rafmacalaba/datause-encoder-data huggingface dataset:
select industries that are arguably the most _unrelated_ to the marine pollution crisis, namely manufacturing, construction, and retail.

The fundamental “parallel trend” assumption of a DiD model implies that the average monthly earnings of the treatment and control individuals follow similar pre-trends, and that the average earning’s movement of the counterfactual group is “smooth” across the _Formosa_ incident timeline. Recall that we discuss several empirical supports for the validity of this assumption in Figure 3 and Table A1. Therefore, we are able to quantify the Average Treatment Effects (ATE) of _Formosa_ by measuring (1) the changes in fishery earnings in the treated location relative to the control region (Panel A), or (2) the changes in fishery earnings relative to the average earnings in unaffected industries in the _Formosa_ zone (Panel B). In both exercises, the average impact of _Formosa_ for the rest of 2016 (i.e. after the incident ˆ took place) is estimated by _α_ 1.

Overall, using LFS 2016 data and controlling for the month-specific and individual-specific fixed effects as well as clustering the standard errors at the district-level, our estimation robustly indicates a massive and statistically significant decline in fishery monthly incomes caused by _Formosa_ , with the magnitude ranges from 30 percent in the “pooled” regressions (columns 1-2) to approx. 45 percent under the individual fixed-effect specification (columns 3-4). The fact that fishery income reduced by almost half after the crisis indicates how destructive the disaster was. From the supply-side perspective, safe seafoods became much more costly to catch. In the subsequent section of the paper, we show that affected fishers, depending on their location within the contaminated zone, either coped with the shock by traveling longer distance to safe fishing grounds with an inevitable cost, or had to substitute fishery hours with working secondary jobs.

# **4.2 Impact on satellite-detected fishing activities**

Having shown a significant decline in average fishery earnings during the immediate period after _Formosa_ took place, we next provide corroborating evidence on the damage caused to fishing intensity in the contaminated zone. We rely on VIIRS’ Satellite Boat-Detection data, which provide a direct measure on the prevalence of night-time fishing boats. Specifically, we build upon the initial visual inspection discussed in Figures 2 and 4, and empirically estimate the causal impact of _Formosa_ under a DiD setting. We use a balance panel of monthly marine grids from 2013 to 2016, with each cell resampled to a 10-mile-square resolution. We focus on – two key measures of fishing intensity, including (1) boat detection prevalence the average 14
participation is reported for all expenditure quartiles this is not true: the elasticity of savings balances to expenditures is about 1.7 for those households reporting some balances.<sup>9</sup> _Households’ share of aggregate financial assets_ . Wealthy households hold a sizable fraction of aggregate bank deposits. The household sector in aggregate holds more than 63 percent of total deposits at financial intermediaries in the United States, and 80 percent of deposits from nonfinancial nongovernment domestic sectors.<sup>10</sup> For the eurozone, comparable percentages are reported: 60 percent and 78 percent respectively.<sup>11</sup> Individuals hold 43 percent of all resident deposits in the UK, a figure that rises to over two-thirds if deposits of financial institutions and public administration are excluded.<sup>12</sup> Just over a half of the large deposit base of Chinese financial institutions is in the form of household savings deposits.<sup>13</sup> In Egypt, households account for 78 percent of total deposits.<sup>14</sup> In the Eastern Caribbean Currency Union, household deposits are 60 percent of the total, or 68 percent if financial and government deposits are excluded.

In addition, households’ claims on managed funds, combined with their direct holdings, can be considered to finance a large share of traded equities in the more advanced economies.

_Offshore assets and liabilities_ . Although households, like other investors, tend to display a degree<sup>15</sup> of home preference that discourages cross-border holdings of financial assets, nevertheless, reasons of political risk and tax avoidance can provide an important push in the opposite direction, resulting in capital flight and the holding of sizable and often

> 9 The data for Jamaica is again out of synch: the elasticity here is below 0.7.

> 10 Tables L204 and L205 of http://www.federalreserve.gov/releases/z1/current/z1r-4.pdf.  The percentages include savings and time deposits as well as checking accounts and currency.

> 11  Households accounted for €4.3 trillion of a total of domestic non-government deposits of €7.0 trillion in eurozone monetary financial intermediaries at end-2005.  Non-government nonfinancial deposits amounted to €5.6 trillion (https://stats.ecb.int/stats/download/weas02_05/weas02_05/weas02_05.pdf).

> 12 <u>http://www.bankofengland.co.uk/statistics/abl/2005/dec/tableb.pdf.</u>

> 13 Including demand deposits and time deposits (http://www.pbc.cov.cn, Table: “Sources and Uses of Credit Funds of Financial Institutions”).

> 14 <u>http://www.cbe.org.eg/timeSeries.htm.</u>

> 15 Albeit diminishing, cf. Huizinga and Jonung (2005)

11
6 approach's drawbacks are discussed in Section 6.

The analysis in carried out aong two separate dimensions. At one level, an attempt .s made to provide a broad profile of utilization and subsidies incidence for 1987. Numbers are presented by quantiles of per capita expenditures, most frequently decilee. At a second level, the paper attempts to characterize the changes which have occurred in the incidence picture since the late 1970s. In the early 1980s work was done in the World 3ank by Oey Messook and Dov Chernikovsky on the 1978 SUSENAS (Meesook, 1984; Chernikovsky and Meesook, 1985 and 1986). Among other questions, they examined the incidence of public expenditures on education and health. This provides us with a benchmark study for making a comparison of the distribution of access and utilization across income groups between the two dates.

In 1978, the full survey covered 24,000 households interviewed over a series of 4 subrounds. The work by Meesook (1984), and Chernikovsky and Meesook (1985 and 1986), is based on the May subround which covers 6,000 households. In 1987, the entire survey comprising 55,000 households was held during January. The timing of the surveys shouldn't make much difference to the kinds of variableq this paper will be comparing across the two dates.<sup>6</sup> The 1987 analysis is based on a much larger sample.<sup>7</sup> Overall, survey methodologies and questionnaires are generally comparable across the two surveys. Any dissimilarities are discussed in the text when they arise. The problems encountered in this kind of replication exercise are more to do with the limitations that are imposed by which questions were explored as well as reported for the earlier data and which were not. The method of conveying results can also be confining. For example, the 1978 incidence figures are mostly given in the form of shares. Without knowledge of the underlying magnitudes, manipulation of the data is 6 It could make a difference to reported illnesses if these are correlated with the seasons, for example.

7 In their work on the 1978 data set, Meesook and Chernikovsky also used the expansion factors.
PROVISION OF WATER TO THE POOR IN AFRICA individual connections have not kept pace point to the serious impact prohibition of this source would have on poor urban households.

Given the coverage gap and the ready distribution system that household resellers provide, a valid question is whether to explore methods to partner with private households to increase coverage. Abidjan is one of the few cities with experience in attempting to regulate this sector, though they also the focus was on removing illegal connections; although the results have been disappointing because of a lack of incentives, there is still potential to explore better mechanisms for using this source. In the early 1980s, the utility SODECI and the national government decided to address the increasing growth of household water resellers that tapped in to illegal connections to the network. They would provide permits to the household water resellers as long as they converted their connections into formal ones. The expected outcomes were an increase in sales among the poor, a reduction in illegal activity and an improvement in revenue collection. The campaign did not provide any incentive to the resellers; they were billed as domestic customers and faced an

**Table 3.5   Regulation of household water resellers**

|Country|City|Prohibited|License|
|---|---|---|---|
|Benin|Cotonou|no|no|
|Ethiopia|Addis Ababa|no|no|
|Mozambique|Maputo|no|no|
|Nigeria|Kaduna|yes|N/A|
|Rwanda|Kigali|no|yes|
|Senegal|Dakar|yes|yes|
|Lesotho|Maseru|N/V|N/V|
|Malawi|Blantyre|N/V|N/V|
|DRC|Kinshasa|no|no|
|Sudan|Great Khartoum|yes|no|
|Zambia|Lusaka|no|no|
|Ghana|Accra|no|no|
|Chad|N'Djamena|no|N/V|
|Cote d'Ivoire|Abidjan|no|yes|
|Madagascar|Antananarivo|no|no|
|Tanzania|Dar es Salaam|yes|no|
|Uganda|Kampala|no|no|
||% yes|24|18|

Source: AICD WSS database 2007.

increasing block tariff (IBT). Moreover, the water vendor was required to provide a title deed for the permit and to invest in an extension from the meter to the water point. As a result, only 1 percent of the total resale at the household level is currently conducted through legalized resellers (Kariuki and others 2003).

# Mobile distributors<sup>_24_</sup>

Mobile distributors charge the highest prices among all the different water service providers. The water delivered by water tankers and carters costs between $2 and $8 per cubic meter. But the economic burden among the unconnected people in different cities is not evenly shared. There are cities where the lack of access to the network makes unconnected people heavily reliant on water tankers, as in Accra and Luanda, or heavily reliant on water vendors, as in Khartoum. Figure 3.4 presents a comparison of the prices of water tankers and water vendors in cities where data is available. There is little difference between the unit sale price of water tankers and water vendors, probably because of the strong competition between the two for the provision of water to certain types of clients.

> 24 Analysis based on data from 15 cities.

30
<!-- Start of picture text --> Self-employed<br>Wage-earner<br>Not<br>working<br>I |<br>February 2020 2021<br>HICs: 4; UMICs: 23; LMICs: 7; LICs: 4.<br><!-- End of picture text -->

<!-- Start of picture text --> Agriculture —— ——_ ~—s<br>Mining/Manuf. a<br>Commerce 7<br>X ra<br>Other services WN<br>—ZA— =<br>Not working<br>| i<br>February 2020 2021<br>HICs: 4; UMICs: 21; LMICs: 10; LICs: 6.<br><!-- End of picture text -->
# 3. Cross **Sectional Regressions**

We next test these hypotheses on a cross section of countries for which a consistent set of productivity and distortion variables are available. In the first set of regressions, we examine the determinants of self-employment as a share of the total work force. In the second set, we focus on two measures of turnover. Using deviations from the predicted values of these regressions, we construct somewhat speculative measures of the magnitude of distortions and of rigidities.

# **_3.1 Variables_**

The data sources and more detailed descriptions are listed in Appendix I.

# _3.1 Dependent Variables_

Share of Workforce in Self-employment: The OECD tabulates the share of non-agriculural workers in self-employment or as owners of firms. To the degree possible, the same variable was created from the employment and household surveys from Latin America. We focus on selfemployment rather than informality more generally for two reasons. First, we believe that it is the act of opening a business that is the central issue and that informality while important and often highly correlated is secondary.'<sup>3</sup> Second, data on informality in the OECD is largely unavailable and the LAC data sets differ in the variables available to use as proxies. Third, data on those employed in micro-enterprises is not available for the OECD. We assume that the total population employed in the self-employed sector, both as owners and workers is proportional to the share declaring themselves self-employed.

Mean Tenure in the Manufacturing Sector and Share of the Manufacturing Work Force with under Two Years of Tenure: These are two alternative variables available from the OECD and then generated from the LAC household and labor market surveys. We focus on manufacturing turnover because this is the best proxy for formal sector turnover that is available.

# _3.1.a Formal Sector Labor Productivity/Profitability (Z)_

Industrial Value Added (Indust. V.A.): The log of industrial value added per industrial worker is

> **13** See Levenson and Maloney(1998) for a development of this view.

13
||**Y= RIF es**|**timate of Log**|**[ Self-repor**|**ted Productiv**|**ity (XOF/ha**|**)]-Food crop**|**s**|||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|Household head has Secondary<br>education|<br>(0.030)|<br>(0.020)|<br>(0.016)|<br>(0.024)|<br>(0.019)|<br>(0.017)|<br>(0.013)|<br>(0.017)|<br>(0.033)|
|Household head has Tertiary|-0.002|-0.003|-0.003|-0.000|0.001|0.002|-0.001|0.009|0.017|
|<br>education|(0.004)|(0.003)|(0.003)|(0.002)|(0.002)|(0.003)|(0.002)|(0.009)|(0.017)|
|Household head disability|0.138*|0.035|0.020|-0.015|0.059|0.046|0.077*|0.075*|0.074|
||(0.079)|(0.046)|(0.040)|(0.039)|(0.042)|(0.044)|(0.044)|(0.041)|(0.069)|
|Dependency ratio|-0.263|-0.131|0.054|-0.013|0.105|0.130|0.031|-0.063|-0.157|
||(0.178)|(0.108)|(0.097)|(0.104)|(0.105)|(0.100)|(0.103)|(0.104)|(0.170)|
|Household Male Labor Use|0.051|0.124*|0.073|0.119*|0.067|0.025|0.020|0.034|-0.066|
|(Number)|(0.137)|(0.066)|(0.066)|(0.066)|(0.067)|(0.062)|(0.061)|(0.056)|(0.098)|
|<br>Household Female Labor Use|-0.520*|-0.140|-0.082|-0.024|0.241*|0.146|0.215|0.359**|0.171|
|(Number)|(0.274)|(0.149)|(0.130)|(0.144)|(0.141)|(0.146)|(0.148)|(0.151)|(0.246)|
|<br>Household Child Labor Use|-0.012|0.011|0.011|-0.001|0.034|0.007|0.017|0.082**|0.144***|
|(Number)|(0.073)|(0.034)|(0.032)|(0.030)|(0.031)|(0.032)|(0.033)|(0.036)|(0.053)|
|<br>Hired Labor Use (Number)|-0.023|0.010|0.027|-0.049|-0.086*|-0.097*|0.016|-0.054|-0.026|
||(0.090)|(0.049)|(0.047)|(0.052)|(0.046)|(0.052)|(0.048)|(0.051)|(0.087)|
|Number of Fields Managed by the|0.736|0.599**|0.073|0.024|0.119|-0.003|0.160|-0.055|-0.472|
|<br>household|(0.486)|(0.284)|(0.222)|(0.216)|(0.200)|(0.217)|(0.250)|(0.178)|(0.379)|
|Total number of crops produced|-0.977*|-0.699**|-0.203|-0.341|-0.395*|-0.414*|-0.595**|-0.619***|-0.857**|
||(0.566)|(0.321)|(0.219)|(0.219)|(0.206)|(0.240)|(0.288)|(0.187)|(0.379)|
|Household used any purchased seed|0.061|-0.021|0.087|0.051|0.090|0.048|-0.061|-0.077|-0.055|
||(0.182)|(0.078)|(0.073)|(0.074)|(0.065)|(0.062)|(0.057)|(0.065)|(0.106)|
|Plots that Use Chemical Fertilizer|0.151*|0.093**|0.045|0.026|-0.018|-0.043|-0.060|-0.021|0.006|
|(% of total)|(0.090)|(0.040)|(0.032)|(0.030)|(0.031)|(0.035)|(0.037)|(0.038)|(0.052)|
|<br>Plots that Use Pesticide (% of total)|-0.290*|-0.257***|-0.095|-0.124|-0.210**|-0.116|-0.144|-0.179*|-0.467***|
||(0.164)|(0.095)|(0.086)|(0.090)|(0.094)|(0.110)|(0.105)|(0.093)|(0.164)|
|Constant|8.520***|2.216**|0.886|0.697|0.067|0.209|0.905|0.760|1.847*|
||(2.037)|(1.085)|(0.734)|(0.824)|(0.718)|(0.760)|(0.704)|(0.615)|(1.077)|
|Observations|2749|2749|2749|2749|2749|2749|2749|2749|2749|

49
10 literature that the returns to experience decrease over time (Lagakos et al., 2018b; Jedwab et al., 2021c). If _<u>re</u>_<sup>_<r_ ̄</sup><sup>_e_, then the returns embody a college premium.</sup>

# **2.2. Returns Parameters**

We follow the methodology of Lagakos et al. (2018b) and Jedwab et al. (2021c) to estimate the returns to education and experience by college status: _r_ ̄ _s_ , _<u>rs</u>_<sup>, ̄</sup><sup>_re_and</sup><sup>_<u>r</u>_</sup> _e_<sup>.</sup> **Sources.** The data source is the _International Income Distribution Database_ (I2D2) of the World Bank. The database consists of a large number of household and labor force surveys and census samples. The data was initially compiled by the World Bank’s _World Development Report_ (WDR) unit between 2005 and 2011. The database has since been expanded.<sup>17</sup> Only select members of the WDR team or research team or individuals in charge of harmonizing the data can access the database. However, a significant number of the surveys can be accessed online or after entering an agreement with the countries’ respective statistical office.<sup>18</sup> Finally, the surveys are nationally representative and large enough for our purpose.<sup>19</sup> Jedwab et al. (2021c) also verify that the database generates global patterns that are consistent with patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank.<sup>20</sup>

**Sample Size.** I2D2 includes about 1,500 survey/census samples. However, wages are only reported for two-thirds of them. We restrict our analysis to workers aged between 18 and 67 with data on age and education. The baseline sample that we obtain includes 24,437,020 individuals from 1,073 surveys and 11 censuses in 145 countries from 19902016 (median = 6; mean = 7.5; min = 1; max = 44). We include surveys from the 1990s to increase sample size. The 145 countries comprise 95% of the world’s population.

**Variables.** Data are available on the monthly wage, age, and number of years of education. For a substantial subsample, information is available for the hourly wage, the number of hours worked, and the employment status (e.g., self-employed or paid employee). Note that I2D2 calculates the wage of self-employed individuals as the amount of salary taken from the business. Next, we calculate _potential work experience_

> 17We use the expanded December 2017 vintage of the database.

> 18World Bank (2013) writes that I2D2’s aim is “to provide the World Bank and its client countries reliable micro economic data that is in an easy to use format. The data will be used to calculate a variety of indices and in the future can be used for complex economic analysis. The micro level data will be strictly confidential; however, the aggregate data can and will be used for information and research purposes.” 19World Bank (2013) writes: “In practice we frequently see samples sizes of 10,000.”

> 20They find that the samples are globally representative in terms of per capita income, age structure, education, and self-employment. The Mincerian R-squareds are also not too different across countries.
- Fernandez, R. (2007). “Women, work and culture.” _Journal of European Economic Association_ , 5(2), pp. 305-333.

- Ferrant, G., L.M Pesando, and K. Nowacka. (2014). “Unpaid Care Work: The Missing Link in the Analysis of Gender Gaps in Labour Outcomes”. OECD Development Centre.

- Floro, M. S., and M. Miles (2003). Time Use, Work and Overlapping Activities: Evidence from Australia. _Cambridge Journal of Economics_ , 27, pp. 881–904.

- Floro, M. S, and H. Komatsu. (2011). “Gender and Work in South Africa: What Can Time-Use Data Reveal?”. _Feminist Economics,_ 17(4): 33–66.

- Folbre, N. (2006). “Measuring Care: Gender, Empowerment, and the Care Economy.” _Journal of Population Economics_ , 7(2), pp. 183–199.

- Folbre, N., and J. Yoon (2007a). “What Is Child Care? Lessons from Time-use Surveys of Major English-speaking Countries.” _Review of Economics of the Household_ , 5, 223–248.

- Folbre, N. and J. Yoon. (2007b). “The Value of Unpaid Child Care in the U.S. in 2003.” In _How Do We Spend Our Time? Recent Evidence from the American Time-Use Survey_ , J. Kimmel. Kalamazoo (ed.), Michigan: W. E. Upjohn Institute for Employment Research.

- Folbre, N., J. Yoon, K. Finnoff, and A. Sidle Fuligni. (2005). “By What Measure? Family Time Devoted to Children in the United States.” _Demography_ 42(2), pp. 373–390.

- Foster, G., and L.S. Stratton. (2017). “Do Significant Labor Market Events Change Who Does the Chores? Paid Work, Housework and Power in Mixed-Gender Australian Households.” IZA Discussion Paper 10831.

- Galiani, S. and F. Weinschelbaum (2012). “Modeling Informality Formally: Households and Firms.” _Economic Inquiry_ , 50(3), pp. 821-838.

- Gammage, S. (2010). “Time Pressed and Time Poor: Unpaid Household Work in Guatemala.” _Feminist Economics_ , 16(3), pp. 79–112.

- Giddings, L., J. M. Nunley, A. Schneebaum, and J. Zietz. (2014). “Birth Cohort and The Specialization Gap between Same-Sex and Different-Sex Couples.” _Demography_ , 51(2), pp. 509–534.

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24
17 for Russia as an economy in transition with a high share of incomes generated in the shadow sector, which leads to additional distortions in regional income statistics. An alternative measure of the achieved development level is the infant mortality rate, which also has significant variation across Russian regions and is positively correlated with income. We use both the 1990 figures that reflect the level of _initial_ pre-transition development of each region and the concurrent infant mortality. Alternatively, the indicator of urban landline phone connections is employed (the number of landline phones per 1,000 urban populations). Another important indicator of development achievements is the level of education. We measured it in two ways: using the number of students per 10,000 populations (variable _students_ ) and the share of the labor force with university degrees (variable _education_ ).

Thirdly, the structure of regional fiscal revenue may matter. It is well known that in intergovernmental relations money tends to stick where it hits, leading to a so-called “flypaper effect”. Namely, the elasticity of regional government expenditure with respect to regional income is consistently estimated to be a small fraction of the elasticity of regional government expenditure with respect to central government transfers (the estimates of the latter range from 0.5 to more than unity).<sup>22</sup> This generally suggests that governments spend “earned” and “unearned” (or “windfall”) revenue in different ways.<sup>23</sup> This would suggest that “earned” revenue could be decentralized in some “normal” way, while windfall revenue is likely to be shared differently, with a much larger share retained by the regional government. In the Russian context one can identify two main sources of windfall regional government revenues: transfers received from the federal government and budget revenue derived from availability of local rents, primarily through implicit and explicit taxation of highly profitable local firms in extraction industries such as oil and gas. The former source

> 22 See the survey by Hines and Thaler (1995) and the study by Knight (2002). 23 Numerous studies (e.g., Blanchard et al., 1994) found the same was true for corporate entities.