rafmacalaba/data-use-mentions-extended huggingface dataset:
--|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Availability & Reliability Index|‐0.01|1.01|0.02|0.99|
|Household Satisfaction Index|0.02|1.02|‐0.04|0.97|
|Household Cost of Service Index|0.00|1.06|‐0.01|0.89|
|Operation & Maintenance Index|0.17|0.86|‐0.30|1.16|
|Institutional Sustainability Index|0.13|0.96|‐0.91|0.81|
|Overall Performance Index|0.13|0.96|‐0.76|0.99|

The relative performances are mixed when Jalanidhi schemes are compared with other community managed schemes. On average, Jalanidhi schemes perform better in areas of household satisfaction, household cost of service, O&M, institutional sustainability, and overall performance. Community managed schemes perform better in availability and reliability of service. However, for many of these indices, the differences in z-scores between these two groups are relatively small.

# 5.2 _Results from Propensity Score Matching_

A propensity score matching exercise was done to assess the performances of Jalanidhi schemes vis-àvis other approaches by using propensity scores to identify comparable treatment and control groups from the surveyed schemes. There were two comparisons done for the analysis. First, the performance of the decentralized demand responsive Jalanidhi schemes were assessed using the traditional supply driven KWA-BM schemes as control group. In the second analysis, Jalanidhi schemes were compared with the other community managed schemes. The PSM analysis was done using the ‘psmatch2’ command in STATA. Since ‘psmatch2’ does not provide the correct standard-errors and t-statistics for the estimates of ATT, the respective standard errors and t-statistics were estimated using bootstrapping.
from national accounts 5 Castaneda et al. (2022) discuss the available 2020 surveys and how COVID-19 led to changes in survey methodologies.

6 At the time of writing, such surveys have been conducted in 85 countries across all developing regions. We only use a subset because some of the countries also conducted a traditional household survey (which we used instead) and because some of the phone surveys do not contain answers to the questions we need. 7 The HFPSs are not nationally representative for a few reasons. First, households without a phone are outside the sampling frame. Second, among the households contacted, a non-negligible share declines to be interviewed. Third, there are many missing values in our key variable of interest, the share of households that have experienced income declines or increases. Fourth, in a few countries, the surveys did not cover the entire country. In the Democratic Republic of the Congo, most notably, the survey only covered Kinshasa, less than 20% of the whole population. It is important to note that reweighting cannot address these issues if the households captured are fundamentally different from households not captured, conditional on covariates. We could further post-stratify the weights based on the missing values of our variable of interest. However, we use these surveys only to get statistics for types of households (e.g. rural households that experienced an income loss with the head-of-household between 31 and 45 years of age, with any secondary schooling, and living in a family of between 2 and 4 people). Since we would have used the same variables to post-stratify and define types, the post-stratification would have only shifted the total weight from some types to others, without affecting the distribution of weights within types.

8 We use sectoral growth rates data from the April 2022 vintage of the World Bank’s Macro and Poverty Outlook (MPO).

6
percentage point greater fall in sales than larger firms, whereas for medium firms the fall is on average 6 percentage points. Similarly, small firms predominantly explain the exposure of SMEs to financial shocks, as one would expect. In contrast, small and medium firms have similar increased exposure to supply shocks and are equally unlikely to experience positive demand shocks or export demand shocks than larger firms.

COVID has led to massive increases in uncertainty that is likely leading firms to postpone investments in capital, skills and innovation – with likely scars for the post-COVID recovery. Measuring uncertainty is not straightforward, with scarce data for developing economies. SMEs typically report higher uncertainty over sales than larger firms, in part, due to poorer access to information and due to the fact that sales is where the impact by firm size is more severe. Evidence for the United Kingdom and the United States finds that while SMEs face around 20%-30% higher uncertainty over sales both before and after COVID, firm uncertainty has reached the highest levels since records began and dwarfing the financial crisis (Altig et al., 2020).<sup>10</sup> Results from the 13 countries surveyed by the World Bank are mixed, with variation in the overall expectations of return to normalcy lower among SMEs than large firms in some countries (see Figure 5), suggesting somewhat greater certainty in overall outlook. Large firms might be better able to cope with the challenges, but often have additional uncertainty to take into account such as foreign suppliers or the stock market, which SMEs are less likely to be listed.

> 9 Smaller firms may however be indirectly affected through disruptions to inputs imported by wholesalers. The World Bank Enterprise follow-up surveys do not allow measurement of the import supply shock.

> 10 The comparison group here is firms with more than 250 employees, but SMEs are defined as having fewer than 100 employees (as in the rest of this paper).  Their records began in 1985.

8
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definitions of the middle class—as suggested by Birdsall (2007), Ravallion (2010), Chun (2010), and Ferreira et al. (2013)—to household data for Egypt and the MENA region as a whole. They show that the different definitions lead to dramatically different middle-class sizes in low-income and middle-income regions. For example, the estimated size of the MENA middle class is either implausibly small, representing under 5 percent of the population with the definitions proposed by Birdsall (2007) and Ferreira et al. (2013), or implausibly large, accounting for more than threequarters of the population with the definitions in Chun (2010) and Ravallion (2010). The other paper by Ali (2011) uses the national poverty line converted to PPP dollars and $13 per day as the lower bound and the upper bound for the middle class, respectively, but also appears to overestimate the size of the middle class as vulnerable households with consumption levels just above the poverty line are considered part of the middle class.

Several limitations exist, however, with respect to the definitions that rely on fixed thresholds. Absolute thresholds will have to differ across countries and time in order to be useful for making inferences about the size of the middle class. The MENA region is comprised of low-income, lowmiddle-income, high-middle-income, and high-income economies, and the heterogeneous income distributions of these economies may not even overlap. Furthermore, fixed thresholds cannot capture the differential price effects within countries and other important welfare effects that are incorporated in the estimation of national poverty lines. An alternative is to define income groups by asking people if they considered themselves to be poor, middle class, or rich and cross check the outcomes with those obtained using objective definitions based either on absolute or relative threshold measures (Ferreira et al., 2013).

We follow two approaches in setting the welfare thresholds (lines) that define the different income groups (i.e., the low-income, middle-income, and top-income groups). Our
and finally: Therefore the log of the likelihood function for all individuals in the panel through all periods will be: Employing the Newton-Raphson algorithm, the first and second derivative of the likelihood function with respect to the parameters are calculated as: which permits us to estimate /3, and _V(,B)._

# **IV. Data:**

The National Urban Employment Survey (NUES) conducts extensive quarterly household interviews in the major metropolitan areas and is available from 1987 to 1993.

It is structured as a rotating panel where in each quarter, a fifth of the sample is dropped and replaced by individuals who will be interviewed for each of the next five quarters. In 24 overlapping panels spanning 1987-1993, individual workers can be followed as the 10
c. By categories of main income source (Each income source accounts for more than 70% of total household income) <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>Fell into poverty Fell out of poverty Stayed in poverty Stayed out of poverty<br>Dependence on: Wage/salary agr. income Dependence on: Self‐employed agr. Income<br>Dependence on: Wage/salary non‐agr. income Dependence on: Self‐employed non‐agr. Income<br>Dependence on: Non‐labor income<br>category<br>Likelihood of being in each poverty‐transition<br><!-- End of picture text --> Note: Other religions include Christian, Sikh and Jain. Of those who were poor in 2005, the distribution is: Forward castes: 9.3%; OBC: 33.7%; Dalit: 27.6%; Adivasi: 13.5%; Muslim: 15.1%; Christian, Sikh and Jain: 0.9%, for total of 100%. Non-labor income includes the following income categories: remittances, benefits and other income.

Source: Calculations based on IHDS, 2005 and 2012.

Compared to other caste/religion groups, upward mobility is low for Dalits and even lower for Adivasis; and conversely, downward mobility is the highest for Adivasis and the second-highest for Dalits.<sup>12</sup> Figure 3 shows that conditional on being poor in 2005, an Adivasi is 25 percent and a Dalit is 8 percent less likely to be non-poor in 2012 than an OBC (the group with the next lowest probability). And conditional on being nonpoor in 2005, an Adivasi and a Dalit are 12 and 5 percent more likely to be poor in 2012, respectively, than a Muslim (the group with the next highest probability). These results
Our use of these three sets of surveys offers a number of advantages. First, the years considered in the analysis coincide with a period of sustained income growth and reduction of poverty and inequality in the Latin America region; mobility in and/or out of poverty is expected to be large. Second, having several years of panel datasets allows us to validate the technique for different lengths in time both _within_ and _across_ countries ranging from five to ten years in Chile, from three to eight years in Nicaragua and from one to two years in Peru. Some additional information on each dataset follows.

# _3.1 Chile (CASEN Survey)_

We use the 1996, 2001, and 2006 CASEN Panel survey. The CASEN survey is carried out jointly by the Foundation for Overcoming Poverty (FSP), Ministry of Planning (Mideplan) and Social Observatory of the Universidad Alberto Hurtado (OSUAH) and its main objective is to study poverty dynamics and vulnerability.

The first round of the CASEN panel survey interviewed 20,948 individuals in the Third, Seventh, Eighth, and Metropolitan regions of Chile, representing approximately 60 percent of total population. The survey was carried out between November and December 1996 and it has information mainly on education, employment, income, health, labor history, participation, and housing. The second round of the CASEN survey was conducted between November and December 2001 and surveyed 18,851 individuals; from which 15,038 were interviewed in 1996 (the corresponding attrition rate is 28.2 percent). Finally, the last round of the survey was performed between November and December 2006 and January and February 2007. This round interviewed 14,996, from which 10,287 were surveyed in 1996. The last two rounds of the survey provide the same information as the CASEN 1996 survey.

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# <u>Measuring participation in the stimulus programs</u>

Specifically, we focus on the three components of SP1 that had the greatest share of their budget disbursed by mid-2009 (Jitsuchon and Patanarangsun 2009). These programs also have the important advantage that they can be matched with the SES data to determine which households were targeted. The three programs we examine are as follows: (1) The extension of free public education to 15 years in the 2009 school year, providing students with uniforms, textbooks, and exercise books free-of-charge, as well as subsidizing other school costs. We assume the benefits flow to households with children 18 years of age and younger. The total budgeted allocation was 19 million baht, of which 82 percent was disbursed between March and May 2009.

(2) The distribution of 500 baht allowances for a period of six months to senior citizens of 60 years or older who do not receive support from other government institutions, and who register at local administration offices. Unlike previous schemes, participants were not selected based on their degree of poverty. The total budgeted allocation was 9 million baht, of which 67 percent was disbursed by May 2009.

(3) The provision of one-time 2000-baht subsidies (Saving the Nation checks) to workers who were contributing to the Social Security Fund, state enterprise employees, and civil servants (including tambon and village heads), who were earning less than 15,000 baht per month. The handouts were paid in cashier's checks to be used to purchase goods at local stores, later cashed by businesses at designated banks. The total budget was 19 million baht, of which 93 percent was disbursed by May 2009.

9
||targeting on specific sectors can be<br>introduced in GRACE as it is a multi-<br>sectoral model.|||
|---|---|---|---|
|**IFs**|Prepackaged scenario intervention files<br>also allow integrated analysis of scenario<br>sets including the UNEP GEO-4 set and<br>the Shared Socioeconomic Pathway<br>(SSP) set|Deterministic (scenario analysis)|ICT Contribution to Increased Carbon Emissions (Moyer<br>et al., 2012). Scenarios for Vulnerability: Opportunities and<br>Constraints in the Context of Climate Change and<br>Disaster Risk (Birkmann, et al., 2013.) Eradicating Poverty<br>in Fragile States (Burt et al. 2014)|
|**PHOENIX**|||Impacts of long-term temperature change and variability<br>on electricity investments (Khan et al., 2021)|
|**WorldScan2**||Deterministic (scenario analysis &<br>sensitivity analysis)|‘Four Futures of Europe’ (De Mooij and Tang, 2003, and<br>Lejour, 2003) and its companion ‘Four Futures of Energy<br>Markets and Climate Change’ (Bollen, Manders and<br>Mulder, 2004)|

**Table 4.b Representation of policy, uncertainty and application for Partial Equilibrium**

|**Model**|**Policy**|**Uncertainty**|**Application**|
|---|---|---|---|
|**DNE21+**|Based on the scenario setup, energy- and<br>climate-related policies are explicitly<br>represented in DNE21+. This includes carbon<br>pricing, emission cap and trade system, carbon<br>tax, preferred tax on specific energy sources,
and Iacovone, Rauch and Winters 2010 for an empirical test of this hypothesis). For each multiproduct firm _f_ and product _p_ , we proxy how close is that product from the firm’s ‘core’ by ln _zfp_ , the log of its share in the total firm’s export sales. Results suggest that it correlates positively with first-year survival probability even after controling for dollar initial value; that is, the probability of survival for ‘core’ products is substantially higher than for others. For instance, a product representing 80% of the firm’s export sales (all destinations together) would have a first-year survival probability on a given destination higher by

> 12Confirming this interpretation, when we exclude the destination fixed effects, the coefficient on this variable becomes positive, suggesting that _permanently_ more attractive markets are associated with longer survival, which is consistent with our interpretation. This “crowding-in” result is also consistent with a finding by Bussolo, Iacovone, and Molina (2010) who found, using firm-level data from the Dominican Republic, that the reduction of tariffs following the signature of CAFTA led to some over-crowding of Dominican exports, followed by retrenchment.

21
nor regular, as may be the case for the labor of smallholder farmers over an agricultural season, respondents are unable to use rate-based or recall-and-count strategies and, so, are likely to yield erroneous reports of labor.

In the absence of episodic or rate-based information, respondents may revert to their general assumptions about the state of the world in their search for answers to survey questions. These assumptions then form a benchmark that is used to infer previous behavior. Indeed, the spuriously high recall-surveyed labor we find in our study can stem from this sort of inference (see below). Schwarz and Oyserman (2001) cite evidence that retrospective estimates of income and of tobacco, marijuana, and alcohol consumption are unduly influenced by people’s income and consumption habits at the time of the interview. Thus, they infer their previous behavior based on their current or recent behavior. Similarly, de Nicola and Giné (2014) show that survey responses on income from small-scale boat owners in coastal India rely more on inference and less on true recollection as the recall period increases. The authors show that, while this bias has little influence on the mean (because, in their case, fishermen base their inferences on average earnings), it does lead to an underestimation of income variability as the recall period increases. The information and assumptions held by respondents are also important if people report on the behavior of others, a common practice in the collection of labor data in household surveys (Bardasi et al. 2011; de Nicola and Giné 2014).

Respondents may also be suggestible and base their inferences on what they believe _should_ have occurred. Ross and Conway (1986) allowed students to participate in a skills-training program that did not, in fact, influence their skills. After participating in the study, the students 6
level helps to mitigate the bias introduced by other confounding variables that potentially affect the number of transaction accounts and remittances (e.g., local shocks).

_G. Household remittances:_ Data from the National Survey of Household Income and Expenditure (ENIGH) provide information about household income and expenditure sources, uses, and distributions, including income from remittances. ENIGH also details the occupational and sociodemographic characteristics of household members. We obtained access to the 2016, 2018, and 2020 surveys. Thus we can provide estimations at the household level of total remittances—that is, those received through both channels.

_H. Wage replacement rates:_ To test the hypothesis that the CARES Act contributed to the observed growth in remittances across Mexican municipalities, we used microdata on the unemployment insurance (UI) system to estimate wage replacement rate distributions by US state from Ganong et al. (2020).

_I. Additional controls:_ To control for potential confounding elements, we collected data from two additional sources. First, we obtained measures of COVID-19 incidence at the municipal level from the COVID-

> 13This Bartik-style instrument for labor demand shock at the local labor market level has been widely used in the literature. For a recent article, see Notowidigdo (2020).

7
temperature from ERA-5 to construct historical average temperature and probability distribution functions for the period 1979 – 2019. We then calculate the projected changes in temperature as the difference between the projected temperature, taken from CMIP6, and the historical average temperature. Finally, the temperature changes are used to calculate poverty rates (inequality) by multiplying with the baseline estimates in Columns (2), (4), and (6) of Table 1 (Columns (2) and (4) of Table 2). We select the estimates from the long differences model since it embodies any adaptations that farmers have undertaken to short-run change in climate, and thus projections of future climate change impacts would appear more trustworthy than those based on either panel or cross-sectional methods (Burke and Emerick, 2016).

Table A13 (Appendix A) provides a summary of the projected changes for temperature and poverty for different emission pathways. The temperature is predicted to rise by a range of 1.4°C to 5.0°C by 2099. This escalation in temperature may lead to an increase in poverty by 0.7 to 2.7 percentage points, equivalent to a rise of 7.4 percent to 26.6 percent, employing a poverty threshold of $1.9 per day (Panel A). Similarly, the projected impacts on inequality are expected to result in a 0.5 to 1.7 percentage point rise in the Gini index, translating to an increase of 1.4 percent to 4.9 percent (Panel B). The largest increase in poverty and inequality would occur in the scenario without any countervailing strategies based on renewable energy to address climate change. Finally, Figures A8 and A9 (Appendix A) present the projected temperature effects across regions in our sample under both emission pathways and reaffirm our previous findings that poor countries in Africa continue to be most vulnerable to hotter temperature.

# **References**

- <mark>Barbier, E. B., & Hochard, J