rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Firms’ Access to Finance**

Previous literature using global firm-level data finds that SMEs face greater financing obstacles than large firms and small firms use less external finance, especially bank finance.<sup>6</sup> Improvements in the functioning of the formal financial sector can therefore reduce financing constraints for small firms and others who have difficulty in self-financing or in finding private or informal sources of funding. To analyze firms’ access to finance in Africa, we use data from Enterprise Surveys<sup>7</sup> , which cover more than 130,000 firms in 127 countries. Data are collected across the world using consistent questionnaires and sampling methodology. Data is typically collected on a 3 to 4 year rotation in each major World Bank region.<sup>8</sup> On average, percent of enterprises in African countries with a bank account (across all firm size groups) is comparable to or greater than the percent of enterprises with a bank account in all other developing economies. For instance, 83% of small and 94% of medium sized enterprises in Africa report having a bank account as compared to 87% of small and 93% of medium sized enterprises in other developing economies.

Yet, firms in Africa have notably limited access to external funding.<sup>9</sup> Enterprise Surveys data show that on average, only 22% of enterprises have a loan or a line of credit. In comparison, the average of enterprises with a loan or a line of credit in other developing economies excluding Africa is 43%. Like elsewhere, we find that small firms in Africa are at a relative disadvantage in accessing external credit (Figure 7). In Africa, 45% of firms in Africa cite access to finance as a major constraint to growth. However, a higher percentage of small firms report access to finance as a major constraint relative to medium and large enterprises.

> 6 See, for example, Beck, Demirguc-Kunt &
# **5.3** **_Real incomes_**

The SILC survey collects detailed household data on disposable income. Figure 7 and Figure 8 display the evolution of real incomes between 2011 and 2018 for the 1st and 10th deciles as well as the average income and the overall CPI.<sup>13</sup> All the numbers are converted into real values using decile-specific inflation rates and normalized to 100 using baseline values as a reference. The graphs confirm the inclusiveness of economic growth in Turkey during the 2010s. The incomes of households from the bottom of the distribution expanded faster than the incomes of the wealthiest households and also faster than the average income. However, the pace of real income growth slowed down as Turkey entered a new period of high inflation in 2017. Between 2011 and 2016, the annualized real growth rate of 1st decile income was 6.3%. Between 2016 and 2018, the corresponding figure dropped to 1.2%. Nominal income figures hide the slowdown since nominalbased growth was similar across the two period (15.2% for the years 2011-2016 and 14.9% for the period 2016-2018). In other words, incomes have been increasing at a similar rate, but this has not translated into stronger purchasing power for the 1st decile after 2016.

Figure 7: Average and 1st decile real income (2011-2018, 2011 =100) <!-- Start of picture text --> 150 18<br>16<br>140 136.0 135.1<br>139.3 14<br>126.7<br>130 12<br>116.5 10<br>120 114.1<br>8<br>107.6<br>110 6<br>100<br>4<br>100<br>2<br>90 0<br>2011 2012 2013 2014 2015 2016 2017 2018<br>1st Decile Overall CPI<br>Real Income
methods are in are in close accord, at least in the near term. Trajectories for poverty in 2015 by the second method are reported in the 2012 _Global Monitoring Report_ (GMR), drawing on the Bank’s country-specific growth projections. This method gives a poverty rate of 16.3% for 2015 (World Bank, 2012a).<sup>16</sup> This is very close to that implied by the first method, namely 15.9%. Table 1 includes the forecasts by region for 2015 from the GMR.

The proposed “optimistic trajectory” for 10 years ahead is the simple linear projection, implying a benchmark of 9% for 2022. There are a number of points to note about this trajectory.

> 15 The Bank’s current population projections in millions for the developing world, as defined in 2012, are 5884 in 2012, 6230 in 2017 and 6561 in 2022. Using the 1990 classification of developing countries (to be consistent with PovcalNet) the numbers are 6009, 6355 and 6687 respectively. This difference does not change the poverty counts reported in this paper at the stated number of significant figures.

> 16 These forecasts factor in historical gaps between growth rates in the survey means and growth rates in private consumption per capita in the national accounts. For most countries, about 90% of the national accounts growth rate is passed onto the survey means, but for India it was only about half, consistent with the larger gap between the two growth rates for India. In addition an allowance is made for rising inequality in both China and India. For further details see World Bank (2008).

7
the ‘best possible life’ evaluations in the daily Gallup Poll appear to be affected more by Valentine’s day than by the doubling of unemployment in the United States.<sup>5</sup> On the other hand, subjective well-being measures have been shown to exhibit consistent patterns across surveys and regions. For instance, Diener et al. (1995) examined four subjective well-being surveys in a total of 55 countries with a combined population of 4.1 billion people and a total survey sample of 100,000 respondents, and found “strong covariation among surveys, despite different years, sample populations, wording, and response formats." Using data from the first three waves (2006-2008) of the World Gallup Poll, Helliwell et al. (2009) find that international differences in life evaluations are due to differences in life circumstances rather than differences in structural relations between circumstances and life evaluations. As they note, the “[a]pplication of the same well-being equation to 125 different national societies shows the same factors coming into play in much the same way and to much the same degree.” In other words, international differences in subjective well-being are found not to be driven by different

> 2 Frey and Stutzer 2002, Dolan 2006; 2011; Graham 2010.

> 3 Subjective assessments of welfare began to be included in the World Bank’s Living Standards Measurement Study (LSMS) surveys in 1993.

> 4 For instance, the possibility that the respondent may be influenced by the preceding questions, or by the type of organization implementing the survey (see Dolan 2011).

> 5 In fact, subjective well-being data have been found to be reasonably stable over time, although the degree of stability is somewhat lower for life satisfaction measures than for measures of affect (Lucas and Diener 2008). This stability is partly the result of stable personality traits, and partly the result of adaptation to events.

2
# **1 Introduction**

The corporate income tax is a key source of government revenue, particularly for developing countries where it constitutes 15-20% of tax revenue. Yet, a common perception is that many firms, and especially large corporations, increasingly pay lower taxes on their profits. This view is fueled both by a race to the bottom in statutory tax rates and by increasing competition between governments to offer tax incentives to attract investment, such as tax credits, income exemptions and reduced rates. In this paper, we document how the take-up of tax incentives lowers effective tax rates and To describe effective tax burdens, we leverage a unique dataset comprising all firm-level corporate income tax returns in 13 developing countries spanning a wide range of income levels. On average, we find that countries spend a substantial amount — 0.9% of GDP — on corporate tax incentives, consistent with the common labeling of these incentives as ‘tax expenditures’. The benefits of tax expenditures accrue disproportionately to both small firms and very large firms (top 1% in sales). In the median country in our sample, 30% of the largest 1% of firms face effective tax rates (ETRs) below 15%, the minimum tax rate agreed upon by the OECD/G20 Inclusive framework’s Pillar II. Given the incentives that a global minimum tax could create, we estimate that corporate tax revenue could increase by up to a quarter in the median sample country.

While tax evasion and profit shifting have been extensively studied before (e.g. Carrillo et al. 2017; Tørsløv et al. 2022), tax expenditures have received less attention. This is in part due to limited access to high-quality microdata. Our first contribution is to develop a consistent measure of effective tax rates across multiple countries, relying on comparable data sources and definitions. The data cover 13 countries with varied income levels and population sizes in Africa (Ethiopia, Eswatini, Rwanda, Senegal, and Uganda), Latin America (Costa Rica
<!-- Start of picture text --> Spain (1999)<br>4 .<br>a.<br>6<br>0.0 20 40 6.0 8.0<br>Debt-to-Own Revenue Ratio<br><!-- End of picture text --> <!-- Start of picture text --> Spain (2006)<br>a<br>g 8<br>6<br>0.0 0.5 1.0 1.5 2.0<br>Debt-to-Own Revenue Ratio<br><!-- End of picture text --> <!-- Start of picture text --> Canada (1996)<br>ce ene<br>a 8 om<br>6 ~<br>0.0 10 20 3.0<br>Debt-to-Own Revenue Ratio<br><!-- End of picture text -->
, and consistent with our survey data, incidence did not significantly change in rural settlements, where non-solid dwellings are substantially more common. Moreover, while the campaign had no impact on the number of tests conducted to diagnose malaria, the urban positivity rates (positive cases/tests) decreased by 2 p.p. and the effect is null in rural areas. These results are robust to different model specifications and estimators.

Overall, our evidence indicates that the MNM campaign benefited households residing in solid dwellings and in urban settlements, but less so those that are particularly susceptible to malaria. The potential cause behind this distinct effect could stem from two factors: (1) content effectiveness (where, upon receiving the ad, it only influenced households residing in solid dwellings) or (2) targeting (the campaign did not sufficiently reached households living in non-solid dwellings). To discern which mechanism might underlie this effect, first we consider ad recall outcomes at the end of the campaign. If the ads reached both solid and non-solid dwellers at similar frequencies, then one would expect both audiences to equally recall them. Yet, we find that ad recall is larger only among treated individuals in solid houses, suggesting that non-solid dwellers were not sufficiently exposed to the ads.

To corroborate this mechanism, we nest an individual-level _feed_ experiment within our crosssectional sample. We use Facebook’s Custom Audience tool to directly retarget ads to a randomly selected half of our survey respondents. Over a 2-week campaign, we reached individuals living 4
treatment. To create a counterfactual for the subgroup of households with children attending the main school in the treated districts, we also chose a subsample clustered around the main schools in the districts of the control group to serve as a control group for the school intervention.

In what follows we assess the impact of the two treatments: mass media and community on a wide range of outcomes and we also study separately the school level component in the community intervention. We will refer to the households employed to explore the community treatment as _community sample_ and to the households used to investigate the . school component of the community treatment as the _school sample_

# **4   Data Collection**

We conducted baseline and follow-up surveys. The baseline survey was conducted from May through August 2008 in a total of 3,576 households. The follow-up took place four months after the project activities ended—from March through June 2011. We located 2,847 of the original households interviewed for the baseline survey during the follow-up round. We replaced the households we could not find with new households that fit the following criteria: the family had lived in the dwelling for at least the two previous years (e.g., when the intervention took place) and had a child under two years of age at the time of the baseline survey.

For the household-level sampling, in each of the 125 districts allocated to treatment and control groups, we randomly selected between 15 and 20 households from a census conducted prior to our baseline survey that listed all households with children under two years of age. From the 44 districts assigned to the community treatment, we randomly selected an additional set of 15 to 20 households with children under two years old and with at least one sibling attending the main treated schools in each district to assess the effect of the school subcomponent of the district-level intervention. To serve as counterfactual for this last group, in each of the 41 districts allocated to the control group, we randomly chose a second set of 15 to 20 households
**Table 3. Panel B. Regressions with Savings Related Measures and TFP, ICA data**

|Dependent<br>variable|(1)<br>Operating<br>Income to<br>Sales|(2)<br>Any<br>Investment|(3)<br>Any<br>Investment|(4)<br>IK|(5)<br>IK|(6)<br>Ret.<br>Earnings<br>WK|(7)<br>Ret.<br>Earnings<br>WK|(8)<br>Ret.<br>Earnings<br>Invest|(9)<br>Ret.<br>Earnings<br>Invest|
|---|---|---|---|---|---|---|---|---|---|
|Small size|0.03**|-0.13***|-0.12***|-0.07***|-0.07***|21.69***|20.36***|-37.34**|-40.70**|
||(0.025)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|(0.049)|(0.040)|
|Foreign|-0.00|0.03|0.02|0.03|0.02|-21.19**|-20.55*|-54.63*|-73.18**|
||(0.864)|(0.408)|(
, the second largest group as estimated from the remittance data is not Nepalese but Bangladeshis. This might be explained by two plausible scenarios: (i) not many Nepalese use MSPs, instead using alternative channels to send money, especially in Selangor, and (ii) a higher proportion of Bangladeshis are irregular workers than are Nepalese. Also, our estimates from the remittance data show smaller numbers of Myanmar, Thai, and Chinese foreign workers compared to MOHA’s data (Figure 7b). Chinese foreign workers are known to use banking channels, possibly because they tend to earn more than average foreign workers<sup>**</sup> and thus can afford higher remittance costs, or because the availability of MSP counterparts in China is limited. Filipinos take up a larger share in our results than in the MOHA data set, perhaps in line with anecdotal evidence that Filipinos in general have a higher level of financial literacy

** Based on our analysis of the average annual remittance amount by nationality, most of the individual remitters send home RM 3,000 and below per annum, except for remittance transactions to China and Vietnam.

10
the Malaysia subsample so, in the interest of parsimoniousness, mostly present the results from the Malaysia subsample.<sup>29</sup> Results for the occupation skills profile (unweighted by the distribution of occupations in a particular country) and replications for other countries are presented in Annex 3.

> 27 Since the outcome is binary and we are using a probability linear model, we can interpret the coefficient of a dummy variable to be a percentage point contribution towards the probability of the outcome. For illustration, assume a PLM model with a constant value of zero. If we analyze one non-zero dummy variable and let all the other variables take a value of zero, then the coefficient of the non-zero dummy variable will equate to the probability of the outcome. Adding another non-zero dummy variable will linearly increase or decrease the probability according to the value of the coefficient. A regression using the raw skills count is available from the authors.

> 28 Studies on job skills demand and technological similarly combine data on job tasks at the occupational level from the O*NET database and individual-level employment survey that weight each occupation by the percentage of the employed population in each occupation (Autor, Levy, and Murnane 2003; Hardy, Keister, and Lewandowski 2018; Caunedo, Keller, and Shin 2021).

> 29 By construction, the variation between the results with occupational skills profile and those for the employed populations are only due to differences in the distribution of the employed population across occupations.

21
To shed light on whether GDP manipulation is widespread among Chinese cities, we first look into the distribution of a city’s over-performance, defined here as the gap between the reported growth and the target growth. We then conduct the manipulation test proposed by Cattaneo, Jansson, and Ma (2018) to test for a discontinuity in the distribution of the over-performance around 0, the threshold for meeting the target. As shown in Figure 2, the gap between the density estimate on the two sides of zero is large and statistically significant ( _p_ =0.001). At the discontinuity point, the density is significantly higher on the positive side than on the negative side.

A similar test using the cumulative _quarterly_ GDP growth data is also revealing. Since the evaluation of bureaucrat performance is based on annual rather than quarterly data, if local governments indeed manipulate GDP data, the need for manipulation emerges only as the year-end approaches. Thus, the discontinuity in over-performance should emerge mainly in the latter half of the year (Lyu et al. 2018). To test this, we use the provincial-level data here since the city-level quarterly GDP data are not widely available. As shown in Appendix Figure A.1, the break in density of overperformance in growth is significant only in the third and fourth quarters, and the magnitude steadily increases over the course of the year. This result is again suggestive of performance manipulation among the Chinese cities.

# **3 Data and Variables**

## **3.1 Nighttime lights data**

The nighttime lights data were collected by the U.S. Air Force Defense Meteorological Satellite Program (DMSP) using the Operational Linescan System (OLS) sensors. They were processed and distributed by the scientists at the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC), available from 1992 to 2013. The DMSP satellites observe every location on the planet each night at the local time from 8:30 pm to 10 pm.
inflation are significant in the estimates of reaction function over 1980-2008; the coefficient of output is more significant than inflation. Patra and Kapur (2012) estimate several different models using data for 1996-2011 and found that even though the quantitative results are specific to the estimated model, both inflation and the output gap are significant in the policy reaction functions.

Studies using longer time series data show the relative importance of inflation and the output gap in policy reaction function has evolved over time. Singh (2010) e.g. shows the output gap to have a larger influence on monetary policy until the 1980s, and the effect of inflation to be stronger in the following two decades. Mohanty (2013) also finds the impact of inflation to be stronger than that of the output gap (and the latter to be statistically insignificant) during a more recent period, 2000-01 to 2012-13.

How may concerns related to exchange rate or capital flows affect monetary policy in an emerging country? As discussed in Section 2, capital inflows usually cause exchange rate appreciation and capital outflows result in exchange rate depreciation. Central banks in order to resist these exchange rate movements, may lower the policy rates to deflect capital inflows and raise them to deter outflows. In addition, central banks may buy foreign exchange to build its reserve cover or to avoid exchange rate appreciation during capital inflows or sell them during outflows, but may choose not to sterilize them fully. Thus the monetary stance often becomes expansionary during capital inflows and contractionary during outflows.

Has monetary policy in India been affected by issues related to capital flows? The evidence is more tenuous on the impact of exchange rate or capital flows on monetary policy in India. Mohanty and Klau (2004) find changes in policy rates to be correlated with exchange rate in India as well as other emerging markets, highlighting the support for the “fear of floating” hypothesis. Additionally, they find the response of policy rates to the exchange rate to be larger than that to inflation or the output gap.<
# **1. Introduction**

This paper reviews some of the challenges and opportunities raised by international trade for newly industrializing countries considering a green growth strategy.

One of the main deterrents to adopting fully efficient environmental policies is concern about the effects of such policies on international competitiveness in the manufacturing sector. This is often characterized as a trade-off between income and environmental quality. From the perspective of a newly industrializing country, the issue is whether growth in a competitive world economy is dampened by effective environmental policy.

There has been extensive research on this issue, most of which uses data from OECD countries. This work is reviewed in section 2 of the paper. In many sectors there is either no trade-off between international competitiveness and environmental policy, or at best a short run trade-off. For pollution that is generated by consumption (such as from automobiles or home heating), tightening up environmental standards does not reduce international competitiveness, and in some cases may promote local firms at the expense of imports. This is because emission standards targeting consumption-generated pollution apply to imports as well as local production and do not place domestic industry at a cost disadvantage. Moreover, high emission standards may yield long term export benefits by encouraging local industry to develop the expertise to build products that meet emission standards in foreign markets.

For environmental problems that lead to the depletion of natural capital, such as deforestation, fisheries depletion, soil erosion, or toxic emissions that harm human health, weak environmental policy may yield short run income gains but long run income losses. In such cases, effective environmental policy is an important component of a policy 2
Outstanding Borrowing for Six Purposes

||(1)<br>Housing|(2)<br>Education|(3)<br>Medical|(4)<br>Consumption|(5)<br>CD|(6)<br>Business|
|---|---|---|---|---|---|---|
|Kerala*Post|0.030<sup>_∗∗∗_</sup>|0.003|0.016<sup>_∗_</sup>|-0.081<sup>_∗∗∗_</sup>|-0.009|0.001|
||(0.008)|(0.006)|(0.007)|(0.020)|(0.012)|(0.006)|
|HH FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Kerala*Timetrend|Yes|Yes|Yes|Yes|Yes|Yes|
|N|26,267|26,267|26,267|26,267|26,267|26,267|
|Households|7,968|7,968|7,968|7,968|7,968|7,968|

Note: Standard errors are clustered at the household level. Kerala*Post denotes the interaction dummy for household residing in Kerala for the months post May 2018. HH = households; FE = fixed effects; CD = commercial bank loans. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 in table 5 show that they were more likely to have outstanding borrowing for
to capture the spatial and temporal variation in weather shocks with higher precision to quantify households’ vulnerability to such shocks to inform future policy decisions.

The rest of the paper is organized as follows. Section 2 briefly presents related literature. Section 3 describes the socioeconomic and weather data, followed by a description of how we construct weather anomalies in 2018 in Section 3.5. Section 4 describes the empirical strategy. Section 5 presents key results and discusses the heterogeneity in impact estimates. Finally, Section 7 concludes.

> 6See Fisker et al. (2021) for details on how to use the data in economic applications and a detailed list of literature using remote sensing data.

4