to be incomplete and was not sufficient to draw the target sample. A block enumeration was also undertaken in order to build an additional list. The block enumeration allowed a list of establishments to be physically created, from which the sample was drawn.

Based on the ES, the employment stock in 2012 is about 170,000, including permanent and temporary employees, equivalent to about 15 percent of the employment stock estimated in 2012 from Ghana Living Standard Survey round 6 for private workers in the same sectors (about 1.24 million). Considering that not all the private wage workers in those sectors are working in formal firms, and that only about 30 percent of private wage workers report having either a written contract or any type of social insurance benefits (to proxy formal workers), the employment stock captured in the ES formal firms represents about half of the “formal” employment stock estimated through the GLSS6 in the same sectors (World bank 2016). The representativeness of the ES will be further validated once the Establishment census data recently collected by the Ghana Statistical Service will be available.

Two broad metrics of private sector employment are informative.<sup>12</sup> The first is the static
poor, has generated greater uncertainty about poverty estimates from national accounts based extrapolation of the latest available survey.<sup>31</sup> Extrapolations or projections of poverty beyond the World Bank’s latest reference year that use national accounts growth rates commonly use an adjustment factor to adjust for the discrepancy in growth rates between national accounts and surveys. But even if the systematic bias and overall error is reduced by applying the adjustment

> 31 In recent years no survey has been available for India, making global poverty estimates for these years highly uncertain. Nowcasting approaches that take into account the limited pass through of national accounts growth to household consumption growth, have been tested. For a closer assessment of the current method applied by the World Bank for the situation in India see Newhouse and Vyas (2019).

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