rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.2 Focusing on DS Registration**

Figure 1 shows that registration at all levels increases markedly with the number of employees. While most firms with 1 or 2 workers are entirely unregistered, a majority of firms with 10 or more workers are registered with each of the relevant agencies. Our goal is to understand the demand for formality among firms in this size range. We focus on one particular dimension of formalizing, registering with the DS. This DS registration most closely corresponds to the concept of formalizing underlying much of the discussion of formalization in the literature, because it establishes a legal and tax presence, and is the prerequisite for selling to the government and other firms which require registration, as well as for applying for most bank loans.

# **3. The Experiment**

# **3.1 Selection of the Sample**

We chose to carry out our study in the two largest cities in Sri Lanka—Colombo and Kandy. Since the goal of our intervention is to gauge the demand for formality among informal firms, we needed to construct a sample of firms not registered with the DS at the time of the baseline survey. There was no existing sample frame which could be used for this, so we carried out our own screening exercise.<sup>5</sup> We randomly selected 5 Divisional Secretariat Divisions in each city. In December 2008, we had research assistants go door-to-door in these areas to screen firms, with the goal of surveying approximately 50 unregistered firms in each selected DS. Firms were selected for the baseline survey if they were not registered with the DS, were not in seasonal agriculture or fisheries, had 1 to 14 paid employees, and had an owner aged 20 to 55 who worked at least 20 hours in the enterprise each week. Few firms of this size are owned by women, and so we chose to restrict the sample to male owners and to businesses jointly owned by a husband and wife. A sixth DS Division was
secondary education ii,ii−5<sup>(at tt= 2001) is constructed from the 1996 wave of the Brazilian School Census.</sup> (which is the responsibility of the states). Excluding education expenditure leaves the estimates virtually unchanged. Results available from authors.

> 24 In an earlier version of this paper, we instrumented secondary school enrolment using the expansion of secondary schools in year t-4. This has the benefit of normalizing the estimates to be in terms of per student enrolled, rather than “school” which is not a well-defined unit of measurement and can vary in size across municipalities. It has the disadvantage in that it may ignore important effects of introducing schools into a municipality that arise through channels other than school enrolment. The results reported here are qualitatively similar to our previous findings (Foureaux Koppensteiner and Matheson 2019) .

16
(2005, 2006, 2009); EE (2004, 2006, 2008); LV (2007, 2009); SI (2004, 2006, 2008); BG (2006, 2009); RO (2008); ES (2004, 2006, 2008); PT (2004, 2006, 2008); GR (2005, 2009). Models [1a], [1b], [1c] include also Italy (2006). See Annex 1 for details on the explanatory variables. Constant terms are not reported.

(*) , *, **, *** - coefficient is significantly different from zero at 11%, 10%, 5%, 1% level, respectively. _Sources:_ Dependent variables: Calculations with ESS data (see Table 1 for details). Explanatory variables - see Annex 1 (Table A1). Model estimates - own calculations ( _Stata_  simultaneous estimates with clustered robust standard errors have been applied after fitting linear models by OLS).

41
(0.062)|(0.060)|(0.088)|
|Observations<br>58891|47707|11184|
|R-squared<br>0.098|0.111|0.184|
|_F-test (p-value): Impact on boys_|||
|In-Kind<br>0.065|0.074|0.478|
|Recognition<br>0.389|0.102|0.614|
|_F-test (p-value): In-Kind vs Recog_|_nition_||
|Girls<br>0.635|0.812|0.391|
|Boys<br>0.372|0.842|0.226|

_Notes._ Each column represents a separate regression on the midline data, estimated using equation 2. Data are restricted to same sample as columns (1) to (3) in Table 2. The bottom four rows report the p-values of the ˆ ˆ ˆ ˆ following respective F-tests: _α_ 1 + _α_ 3 = 0; _α_ 2 + _α_ 4 = 0 ˆ ˆ ˆ ˆ ˆ ˆ _α_ 1 = _α_ 2; _α_ 1 + _α_ 3 = _α_ 2 + _α_ 4. Standard errors are in parentheses. . _∗p <_ 0 _._ 1, _∗∗ p <_ 0 _._ 05, _∗∗∗p <_ 0 _._ 01 19
|0.43|

Source: Author’s calculation based on Colombian customs data.

Note: This table displays Colombian firm-level export dynamics to Latin America and the Caribbean (LAC), measured in absolute (number of firms) and relative terms (share in total number of exporting firms). The third and fourth rows report the number and the share of firms among those exporting to some LAC country that exports exclusively to the region.

19 HS 6-digit products in 2007 to 26 products in 2017. Third, the increase in product diversification is driven by the expansion of regional export relationships, rather than extraregional ones. Put in perspective, Colombian exporting firms show a relatively high level of product diversification; Peruvian firms, for example, only export, on average, 8.5 HS 6-digit products (Fugazza et al., 2018).

Table 3: Number of exported products per firm

||Me|an<br>|Med|ian<br>|Maxi|mum<br>|
|---|---|---|---|---|---|---|
||LAC|All|LAC|All|LAC|All|
|2007|19.5|17.4|10.0|9.0|128|131|
|2008|20.5|18.3|10.0|9.0|133|133|
|2009|22.8|20.1|11.0|10.0|180|181|
|2010|23.3|20.8|11.0|10.0|175|175|
|2011|26.0|22.9|12.0|11.0|193|195|
|2012|27.0|23.7|13.0|11.0|202|204|
|2013|29.0|25.7
160|1.3|0.8|-60|
|**Unskilled Labor**|-5.2|-4.1|106|0.7|0.3|-6|
|**Capital**|2.1|3.1|67|2.0|1.5|33|
|**Sk/Unsk wage gap**<br>**_Price indexes:_**||2.1|||0.6||
|**Food price index**|-2.0|||0.6|||
|**Non food price index**<br>**CPI**|-0.4<br>-1.0|||0.4<br>0.5|||

Sources: Author calculations from model results. Notes: The first column, Δ **P** , represents the percent variation of the price of each factor with respect to the initial levels, and Δ **(S/CPI)** is the percent variation of the price deflated by the Consumer Price Index. The column, **% of CAFTA** , shows the percent ratio of the real price changes in the unilateral liberalizations with respect to the bilateral CAFTA case.

As far as factor market effects are concerned, Table 11 shows that the nonreciprocal removal of Nicaragua’s tariffs causes factor prices of the non-farm segment to vary almost as much as with the CAFTA scenario, leaving a small contribution to the full price change to the U.S. unilateral response. Interestingly, the two unilateral liberalizations have contrasting price effects for factors in the farm segment. In the case of the U.S. liberalizing its tariffs, factor prices go up due to the increased export demand and this inflationary effect is not counterbalanced by inflows of cheaper imports. However, these inflows explain why factor prices
where _yf,b,m_ corresponds to the amount loaned to firm _f_ by bank _b_ in month _m_ for all types of loans as well as working capital and investment loans. The interest rate, collateral rate, and maturity of loans to firm _f_ by bank _b_ in month _m_ are additional credit outcomes that we analyze. Equation 2 includes firm-bank fixed effects, γb,f, and month fixed effects, γ _m_ , with robust standard errors double clustered at the bank and month levels. Furthermore, in some specifications, we include firm-month fixed effects, γf,m, to control for changes in the demand for credit.

A key identifying assumption for estimating the causal effects of the change in energy prices is that the trends in the outcomes of interest would have been the same across banks in the absence of the energy price drop. Although this assumption cannot be tested, we test for differences in bank outcomes and their trends before the energy price drop, using the regression outlined in equation 3, constraining the sample to the period before August 2014.

In equation 3, _yb,m_ corresponds to the outcomes of interest for bank _b_ at time _t_ , and the term _Trendm_ consists of a linear trend over time. As before, _ExposureEnergyb,Aug14_ captures bank _b_ ’s August 2014 exposure to the energy sector. Coefficient _β1_ measures whether the average outcomes of banks are statistically different as their exposure to the energy sector varies, whereas coefficient _β2_ measures differences in the trends of outcome _y_ across banks with varying exposures to the energy sector. Fixed effects at the month level, γ _m_ , are included in the regression. The results, summarized in Table A3 in the appendix, give credibility to the identification strategy, as they show that there are no statistically significant differences in the pre-shock averages and trends of the outcomes of interest. We conduct an additional pre-trends test using the loan-level data for energy sector borrowers prior to August 2014. The results, displayed in Table A4, also corroborate that there are no statistically
.1030|0.0848|0.1138|0.1102|
||(1.55)|(1.31)|(1.60)|(1.19)|(1.10)|(1.62)|(1.68)|
|_No. of observations_|1962|2526|3033|3542|4111|4628|5128|

_Note:_ The analysis is based on towns and villages that are matched to the 2013 Economic Census. All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

**Table 7. Employment trends prior to the state split** **<u>(1990-1998, town and village level)</u>**

|||ΔL<br>|og(employm<br>|ent)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.3040|-0.3187|-0.3089*|-0.2411|-0.2860*|-0.2473*|-0.2449*|
||(-1.56)|(-1.87)|(-1
Bank return on equity (%, after tax)|<br>33,449|12.87|12.11|-30.14|52.53|
|<br>Lerner index|<br>28,374|0.34|0.19|0.15|1.48|
|Five-bank asset concentration|<br>32,133|65.03|18.53|34.94|100|
|Foreign bank among total bank assets (%)|<br>28,552|24.93|28.25|0|100|
|_Country and institutional variables_||||||
|<br>GDP (current US$, billions)|33,449|628.11|866.47|1.80|2,300.00|
|<br>GDP per capita (constant 2005 US$)|<br>33,449|3,506|3,053|391|<br>14,117|
|<br>Ease of Doing Business|<br>33,449|<br>59.30|<br>8.90|43.80|<br>81.44|
|<br>Getting Credit|<br>33,449|60.43|19.85|5.00|100.00|
|<br>Enforcing Contracts|<br>33,449|52.84|12.55|29.04|73.24|
|<br>Financial Crises|33,449|0.25|0.54|0|2|

_Note:_ GDP = gross domestic credit; SME = small and medium-size enterprises.

All the institutional variables are expressed as scores. These scores capture the gap from the best performance ever attained for a given indicator since the World Bank started compiling the Ease of Doing Business (EADB) survey
digitized for this purpose, divide the city into 1,932 blocks each 250m x 250m, and for regression analysis focus on close to 40,000 block-year-level observations in 2000-2019.

Information on the stock of parcels registered, the flow of those registered for the first time either through a project-supported systematic process or sporadically based on owner applications, and the number of registered land sales and mortgages are aggregated by block to obtain a panel of annual data for 2000 through 2019. Registry data is complemented by annual spatial data on built area and population density as control variables. The amount of built area registered is used as an additional outcome variable to proxy for land-attached investment.

We use these data to estimate short- and medium-term effects of LARP using two independent panel econometric specifications with block fixed effects and time-varying controls. The program’s roll-out over time and space is used to identify short-term effects. To separate medium-term impacts of policy change from those of initial registration, we rely on the fact that policies affected registry operation and sporadic registration of land rights before, during, and after LARP-supported initial registration. We thus use the stock of registered rights, by modality of registration and interacted as needed with an indicator for the policy regime (pre- or post-reform), to estimate the impacts of policy changes and the contribution of systematic initial registration to outcome variables of interest. Three results stand out: First, in line with the literature, we find that LARP’s main short-term impacts derive from systematic first registration of rights. A time-varying indicator variable for blocks with active first registration points to significant program-induced increases in the number of registered parcels, female co-ownership of land, built registered area, and registered sales—but not mortgages. Interacting a policy reform indicator with initial values suggests that policy changes had significant impact throughout; however, estimated marginal effects are smaller than those of the systematic registration program.

Second, beyond the immediate impact of systematic first registration of rights, policy reform had significant impact on the number of registered mortgages
||||||
|Not in labor force|0.26|0.20|0.48|0.02|0.17|0.02|0.29|0.08|
|Unemployed|0.04|0.17|0.06|0.00|0.00|0.00|0.09|0.01|
|Employed in Agriculture|0.24|0.19|0.06|0.66|0.31|0.69|0.36|0.54|
|Employed in Industry|0.20|0.07|0.07|0.07|0.12|0.06|0.05|0.09|
|Employed in Services|0.26|0.37|0.32|0.25|0.40|0.23|0.21|0.29|
|**Access to basic services**|||||||||
|Improved water|0.86|0.61|0.87|0.62|0.85|0.66|0.71|0.77|
|Improved sanitation facility|0.45|0.51|0.52|0.15|0.63|0.13|0.28|0.22|
|Access to electricity|0.41|0.46|0.93|0.21|0.74|0.10|0.56|0.39|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the GHSPOP 1km dataset and the DOU method.

65
**Table 6. Correlations with steady-state employment ratios and country characteristics (continued)**

|<br>Correlate|Number of<br>observations|I<br>Total|II<br>Women|III<br>Men|IV<br>Non-<br>agriculture|V<br>Women in non-<br>agriculture|
|---|---|---|---|---|---|---|
|Average establishment size (Bento and Restuccia 2021)|||||||
|Non-agriculture|46|0.1993***|0.0505|0.3119***|0.2968***|0.1601***|
|Manufacturing|60|0.0157|-0.0024|0.0278|0.0493**|0.0293**|
|Services|55|0.2019***|0.0293|0.3292***|0.3287***|0.1629***|
|Product market regulation index (EFW)|||||||
|Overall index|103|0.0779|-0.0929|0.2105|0.3089***|0.1666**|
|Bureaucracy cost|103|0.0827|0.0412|0.1003|0.2491***|0.2071***|
|Impartial public|103|-0.0181|-0.0256|-0.0198|0.1436***|0.1348***|
|Labor market regulation index (EFW)|||||||
|Overall index|103|-0.0474|-0.0400|-0.0662|0.1265*|0.1086|
|Labor
|||Distribution of students in the<br>classrooms.||
|---|---|---|---|
|SUPERVISION<br>QUALITY|**CALI_S_1:**Quantitative<br>indicator of supervision's<br>quality.|Includes annual frequency of<br>visits, duration, occupations of<br>interviewed people and themes<br>discussed.|0-100|
|PARENTS'<br>PARTICIPATION|**APF6:**Quantitative<br>indicator of parents'<br>participation in the school<br>process.|This indicator weighs the<br>attitudes of parents with respect<br>to teachers' attendance, parents'<br>participation in school activities<br>and relevance of parents<br>associations in the school.|0-100|
|UNIT COST|**Unit cost per pupil**|Presents the fixed unit cost per<br>pupil.||
|CHILD'S PART<br>ACADEMIC RECORD|**HIST_ESC:**Index of<br>historical academic record<br>of the student.|Total years in pre-school, total<br>repetition and dropout years.|0-100|
|DUMMY FOR RURAL|**DUMMYR**|Dummy variable: If DUMMYR<br>= 1 then the observation is of<br>rural areas. DUMMYR= 0 for<br>|0 & 1|
|||other cases.||
|DUMMY FOR NATIVE|**DUMMYI**|Dummy variable: If DUMMYR<br>= 1 then the observation is of<br>native areas. DUMMYR= 0 for|0 & I|
|||other cases.||
|FACTOR -||It's a compound index||
|CHARACTERISTICS OF|**FACI_14**|constructed by principal||
|COMMUNITY AND<br>FAMILY||
# **6. Conclusions**

There has been much public debate about India‘s Mahatma Gandhi National Rural Employment Guarantee Scheme since it was introduced. There have been many media reports and some selective surveys, covering at most a few states and/or selected districts. This paper uses India‘s National Sample Survey of 2009/10 to test some of the claims that have been made in past debates using data for all the major states of India. We have focused on a distinctive and important feature of MGNREGS: the guarantee of employment at the stipulated wage rates.

We confirm expectations that the demand for work on MGNREGS tends to be higher in poorer states. This appears to reflect the scheme‘s built-in ―self-targeting‖ mechanism, whereby non-poor people find work on the scheme less attractive than do poor people.

However, actual participation rates in the scheme are not (as a rule) any higher in poorer states where it is needed the most. The reason for this paradox lies in the differences in the extent to which the employment guarantee is honored. The answer to the question posed in our title is clearly ―no.‖ Rationing is common, but far more so in some of the poorest states.

We do not find that the local-level processes determining who gets work amongst those who want it are generally skewed against the poor. There are sure to be places where this is happening (and qualitative field reports have provided examples). But it does not appear to stand up as a generalization. We do find evidence that the poor fare somewhat less well when it comes to the total number of days of work they manage to get on the scheme. However, despite the pervasive rationing we find, it is plain that the scheme is still reaching poor people and also reaching the scheduled tribes and backward castes.

Participation rates on the scheme are higher for poor people than others. This holds at the official poverty line, but the scheme is also reaching many families just above the official
64

# Table 8S MARKET PRICES OF DEVELOPING COUNTRY DEBT (selected countries)

||October<br>1985||October<br>1986|<br>April<br>1987|March<br>1988|
|---|---|---|---|---|---|
|Brazil|78||76|64|48|
|Mexico|80|e|57|59|49|
|Argentina|65||66|60|29|
|Venezuela|82||74|74|54|
|Chile|69||68|70|58|
|Peru|33||20|18|7|

Source: International Bank Credit Analysis Group (IBCA), London and Salomon Brothers Inc, New York.
# **Abstract**

This paper updates the Demirgüç-Kunt and Sobaci (2001) cross-country deposit insurance database and extends it in several important dimensions. This new dataset identifies both recent adopters and the ones that were not covered earlier due to a lack of data. Moreover, for the first time, it provides historical time series for several variables and adds new ones. The data were collected by surveying deposit insurance institutions and related agencies as well as through the use of various other country sources.

Keywords: Deposit insurance; Deposit protection; Deposit coverage; Banking. JEL Classification: G21, G28.

1
# 1.1 Related Literature

Several studies analyzed the response of fertility in Sub-Saharan Africa to the HIV/AIDS epidemic and reached mixed conclusions. Young (2005) studies the eect of the HIV/AIDS epidemic on the welfare of future African generations. He concludes that thanks to a negative eect on fertility, the epidemic, on net, will increase future per capita consumption.<sup>8</sup> He attributes the reduction in fertility to an unwillingness to engage in unprotected sexual activity and increasing labor opportunities for women because of scarcity of labor. He shows empirically a negative relationship between fertility and HIV prevalence using retrospective fertility histories and seroprevalence in antenatal clinics in South Africa. Using time series cross-country data on fertility and HIV prevalence rates, Young (2007) nds a similar eect. Kalemli-Ozcan (2006), using similar types of data, shows that regressing total fertility rate on HIV/AIDS prevalence can yield both positive and negative eects with dierent estimation strategies and dierent measures of HIV prevalence.

Later studies use the cross-country data from the latest rounds of the Demographic and Health Surveys (DHS). These surveys are nationally representative and contain results of HIV testing of respondents. Fortson (2009) and Juhn et al. (2008) nd lower fertility rates of HIV-infected women than of HIV-uninfected women; however, they nd no eect of local prevalence rates on the fertility of uninfected women and an overall insignicant aggregate eect of HIV on fertility. Fink and Linnemayr (2009), linking historical data from World Fertility Surveys (WFS) with the DHS data, argue that while HIV does not have a signicant eect on aggregate fertility levels, it aects dierently women depending on their educational attainment. They nd that, in the presence of HIV, more educated women reduce fertility more than uneducated women. Analyzing the Malawi DHS surveys from 2000 and 2004, together with HIV rates obtained from
from urban areas, indeed 34 percent of older rural men in the 2006 KLoSA wave report receiving pensions. Nonetheless the continued labor supply of rural men is viewed as an important factor contributing to high rates of economic activity among older Koreans (Lee 2009). In spite of availability of pensions, levels of support are not sufficient to permit retirement of the rural elderly. Lee (2009) suggests that the out-migration of the young has left elderly who remain behind with both a lack of young labor and insufficient wealth to cease productive

> 17More specifically, in Korea, 34 percent of urban men have access to pensions and 15 percent of urban women, and in Indonesia, 27 percent of urban men have access to pensions and 10 percent of urban women. Figures on pension coverage are drawn from the 2006 Wave of the Korean Longitudinal Study of Aging (KLoSA) and the 2007 wave of the Indonesia Family Life Survey (IFLS).

10
, 2005), creating public works employment for women (Ghani, Mani and O'Connell, 2013), lowering crime against women (Iyer et al., 2012) and encouraging entrepreneurship among women in the informal sector (Ghani, Kerr and O'Connell, 2014).<sup>28</sup> Our work builds on such studies of gender differences in entrepreneurship and employment (e.g., Estrin and Mickiewicz, 2011; Minniti, 2010; Minniti and Naudé, 2010; Rosenthal and Strange, 2012). This strand of research basically argues that activating half of a country’s potential workforce can be a significant driver of economic growth beyond promoting gender equality (Duflo, 2012; World Bank, 2011).

Nonetheless, despite significant economic advancement since liberalization, the role of women in the Indian economy still lags well behind that of advanced economies (e.g., Mammen and Paxson, 2000; World Bank, 2008, 2012). Cross-country data from the World Bank Entrepreneurship Snapshots find that India’s rates of business ownership (overall, and female-to-male ratio) and female labor force participation are lower than its stage of development would suggest. This dual

> 25 See Goldberg et al. (2010) for a study on the impact of trade liberalization and product scope among formal Indian manufacturing firms.

> 26 Examples include Mammen and Paxson (2000), Dhaliwal (2000), Mitra (2002), Ghosh and Cheruvalath (2007), Amin (2010), Field, Jayachandran and Pande (2010), Pillania, Lall and Saha (2010), Jensen (2010), Verheul, Stel and Thurik (2006), Bruhn (2009), Munshi (2011), Kobeissi (2010), and World Bank (2008).

> 27 See Pande and Ford (2011) for a comprehensive review of the literature on gender quotas.

> 28 Ghani, Kerr and O'Connell (2013) provide a spatial analysis of gender differences