rafmacalaba/datause-encoder-data huggingface dataset:
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Table D.3: Program impacts on time preferences, 12–13 month survey only

||||I|TT regress|ion (N= 94|7)||
|---|---|---|---|---|---|---|---|
||Control|Ther|apy only|Cas|h only||Both|
|Outcome|mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Summary index of forward-looking time|-0.149|0.149|[.102]|0.105|[.102]|0.209|[.105]**|
|preferences||||||||
|Patience (_δ_) summary index, z-score|-0.240|0.170|[.103]*|0.145|[.096]|0.258|[.099]***|
|Incentivized trade-ofs (0-6)|3.941|0.033|[.173]|0.266|[.166]|0.163|[.172]|
|Hypothetical trade-of (0-6)s|3.345|0.295|[.222]|0.376|[.221]*|0.331|[.222]|
|Hypothetical discount rate (.9 to 4)|2.238|-0.177|[.091]*|-0.121|[.091]|-0.251|[.091]***|
|Self-reported survey questions on|-0.363|0.091|[.093]|-0.032|[.092]|0.139|[.092]|
|patience, z-score||||||||
|Placing oneself on a 5-rung patience|3.757|0.003|[.109]|-0.048|[.107]|0.146|[.110]|
|ladder (0-5)||||||||
|“I consider myself a patient person.”<br>(0-3 scale)|2.267|0.091|[.063]|0.043|[.063]|0.129|[.065]**|
|“If I make good money, I save some<br>for future problems.” (0-3 scale)|2.108|0.041|[.072]|-0.078|[.072]|-0.035|[.073]|
|Time inconsistency (_β_) summary index,|0.129|-0.072|[.083]|0.018|[.087]|-0.059|[.084]|
|z-score||||||||
|Incentivized trade-ofs (-3 - 3)|0.227|-0.024|[.071]|-0.010|[.074]|-0.038|[.073]|
|Hypothetical trade-ofs (-3 - 3)|0.192|0.039|[.099]|-0.042|[.100]|-0.078|[.101]|
|Hypothetical discount rate (-3.1 - 3.1)|0.021|0.048|[.068]|0.060|[.072]|0.117|[.070]*|
|Self-reported (mean of 3), z-score|-0.473|0.185|[.100]*|-0.020|[.096]|0.136|[.103]|
|“If I get money, I spend it quickly.”|2.618|0.047|[.077]|0.020|[.075]|0.121|[.076]|
|(0–3)||||||||
|“If I make good money, I spend a lot<br>celebrating with friends.” (0–3)|2.956|0.106|[.077]|-0.036|[.075]|0.070|[.079]|
|“I avoid going around friends who<br>waste money.” (0–3)|1.902|0.141|[.079]*|-0.025|[.079]|0.036|[.082]|
|Incentivized trade-ofs (patience and time<br>inconsistency), z-score|-0.047|0.029|[.081]|0.101|[.081]|0.084|[.084]|

_Notes:_ The table reports long-run (12-13 month) intent to treat estimates of outcomes that were not a priori specified as of primary interest. We calculate the impact of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. The overall summary indexes are the standardized mean of its composite outcomes, standardized. The final variable in column 1 is the average of the patience and time inconsistency measures from our incentivized trade-offs. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1 xxviii
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47
emphasized. The paper summarizes certain key empirical stylized facts of disasters available in the literature, shows that the predictions of the model developed here are mostly consistent with these empirical stylized facts, and contributes to developing further explanations for some.

# **I. EMPIRICAL EVIDENCE AND STYLIZED FACTS OF DISASTERS**

In this section we present some of the empirical evidence available which is relevant to our analysis. The literature reports two types of empirical evidence: (i) Robust evidence on which there is a degree of consensus in the literature. (ii) Weak evidence on which there is no agreement in the literature; these are often contradictory findings across studies and across disasters. We use the robust evidence to provide certain empirical underpinnings that justify some of the assumptions used in the conceptual model provided in the next sections. Then the results and predictions of the analysis are used to shed light on the conditions under which we expect the various conflicting outcomes reported in the literature as part of evidence (ii) above.

The contradictory empirical evidence is in part due to the poor quality of the information available especially of the cross country data. These data is in general too blunt to allow us to identify effects of disasters which generally affect only a portion of the country. In other cases even micro based data provide decidedly inadequate indicators. This data inadequacy means that empirical studies are unlikely to elucidate certain effects of disasters. This gives even more prominence to the need for a rigorous theoretical analysis that can capture at least a subset of the key complexities of reality. Such a model could be regarded as complementary with empirical studies, especially of aspects on which the quality of the empirical information is rather poor.

_1. Long-run impact of disaster shocks on economic growth_ . An important stylized fact is that disasters, while causing significant one-time reductions on assets and per capita income, do not necessarily reduce long-term economic growth. In fact, three of the greatest known disasters, the 1960 earthquake in Chile (9.5 Richter scale), the 1998 hurricane Mitch in Honduras, and the 2004 earthquake and Tsunami in the province of Aceh in Indonesia, have apparently had very different consequences for long-run economic growth. All three disasters affected a significant segment of the population and devastated an important part of the productive capital. While the Chile disaster caused no discernible 5
s<sup>us</sup> L<sup>log L</sup> t<sup>i</sup> +s<sup>�sus</sup> K<sup>log</sup> KNtt<sup>ii</sup> ++ss��11<sup>.Usingthismodi...edmeasureofproductivity,whereinputsharesare</sup> common across countries, we show in the appendix that equation (22) can be re-written in percapita terms as: If we expand the US intertemporal utility around the US steady state and subtract the resulting equation from (23), we can write: Welfare di¤erences across countries, therefore, are summarized by two components. The ...rst component depends on the di¤erences in the capital endowments. The second component is related to the well-known log di¤erence between TFP levels, which accounts empirically for most of the di¤erence is per capita income across countries (Hall and Jones (1999)). In the development accounting literature it is interpreted as a measure of technological or institutional di¤erences between countries. This interpretation, however, is valid under restrictive assumptions on market behavior and technology (i.e. perfect competition, constant returns to scale, etc.). We provide a di¤erent interpretation of the relevance of cross country di¤erences in TFP, by showing that a "modi...ed" version of the log-di¤erence between TFP levels is essential for welfare comparison across countries, together with di¤erences in their per capita capital endowment. This last term is similar to the capital intensity term used in the development accounting literature. Our result holds for any kind of technology and market structure, as long as a representative consumer exists, takes prices as given and is not constrained in the amount he can buy and sell at those prices. Notice however, that our measure of TFP is modi...ed with respect to the traditional growth accounting measure in three ways. First, measuring welfare di¤erences requires comparing not only current log di¤erences in TFP but the present discounted value of present and future ones. Second, the distributional and expenditure shares used to compute the log di¤erences in TFP need to be calculated at their steady-state value in the reference country. Third, as we will argue in section 4.4, domestic absorption is used, instead of GDP, in calculating the productivity residual.

As in the within-country case, we can conduct the comparison by using the concept of equivalent consumption. In this context, for any country i, Ct<sup>�;i,isde...nedforaconstantlevelofleisure,...xed</sup> at the US steady state level.

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# **3.2 Sample**

The sample for this paper consists of 150,000 business owners across 50 countries where there was sufficient sample size per country to conduct gender-disaggregated analysis. Facebook implements a modified simple random sample for survey invitations within each country.<sup>21</sup> Sampling weights are determined for survey non-response patterns using characteristics most closely associated with non-response to Facebook surveys.<sup>22</sup> We present the descriptive statistics of our sample by gender in Table G1 in Appendix G and describe them here.

The sample consists of male and female business owners and managers that are similar in age, education and household composition. The average age of male and female entrepreneurs is 37 years old; 57% of women entrepreneurs in the sample have at least a university or college degree relative to 54% of male entrepreneurs; and 97% of the sample have at least some secondary education. In terms of marital status: 78% of men and 75% of women surveyed report that they have a long-term partner or spouse. In the survey we ask about the types of dependents in their household: 41% of men and 37% of women in the sample report that they live with children under 5 years old, 59% of men and 66% of women in the sample report that they live with children aged 6-18 years old, and 30% of men and 25% of women in the sample report that they live with elderly dependents aged over 65 years old. The average income for the households in the sample in the past 30 days is USD2,733 for men; and USD2,729 for women, however this is mostly driven by households in North America and Europe.<sup>23</sup> Within their households, 83% of men and 56% of women report that they consider themselves to be the main income earner in their household.<sup>24</sup>

> 21Once a respondent has been invited to a survey, they are ineligible for another survey for six months in order to reduce response burden and maintain response quality.

> 22 Non-response weights are constructed using an inverse-probability weighting approach (IPW) and a logistic regression is used to predict the probability of response given eligibility for the survey. Further details of sampling weights can be found in Appendix A.

> 23Household income and revenues were reported in local currency of the respondent’s country and converted to a US dollar amount by the research analyst using an average of the exchange rate for the survey period. The value was then rounded to the nearest 100 USD to mask information for data privacy reasons.

> 24Breadwinner status was only collected for waves 2-5 and therefore is not used in the analysis for this paper.

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Table 2

# Present and Initial Size of Sample Leather Goods Firmsa

||||Cu|rrent Si|ze|||Ave|raae|
|---|---|---|---|---|---|---|---|---|---|
|Initial<br>Size<br>(Workers)|All|<20|20-49|50-74|75-99|100-20|0 >200|Start|Now|
|<5|9|3|3|1|0|0|2|3.4|66|
|5-9|9|1|6|0|1|1|0|7.0|49|
|10-20|7|0|1|1|1|2|2|14.7|126|
|>20|4|0|1|1|1|1|0|49|73|
|na|5|2|0|2|0|0|1|na|111|
|All|34|<br>6|11|5|3|4|5|7 <sup>.</sup>4<sup>b</sup>|55 <sup>.</sup>5<sup>b</sup>|

a) Data include one firm which started in 1991 with 110 workers and for which current size was not reported. but is assumed to be the same. Also there were three recently founded fi rms for which start-up size was not reported but a figure for two years before the survey was: these figures were taken as start-up levels.

b) Includes firms with data for both start-up size and current size. including in the former the three where start-up size was estimated as reported in note (a) above.

33
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" infrastructure services. In each of these sectors a number of other options exist (e.g. private electric generators, public water taps, private wells, septic tanks, public telephones). These alternatives may be more cost-effective solutions for serving some areas (e.g. septic tanks in rural areas), or may be more desirable for other reasons (e.g. formal utility service may be unreliable). In this section we examine the extent to which poor households that do not have electricity, in-house taps, sewer connections, and private telephones are relying on informal service providers (e.g. water vendors), private sources (e.g. private wells), or community service options (e.g. public phones). Are these services filling the infrastructure gap for poor households? How many poor households are left relying on very basic or unimproved sources?

# 6.1. The energy sector

Electricity is one of several energy sources that households around the world use in their homes. Most households rely on more than one energy source, choosing different fuels for different purposes, or substituting one fuel for another as prices, availability, or quality change. The majority of households in the pooled LSMS sample used electricity for lighting, but very few -- and even fewer of the poor --relied on electricity for their cooking needs.

Households without electricity used other fuels for lighting, cooking, and all other energy needs. Virtually all households without electricity connections used kerosene, gas, or oil lamps for lighting. Very few households used candles or flashlights, and even fewer reported having no source of lighting in the home. In Nicaragua, Ghana, Nepal, and Vietnam, only 1 percent, 4 percent, 7 percent, and 2 percent of households respectively used candles, flashlights, or something else other than electricity or gas, oil, or kerosene lamps for lighting.

In eight of the ten countries where data about households' cooking fuel are available, fewer than 2 percent of all households used electricity as cooking fuel.<sup>28</sup> Households that did not use

> 28 South Africa and Bulgaria are the only two countries in the sample where a significant number of households reported using electricity for cooking (43 percent in South Africa and 75 percent in Bulgaria). In South Africa, almost no households

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# **4.2. Robustness checks**

_Cost-of-living differences_ The consumption aggregates across the TZNPS survey rounds are expressed in 2022 prices using the World Bank Global Database of Inflation (Ha _et al._ 2021), as discussed earlier (Section 2.2). To assess the sensitivity of our results to the approach to standardizing prices – namely the degree of disaggregation of price adjustments by types of commodities, and by frequency of adjustment – alternative approaches were evaluated. Our preferred models relying on deflating prices using annual inflation at the level of commodity groups – food, utilities, and other non-food commodities – are compared to models relying on deflation by the traditionally used overall CPI deflators provided by the International Monetary Fund.<sup>13</sup> Deflation using the annual CPI disaggregated by groups is also compared to that using the quarterly CPI between the midpoints of survey rounds (Q1-2009, Q1-2011, Q2-2013, Q2-2015, Q2-2019, Q2-2021, and Q2-2022) and the monthly CPI (3/2009, 3/2011, 6/2013, 6/2015, 7/2019, 6/2021, 6/2022).<sup>14</sup> Figures 3–5 reveal that, across all models and pairs of base-target survey rounds (with a single exception for Model 4 in imputing into TA 1), annual commodity-disaggregated deflation performs not worse than and frequently better than non-disaggregated deflation. The advantage of commodity-disaggregated deflation is particularly notable in Models 8-9 when imputing into TA 1, Model 8 when imputing into TA 2, and Model 2 when imputing into TA 3.

Regarding the frequency of deflation, there appears to be no advantage to going below the annual level. Deflation using quarterly or monthly CPI performs no better and sometimes worse than deflation using annual (commodity-disaggregated) CPI. While commodity-disaggregated annual deflation performs notably better than IMF CPI in certain models and treatment arms, there

> 13 To access the database, visit: https://data.imf.org/?sk=4ffb52b2-3653-409a-b471-d47b46d904b5.

> 14 Quarterly and monthly inflation rates for food and energy are not available prior to February 2010. For years 2009– 2010, annual inflation rates for those sub-aggregates are used.

21
and captures the marginal (social) utility of higher test scores. This approach allows us to: first, solve the social planner’s solution, as total benefits of providing inputs can be consistently compared with their costs; second, show how the social planner’s solution to providing inputs changes with _⌧_ , a parameter that we do not have measurements of; and, third, compute the relative efficiency of the observed allocation relative to what the social planner would have done.

The costs incurred in providing education are twofold. First, there is the direct cost of the inputs provided by program-school operators. Second, there is a deadweight loss due to the taxes necessary for providing subsidies to program-school operators.<sup>13</sup> To estimate the latter, we assume a deadweight loss of 30 percent, and multiply this by the total annual subsidy, which is a fixed per-student amount of 4,200 rupees per year under the gender-uniform subsidy treatment, which rises to 5,400 rupees per year for girls under the one. gender-differentiated We define the social value of education as the product of the student’s annual adult income and a social externality multiplier. To estimate the effect of education on labor earnings, based on estimates by Montenegro and Patrinos (2014) for Pakistan, we fix upper- and lower-bound wage gains from an additional year of education at 10.8 percent and 6.8 percent, respectively. In addition, Bau and Das (2017) find that an additional year of education in rural Punjab, Pakistan is associated with a test-score gain of 0.4 standard deviations. Combining these two findings, we assume a test-score gain of 0.4 standard deviations to be equivalent to an additional year of education, and, therefore, to produce wage gains of 10.8 percent and 6.8 percent at the upper and lower bounds, respectively. The wage gain is calculated as a function of the baseline wage and the labor force participation rate: where the subscript _g_ indicates the gender of the child, and _b_ the upper and lower bound estimates of wage gains. In rural Sindh, the baseline monthly wage ( _blwage_ ) for men aged 15–34 is 6,600 rupees, and that for women in the same age group is 2,000 rupees; and labor force participation rates for the two are 80 percent and 36 percent, respectively.<sup>14</sup> We inflate the term with the multiplier above to account for social externalities.

For each program school in our sample, we solve the following social planner’s problem:

> 13See footnote 12.

> 14Estimates are based on data from the 2010–11 Pakistan Social and Living Standards Measurement Survey (Government of Pakistan, 2011).

20