rafmacalaba/datause-encoder-data huggingface dataset:
Cohen, Aaron J., Michael Brauer, Richard Burnett, and et. al. 2017. "Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015." _The Lancet, Volume 389, Issue 10082_ 1907-1918.

Cole, Matthew A. 2004. "Trade, the Pollution Haven Hypothesis and the Environmental Kuznets Curve." _Ecologial Economics_ 48 (1): 71-81.

Davis, S. J., and J. Haltiwanger. 1999. "On the driving force between cyclical movements in employment and job reallocation." _American Economic Review_ 89: 1234-1258.

Eskeland, Gunnar S., and Ann E. Harrison. 2003. "Moving to Greener Pastures? Multinationals and the Pollution Haven Hypothesis." _Journal of Development Economics_ 70 (1): 1-23.

Fagerberg, Jan. 1996. "Technology and Competitiveness." _Oxford Review of Economic Policy_ 12 (3): 39-51.

Glaeser, Edward L., and J.D Gottlieb. 2009. "The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States." _Journal of Economic Literature_ 47 (4): 983-1028. Glaeser, Edward L. 1998. "Are Cities Dying?" _Journal of Economic Perspectives_ 12 (2): 139-160. Graff-Zivin, Joshua, and Matthew Neidell. 2012. "The impact of pollution on worker productivity." _The American economic review_ 102 (7): 3652-3673.

Harrell, Frank E. (2010) "Regression Modeling Strategies: with applications to linear models, logistic regression, and survival analysis". _Springer-Verlag New York, Inc._ , New York.

Harrison, Ann, Justin Y. Lin, and Lixin Colin Xu. 2014. "Explaining Africa's (Dis)advantage." _World Development_ 63: 59-77.

Henderson, J. Vernon. 1974. "The sizes and types of cities." _The American Economic Review_ 640656.

Jones, Charles, and Paul M. Romer. 2010. "The New Kaldor Facts: Ideas, Institutions, Population, and Human Capital." _American Economic Journal: Macroeconomics_ 2 (1): 224-245.

Kahn, Matthew E, Nancy Lozano-Gracia, and Maria E Soppelsa. 2017. "Does Pollution Hinder Developing Countries Urban Competitiveness?" _Working Paper._ Kahn, Matthew E., and Erin T. Mansur. 2013. "Do local energy prices and regulation affect the geographic concentration of employment?" _Journal of Public Economics_ 101: 105-114.

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32 Graph 6a Returns to Real State Investments in Mexico and U.S.A. 1976-2001 <!-- Start of picture text --> 800 .5<br>700 - -. 4<br>600- .3<br>500- -.2<br>200 --.<br>Ral nvetrnsto tateInvstmets n MeicoandU.S.A. 9-20<br>-.2<br>1980 1985 1990 1995 2000<br><!-- End of picture text --> 6500- , 2 ^< -.02 550 M ' X **Graph 6b** X2_/\ '.04 -- **Returns** to **Real State Investments in Mexico and** U.S.A. **1995-2001** <!-- Start of picture text --> 4500- --.08<br>800 .06<br>750- 400 . ~~. ... ... .. ,..,.. .. -1 .04<br>700 'A A -,, .02<br>650i ,.-2.00<br>600-\ - -.02<br>.. mexico,,'<br>550 - .04<br>500- \--.06<br>450- --.08<br>401995 '1996 '1997 '1998 '1999 '2000 2001 -1<br><!-- End of picture text -->
<!-- Start of picture text --> 12406<br>2<br>8<br>=<br>x<br>=<br>BH2g 8e+05<br>3<br>2<br>><br>3<br>3<br>6e+05<br>2011 2013 2014 2015 2016 2017 2018 2019<br>Year<br>Ifa commune has IDP HHs — 0 — 1<br><!-- End of picture text --> <!-- Start of picture text --> 1600000<br>s<br>a<br>—<br>@ 1400000<br>5<br>(6)<br>x=<br>=x<br>=£<br>1200000<br>1000000<br>2011 2013 2014 2015 2016 2017 2018 2019<br>Year<br>If acommune has IDP HHs — 0 — 1<br><!-- End of picture text -->
although they are not robust and only significant in the specification including village fixedeffects.

# _D. Mechanisms: Savings and Tontines_

_Tontines_ are an important vector for investment in rural Niger. As mentioned above, one of the particular features of the cash transfer pilot is that it encouraged beneficiaries to set-up _tontines_ . _Tontines_ represent a possible mechanism used by households to invest in productive activities. We therefore also analyze the impact of the project on participation in _tontines_ 18 months after the end of the program.

Project impacts on participation in _tontines_ is estimated by the SD approach because the relevant variables are only included in the follow-up survey (Table 6). Results show large and significant increases in the participation in _tontines_ , in the number of _tontines_ to which individuals contribute, and in the amount invested in and received from _tontines_ . The percentage of beneficiaries participating in tontines in 2013 is twice the percentage of non-beneficiaries (20.7% vs. 10.2%). The average effect of being a former beneficiary on the amount invested monthly in tontines is 385 FCFA (and the average effect on the amount received when one’s turn comes is 3,566 FCFA). Conditional on tontine participation, the average effect is greater: 1,271 FCFA (13,528 FCFA for amount received). Increased participation in _tontine_ translates into a more prevalent use of tontines to support consumption, but also into investments in productive activities and durable goods. As such, _tontines_ are one of the mechanisms that can explain the observed impacts of transfers on productive assets.

Overall, the results suggest that beneficiary households continue to participate in the _tontines_ that were set-up during the project, even 18 months after the project terminated. Still, both the share of beneficiaries participating in the tontines and the amounts invested in 2013 are below levels observed during the project (PPFS-CT, 2011).<sup>22</sup> This is consistent with what is expected after beneficiaries stop receiving regular transfers and encouragement to participate in savings groups.

> 22 Project managers recorded a 90% participation rate in _tontines_ among beneficiaries.

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subsidiaries or affiliated entities mentioned on the Entity List. For example, China Aerospace Science and Industry Corporation (CASIC) was added to the Entity List in 2018. We identified all PATSTAT-listed firms associated with CASIC as a sanctioned entity from 2018 onwards. Since our primary focus is on changes in sanctioned firms’ innovation behavior, we drop all research institutes and government agencies in our sample. However, if the research institutes or government agencies have affiliated firms or subsidiaries identified as firms, these subsidiaries or affiliated firms are included in our sample and identified as sanctioned firms. After matching, we identify 182 sanctioned firms in PATSTAT.

# **3.2 Outcome Variable**

We use the patent count at time _t_ to measure innovation outcomes in that year. Applicants can file patents across multiple patent offices, such as JPO, EPO, USPTO, and WIPO. Using patents filed in one patent office might not fully reflect applicants’ innovation activities. To avoid double counting in patent counts, we track the earliest filing ID of each patent, ensuring the uniqueness of patents in our count. This means a patent’s initial filing ID is used in calculating a firm’s annual patent filings. Moreover, being added to the Entity List could distinctively impact a firm’s patent filing domestically and in foreign offices. The inclusion in the Entity List may reduce a firm’s market share internationally, potentially reducing its incentive to file patents in foreign patent offices. To understand these dynamics, we analyze a firm’s patent applications in its home country, as well as in the USPTO, EPO, and WIPO.

We also evaluate the U.S. sanction’s impact on the scientific significance of a firm’s patent filings. We count each firm’s most important patent filings each year, using three proxies. First, we consider the number of high-technology patents, classified according to the EPO criteria based on each patent’s IPC code. Second, we count “triadic patents”, which are concurrently filed in the EPO, USPTO, and JPO. These patents, known for their high novelty standards, represent a firm’s most important inventions annually. Relatively few Chinese firms have filed “triadic patents”. As an alternative measure, we use international 8
are some systematic differences: the 535-household sample is wealthier, which is consistent with households in the subsample being required to own at least one phone. For instance, while 88% of non-ultra-poor households in the TUP survey own at least one phone, only 72% of ultra-poor households own at least one phone.

**Comparing Survey-Based Measures of Well-Being and Deprivation** As shown in Table 1 and Figure S3, the two survey-based measures of well-being are only weakly correlated. In the full sample, the correlation between the asset index and consumption is just 0.37; in the matched subsample, the correlation is 0.34. These modest correlations may be due in part to the fact that, as discussed above, the consumption data capture short- and medium-term deprivation, whereas the asset index is a better indicator of long-term wealth. Measurement error may also weaken these empirical correlations.

Also notable is the weak relationship between the two survey-based measures of deprivation and the ground truth ultra-poor designation: while the ultra-poor population makes up 27% of the overall sub-sample, less than half of the ultra-poor fall into the bottom 27% of the sample by wealth index or consumption. These differences may be partly attributable to measurement error, but they surely also arise from the fact that they are conceptually distinct constructs: while the consumption and asset indices focus primarily on economic flows and stocks, respectively, the ultra-poor designation was designed to be more holistic and multidimensional, informed in part by community perceptions of vulnerability (Sen, 1992; Alkire et al., 2015).

The fact that the ultra-poor designation is not strongly correlated with the survey measures of consumption and wealth has important implications for the targeting analysis presented below. In particular, it suggests – and our later results affirm – that a policy targeted solely on assets or consumption data will do a poor job of differentiating between ultra-poor and non-ultra-poor. The relatively weak correlation between consumption and the asset index also hints at a later finding that targeting based on a combination of the two data sources performs better than targeting on a single source in isolation.

**Sample Weights** Since the TUP survey oversampled the ultra-poor (by a factor of roughly 12), portions of our analysis use sample weights to adjust for population representativeness. When sample weights are applied, it is explicitly noted; if not mentioned, no weights are applied. After sample weights are applied, the ultra-poor make up 5.98% of the overall population, and 4.63% of our matched subsample.

7
Further, Table 12 shows that majority of the households below poverty line and non‐poor households would not want to consider relocation as an option in future. Only among the poor households, more than half are willing to consider this option. For all the households put together, irrespective of their income category, the most important factors for relocation seem to be (in the order of importance) clean surroundings, access to medical facilities, people from the same community living nearby, cheaper transportation, access to schools and good social networks. For the BPL families, important factors are people from same community living nearby, access to schools, medical facilities, cheap transport and clean surroundings. For the other poor households, clean surroundings and access to medical facilities are important. For the non‐poor, access to medical facilities and schools, clean surroundings and good social networks are important considerations.

Table 12: Factors considered important for relocation by poor and non‐poor households

||**BPL**<br>**Poor**|**Non‐poor**|
|---|---|---|
|**Will you consider relo**<br>|**cation as an option**<br> <br>|<br>|
|Yes|35<br>53.7|39.5|
|No|65<br>46.3|60.5|
|**Factors important**<br>|**for relocation**<br> <br>||
|Job opportunities|25<br>26.2|16.3|
|Same cost of living|20<br>12.3|7|
|Access to transport|20<br>18.9|16.3|
|Cheaper transport|55<br>44.3|39.5|
|Access to schools|45<br>43.4|44.2|
|Access to medical facilities|70<br>51.6|46.5|
|Clean surroundings|65<br>67.2|41.9|
|Good social networks|25<br>36.1|41.9|
|People with similar backgrounds|20<br>32|27.9|
|People from same community|75<br>49.2|32.6|

Source: Calculations based on the data obtained from 200 households Finally, all surveyed households have acknowledged the fact that they can do something themselves to reduce the extent of flooding. Although almost all of them believe that MCGM must take the primary responsibility to reduce the flood risk, even individuals are responsible for the same. Some important suggestions from households regarding individual efforts are cleaning the nearby gutters, stopping the encroachment on nullahs and gutters, stopping dumping garbage and reducing the use of plastic bags that clog the drains. There are instances of initiatives taken up mainly by the MCGM to clean up the areas. But individual efforts seem to be lacking in this regard and households mainly rely on the local government departments to take steps to reduce the flood risks.

# 5. Policy implications

Poor households in Mumbai support a large share of the cost of extreme and recurrent floods. Further, they are vulnerable to floods due to limited resources and have inadequate infrastructure and access to facilities thus limiting their capacity to respond to floods. Therefore, it is important for 25
Median yearly earnings for HE owners are higher than in agriculture and lower than wage employment. Within each income source there is large variation and the income distributions for each source overlap, even if the medians are higher (figure 6). These earnings distributions reflect, among other things, average level of education in each segment of employment (see figure 2), as well as opportunities for more hours worked. Nonetheless, they do suggest that average labor productivity is higher in the HE sector than in agriculture, even though the HEs are very small businesses.<sup>6</sup> To analyze this further, we start with a cross section analysis, which shows the associations between HE earnings and household welfare in 2008. Then we move to the two-period panel where we can see the dynamics – whether given household characteristics, adding a HE improves welfare faster than not adding a HE.

**Figure 6 Yearly earnings (Meticais) from income type, 2008**

<!-- Start of picture text --> Agricultural workers<br>Wage workers<br>Household enterprise<br>10 100 1000 10000 100000 1000000 workers<br>Income per main worker (Mt per year)<br><!-- End of picture text --> _Source:_ NPS panel. _Notes:_ Scale is logarithmic. Income is from primary employment. Series are smoothed by kernel estimates A simple way of analyzing the relationship between having an HE and household standard of living is to run an OLS regressions of log consumption per capita on education, demographics, location and sources of income. We estimate the standard model with a small addition. Our formulation is Yi= a + B1 **X** i + B2 **Zi** + ei where: Y is the log of household consumption per capita,

**X** is a vector of individual and household characteristics such as age, education, location, etc. used here as controls,

**Z** is a dummy variable which takes the value of 1 if an income source, or portfolio of income sources is present, and

e is the error term.

> 6 Data are yearly earnings, so they do not control for hours worked. Controlling for hours worked might result in higher relative earnings for agriculture and HEs compared to wages, because these activities are less likely to be performed year around. But to the extent that doing these non-wage activities results in underemployment, yearly earnings are the relevant outcome comparison.

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producing in the more profitable location, taking into account the optimal choice of worker types and net of the difference in rental costs, is larger than the sum of: (i) the wage premium for the workers; (ii) the direct productivity cost of being exposed to higher pollution (net of protective investments); and (iii) the cost of higher mitigation expenditures.<sup>13</sup> Ex-ante, it is not clear whether we should observe managers sorting to high or low pollution locations. This depends on the empirical strength of the negative correlation between productivity and pollution, and the relative perceived costs of pollution relative to the profitability benefits. Similarly, higher ability managers could be either more or less likely to sort towards more polluted areas. The pattern of sorting depends on the relative costs and benefits, which are themselves mediated by the choice of worker type.

# **4 Sampling Strategy, Data and Descriptives**

In Section 2 we have shown that the urban environment in Uganda is becoming increasingly polluted due to rapid motorization and the resulting traffic emissions, thus plausibly inflicting severe health costs on workers and productivity losses to firms. The simple framework of Section 3 describes how managers’ actions can mediate these negative effects through avoidance of pollution (through location choice) or adaptation (through investment in protective equipment or organizational strategies). In order to study how firms in urban Uganda respond to the challenge created by increasing levels of urban pollution, we need the following types of data. First, we need data on pollution within and across cities to document variation in pollution exposure at a granular level. Second, we need data on the road network and on firm location to examine how firm location choices determine exposure to traffic pollution. Third, we need firmlevel survey data to study the economic benefits and costs associated with different locations, as well as firm’s perceptions of pollution and any adaptation strategies, as well as manager’s characteristics. To the best of our knowledge, such highly granular data did not exist. Hence, a core contribution of our study was to build this data. In the following section we describe the sampling design and data collection strategy.

## **4.1 Sampling Strategy**

We collected pollution measurements and a novel firm survey in a representative sample of urban and semi-urban areas across three of the four macro-regions of Uganda: Central, Western, and Eastern regions. Crucially for the purpose of this study, the pollution measurements and the

> 13Producing in the more profitable location affects the choice of workers’ type and wages through the compensating differential constraint: workers demand higher wages in the more polluted location, and this may be especially true for higher skilled workers. As a result, the optimal choice of workers’ types may be different in the low and high profitability location.

11
with a Heckman selection bias correction (Heckman, 1979). Ignoring the differences in the relative participation of men and women in the labor market when estimating the returns to schooling and skills has been found to result in biased estimates. We use this approach also to address the measurement error due to the unobserved wages of men and women who are unpaid workers. Depending on the size of the nonformal sector, this source of selection bias can be significant.<sup>28</sup> We use marital status, the number of young children, and the presence of other workers in the household as instruments to identify the labor force participation function.<sup>29</sup> Previous studies have concluded that, for women, being married and having young children reduce labor force participation and the probability of paid employment, whereas, for men, being married increases labor force participation and the probability of paid work and having young children does not have a significant impact.<sup>30</sup>

# **_Heterogenous effects: Quantile regressions_**

There is a considerable empirical literature that explores heterogenous effects of covariates along the earnings distribution, based on quantile regressions introduced by Koenker and Bassett

> 28 In developing countries, ILO (2014) reports that more than half of all non-agricultural jobs are in the informal economy, and in many South Asian and Sub-Saharan African countries, the share is as high as 90 percent.  These jobs employ mostly low-skilled workers and women, and typically these jobs offer modest earnings, limited security, and hardly any social protection (Adams, de Silva, and Razmara, 2013).

29 While our paper does not attempt to explain marriage and childbearing choices, we examine how marriage and fertility choices affect earnings through labor market choices. We acknowledge that marriage, fertility and labor market choices may be determined simultaneously, but we treat the number of children and marital status as predetermined.

30 The cross-country study of Bloom et al. (2009) finds that fertility has a large negative effect on female labor force participation, with this direct effect being concentrated among women aged 20-39. Their results imply that with each additional child, female labor force participation decreases by about 10–15 percentage points in the age group 25–39, and by about 5–10 percentage points in the age group 40–49. These results imply a reduction of about four years of paid work over a woman’s lifetime for each birth (Bloom et al., 2007). Other studies have questioned what OLS results show. This skepticism about the causal interpretation of associations between fertility and labor supply stems from the fact that fertility and labor supply could be jointly determined. See, for example, <mark>Agüero and Marks (2008) on Latin American countries, and Jensen (2012) on India.</mark> 17
4.3 considers an alternative account of stunting in India: could it be a mere effect of short mothers causing restricted fetal growth, reflecting historical conditions and not a healthy environment that might exist today? Finally, section 4.4 proceeds with the decomposition, applying linear and non-parametric approaches to explain the India-Africa gap.

# **4.1 Effects of village sanitation: A negative externality**

As a first step towards explaining the height difference between Indian and African children, this section verifies that village-level open defecation predicts children’s height within each region. As in section 3, I use household-level DHS data to find the fraction of households in a PSU reporting open defecation, which I treat as an estimate of village-level open defecation.

Thus, separately for each region _r_ Africa and India, I estimate: where _i_ indexes individual children under 5, _v_ are villages (rural PSUs), _c_ are country-years (DHS surveys) in Africa and states in India, and _r_ are regions (India or Africa). The dependent variable, _heightivcr_ is the height of child _i_ in standard deviations, scaled according to the WHO 2006 reference chart. The independent variable _open defecation_<sup>_v_</sup> _vcr_<sup>isthecom-</sup> puted fraction 0 to 1 of households reporting open defecation in the child’s village (again, implemented as rural primary sampling unit), with a quadratic term included in some specifications. Household-level open defecation _open defecation_<sup>_i_</sup> _ivcr_<sup>isanindicator,0or1,forthe</sup> child’s household. Including both household and village-level open defecation tests whether one household’s open defecation involves negative externalities for other households.<sup>21</sup> In other words, is it only a household’s own sanitation that matters, or do other households’ sanitation matter, even controlling for one’s own?

> 21G ̈unther and Fink’s (2010) working paper version of Fink et al. (2011) conducts a similar analysis, regressing diarrhea and child mortality on household and cluster-mean water and sanitation variables.

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