rafmacalaba/datause-encoder-data huggingface dataset:
These estimates are higher than (but of comparable magnitude to) those in Duflo (2001), although we use more recent data. Petterson (2010) finds a return of 14% using the same data from the same year but a different sub-sample and instrument.

As in most of the literature, our IV estimates of the return to education are larger than OLS estimates. Card (2001) suggests that such a finding indicates that returns to schooling are heterogeneous and the marginal individual induced to enroll in school by the change in the instrument has a higher return than the average individual. Carneiro, Heckman and Vytlacil (2011) show that in the case of college attendance in the US, IV estimates can be above OLS estimates even if the marginal individual has a lower return than the average. Another reason why IV can exceed OLS is measurement error in schooling. Although schooling is relatively well measured in the US (Card, 1999) and other developed countries, that is not necessarily the case in Indonesia.

In appendix table A2 we also present IV estimates of returns for models where we do not interact the instrument with the variables in _X_ . The point estimate is smaller than the one in Table A1, and the standard error is larger. Nevertheless, the main pattern remains: the IV estimate is much higher than the OLS estimate. In a model with heterogeneous returns, it is not surprising that the instrumental variable is sensitive to the choice of instrument. For the remaining of the paper, we present a parallel set of results in the appendix in which we do not interact the instrument with _X_ in the selection equation.<sup>10</sup> Finally, in appendix table A3 we present results were we omit all regional dummies from the model. Our IV estimate is very similar to the ones in tables A1 and A2. This indicates that regional variation in infrastructure, which is correlated with the availability of schooling, is unlikely to be driving our results.

OLS and IV estimates hide considerable heterogeneity in returns and, as emphasized in Heckman and Vytlacil (2005), Heckman, Urzua and Vytlacil (2006), and Carneiro, Heckman and Vytlacil (2011), it is not clear what economic question is answered by the IV estimate. In order to further investigate this issue we use the framework of section 3.

> 10 We do this for two reasons. First, to show that the main patterns in our results are not driven by choosing the specific way the instrument enters the model. Second, because the first stage F-statistic is higher in the case where we use a single IV (F=11.34) than when we use multiple IVs (F=3.62) consisting of distance interacted with different components of _X_ . We will see throughout the paper that using the expanded set of instruments allows us to get similar results and lower standard errors than we use a single (but apparently stronger) instrument.

16
# **Appendix B3: Correlation Matrix**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Life evaluation|1.00||||||||||||||
|Dissatisfaction with standard of living: Yes|-0.36|1.00|||||||||||||
|People cannot get ahead by working hard:<br>Yes|-0.09|0.16|1.00||||||||||||
|Dissatisfied with efforts to increase high<br>quality jobs: Yes|-0.13|0.19|0.14|1.00|||||||||||
|Dissatisfied with freedom to choose life:<br>Yes|-0.10|0.16|0.16|0.33|1.00||||||||||
|Dissatisfied with educational system|-0.13|0.16|0.09|0.17|0.17|1.00|||||||||
|Corruption widespread within government:<br>Yes|-0.07|0.10|0.07|0.19|0.11|0.13|1.00||||||||
|Income (1,000’s)|0.22|-0.16|-0.01|-0.07|-0.08|-0.09|0.00|1.00|||||||
|Self-employed|0.00|0.00|0.00|0.01|-0.02|-0.01|0.02|0.08|1.00||||||
|Unemployed|-0.07|0.09|0.04|0.05|0.04|0.03|0.00|-0.05|-0.06|1.00|||||
|Out of workforce|-0.03|0.01|-0.01|0.03|0.04|-0.02|-0.02|-0.07|-0.20|-0.14|1.00||||
|Underemployed|-0.04|0.07|0.02|0.03|0.02|0.04|0.01|-0.03|-0.06|-0.04|-0.13|1.00|||
|Government employee|0.04|-0.04|-0.03|-0.03|-0.01|0.02|-0.03|0.01|-0.04|-0.07|-0.22|0.04|1.00||
|Undetermined|0.05|-0.02|0.03|-0.04|0.00|-0.01|0.00|-0.04|-0.12|-0.13|-0.42|0.02|-0.20|1.00|

33
Table 3: Estimated Average Treatment on the Treated

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Total|International|Triadic|High-Tech|
|Treatment_×_Post|-0.275***|-0.107|-0.046***|-0.235***|
||(0.107)|(0.075)|(0.047)|(0.116)|
|No. Obs.|6,370|6,370|6,370|6,370|
|Sector-year FE|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|

Note: Columns (1) to (5) are the staggered DID estimates using the method by Callaway and Sant’Anna (2021). Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table 4: Estimated Patent Elasticity on Collaboration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||**Curre**|**nt Collab**|**orators**|**Past **|**Collabor**|**ators**|
||Total|Intl|High-Tech|Total|Intl|High-Tech|
|U.S.|0.147***|0.057***|0.130***|0.472***|0.129***|0.428***|
||(0.010)|(0.010)|(0.011)|(0.021)|(0.028)|(0.017)|
|Euro+|0.102***|0.082***|0.068***|0.087*|0.121***|0.138***|
||(0.026)|(0.006)|(0.006)|(0.046)|(0.017)|(0.024)|
|Adv. Asia|0.143***|0.074***|0.123***|0.239***|0.111***|0.208***|
||(0.028)|(0.011)|(0.024)|(0.014)|(0.013)|(0.036)|
|other|0.110|0.054|0.092|0.402***|0.332***|0.346***|
||(0.127)|(0.130)|(0.135)|(0.044)|(0.031)|(0.059)|
|No. Obs.|5,317|5,317|5,317|5,317|5,317|5,317|
|R-Squared|0.687|0.652|0.706|0.716|0.658|0.730|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients in columns (1) to (3) using OLS on a set of firms that had positive patenting during the sample period. In columns (1) to (3), the dependent variable is the number of collaborators in each year. In columns (4) to (6), the dependent variable is the total number of collaborators up to the current year. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

38
Zhang, J. (1999). Effects of air pollution on respiratory health of adults in three Chinese cities. Archives of Environmental health, 54(5): 373-382.

- Zhou, Y. and R. S.J. Tol (2005). _Valuing the health impacts from particulate air pollution in Tianjin_ . Working paper FNU-89.

Zhou, Y. R. (1997). Association between air pollution and hospital admissions in Chongqing. Journal of Mod. Prev. Med, 24(1): 43-45.

22
negative impact of 15.7 percent on per capita labor income, while IHD had an impact of 3.1 percent, while WEI had an impact is of 9.4 percent. The example of Hurricane Matthew reflects the importance of considering a measure of asset exposure in the indexes. The impacts calculated from the levels of the three indicators for Hurricane Matthew reflect a significantly higher impact when considering the MSWS index. Indeed, when considering MSWS, the impact seem to be 5 times higher than the impact calculated from HDI, even if the coefficient estimated within IHD is significantly higher than the coefficient estimated within MSWS. As discussed in Section 2, each indicator relies on different assumptions that may have significant variations. In the particular case of Hurricane Matthew, the lower effect presented by the IHD and WEI indicators appear to indicate an overestimation of the effect when not considering any exposure measure in the MSWS.

Table 5 Impact of Hurricanes on Poverty and Income at the Household Level

||Labor Income|Total Income|Extreme Poverty|Poverty|
|---|---|---|---|---|
|||_Non We_<br>|_ighted Results_<br>||
||(1)|(2)|(3)|(4)|
|MSWS|‐0.0300***|‐0.0403***|0.0164***|0.0186***|
|Standard Error|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.430|0.472|0.258|0.317|
|IDH|‐0.0353***|‐0.0473***|0.0205***|0.0232***|
|Standard Error|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.430|0.472|0.258|0.317|
|WEI|‐0.0178***|‐0.0243***|0.0100***|0.0119***|
|Standard Error|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.429|0.471|0.257|0.317|
|||_Weig _<br>|_hted Results_<br>||
||(5)|(6)|(7)|(8)|
|MSWS|‐0.0159***|‐0.0272***|0.00966***|0.0109***|
|Standard Error|(0.0002)|(0.0001)|(0.0001)|(0.0001)|
|R‐squared|0.429|0.472|0.257|0.316|
|IDH|‐0.0108***|‐0.0203***|0.00754***|0.00883***|
|Standard Error|(0.0002)|(0.0001)|(0.0001)|(0.0001)|
|R‐squared|0.429|0.471|0.257|0.316|
|WEI|‐0.00908***|‐0.0170***|0.00592***|0.00704***|
|Standard Error|(0.0002)|(0.0001)|(0.0001)|(0.0001)|
|R‐squared|0.429|0.471|0.257|0.316|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the sub‐regional level.

Matthew landed also in Guatemala with a SS scale of 1 in outside the Metropolitan Region with a Maximum Sustained Wind Speed of 81.64 mph.

18
# **_E. Regression results when excluding availability_**

**Table 10: Marginal effect of public transport characteristics on women’s labor force participation by city, excluding availability.**

||**No**<br>|**income restri**<br>|**ction**<br>|**Low**<br>|**er-income gr**<br>|**oup**<br>|
|---|---|---|---|---|---|---|
||(1a)|(2a)|(3a)|(1b)|(2b)|(3b)|
|**Sample**<br>**restriction**|All|Birth +<br>Marriage|Birth|All|Birth +<br>Marriage|Birth|
|**Accessibility**|||||||
|Amman|0.098|0.047|-0.123|0.338***|0.271|0.300|
||(0.09)|(0.16)|(0.29)|(0.12)|(0.24)|(0.43)|
|Beirut|0.098|0.082|0.261|-0.043|-0.027|0.625*|
||(0.11)|(0.14)|(0.24)|(0.16)|(0.21)|(0.36)|
|Cairo|0.229|0.416|1.328**|0.579|0.694|1.815**|
||(0.32)|(0.40)|(0.64)|(0.39)|(0.46)|(0.85)|
|**Safety**|||||||
|Amman|-0.047|0.169|1.028**|-0.022|0.107|0.898|
||(0.16)|(0.25)|(0.43)|(0.21)|(0.34)|(0.65)|
|Beirut|-0.459|-0.363|-0.506|-0.774*|-0.773|-1.56|
||(0.28)|(0.36)|(0.61)|(0.40)|(0.52)|(0.97)|
|Cairo|-0.603|-0.364|-0.204|-0.241|-0.154|0.215|
||(0.68)|(0.72)|(1.21)|(0.81)|(0.84)|(1.53)|
|N|2961|1732|670|1701|999|345|
|R2|0.173|0.180|0.208|0.199|0.214|0.266|
|Adj. R2|0.148|0.138|0.100|0.159|0.149|0.088|

Notes: *, **, *** denote significance at the 10 percent, 5 percent and 1 percent levels respectively. Robust standard errors in parentheses. N: number of observations.

51
Policy Research Working Paper 9459

# **Abstract**

Evidence on the effect of exports on welfare at the local level is scarce. Using a unique data set of international trade and poverty maps for almost 2,000 Mexican municipalities between 2004 and 2014, the study presented in this paper provides new evidence on the impact of a significant rise in exports on poverty and inequality at the local level. The analysis implements an instrumental variable approach that combines the initial structure of exports across municipalities with global trends in exports from developing to developed countries by sector. The results show that a 10 percent increase in the ratio of exports to workers reduces income inequality measured by the Gini coefficient by 0.17 point (using a 0 to 100 scale), but no significant effects on poverty reduction or average household incomes are identified. The lack of impacts on average incomes is driven by a rise in the supply of labor at the local level because municipalities with higher export growth experienced an increase in labor force participation and attracted more net migration, particularly of unskilled workers. Therefore, while total labor incomes grew in response to an increase in exports, average labor income per worker did not change. Declining remittances also blunted the effect of growing exports on household incomes.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at crodriguezc@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure <u>6. Croatia. Distributional Impact of the Tax and Benefit System in 2014</u> <!-- Start of picture text --> 300%<br>250%<br>200%<br>150%<br>100%<br>50%<br>0%<br>-50%<br>-100%<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Deciles of Market income plus pensions<br>All direct taxes Non-pension contributions All Direct Transfers<br>All indirect taxes Education Health<br>Net cash position Total<br>Share of market income plus pensions<br><!-- End of picture text --> Source: Own estimates based on the 2014 Croatian PIS and HBS. The Net Cash position of the household is measured as the difference between consumable and market income plus <u>pensions, and is equivalent to all the cash transfers to households minus all taxes.</u> Since the influence of specific interventions may be different from that of the overall system, a fundamental question in the policy discussion is whether a particular fiscal instrument (or a particular combination of them) is equalizing. If there were a single fiscal intervention, using the typical indicators such as the Kakwani index<sup>7</sup> to determine whether a particular intervention is progressive or regressive would be sufficient to unambiguously determine whether that intervention was equalizing. Given there is more than one fiscal intervention, this one-to-one relationship between the progressivity of a particular intervention and its effect on inequality breaks down. As Lambert (2001) demonstrates, depending on certain characteristics of the fiscal system, a regressive tax—for example—can exert an equalizing force over and above that which would prevail in the absence of that regressive tax. This is because each fiscal intervention interacts with all the others. For instance, the proceeds of a regressive indirect tax could be used very effectively in a pro-poor transfer, leading to a situation where post-fisc incomes are more equal than in the absence of that regressive tax. One way to calculate the effect of a particular fiscal instrument on inequality is to calculate its marginal contribution. The marginal contribution of a tax (or transfer) is calculated by taking the difference between the inequality indicator _with_ and

> 7 The Kakwani index for taxes is defined as the difference between the concentration coefficient of the tax and the Gini for market income. For transfers, it is defined as the difference between the Gini for market income and the concentration coefficient of the transfer. See, for example, Kakwani (1977).

18
fiscal programs in China. Given the magnitude of China’s fiscal pie—its fiscal spending is about 14 trillion yuan (i.e., 2.3 trillion U.S. dollars) in 2013—more evaluations of specific programs are called for, and ours here is a simple example of high cost-effectiveness.

# **2 The Rural Drinking Water Program in China**

Before the 1980s, the rural residents in China largely relied on untreated water from wells, rivers, and lakes. Indeed, more than 70 percent of rural households in the CHNS data drank untreated water even in 1989. The sanitation environment was also poor as human and livestock waste was disposed freely around dwellings within villages. These unsanitary practices caused endemics of water-related diseases. It should be noted that microorganisms, the major drinking water pollutants in many other developing countries, have less adverse consequences in China due to the Chinese tradition of drinking boiled water and eating cooked food (Braudel, 1982; Zhang et al., 2009). In contrast, another key water pollutant, chemical impurities such as toxic metals and inorganic and organic compounds, causes as much health damage in China as in elsewhere. Partly the result of geography as natural soil and rock contain high levels of chemicals, chemicals in drinking water are increasingly caused by the fast industrialization process coupled with weak government regulations in China (Cai et al. 2011). Vast discharges of industrial waste and excessive use of fertilizer and pesticides have led to widespread and severe water pollution, causing the spread of diseases and even deaths. In 2006 a total of 1,115 counties and about 81.6 million people are at the risk of fluorosis via drinking water.<sup>5</sup> Around 66,000 people likely die from water pollution in rural China every year (World Bank, 2007).

After largely completing the urban public water system by the 1980s, the Chinese government started the rural drinking water treatment program, and spent a significant amount of resources trying to improve the _quality_ of drinking water in rural China. The ultimate goal of the program has been to build up water treatment plants where clean water technology and equipment can be installed to eradicate chemical pollutants and microorganisms and where

> Indonesia men.  They interpreted this as evidence of gender discrimination—parents allocated limited food to boys rather than girls in time of food scarcity.

> 5 Based on Chinese National Health Statistics (2007).

6
in 18 “distritos”. A “concelho” is an ensemble of persons resident in a certain geographical area. It is, also, a juridical entity with its own independent administrative bodies. Their average area is 323.4 square kilometers, approximately 10% of the US county (with a standard deviation of 283.3).

All the data was aggregated from the plant level, to focus on “concelhos” and two-digit ISIC manufacturing sectors. Due to the well known problems of estimating productivity in non-manufacturing (see Griliches, 1994) in this paper I analyze only manufacturing sectors. The choice of the plant, and not the firm as the level of analysis is not arbitrary. At the firm level what matters is the regional location of the head office of a firm and, therefore, all the employment in plants outside this region is reported to the region of the head office. The average costs of labor supported by the firm and the potential experience and years of schooling of the workforce were obtained aggregating the worker level data to the plant level using the plant identifier in each worker record.

Figure 3 illustrates the evolution of unemployment rate and the cyclical component of GDP for the Portuguese economy during this period. The period 1985-1994 goes from bottom to bottom of the business cycle, and is sufficiently long to capture long term growth without being too much affected by cyclical considerations. Also these years are nearly equivalent with respect to the unemployment rate. During this period, national employment rose 16.5% and the Portuguese employment structure also registers a reasonably fast change. Figure 4 shows that the share of industrial employment is falling while agricultural employment is almost unchanged. In industry employment increases slightly 1.8%, but total employment in manufacturing falls by 2.5%. Figure 5 decomposes the change in the employment structure for three-digit manufacturing sectors. Wearing apparel and footwear, both with a large tradition in the Portuguese economy, were the sectors registering larger growth. They went from 9.6% and 4.1% of national manufacturing employment to 15% and 7.2%, respectively. Textiles and transport equipment registered the overall worst performances in manufacturing.

These aggregate sector trends in employment growth hide the spatial disparity that each sector registered during this period. The dispersion of employment growth rates within each sector is very large. In more than 80% of the three-digit sectors, the coefficient of variation is above 100% while 53% of the sectors still have coefficients above the 200%. There is also a lot of dispersion in the sector productivity growth rates across regions. The coefficients of variation within three-digit sectors are always above 100%. On average, each “concelho” grew 53% (standard deviation of 63%).

13
|||Self-<br>reported<br>widowed<br>FHH rural|Self-<br>reported<br>non-<br>widowed<br>FHH rural|Per capita consumption of widowed FHHs is around 12% lower than that of all rural<br>households. The results are robust to using an equivalence scale in measuring<br>consumption.|
|---|---|---|---|---|
|||Self-<br>reported<br>widowed<br>FHH urban|Self-<br>reported<br>non-<br>widowed<br>FHH urban|Per capita consumption of widowed FHHs is around 6% lower than that of all other<br>urban households. The results are robust to using an equivalence scale in measuring<br>consumption.|
|9<br>Rogan (2013)|South<br>Afi|Self-<br>reported de<br>jure FHH,<br>de facto<br>FHH, co-<br>resident<br>FHH|Self-<br>reported<br>MHH|Poverty rates are higher in FHHs than in MHHs, irrespective of how headship is<br>defined.|
||rca|Self-<br>reported de<br>jure FHH,<br>de facto<br>FHH|co-resident<br>FHH|Co-resident FHHs are less poor than other types of FHHs.|
|||core FHH|non-FHH|FHH has the lowest risk of poverty|
|||Self-<br>reported<br>FHH|Self-<br>reported<br>MHH|No significant differences between FHHs and MHHs were found regarding<br>consumption, the probability of shock exposure, or vulnerability to poverty in<br>Thailand or Viet Nam.|
|||Self-<br>reported de-<br>jure FHH|Self-<br>reported<br>MHH|De jure FHHs have lower consumption than MHHs in Viet Nam. There are no<br>significant differences between de-jure FHHs and MHHs regarding the probability<br>of shock exposure or vulnerability to poverty in Thailand or Viet Nam.|
|10<br>Klasen_et al._(2015)|Thailand,<br>Viet Nam|Self-<br>reported de-<br>facto FHH|Self-<br>reported<br>MHH|De facto FHHs have higher consumption than MHHs in Thailand. There are no<br>significant differences between de-facto FHHs and MHHs regarding the probability<br>of shock exposure or vulnerability to poverty in Thailand or Viet Nam.|
|||Self-<br>reported<br>single,<br>widowed<br>FHH|Self-<br>reported<br>MHH|FHHs with an absent spouse have higher consumption levels than MHHs in<br>Thailand. Single FHH has a lower consumption level than Viet Nam. There are no<br>significant differences between FHHs and MHHs regarding the probability of any<br>shock exposure in Thailand or Viet Nam. Single FHHs are less vulnerable to<br>poverty in Thailand but more vulnerable to poverty in Viet Nam.|
|11<br>Liu_et al._(2017)|14 Latin<br>American<br>countries|Self-<br>reported<br>married w/o<br>spouse,<br>single,<br>separated,|Self-<br>reported<br>married<br>FHH with<br>spouse|In eight of the 14 countries, FHHs are more likely to live in poor conditions.<br>However, MHHs are in more impoverished conditions than FHHs when married<br>status, urban or rural setting, ownership, and the presence of children are controlled<br>in the regression. Generally, married FHHs with the spouse present are better off<br>than any other category. The worst living conditions are associated with single,<br>separated, divorced, or widowed FHHs.|

43