rafmacalaba/data-use-mentions-extended huggingface dataset:
These jobs include maintenance activities such as building sidewalks, local roads, schools, and improvements in the local infrastructure (Fishback _et al._ , 2004).

Using data on New Deal Grants to each US county from 1933 to 1939, Fishback _et al._ (2005) estimate how relief and public works spending and payments to farmers through the Agricultural Adjustment Administration influenced retail consumption. They find that an additional dollar of public works and relief spending was associated with 44 cents increase in 1939 retail sales. This implies a weak effect of fiscal policy consistent with results from Romer (1992).

Similarly, Fishback and Kachanovskaya (2011) examine the impact of federal stimulus programs and find that state per capita personal income multiplier with respect to per capita federal grants is around 1.1. In another study, Fishback and Kachanovskaya (2015) estimate that an added dollar of federal spending in the state increased state per capita income by between 40 and 96 cents by using an annual panel data set for the period 1930-1940.

Federal grants had stronger effects on consumption than on personal income, but they had no positive effect on various measures of private employment. The multiplier for wages, salaries, and retail sales is substantially less than one indicating “crowding-out”. The multiplier for farm payments to take land out of production was -0.57 (Fishback and Kachanovskaya, 2011). This implies that the program actually reduced personal income. These results may help to explain why measures of income have recovered more rapidly than measures of employment in the 1930s. Based on these findings, the New Deal spending might have raised the productivity of local producers if it was devoted to building infrastructure that cut transport costs to other areas (Fishback and Kachanovskaya, 2010).

> 5 Renamed as Works Projects Administration in 1939.

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# **1 Introduction**

_The agreement to lure Amazon to Long Island City, Queens, had stirred intense debate in New York about the use of public subsidies to entice wealthy companies and the rising cost of living (New York Times, February 14, 2019)_ After facing resistance from local politicians and the residents of New York, Amazon decided to cancel its planned construction in Queens, NY, and focus instead on the second site in Virginia. While some local NY politicians and activists argued that the $7 billion in tax incentives offered to the tech giant outweigh the benefits that would have accrued to the local economy, others have argued the opposite. The argument of rising costs of living, or local inflation, has featured prominently in public debates. However, while we have some evidence on the effects of Million Dollar Plants (MDP) on local productivity and rents (Greenstone et al., 2010), to date we do not know how MDPs affect the local cost of living, specifically prices of retail goods, which ultimately affect real wages, and hence worker welfare. The main challenge in addressing this question is estimating the cost of consumer goods at a micro-region and granular barcode level of goods that is not possible using aggregated consumer price indices.

In this paper, using detailed barcode level prices from the IRI data, we analyze how the entry of an MDP in a county affects retail prices and how that affects the real wages of high and low skilled workers. To understand what rising local inflation means for local within-region inequality, in an event study we compare the wages and hours worked of both high skilled and low-skilled workers in winning compared to runner up counties. To shed light on how an MDP entry changes local prices, we build a partial equilibrium model that introduces a new mechanism in the MDP literature: Changing household optimization decisions over labor supply, leisure, and shopping time choice. The model allows for the possibility that the higher income generated by the MDP makes high-skilled workers spend less
claims<sup>10</sup> by _all_ banks that the BIS tracks (Exhibits II.6 and II.7). These data show that total international claims with a maturity of over 2 years have been falling and, since 2009, longerterm claims growth has been often lower than total claims growth. This suggests international banks are letting their longer-term loans run off. In contrast, although falling, longer-term claims growth to EMDEs has remained positive since 2011. Yet, is has been lower than total claims growth, suggesting international banks have shifted their lending expansion in EMDEs towards shorter maturities.

**European bank deleveraging in EMDEs varies from region to region and from country to country depending on the bank’s business model and nationality.**<sup>**11**</sup> The Eastern European region was particularly affected by the retrenchment of mainly Austrian and Italian banks<sup>12</sup> that provided cross-border funding and local credit. Latin America has a strong presence of Spanish bank subsidiaries although they tend to be more self-reliant and raise deposits in domestic markets. The Middle East and North Africa has a presence of French banks. British banks are active in Asia and South-Saharan Africa among others.

**_II.6_ For all banks combined, growth of international** **_II.7_ As a result, the fraction of longer maturities to** **<u>claims with longer maturities lagged behind... total outstanding international claims has declined</u>** Growth Intl Claims of All Banks Claims with maturity >2yr as a Fraction of Total Intl _%, YoY (2001Q1-2012Q3)_ Claims of All Banks _%,_ _<u>(2001Q1-2012Q3)</u>_

<!-- Start of picture text --> 60% 40%<br>40% 35%<br>20%<br>30%<br>0%<br>25%<
Yemen, and by about 12 percentage points in Pakistan. They do not find any significant effect in African countries, though. Interestingly, in their study the impact of better water access on school attendance is similar for boys and girls.

Nankhuni and Findeis (2004), using a cross-sectional survey of 10,698 households in Malawi, investigated whether time spent by children (aged 6-14) to collect water and fuelwood impacted on the likelihood of attending school. Each child in the survey was assigned the median value of hours spent on fuelwood and water collection for the area of residence (the survey covers 136 areas), which is likely to induce some measurement error. They found evidence that the probability that a given child is involved in fuelwood and water collection is reduced in households with more women, and more members beyond school age. They also found a significant relationship between time spent collecting fuelwood and school attendance so that “children in districts with severe fuel wood deficits (all of the south and most of the central region) are about 9% less likely to attend school than those from fuel wood surplus districts.” They however provided no direct measure of the impact of the time to collect water on school participation.

Ilahi and Grimard (2000) measured how the quality and quantity of water-supply infrastructure (calculated from the distance to the source of water averaged over all households in the community that report collection but excluding the household in question) affect the allocation of time among Pakistani women to various activities, using a crosssectional survey from 1991 of Pakistani women above 15 years of age. Improvements in the water-supply infrastructure were here found to increase the time that women allocate to income-generating activities (although no magnitudes are given). Ilahi and Grimard did not study impacts on children’s school enrollment.

Lokshin and Yemtsov (2005) used community-level data from Georgia to assess the welfare impact of infrastructure rehabilitation projects (school rehabilitation, improvements in road infrastructure, and water system rehabilitation projects) in rural areas between 1998 and 2000
comparing characteristics of firms in the sample with those not taking part in the interview. But since data are, by definition, not collected from firms that refuse/didn’t take part, it is not possible to compare the characteristics of firms who responded with those who did not. But what we can do is compare some of the broad characteristics of the drawn sample to the actual sample of firms we used. Comparisons for governorate, size, export status and many other characteristics are pretty similar. Actual sample characteristic are quite close to the drawn sample.<sup>7</sup> The questionnaire includes 14 modules: basic firm identification data, firm size and employment, firm expectations on recovery and potential exit, changes in firm performance, pandemic transmission channels, ownership and management characteristics, innovation, management practices and use of information technology (IT), production costs, obstacles to operation, exports and global value chains, obstacles to exports, worker training and government support.

The survey includes information on the distribution of female and male employees preand post- COVID, which we use to construct our measure for the gender employment gap. Specifically, we are able to infer the change in employment and other indicators pre- and postCOVID-19 based on a number of retrospective questions in the EIFBS questionnaire. For instance, we ask about the total number of employees in the last financial year prior to COVID19, and the total number of employees recorded at the month of the survey. This allows us to work out changes in total employment post-COVID. Similarly for male and female employment.

_Stylized Facts_

> 5 Precisely between November 19th 2020 and the 5th of February 2021. Precisely between November 19th 2020 and the 5th of February 2021.

> 6 Only four modules are slightly different. The main difference is that for temporarily closed or closed firms there are no values for current variables such as production, exports, employment or revenues. 7 Available from authors upon request.

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**Table 1 continued from previous page**

|**Activity/Sector**|**Category M**|**ale percentage **|**Median earning PPP**|
|---|---|---|---|
|Maintenance and cleaning/surface technician|Not MDS|22.92|80|
|Retail trade: foodstufs|Not MDS|21.39|140|
|Hairdressing/cosmetic care|Not MDS|20.78|140|
|Governor|Not MDS|17.93|180|
|Food/beverage processing|Not MDS|9.77|120|
|Kitchen/pastry/restaurant|Not MDS|5.54|239|
|Other*|Not MDS|NA|NA|
|Miscellaneous services (campaigns, events, etc.)*|Not MDS|NA|NA|
|Chair/chapel rental*|Not MDS|NA|NA|
|Video game room*|Not MDS|NA|NA|
|Discotheque*|Not MDS|NA|NA|
|Telephone booth*|Not MDS|NA|NA|

_N_ otes:

*Not MDS by default.

`[ICT]` and `[Energy]` shows sectors that were assigned respectively to the ICT and Energy sectors. The sector classification is based on data from the ENV 2015 survey, which includes information on respondents’ main activities and their corresponding sectors. Given that the Pro-Jeune survey uses its own sector classification system, differing from the ENV classification, we matched the ENV sectors to those of the Pro-Jeune survey.

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1)|1.3|(1.2)|
|TA’s median Palmer Index<br>-6.4<sup>∗</sup>|(3.7)|-2.7|(3.6)|-7.9<sup>∗</sup>|(4.3)|
|TA’s 98th percentile of rainfall<br>0.1|(0.1)|0.2<sup>∗</sup>|(0.1)|0.2|(0.2)|
|**2. Self-reported shocks**||||||
|Job loss<br>2.9<sup>∗</sup>|(1.7)|0.5|(3.3)|0.7|(3.6)|
|Food price shock<br>-2.0|(1.4)|-5.5<sup>∗∗∗</sup>|(1.6)|-6.1<sup>∗∗∗</sup>|(1.9)|
|Death<br>3.4<sup>∗∗</sup><br>**3. Household Head**|(1.4)|0.4|(2.1)|-0.2|(3.3)|

14 The difference between columns (1) and (2) is only due to the exclusion of IHS 2. The difference between columns (2) and (3) is due to the new dependent variable, which accounts better for transfers. As explained earlier, the coefficients on excess rainfall, as well as the one on job loss, death, and no employment, become smaller.

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<u>2012). The criterion function used to select the probabilities corresponding to the coefficients of</u> the APC model is the entropy measure by Shannon (1948) that is identified by requirements that any measure of uncertainty should be continuous, symmetric with respect to reordering of the outcomes, and additively decomposable, and bounded (which is the case for most demographic and economic data). An appealing feature of the APC ME method is that it overcomes the potential arbitrariness of identification restrictions. We use the ME estimator, but we compare our results to the estimates based on the approach by Deaton and Paxson (1994).<sup>11</sup> We model the job tenure _Tiapc_ of individual _i_ as a linear function of the individual’s age ( _ai_ ), period ( _pi_ ), and cohort ( _ci_ ) effects. We define a (the average tenure of individuals who were age _a_ in survey year _p_ and cohort _c_ ) such that aTT =aa ∑inni1 ii i <u>i</u> ~~,~~ where a is the number of individuals in i i= TT the corresponding APC cell. We also define vectors of age, period, and cohort dummies: TT aa nn ii nn aa = , , and , where _K_ kk is AA the number of age categories, ll _L_ is the number of survey rounds, mm _M_ is the number of generational ll[kk= aa], kk= 1, ... , KK PP = ll[ll= pp], ll= 1, ... , LL CC = ll[mm= cc], mm= 1, ... , MM cohorts, and is an indicator function. Then, ll a = ∑ + ∑ + ∑ a , (1) KK LL MM aa kk=1 kk kk ll=1 ll ll mm=1 mm mm aa where a denotes the error term. TT αα AA ππ PP γγ
high, and very high flood risk during an intense flood event. These estimates are available globally with a resolution of 90 meters, but they are also aggregated to administrative units, including for each country and subnational unit. These estimates are also aggregated to yield regional and global estimates.

**4. Compute the number of poor people exposed to flood risk:** While poverty estimates are not available at the pixel level, the World Bank’s GMD database provides them at the subnational level for most countries. These poverty shares are multiplied with the population headcount that is estimated to be exposed to flooding, in order to obtain an estimate of the number of poor people in each administrative unit exposed to flood risk. Similarly, exposure headcount estimates are multiplied with subnational GDP per capita figures to obtain estimates of flood exposed GDP in monetary terms.

# **4.2. Stepwise computational process**

The following steps are repeated for each subnational region where data are available.

## **Merge coastal, pluvial, fluvial flood hazard maps**

- Crop fluvial and pluvial flood rasters to subnational boundary

- Crop then virtually warp the coastal flood raster, using a nearest neighbors resampling method, to perfectly match the resolution and extent of the fluvial and pluvial rasters

- Merge fluvial, pluvial, and coastal rasters using a maximum value method

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upon both employment shares and fraction of plants. Small industry clearly dominates the poorest regions of Brazil.<sup>5</sup> In the lowest-decile areas, 90% of manufacturing facilities are small plants. This declines to 63% in the highest-income municipios. The small-plant employment share drops steadily from 83% in decile 1 to 31% in decile 9, and then plummets to 19% in decile 10. The share of employment in large plants climbs steadily through 45% in decile 9 before jumping to 62% in the highest-income municipios.<sup>6</sup>

# 5. **Results: Total Emissions and Health** Damage

The results in Section 4 are consistent with the three hypotheses at the end of Section 2. The Mexican data, especially combined with the air dispersion parameters, show that small plants are far more pollution-intensive than large ones. In Brazil, small plants play a much larger role in the industrial economies of poor regions. Furthermore, these poorer regions of Brazil exhibit a greater concentration of plants in the dirtiest sectors. In contrast to poor areas, Brazil's richest municipios are the heartland of large enterprises in relatively clean sectors.

Yet, paradoxically, confirmation of these hypotheses does not imply that pollution damage is actually greater in poor regions. Economic development also promotes two countervailing trends. The first is an increase in the _scale_ of industrial activity, which may lead to greater pollution even if production shifts toward larger plants in cleaner sectors. The second is _urbanization,_ which increases the size of populations exposed to industrial pollution. A larger exposed population will suffer greater aggregate health damage from pollution, even if industrial emissions remain constant. In this section, _5_ The database provided by IEBGE is more comprehensive than any comparable information source we have seen. Nevertheless, it is entirely possible that small enterprises are undercounted in the database. For this exercise, what matters is the effect of income level on the propensity to undercount. Data-gathering is probably less efficient in poor
data at the person-plot level shows, farmers only perform 2.5 weeks of work on a given plot over the entire season, the possibility is large that their reporting as if they were working on every plot up to as often as they work on any plot—for instance, 12.8 plot-weeks at the person-level in weekly visit interviews—introduces errors in plot-person labor calculations. Accordingly, it appears as though farmers may report work that occurs across all plots as if it occurred on every plot, thereby inflating person-plot labor estimates.

# **Reporting on people and plots**

Unlike the case of work time, the direction of the bias in the reporting on people and plots active in agriculture means that forgetting is a plausible explanation for the observed gap between the weekly survey and the recall survey. Indeed, it may be a more plausible explanation in this case than mismeasurement because of poor inference because there is less gray area in reporting on the extensive margin of agricultural labor, that is, it is unlikely that a person would have to infer their participation or a plot’s engagement in agricultural work. What, then, drives certain plots and people to be forgotten? Here, as before, inconsistency plays a role, albeit perhaps a weaker one: where the associated work is rare or infrequent, people and plots are unlikely to be recalled as active in farming.

# _Reporting on plots_

First, we examine the possibility that, in recall surveys, households may have forgotten or otherwise failed to report plots that may have fallen out of the study over the course of the season for legitimate reasons. Put another way, we ask whether, in recall-surveyed households, the people and plots active in agriculture actually represent net end-of-season data, while, in the weekly surveyed household, they may represent cumulative or gross data.

Table 7, panel B shows a clear gap in the reporting on plots in the recalls, wherein roughly half the active agricultural plots are reported as in the weekly surveys. To track
wage bill and capital share across firms in each cell.

Figure A7 in the Online Appendix plots the labor-wedge distribution across locations for each sector in the baseline year. The wedges between the formal and informal sectors are very similar to the ones found by Busso et al. (2012). Formal firms face larger distortions. On average, the wedge in the formal sector is approximately 1.67 times the wedge in the informal sector. Furthermore, panel B of figure A10 in the Online Appendix shows the spatial distribution of labor wedges after I construct ventiles across locations. In places in the center of the city, where there is more economic activity and formal firms locate, wedges are larger.

Moreover, for the counterfactual analysis, I also use a constant wedge for formal firms based on the work from Levy (2018) (Table 7.9). For the labor wedge, I use a conservative value of 0.95, and for the commercial floor space, a value of 0.75. These wedges include several distortions such as implicit taxes on salaried workers, regulations on dismissals and reinstatements, non-contributory social insurance, standard labor taxation like state payroll taxes, and firm taxation including REPECO and value-added taxes.

# **5.5 Other Parameters**

I calibrate other parameters of the model using simple moments of the data, or take them directly from the previous literature. I calibrate the expenditure share on housing using the ENOE and find, on average, a value of _α_ = 0 _._ 75. Similarly, for the labor share, I use data from the Economic Census in 1999 and find a value of _β_ I = 0 _._ 70, and _βF_ = 0 _._ 6. To calculate the total amount of housing _H_<sup> ̃</sup> and commercial floor space _Z_<sup> ̃</sup> in each location, I use the area in square kilometers of buildings in each census tract from the Global Human Settlement Layer
tion.<sup>62</sup> Both the RDD and the matching approach confirm the effects on expenditures on women’s assignable goods and remittances, despite the low statistical power of the RDD approach.<sup>63</sup> The matching procedure also confirms the effects on the decision-making index in wave 1 and girls’ expenditures, though the RDD coefficients are small and insignificant for these two variables.

I further implement a placebo test by comparing outcomes of the women in the top half of the selected group (who I define to receive the _placebo_ treatment) to outcomes of women in the bottom half of the selected group.<sup>64</sup> This means that I am now only comparing women who were participants of the promotion program. The placebo coefficients are all small and insignificant. These null results support the claims of the empirical strategy that (1) the ranking of eligible women in the shortlist is orthogonal to the outcomes of interest, and that (2) the cutoff which separates the selected from the non-selected is the relevant margin of interest.

As explained in Section 3.2, there was some differential attrition between the selected and the nominated group in the second wave of the household survey, for which I present two checks. The balance checks in Table 1 presented earlier only include the respondents whose responses are included in the analysis, and therefore show that the selected and the nominated group remain comparable even after taking attrition into account. Furthermore, bounds following Lee (2009) in Table C.21 confirm the main effects.<sup>65</sup> For the pooled estimates and the estimates only using data from wave 1, the bounds do not include zero for the outcomes I investigate. Also in wave 2, the bounds confirm positive effects on being a supervisor, female expenditures and remittances, though the higher attrition means that the bounds for the decision-making index include zero.

The Online Appendix shows several additional robustness checks.<sup>66
poverty reduction in the 1990s. The impacts are highest in Sub-Saharan Africa. While aggregate poverty incidence has shown no trend increase or decrease in Africa over the bulk of the 1980s and 1990s, the results presented here suggest that without selective mortality, poverty would have been rising in this region by around 0.2-0.4 percentage points per year in a range of plausible estimates of the death rate differential for this region.

The effect of differential fertility is more than enough to eliminate these impacts of mortality. The net effect of the higher natural rate of population growth among the poor is a non-negligible increase the poverty rate, representing somewhere between 10-50% of the trend rate of poverty reduction in the developing world by the $1 a day measures. And again it is Africa where this impact is largest (followed by South Asia). In spite of the high death rate in Africa (due in part to the HIV/AIDS epidemic) the balance of the demographic forces is markedly poverty increasing.

These calculations are at best suggestive about likely magnitudes. Although there have been significant advances in our knowledge about socioeconomic differentials in mortality and fertility over the last 5-10 years (notably in the broader country coverage and deeper analyses of Demographic and Health Surveys), there is still considerable uncertainty about the extent of demographic differentials in the developing world, as well as about the (unconditional) death and birth rates. That is evident in the relatively wide ranges for the impact estimates presented here.

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