rafmacalaba/data-use-mentions-extended huggingface dataset:
due to the elimination of misallocated quota licenses; while the remaining 29% is explained by the removal of misallocation due to the quota itself.

**Credit constraints and informational frictions.** There is an established literature on the positive correlation between financial market development and growth (See Buera, Kaboski and Shin (2015) for an overview). Credit constraints may lead to misallocation, potentially magnifying the persistency of low productivity, as more productive firms take longer time to overcome financial constraints. This empirical question has been tested in multiple frameworks and the resulting estimates tend to vary substantially across studies. On the conservative side are the results by Midrigan and Xu (2014). They calibrate a model with Korean plant-level data and find that losses from misallocation in an environment with borrowing constraints amount to 4.7% of the TFP decline (accounting for over 27% of the change). Borrowing constraints harm growth more through the selection (into a sector) channel rather than through the misallocation (within the sector) channel.

A complimentary analysis is proposed by David, Hopenhayn, and Venkateswaran (2016) who focus on informational frictions. Under their framework, firms have limited knowledge about the demand conditions in their own markets when choosing inputs. The authors estimate a structural model with 2012 data on firm-level production variables and stock returns for three countries: China, India, and the United States. Informational frictions for investment but not labor decisions lead to losses in productivity (of 4, 7, and 10%) and output (5, 10, 14%) (for the United States, China and India, respectively). The authors show that financial markets have limited ability to overcome these frictions given the high level of noise in market prices. <u>Conversely, valuable information can be inferred from private (internal to the firm) sources.</u> As acknowledged by Hsieh and Klenow (2009), their approach relies on restrictive assumptions such as (i) CES aggregation of differentiated
country-level crime experienced by firms may affect our police measure, and thus we limit the problem of reverse causality by using lags of the number of police. However, under reverse causality we expect a positive correlation between crime and the number of police and thus any negative relationship we uncover would be even stronger if reverse causality was explicitly accounted for. On the other hand omitted variable bias is an issue that is challenging to overcome given data limitations. While we cannot cleanly solve the identification problem with the data at hand, we employ several strategies in an effort to consistently estimate the parameters of interest. First, we use a dataset of 644 panel firms in 12 Eastern European economies for the years 2004 and 2007. We caution that the firms sampled in 2004 do not use the same sampling methodology as the firms in 2007, and data are only available for Eastern European economies. We employ firm-level fixed effects and year effects to account for time invariant firm specific unobservables. Although we cannot completely account for endogeneity, our results do seem consistent with estimates in the literature based on overall crime instead of firm-specific crime.

# _3.1 Base Regression Results_

The base estimation results are presented in column 1 of table 1. The coefficient of the number of police is negative and significant at 5%. In terms of magnitude, a one percent increase in the number of police is associated with a 0.43 percent decline in crime losses. This result indicates that the number of police may effectively be a deterrent for crime experienced by firms, just as studies have shown them to be a deterrent for individual-level crime (Di Tella and Schargrodsky, 2004; Levitt, 1997).<sup>3</sup>

> 3 When all controls are excluded from the estimation, a negative relationship between police size and crime persists, but it is not significant unless GNI per capita is controlled for. Thus these results are conditional on the level of income in the country.

12
)||
|Log monthly wage|13.27<br>|13.49<br>|.22<br>|13.06<br>|13.24<br>|.18<br>|13.59<br>|13.86<br>|.27<br>|
||(.70)|(.72)|(.51)|(.66)|(.67)|(.55)|(.64)|(.63)|(.44)|
|**Province-specific Characteristics**||||||||||
|Observations|61|61|61|||||||
|Export Index|2.35|2.93|.59|||||||
||(4.76)|(6.10)|(1.44)|||||||
|Export US Index|<br>.42<br>|<br>.76<br>|<br>.34<br>|||||||
||(.82)|(1.65)|(.84)|||||||
|US tariff cut measure|||.45<br>(.87)|||||||
|Share of agriculture 2002 (%)|.620<br>(.178)|||||||||
|Share of construction 2002 (%)|<br>.041<br>(.022)|||||||||
|Share of government 2002 (%)|<br>.053<br>(.018)|||||||||
|Share of industry 2002 (%)|<br>.113<br>
369)|
|Crop suitability score|1.251**|0.996**|
||(0.583)|(0.443)|
|N|4,388|4,388|
|R2|0.54|0.60|
|Province FE|Y|Y|
|Average of outcome|4.19|7.05|

**Notes.** This table examines the determinants of the share of refugees in 1923 and 1928 and the change in the share of refugees between the two years across municipalities. The unit of analysis is the municipality as defined by the 1920 population census of the Kingdom of Greece. All specifications controls for province fixed effects and a set of municipal-level covariates, which comprises: log population density in 1920; dummy variables indicating whether the municipality was a city in 1920 and whether it is an island; distances to railway network in 1920, shoreline, and national borders; altitude, mean annual precipitation and temperature, and crop suitability of the municipality. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

41
lags well behind that of advanced economies (e.g., Dunlop and Velkoff 1999, Mammen and Paxson 2000, Ghani 2010a, World Bank 2011). Cross-country data from the World Bank Entrepreneurship Snapshots find that India’s rate of entrepreneurship is lower than its stage of development would suggest; similarcomparisons also highlight that India’s gender ratio is lower than its peers. This dual under-performance has cultural and economic antecedents, but it is starting to change. Women are making economic gains in the Indian economy, and further progress represents a tremendous growth opportunity for the country.

This study contributes most directly to two literature strands. First, our work builds upon prior studies of gender differences in entrepreneurship. Parker (2009) and Klapper and Parker (2011) offer a comprehensive review of this literature and appropriate references. Our paper is among the first to study how spatial differences in gender ratios in entrepreneurship relate to local incumbent business structures. Existing research mostly employs cross-country studies of 3
(individuals who do not have a registered business but file taxes under a self-employment regime), and informal employees (employees without a written contract who are not in RAIS).

Table 7 shows the effect of the financial education intervention on receiving the pandemic aid and on the informal proxy. Unlike the previous tables, the analysis here is done in a cross section, not a panel. The results indicate that once all controls are included, treatment students were 1.4 percentage points more likely to access the pandemic aid transfer than control students. About 38 percent of control students received pandemic aid transfers. Treatment students were also 1.1 percentage points more likely to be informal, but this coefficient is only statistically significant at the 10 percent level. We thus find weak evidence that the financial education intervention increased the share of students in the informal sector. Unfortunately, the data do not allow us to disentangle whether this increase comes from informal business owners or employees who are not registered with the government. However, given that the program increased the share of students running a formal microenterprise (MEI) and lowered the share of students working as formal employees, we suspect that the increase is due to students running informal businesses.

# **5. Conclusion**

This paper measures the long-run effects of a high-school financial education program in Brazil on financial behavior and employment outcomes using a randomized control trial with 892 public high schools and administrative data on about 16,000 former students. Unlike previous literature that focuses on short-term effects over one to two years, we follow students for up to nine years after leaving high school.

While some literature has hypothesized that the long-run effects of high-school education would be declining or zero as students forget what they have learned, we find lasting effects. In fact, the effect on the use of expensive sources of credit goes from being positive in the short run to being negative in the long run. That is, in the long run, treatment students are less likely to use expensive
Here we introduce the process of GDP compilation by statistical bureaus in China, and the potential for local government maneuver, drawing heavily from Ma et al. (2014) and Holz (2005). The National Bureau of Statistics (NBS), an agency directly under the State Council, is in charge of the collection, research and publication of economic statistics throughout China. Local governments at the level of county and above have their own statistical division, which is under the joint oversight of the local governments and the statistical counterpart one level up.<sup>15</sup> Take the provincial bureaus of statistics (provincial Bureaus for short) as an example. The provincial governments hold the appointment authority over major statistical personnel and make key decisions on the funding allocation to provincial Bureaus. Meanwhile, provincial Bureaus must conduct statistical works following the rules and guidelines issued by the NBS.

Statistics collected by local Bureaus are reported to the next higher level. Provincial GDP statistics thus depend on the GDP figures reported by each prefecture, as well as on the independent sampling and data processing from a higher-level statistical bureau. The local Bureaus compile local GDP by combining all related data from the next lower-level bureaus, from other departments, and from the NBS survey teams. The local Bureaus are mainly in charge of raw data collection from firms above the cutoff level in industry,<sup>16</sup> wholesale and retail, and hotels and catering sectors as well as from construction enterprises with qualified certification and independent accounting. The NBS survey teams are responsible for sampling surveys on firms below the cutoff level in the corresponding sectors, but they are not involved in collecting economic statistics in the construction and banking, securities and insurance sectors.

This process of GDP compilation may open doors to GDP manipulation. Since local leaders are rewarded for economic growth, they have incentives to exaggerate local growth. First, because the local government has appointment authority over major personnel of the local Bureaus, the local leader’s “requests”
The use of panel data may have many benefits but their use also raises a number of potential econometric problems including:

# 1) _Coefficient heterogeneity across countries_ .

We have strong prior views that countries will differ consistently in their intercepts according to persisting, largely time invariant local factors. For this reason our maintained hypothesis is that a fixed effects model is more appropriate than a random effects approach. In addition, the fixed effects static model avoids the potential biases which could arise in the random effects model owing to correlation between the included exogenous variables and omitted country attributes.

# 2) _Dynamic structure_

Static models, which assume that all adjustment to disequilibrium occur within the period defined by observation frequency may be inappropriate. In particular, investment in electricity is not usually completed in a year so we would expect that scope for some adjustment process would need to be incorporated into our model. Such processes can be modelled generally by a combination of lags on the dependent variable (autoregressive) and on the explanatory variables (moving average).

However, the presence of a lagged dependent variable in a fixed-effects model can result in a biased estimates for the lagged dependent variable coefficient. The size of the bias will depend on the number of time series, _N,_ the length of the time series, _T,_ and the influence of other exogenous variables in the determination of the dependent variable.<sup>19</sup> The problem is mainly significant in short panels. For T=21 we have estimated the asymptotic bias (as N increases) to be of the order of 3%. This is an upper limit given the presence of other major influences on the dependent variable.

There is also the potential problem of spurious correlation eg if both electricity capacity and GDP per capita were both strongly trended across our countries. In fact, they are not in our data set, but we have considered carefully how the dynamics should be modelled and we report a selection of the key results.

# 3) _Endogeneity and Causality_
error and changes in the disease environment to the (Deaton and Dr`eze 2009) puzzle of declining measured caloric intake . (Smith 2015) argues that the NSS estimates of caloric intake may be biased down, especially in recent years, due to rising consumption of food away from home.<sup>2</sup> Evidence on the magnitude of recall bias identified using different survey recall periods ((National Sample Survey Organization 2000; NSSO 2013)) and the sensitivity of estimated caloric intake to assumptions about the caloric value of food expenditures is reviewed. The findings are also discussed in the context of (Duh and Spears 2017), who test one of the potential mechanisms dis-

> 2See also (Fiedler and Yadav 2017) for evidence from the 2011-2012 NSS survey and (Smith et al. 2014) for a general review of the reliability of food data collected in consumption and expenditure surveys.

3
# **Figure 10  Real wages rebounded in the mid-1990s**

## **Real Wage Growth**

<!-- Start of picture text --> 150.0<br>100.0<br>50.0<br>1989-1995<br>1995-2001<br>1989-2001<br>0.0<br>EU member TE EU accession TE Russia and Ukraine CIS-7<br>-50.0<br>-100.0<br>percent<br><!-- End of picture text --> Source: UNICEF TransMONEE database; Bank staff calculations Individual country experiences regarding wage dynamics vary strongly across the region (Figure 11). Among CEE, wages in the Czech Republic and Poland are almost 20 percent higher than before the transition and close to the pre-transition level in Hungary and Slovenia. At the same time, in Bulgaria and Lithuania wages much lower than before the transition.

**Figure 11  Real wages are close to the pre-transition level in CEE countries and below it in CIS-7.**

<!-- Start of picture text --> Real Wages in 2001<br>1989=100<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Czech RepublicCroatia PolandGeorgiaHungarySloveniaSlovakia _CEERomaniaEstonia LatviaMoldovaUkraine _RUKazahstanMacedonia, FYRRussia BulgariaAzerbaijan_CIS-7 + KArmeniaLithuaniaKyrgyzstanUzbekistanTajikistan<br>Percent<br><!-- End of picture text --> <!-- Start of picture text --> Source: UNICEF, TransMONEE database; Bank staff calculations.<br><!-- End of picture text --> 31
<!-- Start of picture text --> Sub-Saharan Africa GDEs by categories nN<br>(g) ©<br>Cabo hi Mauritania 2, . - 5 . : \<br> Verde ‘sg Mali 2 3 7 “ 4<br>a K nis ” Cha = 1 te \.<br>~~ Gambia rnee "NG scv tit Ge eo : © 48 8% 6 bag886 Djtoys<br>a Guinea Or ¥ Benin” - . “hy . a]<br>Sierra Leone“a Cote“ cvore Ghana Togo Nigerta .Pn4 “ wah Eth.—<br>GDE categories (Cat I) . a - oscc , Somaiia<br>af a<br>me | Inland surface waters Siateniendiacge .,' § os i<br>| Terrestrial springs any Boy ORE a ay<br>| Spring: a“ Burundi 2. te “3 A<br>= Coastal and marine ’ % ryAy4 t<br>a }| Terrestrial vegetation “Nc oh7 B ‘are" JA B) Malawi) feed<br>HM inland surface waters/ Coastal and Marine ay: ee ;<br>Terrestrial springs / Inland surface waters ae; KKSy . seats<br>HM _siinland surface waters/ Terrestrial vegetation ? Syiate\" a Madagascar %<br>tjadiba Botswana pe,<br>Administrative boundaries 7 WHOr op \<br>Studied countries re .<br>Countries with insufficient data 2. DP \gstho ,<br>Non-studied countries 5) On a<br>"a 0 500 1000km<br>Sources: Dataset from the SSA GDE inventory (Rambhunjun 2022), basemap from gadm 2022. Map created with OGIS and Inkscape. by Précila Rambhunjun. july 2022 | |<br><!-- End of picture text -->
|-0.027|0.021|0.069|0.048***|
||(0.010)|(0.020)|(0.026)|(0.005)|(0.021)|(0.015)|
|N|693|121|814|944|143|1,087|

**Notes** This table shows the average literacy and education of refugees and Greek natives in different dataset. In Panel A, the sample consists of individuals born in 1858-1907, i.e., 16-65 adults in 1923. In Panel B, the sample consists of individuals born in1908-1922, i.e., 0-15 children in 1923. In Panel C, the sample consists of secondgeneration refugees and natives’ offspring born in 1923-1950. Standard errors in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 and 2001 Greek population censuses and European Social Survey (ESS).

29
# **Who Benefits Most from Rural Electrification? Evidence in India**<sup>*****</sup>

**Shahidur R. Khandker (Corresponding author, sector mapping Poverty Reduction) Hussain A. Samad Rubaba Ali Douglas F. Barnes**

_Keywords_ : Rural Electrification Impacts; Rural India; Distributional Impacts of Electrification; _JEL classification_ : I38, O18, O53

*Shahidur R. Khandker is a Lead Economist in the Development Research Group of the World Bank, Hussain A. Samad and Douglas F. Barnes are Consultants in the World Bank, and Rubaba Ali is a PhD. Student in the University of Maryland. The paper’s analysis is based on the India Human Development Survey (IHDS) of 2005.  The authors would like to thank Will Martin and Giovanna Prennushi for very useful comments and Norma Adams for editorial assistance. Views expressed in the paper are those of authors alone and do not reflect the views of the World Bank or its affiliated agencies.
Another, and more important limitation in the HFPS data relates to self-reported loss of household income. Income losses are collected through a binary indicator on whether households’ incomes recently declined or not, which is susceptible to measurement errors and makes it difficult to quantify the COVID19 impacts on their incomes. In addition, the mobility trend in the Facebook data may not represent Kinshasa residents, as the data is collected from the smartphone app users, who are likely to be richer and younger than other urban populations.

With these limitations in mind, the results of our analyses highlight the spatial heterogeneity of COVID19 impacts on urban households’ incomes as well as the key role of mobility and accessibility in urban agglomerations. In Ethiopia, households in large and dense towns were more likely to lose their labor incomes in the early phase of the pandemic, and their recovery was slower than other households. Disadvantaged households in those large and dense towns are particularly affected, such as those with female heads, low-skilled heads, self-employed heads, and poor households. The city-level analysis for Kinshasa demonstrates that workers’ income is more elastic to mobility shocks in suburbs and precarious and highly densely populated neighborhoods. Disadvantaged workers, such as female and low-skilled workers, in those areas were particularly affected. These findings highlight the need for policies and infrastructure in the medium and long terms to enhance mobility and connectivity and build functional urban agglomerations.

Our study is related to two strands of literature. First, we contribute to the rapidly growing literature of the impacts of COVID-19 on individual/household labor and economic outcomes. Various studies analyze economic and welfare impacts at the household level, such as Adams-Prassl et al. (2020); Baek et al. (2020); Chetty et al. (2020); and Crossley, Fisher, and Low (2021) for developed countries and Bundervoet, Davalos, and Garcia (2021); Egger et al. (2021); and Khamis et al. (2021) for developing countries. While a few studies examine
expand capacity, start a new product line, or change location. Delicensing reform meant freedom from constraints on output, inputs, technology usage, and location choice as well as easier entry into delicensed industries. Freedom from these constraints allowed firms to take advantage of economies of scale, more efficient input combinations, and newer technologies. Furthermore, greater domestic competition as a result of free entry into delicensed industries provided firms with incentives to innovate, increase productivity, and improve product quality.

Most of the existing studies on innovation use patenting activity/citations, total factor productivity (TFP), and data from innovation surveys.<sup>2</sup> R&D expenditures which are sometimes used to measure innovation intensity should be considered only as an input factor for innovation process. TFP measures have the well known problem that they are biased in the presence of imperfectly competitive product markets. Moreover there is a general problem with interpreting TFP findings as they usually use revenue data which makes it difficult to identify the effects of policy reforms on physical efficiency separately from its the effects on firm markups, product quality, and in the case of multiproduct firms the range of products produced by the firm (see Foster et al. (2008) and Katayama et al. (2009)). The patent data provides a better measure of innovation compared to TFP or innovation survey data but it usually has low data coverage. Moreover in developing countries the patenting mechanism usually does not work efficiently and thus provide a very limited picture of overall innovation activities. The innovation measure I use in this paper is introduction of new products to the market. This information is

> 2  An example of these surveys is Community Innovation Surveys (CIS) which is developed by European Commission for member countries of European Union.

2
areas, but have a muted impact for urban households.

Tables 8, 9 and 10 focus solely on rural households and examine the source of increase in average monthly per capita expenditures identified in Table 6. We note that consistent with the evidence shown in the binned scatter plots, rainfall shocks have little impact on household food and essential food expenditures (Tables 8 and 9). Only households with any member employed as a wage laborer or employed in informal activities register a significant increase in either of these expenditure categories in response to a positive rainfall shock. On the contrary, consistent with the descriptive evidence in Figure 3, Table 10 records a positive and statistically significant impact of a positive rainfall shock on luxury expenditures. As seen in columns (2)-(6) of Table 10, this increase is observed amongst all categories of households, suggesting that the increase in monthly per capita consumption for rural households is driven by luxury expenditures. As luxury expenditures include snacks, beverages, packaged foods, sweets and dry fruits, the findings in this section are consistent with those in Section 4.2, where we saw that rainfall shocks had a positive impact on the revenues of rural non-farm enterprises engaged in retail trade.

# **5 Policy Implications**

Our empirical results allow us to draw four clear policy implications. The first is that rural non-farm enterprises comprise a major source of employment in the rural economy. Applying the survey weights to the 2015 survey round, the sample indicates the presence of 2.6 million – enterprises, providing employment to 4 million workers approximately 16% of the rural workforce as per Census 2011. Despite being such a large component of the economy, these 13