rafmacalaba/data-use-mentions-extended huggingface dataset:
One aspect of these markets that we had not anticipated was the significant churn in the private sector. Of the 301 private facilities in the control group operational at randomization, 57 (19%) had exited by August 2018 and 55 (15%) new facilities had entered. These closure rates far exceed the 8.2% reported by McKenzie & Paffhausen (2019) for small firms in LMICs. In our 2015 census itself, we were able to identify 202 (21%) facilities from the government master facility list in February 2015 that were no longer operational, and 379 (40%) facilities that were not part of the 938 facilities listed in the government records.

A second key feature of our data is the close link between the JHIC score, licensing status and market outcomes, which shows up in every aspect of facility performance. In the private sector at baseline, the JHIC score for unlicensed relative to licensed providers was 21% lower.<sup>19</sup> JHIC scores and licensing status were also strongly correlated with facility exits in the control group, with a 1 SD increase in the JHIC score (9.6 percentage points) associated with a 7.7 percentage point decline in exits (Table 2). However, facilities that exit the market by endline tend to be small and represent only 3% of all patients in the data (Table S9 of the Supplemental Material).

Column 1, Table 2 then shows that a 1 SD increase in the JHIC score (12.1 percentage points) was correlated with an increase of USD 1.9 (PPP) in out-of-pocket (OOP) payments per visit, a correlation that remains robust to the inclusion of machine-selected controls. As is well understood, this association between prices and the JHIC score does not identify the structural hedonic parameter in the presence of patient sorting. While we cannot address patient sorting fully, we can assess the sensitivity of our estimates to select features of the patient population in each facility,
- Start of picture text --> 110%<br>90%<br>70%<br>50%<br>30%<br>10%<br>-10%<br>-30%<br>2019 2020 2021 2022 2023<br>Gasoline Diesel Kerosene LPG<br><!-- End of picture text --> Source: authors’ estimations based on IGAPE As in many oil-producing countries, Angola's government provides substantial subsidies for fuel consumption. In 2011, fuel prices in Angola were 67 percent lower than the average price in SubSaharan Africa, and among the lowest in the world, due to tax and duty exemptions on petroleum products. With the decline in oil revenues from 2015 onward, the government started a strong fiscal consolidation that left fuel subsidies as the only de-facto social spending, while spending on health education, social protection, and water infrastructure declined. In end-2015, the government removed gasoline and diesel subsidies by increasing retail prices, but subsidies reemerged as soon as market 6
. When nights lights are used, fixed effects for either OLS (average over DMSP satellites; pre-2014) or VIIRS (2014-2020) are included. Robust standard errors, clustered by country are shown in (parentheses) and _p_ -values in [brackets]. R-Squared is adjusted and within fixed effects. Three countries, Cura ̧cao, Palestinian Territory, and Sint Maarten, are ungraded.

and Nordhaus (2011). On the other hand, the effect of NO2 is statistically significant across all grades. We reject the null hypothesis that the effect is the same across-grades for lights with a _p_ -value of 0.053, but fail to do so at any reasonable significance level for NO2. NO2 is the only signal which consistently has explanatory power for cyclical economic fluctuations—even when other signals are available—across all countries, and with a common effect size.

**Data Manipulation.** Martinez (2019) uses night lights to quantify the global extent of data manipulation in GDP. This is achieved using the Freedom in the World (FiW) index of autocracy to augment the error-measurement framework for proxy estimation.<sup>16</sup> Building on this insight, the FiW index is now added to the baseline specification in Table 2 in levels, squared, and as an interaction with lights. The effect on the interaction, known as the autocracy gradient, is the key parameter. When positive, this is indicative of data manipulation, because GDP increases more per unit increase in light radiance in autocracies than in democracies, and there is no reasonable alternative explanation but manipulation.

Table 4 Column 1 replicates Martinez (2019)’s results for annual fluctuations over 19922008. The interaction between lights and the FiW index is positive and strongly statistically

> 16Freedom in the World (FiW) index: `https://freedomhouse.org/report/freedom-world` .

18
introduce the professional civil service gradually, while eliminating the worst cases of corruption with his new personnel statute.

In Mexico the SAT’s Fiscal Career Service (SFC), intended to be a merit-based system, did not quite get off the ground during the initial reform period. Due to union opposition, entry into the SFC was made optional for existing employees and mandatory only for new employees. In addition, the SFC was to cover only a certain percentage (unspecified) of employees; that is, there would still be a category of “freely designated” employees, who would not have to fulfill any SFC requirements. This category not only included high level managers, but also specialists and technical professionals.

Survey data on the reforms in Latin America are consistent with the above evaluation of the extent of personnel reform. Indeed, the differences are highly pronounced. Perceptions of SUNAT’s and SENIAT’s human resources are much more favorable than those of the SAT’s. Eighty five percent of Peruvian respondents said that tax agency employees are _Frequently_ , _Mostly_ , or _Almost Always_ more qualified professionally than other public sector employees, as compared with 75% in Venezuela, and 52% in Mexico. In terms of the impact of the reform, 81% in Peru and 61% in Venezuela said tax agency employees were _Substantially_ or _Much More Qualified_ now than before the reform, as opposed to 16% in Mexico.<sup>20</sup> The African cases also show a mixed record in personnel management, from Kenya, where the reforms were consistently moving in the right direction, to Uganda, which showed forward as well as backward movement. The advent of the KRA is widely regarded to have increased the professionalism of the tax agency’s staff and reduced, though not eliminated, the level of corruption. KRA improved staff quality through a number of measures. The KRA increased salaries in real terms, which was a break from the previous salary policy, and actually reduced the total number of
Brown et al. (2011) for a somewhat larger sample of BEEPS firms.<sup>30</sup> This is also confirmed by the responses to the 2008-9 survey for firms that had a loan in 2005. Only 37.9% of small firms that had loans in 2005 also had a loan in 2008-9. For large firms, 76.2% of firms that had a loan in 2005 also had one in 2008-9, suggesting greater persistence in the lending relationships for large firms.

There was also persistence among small firms that reported having no need for a loan. Sixty-five percent of those that reported no need in 2005 also reported no need in 2008-9. Large firms showed less persistence on that measure. Only half that reported no need in 2005 also reported no need in 2008-9; 41.7% of them reported that they had a loan in 2008-9 while 8.3% reported that they applied for a loan but were rejected. A relatively high share of the large firms that had loans in 2005 had their loan applications rejected in 2008-9 (14.3%). In comparison, the share of small firms that had their loan applications rejected in 2008-9 was only about 7%, a figure that was similar regardless of the firms‘ credit status in 2005. This pattern is also consistent with the notion that small firms were unlikely to apply for loans if their owners felt the probability of rejection was high. It is also telling that a large share (22-37%) of small firms was discouraged from applying for loans in 2008-9 regardless of their credit status in 2005. By contrast, no large firm reported being discouraged in 2008-9.

In all, the patterns suggest strongly that small firms had substantially less access to credit than large firms, and were therefore less willing to apply for loans, both in 2005 and 2008-9. By contrast, large firms, many of whom reported having no need of a loan in 2005, turned to banks as a response to the crisis in
data provides an opportunity to expand the use of the hedonic pricing approach to evaluate the benefits of urban resilience and infrastructure investment in countries that rely heavily on household survey data and where hazard risk and property value data may not be readily available. The household survey data are complemented by information on access to jobs.

> 1See Beltr ́an et al. (2018) for an extensive review of the effect on property prices of housing location in a flood plain.

3
>a<br>02 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Reduced Coping Strategies Index (inverted and standardized)<br>6 months 12 months 18 months<br>co / 4 4<br>/<br>/<br>= cect” / /<br>aN a eat / y<br>~<br>-“7 “Ts. S a YY ~S<br>° rey ° —<br>- poe eer NUE<br>NL es<br>_-<br>~\<br>\ \ “é“<br>\Z \<br>2 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Per capita consumption expenditure<br>6 months 12 months 18 months<br>8 8 8<br>7<br>a“ uc<br>Ig Uc / /<br>° > ed_ —e 7 =<br>ee Uo uo a“<br>7 “ o* ~ ‘<br>A / eo -7 YN .<br>yaa “ \<br>Y \<br>\<br>\<br>g 8 8<br>“0 2 4 6 8 1'°0 2 4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Livelihoods Coping strategy Index (
to the users beyond an equilibrium price can only achieve this goal by reducing production and transportation cost and this amounts to an outward shift of the supply curve. Thus, in principle, there can be no conceptual distinction between production and consumption subsidies. That said, differential gains of both users and producers from a subsidy can certainly be assessed, as was illustrated in Figures 3 and 3a.

Our empirical methodology, detailed below, is supplemented with a scenario-based forecast of countries using the World Bank-IMF joint analytical tool, Climate Policy Assessment Tool (CPAT), which is an Excel based large macro accounting tool calibrated to detailed country-level data. Although CPAT is a partial equilibrium tool, this limitation is overcome by the consistency that we find between our empirical results and the CPAT scenario-based forecasts, at least in terms of focus on CO2 emissions. This is confirmed in nearly all cases. Beyond that, however, the use of this methodology allows us to compare the effectiveness of a possible 12
.9535242|86.44217391|15.6740974|11.87686787|43|
|Slovak<br>Republic|5382449|14989.53|8776060928|27744734233|29861579419|..|89.3970894|30.54339454|7.548500882|27|
|Slovenia|1997000|22132.83|7092050944|16361365166|17758527740|95.54712377|190.9126156|48.77266733|3.589026796|25|
|1Number.<br>2Constant 2005 in<br>3Constant 2000 U<br>4Current US$.<br>5% net.<br>6Km of road per 1<br>7% of GDP.<br>8Annual %.|ternational $.<br>S$.<br>00 sq. km of land ar|ea.|||||||||

Source: DDP Quick Query database of WDI & GDF, World Bank.

6
explained by an increasing urbanization rate. Even if the demand for housing is projected to be flat post2015, it is still increasing at a low rate.

**Figure 3.3:** Annual Housing Demand Evolution in SSA, 1965-2050 (projection)

<!-- Start of picture text --> 6,000<br> 5,000<br> 4,000<br> 3,000<br> 2,000<br> 1,000<br> -<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045<br><!-- End of picture text --> _Source_ : Authors’ calculation using the World Bank database on housing finance launched by Badev et al., 2014: “Housing finance across countries: New data and analysis,” WPS6756.

Many initiatives have been taken in Africa to support the development of the mortgage market. For example, the recent creation of national and regional mortgage refinancing institutions in Nigeria (2013), Tanzania (2011), and the WAEMU (West African Economic and Monetary Union) sub-region (2010) will improve mortgage loan granting. There potentially may be a way to improve household access to long-term liquidity through developing mortgage markets, and at a reasonable cost. Unfortunately, as shown in figures 3.4 and 3.5 below, interest rates are too high in almost all African countries, which undermines mortgage market accessibility.

**Figure 3.4:** Mortgage Market Access and Housing Finance Policy in SSA.

**Figure 3.5:** Mortgage Penetration and Housing Finance Policy in SSA.

<!-- Start of picture text --> 70 8<br>CAP MAS SOU<br>60 SOU 7 DJI CHATOG MAL<br>50 SEN 6 11.708 MAU<br>MAR<br>40 ANG<br>GUB 5<br>30 NAM LIB<br
individuals who have not moved.<sup>22</sup> The most recent round has 50583 observations and 13507 households. The analytical work below uses detailed information collected from 7283 direct respondents aged 45 and over for whom information on pension eligibility was enumerated.

**_Korea_** . The Korean Longitudinal Study of Aging (KLoSA) is a nationally representative survey conducted by the Korea Labor Institute to investigate the overall conditions of people aged 45 or older in South Korea (Excluding Jeju island). KLoSA belongs to the same family of aging surveys as CHARLS, HRS and ELSA. The first baseline survey was conducted at 2006, and it was followed by a job history survey in 2007. While KLoSA is a longitudinal survey, only the baseline survey is publically available. In common with CHARLS and ELSA, but differing from the HRS, the first baseline survey doesn’t cover the institutionalized elderly. Overall, 6171 households and 10254 individuals participated in the baseline survey, and there are 10030 observations with complete information.

# **A Model of the Employment of Older Adults**

Below, we examine determinants of the labor supply (employment) of older workers, recognizing that important correlates, such as access to pensions, may be systematically related to both the ability to retire and unobservable characteristics (e.g., ability). While these models should be viewed as providing descriptive evidence, we choose measures of health status, and proxies for wealth and family characteristics with the aim of minimizing endogeneity biases.

As a framework for understanding the “retirement” and labor supply decisions of the elderly, assume that individuals (or households) maximize utility subject to a family budget constraint, which is a function of wealth, income, available time, health status and non-labor income of household members. From the constrained utility maximization decision, we conceptualize a general model of labor supply:

> 22Even though older residents of both this survey, as well as the CHNS in China, do not move or split off
coagglomeration index between sector _j_ and all other sectors that receive zero central excise tax incentives (or not treated). Everything else has the same meaning as in Equ.(3).

The coefficient on _Target_ now measures the direct effect of non-central excise tax incentives on the employment outcomes of all sectors, and the coefficient on the interaction term _Target X Treat_ measures the direct effect of central excise tax incentives on the treated sectors. Meanwhile, the coefficient on T T T × E captures the indirect impact of the New Industrial Policy through industries’ tendency to collocate with treated sectors. And the coefficient on TT T T T × E captures the indirect impact mm mm through industries’ tendency to collocate with nontreated sectors. NNTT mm mm One disadvantage of the coagglomeration index is that it does not distinguish between colocation tendency due to agglomeration forces from that caused by natural advantages. To better assess the impact of agglomeration forces, we construct indices to measure two important Marshallian mechanisms that can increase industries’ tendency to locate together and give rise to agglomeration, namely, inputoutput linkages and labor pooling.

The index on input-output linkages measures the extent that two sectors buy and sell from each other. It is computed from Input-Output accounts for India. Industries that serve intermediate inputs in the production processes of other sectors can minimize transportation costs by locating close to one another. In such a case, the growth of a central excise tax-incentivized sectors in Uttarakhand can induce other sectors linked through the value-chain to locate within the state.

The index on labor pooling captures the extent to which two sectors share the same type of workers. The information is based on sectors’ occupational characteristics reported by labor force surveys of India. Industries that require similar types of workers can also choose to locate in the same labor market to reduce matching costs and tap into suitable labor force in the market. In this case, the growth of a central excise tax-incentivized sectors in Uttarakhand can induce other sectors requiring similar types of labor to locate within the state.

Using these
in a given country-sector is not exogenous, the identification of causal effects is impaired by the fact that macro data do not allow for determining if private lending would have happened even without MDB involvement. Our approach, based on more granular loan-level data, has the advantage of absorbing all time-varying country- and sector-specific factors which could drive MDB and private sector lending via a large set of fixed effects. In particular, by exploiting country-sector level data, we are able to control not only for country- and year- fixed effects, but also for countrysector, country-year, and sector-year fixed effects; hence, we greatly diminish the possibility of omitted variable bias and increase accuracy in the estimation of the mobilization effects.

Our results—based on regressions at the country-sector-year level—indicate that the volume of syndicated lending, the average number of lending banks per loan, and the average loan maturity increase in the years following the presence of a syndicated loan with MDB participation in a given country-sector pair.

To mitigate concerns of reverse causality, we show (i) descriptive evidence that MDBs tend to be among the first to enter a given country-sector, and (ii) formal evidence of the lack of anticipation effects. Then, we run a number of robustness tests to address lingering concerns about the omitted variable bias, which could potentially affect our results, as long as there are confounding factors driving both MDB and private bank lending. In particular, we show that including a large set of confounding factors in the baseline model—the presence of the largest global banks, Chinese lending, aid flows, corporate bond issuances, and value added growth in the country-sector pair— does not affect the significance or the size of the estimated MDB mobilization effects. In addition, we control for sector-specific linear and quadratic trends and identify the effects in deviation from trend, and we further control for unobserved heterogeneity computing the Oster (2019) bounds.

We perform a number of extensions. First, to have a better sense of the macroeconomic implications of our
number of stores the previous year, (4) reallocation rate, the sum of entry and exit rates, and (5) log count per population, the log of the total number of stores per population. Second, we use County Business Patterns (CBP) data from the Census Bureau to obtain two additional annual outcomes at the county-level: (6) log establishments per population, the log of the number of establishments per population, and (7) HHI, the sum of squared market shares calculated using employment size as a measure of firm size.<sup>67</sup> We then estimate equation (3) at the county-month level using the seven outcomes, again using data from 2007 to 2010 to focus on the periods in which SNAP benefits were increased.

As shown in Appendix Table N11, the estimated effect on entry and exit is positive but almost all of the estimates are statistically insignificant except the reallocation rate, suggesting that SNAP-benefit changes had little impact on entry and exit margins and market structure. A 10% increase in benefits per population increases the reallocation rate by 0.01, compared to a mean of around 0.17 for grocery stores in the SNAP panel. One possible reason for these small effects is that most grocery stores already accepted SNAP before our sample period. Many non-grocery stores also started accepting SNAP at the chain level over this time period so the competitive landscape may have changed in ways not fully captured in our analysis.

> 66Data from the Economic Census also show the grocery store industry is much less concentrated and less dominated by chains than the drug and merchandise store industries. For example, market share of the 4 largest firms is 30.7%, 54.4%, and 73.2% for grocery stores, drug stores, and merchandise stores respectively in 2007.

> 67Since the CBP only provides employment size in 13 size brackets, we use the mid-point of each bracket to construct the HHI.

57
# **9. Impact on the general consolidated budget**

The impact on the budget was assessed by taking into account the effects of the increase in minimum wage on the revenues side (first-round effects, but also second-round effects) and on the expenditure side (for more details see Annex 4).

Between 2012 and 2016, the net impact of the increase in the minimum wage on the general consolidated budget was a positive one, between 3.4 million RON in 2012 and 1,362 million RON in 2016. The impact of the measure on the revenues side was between 56 million RON in 2012, when the minimum wage increased by almost 5% and 1,468 million RON in 2016, when the minimum wage increased by around 19%. The impact on the revenues side has significantly increased in 2015 as the number of employees from the private sector that were influenced by the measure grew by more than 40%. This trend continued in 2016.

_Figure 34. Evolution of the minimum wage impact on the budget_ <!-- Start of picture text --> thousands RON<br>1600<br>1468.3<br>1431.7<br>1400 1250<br>1200 1050<br>1000 900<br>800<br>800 700<br>600 493.8<br>400 298.1<br>175.8 178.4<br>200 106.0<br>56.3 52.9<br>0.0<br>0<br>2012 2013 2014 2015 2016<br>on revenues on expenditures minimum wage level (end of year)<br><!-- End of picture text --> Source: Authors, based on MPF, Substantiation notes of the Government decisions, NBR calculations On the expenditure side, the impact varies between 53 million RON in 2012 and around 106 million RON in 2016. The maximum impact on the expenditure side was registered
annual investments and does not provide actual FDI flow data. As a result, we have determined annual “flows” by taking the difference in cumulative FDI from one year to the next. We have then compared this to FDI stocks data from UNCTAD and again taken the difference in reported stocks from one year to the next as a proxy for the annual FDI flow.

> 10 Data in columns 2 and 3 are the average over the period 2005‐2007.

> 11 In the case of Senegal, data is only available on the number of newly‐established “EFE” firms each year; no data is available on the number of these firms which are operating actively at any one time – thus the actual number of active firms will be somewhat smaller than what is reported here.

12