rafmacalaba/datause-encoder-data huggingface dataset:
In conducting a difference-in-difference estimation, we may be concerned that other factors may change differentially between the treatment and control groups over time. In which case, it would be these other factors driving the results of interest, and not the exogenous shock of the earthquake. To control for this, the difference-in-difference dummies are interacted with the control variables that vary across time to test whether these variables vary differential in treatment and control area. In this case, the following equation is estimated, The _xirt_ is either the number of children born, education of the mother, age of the mother, age at first birth, urban, or the education of the father. If coefficient  12 is significant, then this implies that that particular variable changes differentially between the treatment and control groups over time, and this would contaminate the estimated coefficient of  3 in equation (12).

The a list of control variables, including regional fixed effects, can never be definitive, and thus the risk of omitted variables means that the difference-in-difference parameter is picking up another factor that changed differentially between treatment and control groups over the same period. To rule out this possibility, a further test is conducted. The core regression in equation (12) is run for a randomly selected group of regions (one region per country) to be the treatment groups and the remainder to be the control groups.

# **4.2 Results**

In Table 2 the pre-shock summary by treatment status statistics are presented. In the total sample across the three countries, we see that before each country’s respective earthquake, the average age of the respondent was 31 years. The average number of children who died per woman is 0.36 on average, but with the stratification by urban/rural we see that the number of children who died per woman is 0.42 in rural areas and 0.24 in urban areas. The average number of years of 17
# **4. Discussion**

Some research shows that experiential measures of food security are consistent with objective, quantitative indicators. Frongillo (1999) and Webb et al. (2002) both show that the United States’ Household Food Security Survey Module (HFSSM), which includes a combination of quantitative and qualitative questions on households’, adults’ and children’s access to and utilization of food, is a strong measure of food security across multiple dimensions and even in multiple developing country contexts. In Malawi and Cambodia, analysis of a household living conditions survey found experiential food security to be correlated with household expenditure and dietary diversity (Headey and Ecker 2012).

However, self-reported experiences of hunger and food insecurity are not necessarily indicative of problems with access to food. Deaton and Dreze (2009) find that reported experiences of hunger in India are not correlated with caloric intake. Headey and Ecker (2012) find that experiential food insecurity exhibits no relationship with caloric intake in Ethiopia. Similarly, using Living Standards Measurement Study (LSMS) data from Albania, Indonesia, Madagascar, and Nepal, Migotto et al. (2006) find that experiential food security indicators are weakly correlated with measures of food utilization, as measured through dietary diversity and anthropometry.

The lack of correlation observed between objective and experiential measures of food security has led to the supposition that experiential indicators are perhaps capturing households’ perceptions of food _stability_ and vulnerability. Because vulnerability refers to a point in the future, and is therefore difficult to measure, it is often assessed using experiential indicators of food security (Dercon 2001). Households may appear to be food secure in all objective measures of availability, access, and utilization at a given point in time and yet, they may perceive pending vulnerability or instability. An extension of this view is that the 19
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Control: These SACCOs served as a comparison group, and community members did not receive the financial education program during the evaluation period. There were 70 SACCOs in this group.

In terms of timeline, the program development and piloting began in December 2014 and continued for most of 2015 in select out-of-sample communities. The _training-of-trainers_ program was implemented in May and June 2016, and we conducted two endline household surveys at six months (October and November 2016) and twelve months (May and June 2017) after program delivery.

# **3.2. Data and Analysis Weights**

The analysis presented in this paper is based on two sources of data: household surveys of SACCO community members that we conducted six months and twelve months after the intervention;<sup>7</sup> and quarterly administrative data on savings at the SACCO level for both baseline and up to eighteen months after the intervention that was obtained directly from RCA.

The household survey methodology was as follows. First, for treated communities, we obtained the full roster of SACCO community members and matched them with the attendance sheets for the financial education training sessions. This enabled us to divide SACCO members into _trained_ and _untrained_ . Within each of these two categories, we randomly selected 10 members per category for our surveys, giving us 20 community respondents per SACCO. For control communities, we likewise obtained the full roster of SACCO community members and randomly selected 20 community respondents per SACCO. Overall, this sampling strategy resulted in a total of 4,000 respondents per survey round.

The stratified sampling approach in treated communities was adopted to ensure that half of our interviewees in each community were recipients of the financial education training program. Given that the proportion of trained and untrained members in each treated community was unequal, we

> 7 We did not conduct a baseline household survey for the sample due to cost and logistical considerations.

10
**Table 4. Understanding the Mechanisms**

|Estimator|(1)<br>OLS|(2)<br>OLS|(3)<br>Bias-<br>Adjusted|(4)<br>2SLS|(5)<br>2SLS|(6)<br>2SLS|(7)<br>Lasso-IV|(8)<br>2SLS|
|---|---|---|---|---|---|---|---|---|
|**Panel A**||||**ln(Tech. Use**|**2012)**||||
|ln(MA1996)|0.273***|0.235***|0.188|0.423***|0.289**|0.366***|0.350***|0.364***|
||(0.06)|(0.04)|(0.70)|(0.14)|(0.12)|(0.09)|(0.09)|(0.08)|
|First Stage|||||||||
|Weak Identification||||18.49|11.14|26.95|12.36|41.70|
|||||0.0000|0.0010|0.000|0.0000|0.000|
|R-squared|0.186|0.538||0.488|0.533|0.5136|0.519|0.514|
|**Panel B**||||**ln(Land Sale,**|**2012)**||||
|ln(MA1996)|1.172*|2.330**|3.093***|-1.537|1.902|-0.072|0.433|1.127|
||(0.66)|(0.90)|(0.96)|(2.65)|(3.13)|(2.04)|(2.11)|(1.90)|
|First Stage|||||||||
|Weak Identification||||18.49|11.14|26.95|12.36|41.70|
|||||0.0000|0.0010|0.000|0.0000|0.000|
|R-squared|0.014|0.163||0.080|0.162|0.131|0.143|0.155|
|**Panel C**||||**Credit: Form**|**al Bank**||||
|ln(MA1996)|0.017|0.014|0.011|0.067|0.064|0.066*|0.063*|0.057*|
||(0.01)|(0.01)|(7.07)|(0.04)|(0.04)|(0.04)|(0.04)|(0.03)|
|First Stage|||||||||
|Weak Identification||||18.49|11.14|26.95|12.36|41.70|
|||||0.0000|0.0010|0.000|0.0000|0.000|
|R-squared|0.005|0.340||0.282|0.318|0.314|0.317|0.322|
|State Dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|212|212|212|212|212|212|212|212|

Notes: Columns (1) and (2) are estimated by OLS. Column (1) is a bivariate regression without any controls while column (2) includes state fixed effects and other controls. Column (3) is estimated by Oster's bias-adjusted OLS estimator. Columns (4) and (5) are estimated by 2SLS using railroad length and distance to GQ as IVs, respectively. Column (6) is estimated by 2SLS using predicted market access as the IV; which is estimated from a zero stage OLS regression including both railroad length and distance to GQ. Column (7) reports the Lasso estimates using a broad set of instruments that includes railroad length, distance to GQ, and each of their interactions with the exogenous control variables as IVs. The three IVs chosen by the lasso are: distance to GQ interacted with slope, distance to GQ interacted with elevation, and railroad length interacted with crop suitability. Column (8) is estimated by 2SLS using predicted market access estimated from a zero stage OLS regression including railroad length, distance to GQ, and all the lasso chosen IVs. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
12 Table 2. Regression results, aid-allocation equation

|Dependent variable:||<br>Indicator<br>Log (aid f|<br>or trade/GDP)|<br>|
|---|---|---|---|---|
|Trade costs|Simple<br>average|Weighted<br>average a/|Weighted<br>average b/|Decomposed|
||(1)|(2)|(3)|(4)|
|Ln (GDP per capita)|-1.566***|-1.520***|-1.518***|-1.569***|
|Rule of law c/|1.016***|1.085***|1.089***|0.989***|
|TCio|-0.963***|-0.693***|-0.705***||
|Infrastructure d/||||-0.179**|
|Incentives e/||||-9.609***|
|Institutions f/||||-1.218***|
|Observations|232|232|232|232|
|Year dummies|YES|YES|YES|YES|

# Notes

a/ Weighted average using cross-section gravity estimates b/ Weighted average using pooled gravity estimates c/ Using Kaufman’s index, standardized d/ Using Limao-Venables’ index, standardized e/ Using the MFN TRI, standardized f/ Using the Customs component of the LPI, standardized Column (4) shows the separate effects of the quality of infrastructure, incentives, and institutions on the aid for trade allocation decision. Controlling for other factors, countries with the worse infrastructure and institutions receive more aid for trade. Aid effectiveness and GDP per capita also influence the decision on the aid for trade allocation as predicted. However, disincentives (i.e. higher trade restrictiveness applied by the recipient country against import from all the countries of the world) seem to play a larger role and negative role in deciding the allocation of aid for trade.
13 The estimates in Table 8 strongly confirm the proposition that the minimum wage substantially affected employment in manufacturing firms in Colombia. The sizable minimum wage effects are robust across alternative specifications and using both lagged and contemporaneous minimum wage information. The elasticity of employment with respect to the minimum wage is in the range of .15 to .33 in the case of unskilled workers, and .03 to .24 in the case of skilled workers<sup>22</sup> , depending on lag structure and the exact specification of the other parameters. The firm panel data is therefore crudely consistent with the magnitude of effects implied by the time series estimates.

The estimated elasticities are presumably a lower bound on the true effect of minimum wages on low wage labor. If we take as a guide the fact that 27 percent of Colombian firms report wages within a range of 1.5 times the minimum wage, and as a crude approximation assume that roughly the same percentage of individuals are so affected, then the implied mninimum wage elasticities for minimum wage labor may be as high as .55 to 1.22, depending on specification. 23 In any case, without more precise information, it is probably best to think of the two sets of estimates as bracketing the true effect of the minimum wage on low wage, low skilled Colombian labor. Together, they suggest that the roughly 10 percent rise in the real value of the minimum wage from 1981-1987 (Table 2) reduced low skilled low wage Colombian employment in the range of 2 to 12 percent over the period.

# _Alternative Specifications with the Colombia Data_

Several relevant issues of design may weaken the Colombia firm level employment regression results. First, although a dual structure to mninimum wage fixing with differential rates for large and small cities persists in Colombia through the 1984 period, by 1985 the country had established a single national minimum wage standard. This eliminates the valued heterogeneity in the minimum wage over the later period and makes interpretation of the minimum coefficient difficult at least over this range. If 22The differences between the average wages of skilled and unskilled wages are relatively small in Columbia (a .07 In point differential) which might explain the impact of minimum wages on the employment of this group.

> 23This assumes implicitly that the distribution of wages is roughly normally distributed _within_ firms (as it is across firms) and affected firms are no bigger than average size, as a lower estimate on the effect. In fact from Table 3 it is clear that affected firms tend to be the smaller firms, implying if anything a greater multiple effect.
_Source:_ Authors’ calculation based on data from NCDC.

# **5. Simulating the impacts of recent climate change**

In this section, we will use the consumption model estimated in Section 2 to simulate the impact of the climate change experienced during the last 50 years, as indicated by the analysis in Section 3.

We will compare two scenarios, one with Climate Change, which is the factual scenario, and one with No Climate Change, which is the counterfactual scenario. The temperatures in the Climate Change scenario, _ti,CC_ , are the actual temperatures, whereas the temperatures in the counterfactual scenario, _ti,NCC_ , are the temperatures that would have been if temperatures had not changed during the last 50 years. That is, for all highland municipalities temperatures are 1oC higher in the No Climate Change scenario compared to the Climate Change scenario, whereas for all lowland municipalities temperatures are 0.25oC lower in the No Climate Change scenario.

As there has been no systematic change in precipitation, the precipitation terms cancel out, and the other factors, education and urbanization rates, we will maintain constant, so as to isolate the climate change effect. Thus the ratio of Climate Change Consumption to No Climate Change Consumption can be written as: After estimating this ratio for each municipality, it is straightforward to calculate the percentage change in consumption levels that can be attributed to climate change. At the national level, the model estimates that climate change during the last 50 years has caused a reduction in consumption of about 1.3 percent.

Table 8 shows the results disaggregated at the state level. The most disadvantaged states are the highland states Oruro, Potosí and La Paz, which are estimated to have lost almost 3 percent of their consumption capacity due to the already cold climates becoming colder. Chuquisaca and Tarija have also lost out according to this simulation, but a bit less, as they were initially warmer, and the slope of the temperature/consumption relationship was thus less steep. All lowland states have gained slightly.

**<u>Table 8: Effects of recent climate change on consumption levels, by state</u>**

||Effects of past temperature changes on<br>consumption levels|
|---|---|
|**State**|(% change)|
|Beni|0.16|
|Chuquisaca|-1.74|
|Cochabamba|0.54|
|La Paz|-2.44|

14
survival that we do not observe. Additionally, since our analysis of reform episodes relies solely on time series variation, it results in weak identification. However, the findings can still provide a correlation between reform episodes and firm survival.

# <u>5.2.1 Firm dynamics and growth</u>

We begin by summarizing firm and job dynamics, relative to annual GDP growth. Figure 6, Panels A and B, shows the absolute number and one-year growth rates of entering and exiting firms, by year, relative to one-year GDP growth. For most of the period, we find that the formal private sector both gains and loses about 200 firms per year, which corresponds to average entry and exit rates of around 10%.

Next, in Figure 7, Panels A and B, we present changes in new employment at new and exited firms (Panel A) and jobs created and lost at incumbent firms (Panel B), relative to annual GDP growth. Over time, net employment is always positive, that is more jobs are created at new firms than lost at exiting firms. However, the absolute number of new jobs added to the private sector by new firms is remarkably small – on average, fewer than 3,000 formal, private sector jobs are added on an annual basis. In comparison, the net number of new jobs generated at incumbent firms is negative for all years from 1979 through 1995. The largest net job losses occurred during the 1980 macroeconomic downturn – over 10,000 jobs were shed in 1979 – although net jobs at incumbent firms remained negative even during periods of GDP growth.

# <u>5.2.2 Empirical model</u>

Our comprehensive panel data allows us to study the firm-level determinants of firm survival. Unlike previous studies, with the exception of Shiferaw (2007, 2009) and Bedi and Shiferaw (2009), we rely on census-based panel data, which allows for better tracking of firm entry and exit. However, we go beyond existing studies since we do not face a stock sampling problem (survivorship bias) and we use a long panel that allows firms sufficient time to follow their natural lifecycle and therefore minimize right-censoring. We also include firms outside of the manufacturing sector, which is important given the historically small size of manufacturing sectors in Sub-Saharan Africa.

16
more children are being enrolled since 2000 but learning becomes a serious concern. It is estimated that more than 617 million children and adolescents are not achieving minimum proficiency levels in reading and mathematics and most of these individuals are living in developing countries (UIS, 2017). The World Bank’s Human Capital Index (HCI) shows that the next generation of workers in low‐ and middle‐income countries would only have 51 % of the productivity that they could have if they had complete education and full health.<sup>2</sup> A decomposition of the average HCI shows that the education component is associated with the lowest achievement and the average of the learning‐adjusted years of schooling (LAYS) seems very low in most developing countries and far below the benchmarks.<sup>3</sup> A simple visualization of the relationship between the total public education expenditure (government expenditure + donors’ contribution) per school‐age individual and the LAYS shows that there could be some correlation between public expenditure on education and access to quality education (Figure 2). This relationship has been reported in previous studies (Jung & Thorbecke, 2003; Dissou, Didic, & Yakautsava, 2016; Al‐Samarrai, Cerdan‐Infantes, & Lehe, 2019). More interestingly, Figure 2 shows that countries with similar levels of average public expenditure on education over the past two decades register different achievements in terms of access to quality education. This indicates that countries may face issues related to their ability to translate resources into quality education.

**Figure 2 Average public expenditure per school‐age individual and the LAYS**

<!-- Start of picture text --> lays = 5.2593 + .00175 expeduc R 2 = 43.0%<br>RUS<br>KAZ<br>SRB<br>HRV<br>VNM BGR UKR BGR<br>MUS MNG MUS<br>MYS<br>TJKLKAKENPHLARGKGZARMGEOLKAIDNMEXALBPERMDAAZE JORCOLIRNECU THAIRNTURMEXROUBRA CRIARG<br>PRY NICKIR PRYJAM PANJAM<br>BGDMMRKHMBGDHTINPLLAOZWEDOMTONMARGTMGUYSLVHNDTUNLBNZWEEGYMARMKD DZATUN<br>VUT GAB<br>MWIZMBTLSBDIBDIMWITGOCOMBENCMRCMRINDSLBCOGLSOGHA SWZSWZNAM BWA ZAF ZAF BWA<br>CODGMBGINSLETZAMOZUGAETHAFGGMBSDNTZPSENPAK A K<br>MDGBFACIVAGOYEM<br>RWA<br>MRT<br>NERTCDNERMLI<br>SSDLBR<br>0 1000 2000 3000 4000<br>Average public expenditure per school age individual 2000-2015 (2010 constant PPP)<br>n = 122 RMSE = 1.547724<br>12<br>10<br>8<br>LAYS<br>6<br>4<br>2<br><!-- End of picture text -->

> 2 A population weighted average of the HCI was calculated based data available in 133 low‐ and middle‐income countries. The average value of the HCI is 0.5089977. A decomposition of the HCI into its component shows that the survival component value is 0.9665485 while the health and the schooling components’ values are respectively 0.9039009 and 0.5777548.

> 3 See Kraay (2018) for a discussion on the HCI benchmarks.

3
# **D. Foreign bank acquisitions and outreach – the bank-municipality-level evidence**

While the results using country-wide time-series data for Mexico show the aggregate effect of foreign bank participation on outreach and the bank-level regressions allow us to identify changes in outreach following acquisitions, the bank-municipality regressions in Table V enable us to assess the effect of foreign acquisitions on outreach as a function of municipality characteristics such as income and degree of urbanity. Further, given that for each of the six banks we have for more than 1,163 municipalities over 36 quarters between 1997 and 2005, these specifications give us greater power and allow us to verify whether the findings at the national and bank-level hold up when we focus on a smaller geographical entity.<sup>25</sup> All regressions include municipality, bank, and quarter dummies, so that we measure the effect of foreign acquisition relative to the average for each bank, municipality, and time period. Panels A, B and C present (i) the baseline regression with the foreign acquisition dummy, (ii) regressions

> 25 There are over 2,400 municipalities in Mexico; however, the available data aggregate the branches, deposits and loans for some of the smaller municipalities into a broader category labeled “others”.  There are 29 states which report this “other” category. Combined the municipalities included under the “other” category account for less than 3 percent of the Mexican population.

23
# **The Value-Added Tax Reform Puzzle**

Jing Cai University of California, Berkeley

## Ann Harrison

University of California, Berkeley and NBER August, 2011 Abstract In this paper, we explore the impact of a tax reform in some provinces of China which eliminated the value-added tax on some investment goods. While the goal of the experiment was to encourage upgrading of technology, our results suggest that there was no evident increase overall in fixed investment, and employment fell significantly in the treated provinces and sectors. The reform reduced the total number of employees for all types of firms. For domestic firms, it reduced employment by almost 8%. Our results are robust to a variety of approaches, and suggest that the primary impact of the policy has been to induce labor-saving growth. This experiment has since been extended to the rest of China.

1