rafmacalaba/datause-encoder-data huggingface dataset:
we used log-transformed HAP variables as dependent variables in the regressions by taking natural logarithm of PM2.5 and CO concentrations.<sup>27</sup> All the results presented in Table 4 highlight the importance of clean fuel for reduction of PM2.5 concentrations, as expected. Column (1) indicates that use of clean fuel (ethanol) reduces, and use of traditional stoves for burning wood increases, the concentration of PM2.5 significantly compared to the use of charcoal in traditional stoves.<sup>28</sup> We find no significant impact of improved charcoal stoves or improved wood stoves on PM2.5. Although this might be contrary to expectations, in the case of the improved charcoal stove, controlled cooking tests conducted in an earlier phase of the study found that this stove had essentially the same thermal efficiency as the traditional charcoal stove. While the emissions of the two charcoal stoves were not measured, the similarity of the thermal efficiencies implies that the PM2.5 emissions from the two stoves are likely similar. In the case of the improved wood stove, one of the main issues reported by households was the fact that it took time to cut wood to the correct size so that it fitted into the stove. This observation may suggest difficulties with ignition and fuel addition, which have been found to cause emission of significant quantities of particulate matter from improved wood stoves with chimneys.<sup>29</sup> The estimates after the introduction of kitchen configuration variables, presented in column (2), indicate that while most of the kitchen characteristics have the right sign, only the size of the kitchen has statistically significant impact on kitchen PM2.5. In particular, an increase in the size of a kitchen by one sq. ft. is expected to lower concentration of PM2.5 in the kitchen by 2%. Column 2 also shows that after controlling for the kitchen characteristics, the use of a traditional stove in burning wood increases PM2.5 by 33.4%, and the use of ethanol reduces the concentration of PM2.5 in the kitchen by 50.4%, compared to the traditional charcoal stove.<sup>30</sup> Finally, the introduction of ambient environment related variables in column (3) indicates that

> 27A log transformation of a dependent variable is advocated for estimation when the distribution of the variable is positively skewed. In our case, distributions of both PM2.5 and CO are positively skewed, with numerical values of skewness of PM2.5 and CO being 10.8 and 2.4 respectively.

> 28 Use of charcoal in traditional stoves is the excluded category, hence the comparison is with respect to the use of charcoal in traditional stoves.

> 29Roden et al., 2009

> 30 The regression coefficient of a log-transformed variable can be interpreted as the percentage of change only if its numerical value is less than 0.1. Otherwise, the percentage change is given by the expression: _exp(β)_ − _1_ , where _β_ is the regression coefficient and _exp_ is the exponential function.

14
constraints cease to bite, but nor is there any obviously better basis for setting a threshold. I will also consider a lower line of $1.25 a day and a much higher line of $13 a day in 2005, corresponding to the US poverty line. The $1.25 line is the expected value of the poverty line in the poorest countries in terms of consumption per person;<sup>21</sup> the $13 per person per day is the official poverty line in the US for a family of four in 2005.<sup>22</sup> <u>Inequality</u> is measured by the usual Gini index ( _Git_ ).The initial index ranged from 19.4% (Czech Republic) to 62.9% (Sierra Leone), both around 1990, and about one quarter of the sample had a Gini index below 30% while one quarter had an index above 50%. Between the earliest and latest surveys, the mean Gini index stayed roughly unchanged at about 42%.

Four measures of the middle class are used. The first is the population share living between $2 and $13 a day, denoted _MCit_  _Fit_ (13)  _Fit_ (2) where _Fit_ ( _z_ ) is the distribution function for country _i_ at date _t_ (so _Hit_  _Fit_ (2) ). This is interpreted as the middle class by developing-country standards; while the bounds are somewhat arbitrary, this definition appears to accord roughly with the idea of what it means to be ―middle class‖ in China and India (Ravallion, 2010). By contrast, those living above $13 a day can be thought of as the ―middle class and rich‖ by Western standards. These are absolute measures. The third measure uses a <u>relative</u> definition of the middle class, namely the consumption or income share controlled by the middle three quintiles, denoted _MQit_ , as used by Easterly (2001). Finally, I will also consider the ―miser index‖ proposed by Lind and Moene (2010), given by _Hit_ (  _it_  _itH_ ) where  _it_ is the overall mean and  _itH_ is the mean below the poverty line. Lind and Moene propose this as a measure of polarization between the rich and poor, but it can equally well be thought of as an inverse measure of the middle class.

As one would expect, there are some strong correlations amongst these parameters of the initial distribution.<sup>23</sup> The Gini index is highly correlated with _MQ_ (r=-0.969 for the earliest surveys). The poverty measures are also strongly correlated with the survey means; ln _Hit_  and ln  _it_  have a correlation of -0.743. The least-squares elasticity of _H it_  with respect to the

> 21 This is based on a threshold model calibrated to national poverty lines, as documented in Ravallion et al., 2009). $1.25 is also the mean poverty line for the poorest 15 countries. 22 See Department of Health and Human Services.

> 23 The following descriptive statistics apply to the sample of 90 countries used in the main econometric analysis, for which a non-zero proportion of the population live below $2 a day.

11
<!-- Start of picture text --> Japan: real credit medium and short term cycle<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle Short-term credit cycle<br>UK: real credit medium and short term cycle<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle Short-term credit cycle<br>10<br>5<br>0<br>-5<br>cyclical component<br>-10<br>-15<br>20<br>10<br>0<br>cyclical component<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Spain: real credit medium and short term cycle<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle Short-term credit cycle<br>10<br>5<br>0<br>cyclical component -5<br>-10<br><!-- End of picture text --> 50
there is no improvement in enrollment and graduation rates from those observed at time _t_ . In other words, the average educational attainment of these young cohorts in the future is equal to the average educational levels of the 20 to 24 cohort of time _t_ . This is a conservative assumption given that the 20 to 24 cohort observed at time t may not have the maximum educational level attainable.<sup>21</sup> The second step is to adjust factor returns by skill and sector in accordance with the results of the CGE model. The GIDD imposes an entirely new vector of earnings on each worker, conditional on that worker being in sector s and having and educational attainment e. The third step adjusts the average income/consumption per capita to guarantee that it changes exactly in line with the CGE results. Lastly, GIDD constructs a household-specific deflator to adjust for changes in relative prices. The price deflator is constructed using initial and final prices indexes of food versus non-food from the macro model and household-specific budget consumption shares for food and non-food observed in micro data.

Beginning with a distribution of earning from labor by sector and skill [y�,�] in the macro data, define a set of wage gaps as follows: and a similar set of wage gaps for the macroeconomic counterfactual scenario: where y1,1 is the average earnings from labor of unskilled workers in agriculture and y��,� and y��,� are their predicted values from the CGE model in the counterfactual scenario. All right hand side values in equation 1 are known data in the CGE model benchmark data set, and all right hand side values in equation 2 are known values in the CGE model simulations.

The micro data will have also a set of wage premia which, in general, will differ from the CGE data. Analogous to equations 1 and 2, define: � where g�,� are the wage premia based on averages by skill group and sector in the household data; y′�,� are the average earnings of labor in sector s and skill group e based on the household data; y′�,� are the average earnings of unskilled labor in agriculture based on the household data; and the g′<sup>�</sup> are the predicted values at the household level as a result of the policy change. All right hand side values of equation 3 are known from the initial household data. In order to calculate g��,�� , we define:

> 21 In practical terms, the micro-simulation model recalibrates each household sample weight to match the age, gender, and education projected totals. A new probability distribution can be obtained by solving an optimization problem based on a minimum cross-entropy criterion as in Olivieri et al. (2014). The minimum cross-entropy method assures that the new sets of age, gender and education, deviate as little as possible from the initial distributions. See Wittenberg, (2010) for a technical description and implementation of this method.

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# 1. **_Introduction_**

The US subprime crisis, and the likelihood that the attendant downturn will spread from developed to developing countries has left policy-makers and academics concerned about the “human” costs of the crisis for poor countries. These human costs could in principle take a variety of forms—lower income and consumption levels, higher unemployment, lower investments in education by families, and worse health and nutrition outcomes. Of particular concern are changes that could have irreversible consequences for the wellbeing of children throughout their lives—for example, school dropout or impaired child development at critical, early ages. No outcome is more dramatic than infant death, and in no region is infant mortality higher than in Sub-Saharan Africa (henceforth Africa, for short).<sup>1</sup> In this note we assess the likely impact of the current financial crisis on infant mortality in Africa. To do this, we first use data from all available Demographic and Health Surveys (DHS) for the region to construct retrospective birth and death histories, and assess the impact of past crises on infant mortality in Africa. We then use the most recent (April 2009) IMF growth projections to estimate the likely impact of the current downturn on infant mortality.

The basic messages of our analysis are two. First, although severe, the consequences of the current economic crisis for infant mortality in Africa are likely to be much smaller than many of the estimates that have been frequently cited on blogs or in the popular press.<sup>2</sup> Specifically, our estimates suggest approximately 30,000 to 50,000 excess infant deaths in Africa in 2009. Second, however, we find that virtually all of these excess deaths take place to girls, not boys. Furthermore, these asymmetries in the impact of the crisis by gender are particularly apparent for sharp (rather than modest) contractions in per capita GDP—a finding that is important if the current growth projections were to be revised further downward.

# 2. **_Data and methods_**

The DHS ask women a set of questions about the date of birth, current vital statistics, and date of death (if deceased) of all children ever born. We use the responses to these questions to construct retrospective birth and death histories for every woman in the surveys. Our measure of infant mortality is an indicator that takes on the value of one if a child died at a reported age of 12

> 1 In 2005, the IMR per 1,000 children born was 90.8 in Sub Saharan Africa. As a comparison, in this same year the rate was 24.2 in Latin America and the Caribbean, 62.2 in South Asia (57.7 in India), and 24.8 in East Asia (21.4 in China).

> 2 See <u>http://africacan.worldbank.org/a-sub-prime-crisis-in-the-us-and-infant-deaths-in-africa; Harvey</u> Morris, “Forgotten victims of the downturn”, Financial Times March 11, 2009, p. 4; Nicholas Kristof, “At Stake Are More Than Banks”, New York Times, April 1, 2009.

2
# **3. Regression Discontinuity Design and Survey**

This scoring process has several features that make it suitable for use in regression discontinuity analysis. The score is a clear cutoff used to decide funding, and the cutoff score is unknown to peer reviewers and determined by funding availability. There is not a sharp discontinuity at the threshold for two reasons. First, there were two consortia applications that received scores at or above the threshold which were not funded – one withdrew their application and decided not to sign a funding agreement, and one was rejected to avoid a risk of double-funding. Second, 14 of the 159 funded applications had initial scores below the cutoff, and were approved following appeals on the original scores.

We combine the two rounds for analysis by subtracting the round-specific cutoff scores. Figure 1 then plots the proportion of applications with each score receiving funding, showing a large jump at the standardized cutoff point of zero.<sup>8</sup> The size of the jump is between 0.68 and 0.82 depending on the bandwidth and whether a local linear or local mean is fit, and is always highly significant.

# **3.1 Manipulation Tests**

We conducted two different manipulation tests to check for potential sorting around the cutoff. These tests estimate the density of scores on each side of the cutoff and then check whether there is a jump in these densities at the cutoff, which would suggest that the scores may have been manipulated. The first test, the McCrary test, gives an estimated log difference in the density functions at the cutoff of 0.382 (standard error 0.191). The second test uses the _rddensity_ command in Stata and gives an estimated difference of -0.236 in the density at the cutoff (p-value 0.813). While the McCrary test suggests that there is an upward jump in the density at the cutoff, the _rddensity_ test does not show a significant difference in the density at the cutoff and in fact the sign of the coefficient here suggests a downward jump. We believe that the difference in results is due to the different procedures for smoothing the discrete score variable used in both approaches.

As a further check, we plot the distributions of scores on each side of the cutoff in Figure 2. The distributions look very similar on both sides of the cutoff.<sup>9</sup> Also, based on program information we have no reason to believe that scores could be manipulated around the cutoff since judges did

> 8 For scores that were not integers, we rounded down to the nearest integer when plotting Figure 1. 9 Most scores have integer values or a 0.5 decimal point, but there are some values with 0.17, 0.33, 0.67 and 0.83 decimal points, which account for the short bars in Figure 2.

9
of higher inflation, firms reduce their holdings of liquid assets to finance unexpected working capital expenditure (see equation (10)). Column 3 of Table 2 shows that the probability for Table 2: Harmonized firm panel for 139 countries from 2006-16: Inflation reduces firms’ liquidity holdings and innovation

||Retained|Bank|Innovation|New|New|ln(Inv)|
|---|---|---|---|---|---|---|
||earnings|fnance||product|process||
||fmlo|git|probit|(margina|ls)|ols|
|infation|-.037<sup>_∗∗_</sup>|.005|-.043<sup>_∗∗_</sup>|-.029<sup>_∗∗_</sup>|-.030<sup>_∗∗_</sup>|.005|
||(-3.53)|(0.35)|(-3.52)|(-2.22)|(-3.19)|(0.25)|
|ln(empl)|-.041|.202<sup>_∗∗_</sup>|.061<sup>_∗∗_</sup>|.044<sup>_∗∗_</sup>|.062<sup>_∗∗_</sup>|.726<sup>_∗∗_</sup>|
||(-1.46)|(7.61)|(12.6)|(13.1)|(9.01)|(14.7)|
|ln(age)|.014|.040|-.031<sup>_∗∗_</sup>|-.028<sup>_∗∗_</sup>|-.043<sup>_∗∗_</sup>|-.087|
||(0.21)|(.54)|(-3.12)|(-3.29)|(-2.93)|(-1.01)|
|subsidiary|.093|.251<sup>_∗_</sup>|.108<sup>_∗∗_</sup>|.111<sup>_∗∗_</sup>|.081<sup>_∗∗_</sup>|.342<sup>_∗∗_</sup>|
||(0.68)|(1.81)|(3.49)|(3.49)|(4.84|(3.88)|
|ln(real GDP pc)|-1.60<sup>_∗∗_</sup>|-.182|-.625<sup>_∗∗_</sup>|-.529<sup>_∗∗_</sup>|-.969<sup>_∗∗_</sup>|-3.23|
||(-2.74)|(-0.26)|(-3.44)|(-3.08)|(-4.43)|(-1.57)|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms|103,050|103,050|72,643|72,451|70,909|41,155|

World Bank Enterprise Surveys establishment-level data in 139 developing countries from 2006-16. Marginal effects are reported for the Probit estimations. Median inflation: 6%. Heteroscedasticity robust s.e. clustered at country _∗_ year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

establishments in a given country to engage in innovation activities is reduced during episodes of higher inflation. Higher inflation diminishes the probability of both product and process innovation (columns 4-5). The corresponding coefficients are significant at the 5% level. By contrast, inflation has no effect on total investment (column 6), implying that inflation influences the composition but not the overall volume of investment.

The results thus suggest that inflation shifts establishments’ technology choice from advanced to basic processes/products that are less exposed to liquidity risk, confirming hypothesis (i) of Proposition 1 in the model. The marginal effects reported in column 3 of Table 2 imply that a 1 pp increase in inflation reduces the innovation probability by 4.3%. The adverse impact of inflation on firms’ technology choice is thus statistically and economically significant.

21
2005). Contracting institutions refer to those that “enable private contracts between citizens”, while property rights institutions refer to those that “protect citizens against expropriation by the government and powerful elites.” They argue that government officials, and to a lesser extent, economic elites, can use force or other means to back up their demands and therefore pose real dangers. In contrast, when contracting institutions increase the costs of doing business in some way, contracting parties may use other ways of transacting. For example, instead of using bank finance, firms may rely on reputation-based financing methods such as trade credits (Coase 1960; Allen et al. 2005; Cull et al. 2009; Long and Zhang 2011). Cross-country empirical analysis tends to find much more pronounced effects of property rights institutions relative to contracting institutions (Acemoglu and Johnson 2005; Knack and Xu 2017), while China-specific firm study finds both to be relatively important (Cull and Xu 2005).

China has made significant progress in the legal system, though it likely remains weak in comparison to developed countries. A decade ago, the prevailing view was that China had a rather weak legal system (Clarke, Murrell and Whiting 2005), and the Chinese legal system likely has changed little since. Key issues with the legal system include government intervention, the lack of judicial independence, and the low density of lawyers in the population. For instance, there was one lawyer per 9,000 people in China in 2005 (right in the middle of our sample period), and the density was one lawyer per 300 people in the United States (Long 2010). Furthermore, firm-level Chinese data in the early 2000s suggest that firm-perceived quality of contract enforcement does not affect FDI inflow to Chinese districts (or counties), but local leadership characteristics matter, indicating relative importance of “rule of man” rather than the “rule of law” (Wang et al. 2012).

However, despite this prevailing view and related evidence, there are also indications that China has made progress in the legal system (Long 2010). Between 1983 and 2001, economic disputes accepted by courts of First Instance increased annually by 19.9 percent, much higher than those of civil disputes (8.8 percent). Between 1983 and 2005, the number of lawyers grew from 8,600 to 154,000, annually by 13.4 percent. Thus, both the usage and the human resources of the legal system have grown dramatically. There is also evidence of its positive effects. Cull and Xu (2005) find that contract institutions (e.g., the usage of courts, signing formal contracts, 9
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