rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_6.1 The case of India_**

As mentioned earlier, in India, 35 percent of account holders report not using their account at a bank or similar financial institution for a single deposit, withdrawal, or digital payment in the past year. One potential contributing reason for India’s share of account inactivity may be the large number of accounts that were opened as part of the Indian government’s Jan Dhan Yojana program, which offered no-minimum balance, no-fee accounts to all adults with an Aadhaar biometric ID. Launched in August 2014, it brought an additional 450 million Indians into the formal banking system, most of them before the collection of the Global Findex 2017 survey data (Indian Ministry of Finance 2022). The launch and trajectory of Jan Dhan Yojana corresponds with a significant increase in inactive accounts in India.

India’s account ownership initiative is broadly viewed as a successful example of a government investing in financial access for development purposes. However, the high level of financial inactivity correlated with the program is a reminder that account ownership alone is not enough. Usage is also important.

Although there is no gender gap in account ownership in India, the gender gap in account use is high. Forty-two percent of female account holders in the country are inactive compared to 30 percent of male account holders, a 12-percentage point inactive account gender gap. In contrast, in all developing countries outside of India, women and men are equally likely to have inactive accounts. Moreover, regression results in Table 3, columns 1-2, show adults in India with inactive accounts are significantly more likely to belong to typically underserved groups, such as women, the poor, those less educated, and adults who are self-employed, unemployed or out of the labor force.

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of the existing teachers, which is the other robust result in the panel regressions presented in Table 5 (again, we use the most conservative estimate). Thus, the estimates in column 6 account for the fact that the marginal rate of absence from hiring an extra teacher is higher than the average absence rate, and that increased spending on hiring teachers is correlated with an increase in inefficiency.

The difference in the relative cost effectiveness of the two policy options is large enough, that the policy recommendation of hiring more supervisors rather than teachers (on the current margin) would be unchanged even if the supervisors were to work less efficiently than assumed in these calculations. For instance, if supervisors were absent at the same rate as teachers (say 25 percent), allocating marginal funds to hire an additional supervisor would still be nearly ten times more cost effective at reducing ESTR than using those funds to hire an additional teacher.

# **6 Policy Implications**

The main caveat to using our results to recommend a universal policy of hiring more supervisors to scale up the frequency of school inspections is that our estimates are based on correlations and may not be convincing enough to warrant a universal scale up. Nevertheless, it is worth noting that both our key results - the correlation between increased monitoring and reduced teacher absence, and the correlation between lower STR and increased teacher absence - are consistent with experimental evidence from smaller-scale, which increases our confidence in their validity. Further, our estimates are based on an expansion of existing system of inspections, and use nationwide panel data (which mitigates omitted variables concerns) representing close to a billion people, and complement results from smaller-scale randomized experiments warranting them greater external validity for several reasons.

First, while our results support results from smaller randomized experiments, there is evidence that experimentally-estimated positive results of interventions that are implemented by NGOs may not be replicated when the programs are implemented by governments (Banerjee, Duflo and Glennerster, 2008). Second, there is also evidence of site-selection bias where
the evolution of firm formation document that economic growth in both Canada and the U.S. is driven by new formal business entry rather than by the growth of existing firms (Brander, et al., 1998; Haltiwanger, 2009).

Earlier studies also show that entrepreneurship can foster competition and economic growth (Barseghyan, 2008; Klapper, et al., 2006; Djankov, et al., 2006; Black and Strahan, 2002; Hause and Du Rietz, 1984) and a reduction in informality (Antunes and Cavalcanti, 2009; DabiaNorris, et al., 2008). Previous cross-country studies have found that new firm creation is significantly related to country-level indicators of economic development and growth, the quality of the legal and regulatory environment, ease of access to finance, and prevalence of informality (Wennekers, et al., 2005; Klapper, et al., 2010; Ardagna and Lusardi, 2010).

Our paper is most closely related to the literature showing that costly entry regulations may impede the setting up of businesses and stand in the way of economic growth (De Soto, 1990; Djankov et al., 2002,Klapper, et al., 2006; Kaplan, et al., 2006; Bruhn, 2011). For example, a study of entry regulations across 34 European countries shows that onerous entry regulations are related to lower firm entry, specifically in industries with higher average entry rates (Klapper, et al., 2006).

Some studies, however, have cast doubt on the relationship between Doing Business measures and the real world business environment as perceived by actual business owners (Hallward-Driemeier and Pritchett, 2010). This paper helps to reconcile this recent skepticism with an analysis of the relevance of Doing Business measures using a real-world output: new firm registrations.

Importantly, this paper offers policymakers empirically-based insight into the impact of the reform process on new firm creation. For instance, insufficiently large reforms may not have 5
of the inheritance rule. Finally, I obtain the men’s total number of surviving siblings and an indicator for having at least one surviving brother nine months before the date of birth of each child ever born. If a brother was alive before or at the time of conception, even if he was dead at the time of birth, his presence could have influenced the decision to have a child. Appendix 1 provides summary statistics for the data.

> 8 Information on fertility preferences includes the ideal number of sons, daughters, and children of either gender; ideal total number of children and desire for additional children. Birth histories report the total number of live births; the age, sex, and survival status of each child, and the age and year of death of the deceased children. Sibling histories give a detailed account of the survivorship of all of the live-born children of the respondent’s mother (i.e., maternal siblings), the current age of surviving siblings, and the age at death and year of dead for deceased siblings.

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currency. Payments technology may a¤ect the use of domestic currency transactions accounts by residents. The monitoring of these accounts can in‡uence the decision to grant credit, independent of the denomination of the debt. Moreover, while changes in domestic currency payments technology may at the margin favour domestic credit over foreign currency credit, for lack of data we are unable to explore whether payment system reforms might have brought about changes in the currency composition of the credit extended.

> 14We will later test the robustness of our results to alternative numbers of lags.

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household demographic characteristics<sup>28</sup> and designate those with a higher likelihood of receiving NAF as ‘predicted beneficiaries’ until we reach the levels reported in the administrative records.<sup>29</sup> The value of the transfer received corresponds to the household report of NAF or MoSD cash income<sup>30</sup> in HEIS for the directly identified households and for the predicted beneficiaries who reported MoSD cash income.<sup>31</sup> For the remaining predicted beneficiaries, the benefit is calculated as JOD 45 per person per month up to a maximum of JOD 180, which corresponds to NAF’s basic benefit.

HEIS does not inquire directly about income from the bread compensation scheme transfer as the scheme was introduced in the middle of the survey collection,<sup>32</sup> so it is not possible to follow a direct identification method in this case. According to government figures, the bread compensation transfer reached 5.2 million Jordanian households, which corresponds to 75 percent of the households in the census. We allocate the bread compensation transfer to the poorest 75 percent of households in HEIS (ranked on their per capita expenditure), making sure to include NAF beneficiaries.<sup>33</sup> We adjust the benefit levels of NAF and the bread compensation transfer to match the administrative figures in the budget.

For smaller direct cash or near-cash transfer programmes (Zakat fund and other non-identified programmes), we use direct identification to determine whether a household received the benefit and to determine the magnitude of the amount received. This also applies to other unidentified social-security dues.

Allocating indirect subsidies: Water and electricity<sup>34</sup> To estimate the subsidy received per household we estimate first the quantity used based on the household reported expenditure for the utility,<sup>35</sup> and then matching the subsidy corresponding to this quantity. Of the households, 18 percent and 14 percent do not report
formal and informal (including subsistence) forms of work.<sup>3</sup> The data for baseline real output are from the World Bank’s _Global Economic Prospects_ database, supplemented with data from WDI for earlier years, spliced by sector using WDI’s sectoral gross value-added data. Productivity is calculated as the ratio of real output to the number of workers.

Total and working-age population come from WDI. The working-age population is defined as the number of people aged 15–64 years. Data on employment, real output, and population spans 1960– 2022 for 145 EMDEs. However, the analysis focuses on developments since 2000 because of limited data availability of policy variables for earlier years.

Data for the policy variables come from WDI, the World Bank’s _Women, Business and the Law_ database, the International Monetary Fund’s _Financial Access Survey_ , the World Bank’s _Enterprise Survey_ s, Bento and Restuccia (2021), and the Fraser Institute’s _Economic Freedom of the World_ (EFW) database.

# **3. First-stage regression results: Steady-state employment ratios**

The first-stage regression results in Table 1 provide clear evidence that EWAPs in EMDEs converge toward country-specific steady-state levels. The baseline regression results also suggest that slower productivity growth and faster working-age population growth have been associated with significantly faster increases in EWAPs in EMDEs. This is the case for aggregate EWAPs

> 3 For Nepal, adjusted employment data is drawn from Ruppert Bulmer, Shrestha, and Marshalian (2020), which also includes subsistence employment, consistent with the baseline employment measure.

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adjustment possibilities.<sup>7</sup> Also, these negative effects are larger for children who were exposed longer to the sanctions and children at crucial ages (high school dropout age and matriculation at a university). In particular, the years of schooling and probability of attending college significantly decreased by 0.4 years and 6.1 percentage points, respectively among cohorts whose more years of schooling career overlapped with the sanctions. Moreover, the economic sanctions decreased the enrollment rate at the high school by 4.3 percentage points among children at high school dropout age (16 years old) with a larger effect among girls and decreased the probability of attending college at age 18 (the average age of matriculation) by 15.4 percentage points. These effects worsen when children reach the age of 16 and 18 at a later stage of the sanctions period. A simple back of the envelope calculation shows that if these children were able to enroll in college at the same rate as college enrollment in the year 2006 and have the wage rates of the year 2006, their lifetime earnings would increase by 41%. Also, 45% of the costs to the society associated with the reduction in earnings comes from decreased earnings for the current workers, and 55% comes from decreased earnings for the next generation. It suggests that the cost estimates using only earnings of the current generation may only capture less than half of the overall cost.

Second, this paper examines the effects of the sanctions on investment in children’s education by looking at household spending on education. This paper results show that after the implementation of the sanctions,

> 7Acemoglu and Pischke (2001) find a 10% decrease in family income is predicted to decrease college enrollment by 1-1.4 percentage point. Other studies find even smaller effects, for example, Hilger (2016) finds a father’s layoff reduces children’s college enrollment by less than half of one percentage point, despite dramatically reducing current and future parental income (
social assistance jumps up in the second bimonth of 2018 in response to the Venezuelan influx and again in the fourth bimonth of 2020 as the response to the COVID-19 pandemic in addition to the influx of Venezuelan refugees and migrants. Civil defense (police) spending shows no change in 2018 and only increases in 2020. Figure 6 seems to suggest that the immediate consequence of the population shock is a temporary increase in social assistance expenditure as government of Roraima along with the Federal government spend more on welfare programs to accommodate the displaced Venezuelans but there seems to be no substantial impact on provision of public services.

Figure 6: Different categories of expenditure (Millions of Brazilian reais) by functions in Roraima.

<!-- Start of picture text --> Investment Personnel Spending<br>−20 −10 0 10 20 −20 −10 0 10 20<br>Education Health<br>−20 −10 0 10 20 −20 −10 0 10 20<br>Social Assistance Police<br>−20 −10 0 10 20 −20 −10 0 10 20<br>60 200<br>40 150<br>20 100<br>0 50<br>0<br>−20<br>40 80<br>60<br>30<br>40<br>20 20<br>0<br>10 −20<br>4 15<br>2 10<br>0 5<br>−2 0<br><!-- End of picture text --> Note: The dots show the bimonthly data on government expenditure by functions or categories in Roraima from year 2015 to 2020 and the x-axis shows distance from the second bimonth of 2018 in units of 2 months with 0 representing April 2018 and y-axis is in millions of Brazilian reais and is adjusted for inflation using national IPCA. The solid line represents the predicted values of a local linear smoother estimated using raw data on each side of the
50|0.40|0.54|0.41|

_Source_ : See Table 1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. These regressions are the same as Table 2 in the paper, with import and export price series data replacing the quantity measures used in our core specifications. Prices on imports are taken from data provided to the authors by Robert Lawrence at the 4-digit NAICS level, and converted to our consistent industry classification for the CPS MORG. Export price data is taken from the Bureau of Labor Statistics. Details are available in the data appendix. Sample sizes differ from Table 2 due to more limited price series data on imports and exports than on quantities.
All prices from official Government price change announcements obtained from East Asia adn Pacific Country Programs Department, Philippines Division, World Bank and EGYD1, World Bank. Fuel oil price is wholesale. January 1986 prices are pre-election price reductions. Average product prices before the price change of 25 January were 60 Centavos/liter higher. January 1986 prices converted to $US at 19.1P = $1; June 1986 prices at 2.05 P = $1.

7/ SOURCE: Eastern and Southern Africa, Country Programs Department, South Centrall Division, World Bank. All prices are Reference Prices (without distribution margin) in Kinshasa and are, therefore, retail prices in Kinshasa. Diesel prices are for "gasoil" in Zaire. January 1986 prices coverted to $US at 55.1 = $1; August 1986 prices at 58.75 Z = $1.

8/ SOURCE: 1985 prices from Kenya Solar Water Heating Project Green Cover, March 1986, World Bank. July 1986 prices were announced in the June 1986 Budget, Government of Kenva. Diesel fuel prices for "gasoil" in Kenya. Industrial diesel prices are for 2500 sec fuel oil andi tuel oil prices are for 1000 sec fuel oil. November 1985 prices converted to $US at 16.5 Ksh = $ 1; July 1986 prices at 16 Ksh = $1.
# **3.4.2 Exposure to increased cost of imported inputs**

Alongside potential protection of US industries, tariffs may have the additional negative impact of increasing the cost of inputs to these or other US industries. We calculate this exposure by taking the output measure from above and, using US input-output tables, weighting it by the share this ‘input’ industry makes up in all of the ‘output’ industry’s inputs.<sup>12</sup> Specifically, the measure is calculated as follows: = <u>1</u> input tariff exposure _kt_ L _k_ 2015 � _j_<sup>_Sjk_</sup> � _cp∈j_<sup>US imports</sup> _pc_ 2016<sup>_×_US tariff</sup><sup>_pct_</sup> (3) where _k_ is the output sector, _j_ is the input sector (6-digit BEA level), and _Sjk_ is the inputs from sector _j_ as a share of total inputs used by sector _k_ sourced from input-output tables. In other words, this variable captures the cost of tariffs on inputs per worker employed in the output industry.

Figure 3 provides a map of the input tariff exposure measures once weighted by employment composition as discussed in Section 3.3. As for the output tariffs, the map shows a concentration in the East with lower exposure in the Great Plains and West, but with a different pattern of variation within these regions.

# **3.4.3 Exposure to retaliatory tariffs**

In response to the imposition of US tariffs, a number of countries implemented retaliatory tariffs on US exports to their countries (see Table 1 for an overview). A sectoral exposure measure can be constructed as for output tariffs, but using foreign tariffs and US exports instead of imports: where US exports _pc_ 2016 is the value of US exports of product _p_ (HS-6) to destination country _c_ in 2016, and Foreign tariff _pct_ is
The use of PSM and longitudinal data are two features that distinguish the present study from that of Trivedi (2003), who used regression analysis on the 1997/98 cross-section data.  The present study also looks beyond health care use and health care spending.  There are also differences in variable definitions that further make comparisons rather difficult.  For example, Trivedi defines annual health spending as the sum of spending in the last 4 weeks and spending in the last 12 months, whereas we treat them as two separate variables, assuming that one measures spending in the last month and the other spending in the last year.

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cyclicality of the current account for the two subgroups and test whether they are significantly different.<sup>24</sup> Figures 2(a)-2(d) present the results of this robustness check for various subsamples of the data. In each figure, the bold (solid) line plots the cyclicality of the current account in debtor (creditor) countries for the corresponding level of adjusted debt at which the sample is divided in two, indicated on the x-axis. The dashed line reports the p-value for a test of the null hypothesis that the cyclicality of the current account is equal in the two groups. Figure 2(a) uses data for all countries over the entire period from 1971 to 1993, and reveals that current accounts are clearly procyclical in creditor countries and countercyclical in debtors. Moreover, for a wide range of values of the threshold, this difference is significant at the 5-10 percent level. Figures 2(b),(c) and (d) present the same information for three subsamples of the data. Figures 2(b) and 2(c) restrict the sample to the 1971-81 and 1982-93 subperiods respectively, and reveal that the difference in current account cyclicality is much more pronounced in the latter period. However, if we drop the two years following the 1973 and 1979 oil shocks from the 1971-82 subperiod, as is done in Figure 2(d), the pattern we emphasize re-emerges.

In the theory developed above, output shocks are saved in both debtor and creditor countries, while the differential current account behavior in the two sets of countries arises from the the differential investment response to output shocks. In debtor countries, a fraction greater than one of these savings is allocated to domestic capital, while in creditor countries a this fraction is smaller than one. The 24 We remove country means from all variables before computing the correlations. We test for equality of current account cyclicality in the two groups as follows: First, we regress the current account on per
to soften for roughly 6 months, and then gradually recover as the easing takes effect with a lag. By three years after the start of the phase, the mean and median measures of activity and the labor market generally settle back around where they began one year before t=0, except that GDP and IP growth settle slightly below their starting points after a tightening phase. These results are broadly consistent with the forward-looking and counter-cyclical nature of monetary policy, as well as with estimates of the length of transmission lags.<sup>22</sup> The means and medians follow very similar patterns—suggesting any skews in the distribution are roughly balanced between positive and negative outcomes.

The evolution of inflation (both headline and core) displays notably different patterns. The pickup in inflation before the start of tightening phases, and deceleration before the start of easing phases, is more muted than occurs for the other macroeconomic variables. After interest rates are increased (at the start of tightening phases), mean inflation continues to pick up for longer than 6 months and only declines very gradually; median inflation barely moves. After interest rates are cut, mean and median inflation continue to fall for an extended period, but only very slowly.

> 20 Macroeconomic data was collected on 31 May 2024 and covers the period from January 1970 through March 2024 for most countries.

> 21 All variables are measured relative to a year earlier, with the unemployment rate measured as the change (instead of percent change). Data for some variables is limited early in the sample. We only include economies in the calculation of the sample statistics if the relevant data is available for at least 6-months before t=0.

> 22  See Clarida, Gali, and Gertler (1998), Woodford (2003), and Lane (2022), among many others, for a discussion of these topics. Havranek and Rusnak (2013), using a meta-analysis based on 67 studies, reports that the average transmission lag of monetary policy to
and more prudent macroeconomic reforms and policies have been implemented. However, if and how these improvements translate into increased trust among investors is debatable according to perception data from domestic investors and the limited FDIs recorded. Not least, the low governance efficiency in Togo has been traced to mismanaged SOEs and lack of public investment execution. As with several of the measures, this may be more connected to previous governments and whether more sustainable and thorough reforms happen in the future remain to be seen.

Governance issues are also related to investor trust in business‐government relations, which according to the analysis is an obvious binding constraint Togo needs to address. Foremost, _barriers and costs of doing business_ are high, especially when starting a business, and compare poorly to countries at the same development level. Even though the over‐arching issue of doing business is critical to tackle, priorities are important. For instance, tax 56