rafmacalaba/data-use-mentions-extended huggingface dataset:
datasets available to measure CBI. For robustness, we also use the data collected by Garriga (2016), which yields similar results.

To capture inequality dynamics, we rely on data from the World Bank’s PovcalNet database. Besides providing an extensive coverage of countries over a comparably long timespan, a key advantage of this dataset is that includes information on income dynamics at the decile level, which allows us to study income dynamics in a more fine-grained manner. Our headline results rely on the share of total income earned by each decile, ordered from the lowest- (bottom 10%) to the highest-earners (top 10%). Our conjecture is that the latter should benefit from CBI, whereas the former should be hurt. Together, this would indicate an increase in inequality.

In addition, we test the validity of the channels through which CBI operates. We discuss these variables in Section 5. Suffice here to say that we examine a range of variables that capture financial, labor market, and social policy deregulation (outputs). In addition, we also investigate the e↵ect of CBI on a range of financial and other economic indicators (outcomes) that should be a↵ected if our argument is correct.

Our baseline models are parsimonious by design. We are wary of including post-treatment control variables that would bias our results (Angrist and Pischke, 2008; Montgomery, Nyhan, and Torres, 2016). Likewise, we are concerned by estimates that depend on complicated modeling strategy (Leamer, 1983). Therefore, we prefer a simple model that uses within-country variation and eliminates time shocks. In the models below, we also control for GDP and population (both logged) to account for di↵erences in baseline economic wealth and size. In the appendix, we include several models with a fuller set of adjustments for potential confounders (see Section 4.2 for a summary). This allows us to rule out competing interpretations of our findings, such as the role played by government
estimating the impact of the pandemic, as highlighted in these early studies, is a lack of reliable data on the spread of the pandemic and associated economic and health damages. These data are needed to design alternative containment policies at minimum economic cost (Stock, 2020). Moreover, high-frequency cross-country data on consumer spending are lacking to assess the global impacts of the pandemic. Our paper fills this gap by using near real-time Google search data that reasonably predict consumer demand.

The remainder of the paper is organized as follows. Section 2 provides a detailed description of the Google Trends data, which capture consumer demand in near real-time, and the search terms that we carefully selected for each service. Section 3 lays out our empirical specification and identification strategies, while Section 4 presents and discusses the results. We 5
significantly.<sup>34</sup> The results here indicate that the closer the state’s congressional delegation was to the president, the more likely the state bank was to be liquidated or privatized. This suggests that the executive might have found it easier to do away with state banks in states where he had stronger ties to the congressional delegates, perhaps because they could be compensated with political currency more readily. The executive simultaneously wielded greater punitive powers within the party as well if party members opposed the elimination of the state bank.

These results have to be interpreted with an important caveat. The small number of observations does not allow us to control for many different factors simultaneously.

Idiosyncratic differences between the states are not captured in our regression. Finally, the time dimension, the fact that the states did not take the decision simultaneously is not explicitly modeled in our regressions.<sup>35</sup>

# **4. Bank-level regressions: data and methodology**

In order to assess the performance and efficiency effects of the different PROES options, we utilize an unbalanced panel of 207 banks with quarterly data over the period January 1995 to September 2003, with a total of 4,864 observations. Our sample contains 19 of the 35 state banks. These banks were federalized, privatized and restructured at different points during our sample period. Liquidated state banks are not included. Further, we had to drop several banks for which we have incomplete data.

> 34 In regressions we do not report here, we also tried measures of the share of a state’s delegation in the House that belonged to the PSDB (the government coalition).  Further, we used a dummy variable indicating whether a state’s governor was member of the PSDB (one of the parties of the government coalition).  In line with the results in Table 3, we find that states with a higher share of PSDB deputies or a governor belonging to one of the parties supporting the Cardoso government were more
Equation (1) can be estimated with different estimators, for example using seemingly unrelated regressions (SUR) with iterated feasible generalized nonlinear least-squares estimator (FGNLS) through nonlinear three-stage least squares (Poi, 2012). A preferred alternative is to use the Iterated Linear Least-Squares (ILLS) estimator developed by Browning and Meghir (1991) and later generalized by Blundell and Robin (1999), which is computationally more attractive (Lecocq and Robin, 2015). It is based on the conditional linearity property – i.e. all equations in (1) are linear in all parameters conditional on the price aggregators functions – and it requires linear SUR to obtain consistent and asymptotically normal estimation of the demand system. In order to allow the level of demand to vary according to household heterogeneity, the intercepts _ɑ_ ’s are parametrized with the sociodemographic variables _s_<sup>_h_</sup> from the household survey and described in Table 4, such that: Lecocq and Robin’s model (2015) allows accounting for endogenous prices by using instrumental variable techniques. the error _u_<sup>_h_</sup> _i_ in equation (1) is augmented with the vector _Z_<sup>_h_</sup> predicted from estimating reduced forms for _x_<sup>_h_</sup> and _p_<sup>_h_</sup> : The independent variables in the reduced form equations are the sociodemographic variables in _s_<sup>_h_</sup> plus the instruments. The instruments used are the unit values calculated for each good _i_ , corrected with price indexes for 2015, so to obtain one-year lagged unit values. Finally, price and expenditure elasticities are calculated at the mean of the household sample.

# **6. Results**

Table 5 displays expenditure and own-price elasticities obtained from the estimation of the QUAIDS with 10 goods and services. Column 1 reports expenditure (income) elasticities, while columns 2 and 3 report the uncompansate and compensated price elasticities. Uncompensated (Marshallian)
# **I. Motivation and Context**

The enhanced market access and increased competition facilitated by greater connectivity are critical drivers of firm dynamics and overall growth, although with theoretically ambiguous effects. On the one hand, increased connectivity through US railway expansion increased productivity and growth through market access and reallocation channels (Hornbeck and Rotenberg, 2019, 2021) and high costs of shipping to distant markets may drive size differences of plants between the United States and India (Hsieh and Klenow, <mark>2012, 2014).</mark> On the other hand, increased competition may cause local firms and industries to contract (Banerjee et al., 2012; Fujita and Krugman, 1999; Chandra and Thompson, 2000).

Ghani et al (2016) exploit a natural experiment offered by the construction of India’s Golden Quadrilateral (GQ) highway connecting the four major cities in India – Delhi, Mumbai, Chennai, and Kolkata – to document that the net effect was to expand manufacturing along the corridor. However, there is little literature on the underlying adjustment dynamics of such interventions – which types of firms generate the observed aggregate responses. Hence, this paper lifts the hood on the previous GQ findings: by creating synthetic cohorts from the rich location-coded plant-level data set, as well as constructing a true plant panel, it details <mark>how the reaction of plants to greater connectivity varies across their life cycle</mark> – <mark>young, mature and old-plant, local initial conditions, and time.</mark><sup>4</sup> <mark>Conceptually, the effects of connectivity could differ radically across these dimensions</mark> – <mark>older firms unaccustomed to competition may contract, while firms born within a more competitive context may be prepared to take advantage of new markets. Alternatively, older firms may have established access to necessary complementary factors – finance, land, or even local product markets that, in line with de Loecker et
the contrary, the weather during the 2009/10 growing season was relatively good while the one in 2003/04 was comparable to the average. The later growing season was preceded by the severe 2002 drought and followed by the dramatic 2005 drought and flooding.

The analysis conducted in this study to examine the impact of droughts and excess rainfall on the welfare of Malawian households shows that exposure to these shocks decreases households’ consumption and elevates their vulnerability to poverty. Weather shocks have severe impacts on welfare of households and push non-poor households into poverty. Drought during crop growing season decreases consumption per capita without aid by significant margin, by 5–12 percent. Similarly, flooding during the first two months of growing season has an adverse effect on consumption per capita. As a result, households’ vulnerability to poverty is generally higher than static poverty. In other words, even if poverty rate is already very high in Malawi, the non-poor households are likely to fall into poverty when exposed to these shocks. Design of safety net programs, such as social cash transfer, need to take into account vulnerability to poverty and such programs can be scaled up during disaster periods to benefit households that become poor as a result.

The rest of this paper is organized as follows: Section 2 describes the data and methodology. Sections 3 and 4 present the results and robustness checks. Finally, we conclude in Section 5 by highlighting policy implications of the main results and acknowledging the study’s limitations.

# **2 Data and Methods**

## **2.1 Data and Descriptive Statistics**

To analyze vulnerability of households to poverty, we used the three rounds of the IHS, which is nationally representative, comparable over time, and used to produce the official poverty statistics. The second round (IHS 2) was conducted from March 2004 to March 2005, and a total of 11,281 households were interviewed. The third round (IHS 3) was implemented from March 2010 to March 2011 and covered 12
PSU (village, town, or neighborhood), such that the estimated differences between Hindus and Muslims are restricted to households in close geographic proximity. Column 5 reports a difference of 5.3 percentage points when jointly including controls for household characteristics, location characteristics, and PSU fixed effects (95% confidence interval: difference – of 1.5pp 9.1pp). The standard errors on the estimates are similar, or decreasing, as additional controls are added because the decrease in degrees of freedom is counterbalanced by the predictive power of the controls. Columns 6 and 7 report this difference separately for urban areas (3.2pp) and rural areas (5.6pp).

For other sanitation practices, panels B and C report little substantive difference between Hindus and Muslims after controlling for the same characteristics. There are some unconditional differences between Hindus and Muslims, with less handwashing and less observed fecal material around Hindu households, but these differences are attenuated when comparing households within similar locations. For handwashing, column 5 reports that Hindus are 1.1 percentage points more likely to report washing their hands after defecating (95% confi– dence interval: 1.7pp less likely 3.9pp more likely). For observed fecal material, column 5 reports that the surveyor is 1.7 percentage points more likely to not observe fecal material near Hindu households (95% confidence interval: 2.2pp less likely – 5.7pp more likely).<sup>17</sup> Table 3 reports estimated differences in latrine ownership for Hindus and Muslims, using data from the DLHS-3. There is an unconditional 22.9 percentage point difference in latrine – ownership between Hindus and Muslims (95% confidence interval: difference of 21.7pp 24.0pp), which is substantively similar to the unconditional difference in latrine ownership in the IHDS data. This unconditional difference is in large part attenuated in columns 3, 4, and 5 when controlling for household location characteristics and/or fixed effects for PSU.
Traditionally, most studies of employment and crime were conducted using American data and many found conflicting or small effects of labor market outcomes on crime (Bushway, 2010). But more recent empirical evidence from the OECD on the relationship between employment and crime suggests more significant effects (Mustard, 2010). Fougere et al (2008) find that increased youth unemployment, instrumented for by changes in industry structure, increased burglaries, thefts, and drug offenses, with particularly large effects for youth. In the US, Lin (2007) estimates that a 1 percentage point increase in the average state unemployment rate raises property crime by about 4 to 6 percent. Gould, et al (2002) document that both wage levels and unemployment rates are significantly related with crime, with estimated elasticities with respect to wage levels of ‐0.55 for property crime and ‐1.08 for violent crime. Finally, a randomized evaluation of the youth job corps, a program for disadvantaged 16‐24 year olds that offers 30 weeks of training and a stipend, found that it reduced crime among all subgroups. However, the monetary benefits of the reduction in crime were estimated to be only about 6 percent of all program costs (Kluve, 2014). Heller (2014) finds that the provision of an 8 week summer job to disadvantaged youth in Chicago reduced the probability of violent‐crime arrest by 43 percent.

The few existing studies from developing countries on how employment‐related interventions affect engagement in illicit activities or violent conflict suggest more nuanced effects than in the OECD. One set of studies examines the effects of India’s National Rural Employment Guarantee Scheme (NREGS), and generally finds weak effects. For instance, Das (2015) examines at the district level, how implementation of India’s National Rural Employment Guarantee Scheme (NREGS) affected official reports of violent crime from 2001 to 2011. The results indicate that the program reduced reports of kidnapping, but most of the relationships between crime and program implementation were
30 any effects of COVID-19 on child labor as argued by ILO and UNICEF (2021).

In addition, even if child labor increases during a pandemic, the evidence suggests that child labor only marginally contributes to total earnings. More precisely, using the I2D2 data for the world, we first show that the employment rate is much lower for children than for adults (Web Appx. Fig. A.8). While more than two thirds of 1867 year-olds work across all countries regardless of the development level, only 3.9, 7.7 and 21.9% of 5-17 year-olds work in the HICs, MICs and LICs, respectively. Children who work work few hours and for low wages (Jedwab et al., 2021b). 5-17 year-olds thus only contribute 0.2, 3.1 and 1.9% of total wages in HICs, MICs and LICs, respectively. As such, ignoring the impact of child labor experience during a pandemic is likely to be inconsequential. Furthermore, allowing for increased work experience due to child labor during a pandemic to mitigate the negative effects of reduced schooling would if anything lower the losses for poorer nations, which would again reinforce our results.

Finally, children that attend school rarely work at the same time. Indeed, using I2D2 data for the world, we show in Web Appx. Fig. A.10 that children attending school on average work far fewer hours than children not attending school (note that these numbers include zeroes for children who do not work at all). Thus, we do not allow children to simultaneously accumulate human capital from education _and_ work.

**Cohort Effects, Measurement, and Selection.** We include decadal cohort FE or important events FE. We then exclude self-employed individuals. Furthermore, we use parameters estimated for men only. Lastly, estimates could be sensitive to the extent of non-employment, which varies across countries and by level of development. This could be because workers with certain characteristics might be more
“I saved money last month”, the amount of total savings relative to the monthly income earned, savings kept at MFBs, savings at commercial banks, outstanding loans from commercial banks, MFBs, suppliers, money lenders, or family/friends. Actual savings behavior is measured through administrative records of those who engaged with representatives of _Accion_ to open an account, and those who actually opened an account at the screening event.

Neither financial knowledge, nor basic numeracy skills were specifically addressed in the movie’s storyline. Nevertheless, the survey also included 6 quiz‐like questions with true and false choices to assess respondents’ understanding of basic financial concepts as well as their numeracy skills. The underlying motivation for including these questions is that economic models of savings and investment choice consider both as indispensable for good financial decision taking (Lusardi and Mitchell, 2013). In particular, respondents were required to do simple divisions, to perform basic calculations related to 9
> Third, we select a 10% random sample of workers from this auxiliary database due to the computational impossibility of using the entire RAIS database.<sup>9</sup> If a worker’s unique identifier is selected for our random sample, her/his entire work history is included. Fourth, our final worker panel database consists of this 10% random sample expanded such that each worker has observations for all consecutive years between the first and last RAIS year (when the worker had a job record) and after the worker’s last RAIS year before 2017 provided the worker’s age does not surpass the retirement age of 65. This expansion of the database by adding observations with zero months worked and missing wages is necessary to construct the cumulative employment and wage measures we use as worker outcomes, described in Section III. Observations with zero

> 5RAIS is a high-quality employer-employee administrative database which has been used extensively in research: e.g., Alvarez et al. (2018); Ulyssea (2018); Dix-Carneiro & Kovak (2019); Dix-Carneiro et al. (2021), and Gerard, Lagos, et al. (2021). As mentioned by Dix-Carneiro & Kovak (2019), firms have an incentive to accurately report to RAIS since they risk fines if they fail to report and workers face similar incentives since their access to government benefits depends on such reporting.

> 6The tradables sector is defined as the set of industries with 2-digit codes below 37 in the Brazilian National Classification of Economic Activities (CNAE) version 1.0 classification.

> 7The choice of the highest paid job in a given month follows that made by previous studies using RAIS (e.g., Dix-Carneiro & Kovak (2019)) as well as that made for similar employer-employee databases for other countries (e.g., Fr ́ıas et al. (2022) for Mexico).

> 8The firm’s exporting status is based on matching RAIS to the customs data as
_i_<br>(1)|_D_<sup>_FNL_</sup><br>_i_<br>(2)|_S _<sup>_FNL_</sup><br>_i_<br>(3)|
|Total Consumption|No control|1.49|2.26|-1.98|
||Lag|6.35|35.00|9.95|
|Consumption expenditures|No control|-10.06|2.29|-1.15|
||Lag|-42.94|10.53|43.23|
|Consumption from stocks|No control|-2.77|6.96|-6.22|
||Lag|-8.11|-6.28|16.79|
|Nonfood spending|No control|-23.82|29.99|-4.00|
||Lag|-16.11|33.16|1538.95|
|TLU|No control|3.55|-4.82|-2.41|
||Lag|22.13|-12.45|-60.60|

_Notes:_ Authors’ analysis based on data described in the text. Each cell of the table reports ratios based on the coefficient corresponding to the respective column: column (1) is the ratio for _returnees_ , column (2) for _demobilized_ and column (3) for the spillovers. There are two lines per outcomes. The first is the restricted model without any control, and the second is a regression that includes the lagged dependent variable. For each outcome, each regression is based on the same sample size is restricted; andand includes province fixed eαˆ _F_ when the regression the full set of controls.ffects. Denote αˆ _R_ the coefficients on the demobilization variables (The reported ratio is calculated as: _R_ , _D_ andαˆ _R_ αˆ− _<
Munley (1984) also estimated water pollution abatement costs based on the framework proposed by Rossi, Young, and Epp.

Goldar, Misra, and Mukherji (2001) identified problems associated with the cost function proposed by Rossi, Young, and Epp, and argue that output of abatement activity should be defined as the reduction in the pollution load. They define output of water pollution abatement as a function of the volume of waste water treated, the difference in the pollution levels of influent and effluent water, and inputs used to purify the water. Golder, Misra, and Mukherji specified a water pollution abatement cost function in which the cost of abatement is an explicit function of the quantum of abatement (i.e., the difference between water quality before and after the treatment) and factor prices. There are some similar studies that did not include factor prices in the abatement cost function (e.g., Mehta, Mundle, and Sankar, 1993).

Another set of studies considered pollution abatement as an inseparable multi-output process, and suggested that the cost of abatement might not be separable from the cost of production (see Pizer and Kopp, 2005; Maradan and Vassiliev, 2005; Boyd, Molburg, and Prince, 1996). Gollup and Roberts (1985) used observed data on utility pollution abatement and production costs to estimate a cost function that included emission control rates as a predictor of production costs. Nordhaus (1994) compared a number of published models in terms of percentage difference of carbon emissions from a baseline path and propose an aggregate formula relating cost to output and reduction of greenhouse gases. In a similar manner, Newell and Stavins (2003) explored the pollution abatement cost heterogeneity (i.e., the relative cost of uniform performance measured in terms of emissions per unit of product output) by using a second-order approximation of the costs around the baseline emissions. Their approach was based on variation in baseline emission rates, thus estimation of the cost function required data on baseline and project