rafmacalaba/data-use-mentions-extended huggingface dataset:
, Lazarus Gonani, Leslie Lipper, and Osborne Sibande for their comments on the earlier drafts of this paper, and (b) the Malawi National Statistical Office (NSO) management and field staff that worked on the 2013 and 2016 rounds of the Integrated Household Panel Survey (IHPS) and the 2015 Flood Impact Assessment Survey. The funding for the research was provided by the World Bank Research Support Budget-funded project “Evaluating the Negative Impacts of Extreme Weather on Household Agricultural Production and Consumption in Malawi.”
Table 1 reports the summary statistics of key variables in three periods. The agriculture sector includes agriculture, forestry, fishing, and hunting. The industry sector includes mining, manufacturing, construction, and utilities. The services sector includes all the rest of the industries.

China experienced rapid structural transformation during the study period. Nearly 70% of employment was in agriculture in 1990 on average. The share declined to 64% in 2000 and 60% in 2005. Average employment share in industry and services remained stable, though the simple average masks huge variations across locations. Moving out of agriculture is also manifested in the sharp decline of agricultural GDP share, which fell from 27% in 1990 to 16% in 2005. Average industry GDP share dropped from 45% in 2000 to 41% in 2000, then increased to 47% in 2005. Services continue to account for a larger share of GDP.

|Table1. Summary statistics|
|---|

||Ye<br>|ar=1990<br>|Year<br>|=2000<br>|Ye<br>|ar=2005<br>|
|---|---|---|---|---|---|---|
|Variable|Obs|Mean|Obs|Mean|Obs|Mean|
||Pa<br>|nel A: Structural t<br>|ransformatio<br>|n<br>|||
|Agri_emps|329|69.22%|345|64.05%|344|60.42%|
|Indu_emps|329|16.03%|345|15.92%|344|16.64%|
|Serv_emps|329|10.72%|345|10.85%|344|10.77%|
|Agri_gdps|298|26.55%|320|23.59%|262|16.
3%|0.6|0.6|0%|
|10<br>_Source_:|0.8<br>Author|0.5<br>’s calcu|-5%<br>lations wit|0.8<br>h data fr|0.5<br>om RI|-6%<br>GA, and|0.7<br>National|0.7<br>Househo|0%<br>ld Surveys|0.8<br>.|0.8|-1%|0.7|0.7|0%|0.64|0.49|-3%|0.5|0.5|0%|

30
|10%|
|Year FE||✓|✓||
|HS6 FE||✓|✓||
|Observations||15,153|15,153||

_Note:_ All estimates are obtained form equation 10 and by varying the dependent variable. Column 1 uses imports and column 2 uses our baseline measure of consumption of imports. Estimates in Panel B separate between the effect of _τzt_<sup>_DD_</sup> for good’s whose tariffs were phased-out (class B,C,C+) and the rest. Standard errors, in parentheses, are clustered at * ** *** HS-6 level, _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.

period lag in the reception and consumption of imports has the following representation: The results of estimating (4) for _h_ = 1 and _h_ = 7 under this version of _q_ ̃ are contained in columns 3 and 4 of Table 6. The estimates are very similar to the baseline results.

_Different Inventory-Sales Ratios._ - In the baseline measure we use a different inventorysales ratio, _kcz_ , for different exporter countries _c_ , but not for the different destination countries _i_ . To check whether our results are sensitive to this lack of data we impose a common _kz_ across all four directions of trade in our baseline measure. Again, as can be seen in columns 5 and 6, the estimates are very similar to the baseline results. Results are also robust to calculating _kcz_ using the average over HS10-district of entry HH indexes or different levels of aggregation of the HH index.

_Demand Shock._ - In our baseline we assume that the usage of inputs is a constant fraction of inventory holdings. Here, we extend the baseline measure of _q_ ̃ by including a demand shock, _νiczt_ . Under this extension the consumption of imports takes the following 21
. 2020. “Impact of Use of Technology on Student Learning Outcomes: Evidence from a Large-scale Experiment in India.” _World Development_ 127: 104736.

Niebel, Thomas. 2018. “ICT and Economic Growth – Comparing Developing, Emerging and Developed Countries.” _World Development_ 104 (April): 197–211.

Nordhaus, William D. 2021. “Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth.” _American Economic Journal: Macroeconomics_ 13 (1): 299–332.

Okunogbe, Oyebola, and Fabrizio Santoro. 2023. “The Promise and Limitations of Information Technology for Tax Mobilization.” _World Bank Research Observer_ 38 (2): 295–324.

Okunogbe, Oyebola. and Victor Pouliquen. 2022. “Technology, Taxation, and Corruption: Evidence from the Introduction of Electronic Tax Filing.” _American Economic Journal: Economic Policy_ 14 (1): 341–72.

38
>improvement in GHG emissions<br>of<br>energy<br>consumption<br>(increase)|Increase<br>in<br>carbon<br>intensity of energy of<br>2.5% per year|
|GHG emissions of energy<br>consumption|Baseline<br>Low|<br>Country-specific<br>estimate<br>of<br>annual improvement in energy<br>intensity of GDP<br>10<sup>th</sup><br>percentile<br>of<br>annual<br>improvement in energy intensity<br>of GDP (decarbonization)|On average, no change in<br>carbon intensity of energy<br>of 0.1% per year<br>Reduction<br>in<br>carbon<br>intensity of energy of<br>2.2% per year|
||High|High variant of UN’s population<br>projections|Country-specific<br>growth<br>rate|
|Population growth|Baseline<br>Low|Medium<br>variant<br>of<br>UN’s<br>population projections<br>Low variant of UN’s population<br>projections|Country-specific<br>growth<br>rate<br>Country-specific<br>growth<br>rate|

30
) (cf. Arrow, 2001, reprinted on several other occasions too), which many consider the founding paper of the sub-discipline, Manning et al. (1987) reporting of the RAND Health Insurance Experiment, Pauly‘s (1968; 1991) comment on Arrow (though not Arrow‘s reply!), and Barsky et al. (1997) with their famous paper on risk tolerance, time preference, and intertemporal substitution.

̳Efficiency and equity‘ as a group has relatively few US contributions and, given the dominance of US economists in the field, this may also explain why the citation rates are not higher. The group includes both methodological contributions and applied studies, including international comparisons, and covers equity in both the financing and delivery of health services. ̳Health statistics and econometrics‘ contains six contributions with citation rates above 1,000 and relatively few published before 2000. Some of these publications (e.g. Angrist et al., 1996) simply have health as an application and the usefulness of the paper extends well beyond the health field. In other cases (e.g. Duan and et al., 1983) the paper sets out to address an
three waves of the China Urban Labor Survey (CULS), which allows one to carefully distinguish employment (under our definition) from labor force participation, it is clear that China’s booming economy and a tight labor market has increased women’s employment across the age distribution for women under 45, but that after 45 employment rates drop sharply (Figure A.2, Panel A). Moreover, when we turn to labor force participation (Figure A.2, Panel B) it is evident that in 2001 and 2005 many of the urban women

> 8We review evidence from the CHNS as it is the publicly available data source that researchers used (pre-CHARLS) to study labor supply, health status and retirement of older workers  in China (e.g., Benjamin et al, 2003; Dong, 2011). As it is based on a panel of households which does not enumerate complete information on family members who have split off of households, one should be concerned that later waves of the CHNS over-represent those who remain in the households. As we are interested in the over-45 population, this is less of a problem than if we were considering the employment decisions of individuals who were children in 1991 and likely to have moved out of households.

> 9In Appendix Figure A.1, we present statistics from the 1990 and 2000 population census and the 2005 one percent population sample. Employment rates are somewhat lower in 2005 for older men and somewhat higher than one observes in the CHNS.

5
used. In Japan, on the other hand, the number of migrants comes out higher under the foreign citizenship criteria.

Where data are available for both definitions, priority is given to data by country of birth, for several reasons. First, country of birth is more appropriate in analyzing physical movements and handling the cases of former colonies and dependencies.<sup>4</sup> Second, while nationality can change, place of birth cannot.<sup>5</sup> Third, naturalization rates vary enormously across destination countries. Differences in laws on citizenship criteria (for both migrants and their children born in the destination country) do not affect data based on place of birth. Fourth, when migrants cannot be assigned to a specific origin, they are often recorded under an aggregated umbrella heading. These categories embody ambiguity about a migrant‘s origin, and since migrants are assigned to aggregated headings more frequently when the citizenship definition is used, the foreign born concept is again favored. Last, for migrants living in disputed territories, such as Kashmir and Western Sahara, an individual‘s status or official citizenship may be unclear, while country of birth is usually more certain.

# _How Are Migrants Recorded?_

Destination countries employ a wide range of tools to enumerate migrants, including population censuses, population registers and registers of foreigners, border statistics, and worker and residence permits.<sup>6</sup> This article focuses on census and population register records, which are widely available, have the broadest geographic coverage, and include similar questions, thereby yielding more standardized responses. For these reasons, they are the primary sources for most data in the Global Migration 4 This discussion of definitions highlights the somewhat paradoxical possibility of individuals being classified as migrants without ever having moved across an international border. As mentioned, this is generally possible only in the case of people born in one country but who are citizens only of another country. A similar situation arises with dependencies and former colonies
in urban areas.

The studies described above are for localised settings in developed countries in the context of fracking but gas flaring in developing countries is mostly in the context of conventional oil and gas production. This distinction may matter as fracking is associated with other pollutants (and other channels of impact on health) that may be less relevant for conventional oil and gas production.<sup>6</sup> Thus, it is unclear how transferrable these findings may be for the health impact of flaring from conventional oil and gas production in developing countries, especially as these countries tend to have weaker environmental regulations and less monitoring than in developed countries.

There are some studies in the Nigerian context, but they are very limited non-econometric studies. A survey of attitudes towards flaring in oil producing communities found that residents perceive gas flaring as hazardous to their health, environment, and general well-being of their community but are resigned to the continued presence of flares (Edino, Nsofor and Bombom, 2009). One case study of 600 households in six Nigerian communities (three with gas-flaring and three without) found that

> 6 Fracking involves injection of chemical additives into wells. Exposure to some of these chemicals has been associated with adverse reproductive health and developmental effects in humans [see Webb et al. 2014].

5
HaryAPraRaja TNadRajaAPraUPraMeghAPraNagaMPraHaryRajaJharUTKarnRajaGujaMizoMahaOrisHaryUPraWBen HPra APraKarnBihar Punj DelhiRajaKeraUPraTpurAPraGoaAPraKeraJKasPun TNad WBenj Karn OrisUPra<br>Delhi<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Bust Distance in Months Average International to Domestic Bust Distance in Months<br>Bananas Coconut Oil<br>UT<br>RajaJKasUPraAPraKeraMahaGoaBihar GoaAPra UPra<br>MizoKeraTNadWBen APra MPra TNad UPraAPraRajaTNadUPraTNadJharRajaHary Oris Delhi Punj HPraMeghHary Naga KeraRajaAPraAPra Punj TpurUT Delhi Karn APraRajaBiharHary MahaAPra TpurKeraKeraGujaTNadAPraWBen Punj PunjKarnKeraUPraTNadTNad Raja OrisMPra<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Bust Distance in Months Average International to Domestic Bust Distance in Months<br>20 20<br>15 15<br>10 10<br>5 5<br>Boom Distance in Months Peak Distance in Months<br>Average International to Domestic Average International to Domestic<br>0 0<br>20 20<br>15 15<br>10 10<br>5 5<br>Boom Distance in Months Boom Distance in Months<br>Average International to Domestic Average International to Domestic<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations.<br><!-- End of picture text --> Markets operate relatively efficiently transmitting signals when they are shocked with sizable international price changes. Booms and busts of food prices, either originating domestically or abroad are transmitted to different Indian markets and in a relatively symmetric manner, even if to different 10 According to Peltzman’s comprehensive study of 165 producer goods and 77 consumer goods “in two out of three markets, output prices rise faster than they fall” (Peltzman, 2000; p. 480). Bacon (1991) was the first to use the term rockets and feathers to describe the patterns of retail gasoline prices in the UK. Varela and Taniguchi (2013) report a similar finding for wheat and
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis Table 10: Countries included in Resource-rich Sub-group, classified by Income Groups

||**Absolute**|**Relative**|
|---|---|---|
|High income|5|16.7%|
|Upper middle income|11|36.7%|
|Lower middle income|10|33.3%|
|Low income|4|13.3%|
|Total|30|100%|

According to the IMF report, “nations [rich in nonrenewable natural resources] produce 85% of the world’s petroleum, 90% of its diamonds and 80% of its copper, generating trillions of dollars in annual profits.” Table 11 summarizes the empirical results of the recursive model estimated for the subsample of resource-rich countries. These figures confirm the results of the full-sample model. The magnitude of the coefficient, as expected, slightly increases relative to the full-sample model, but the sign of the associations and their economic implications remain unchanged.

Table 11. Estimated Results from the Recursive Model Specification (resource-rich subsample)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDP**|**Gini index**|**GDP**|
|Nonrenewable resource dependence|-0.067**|0.008|-0.177***|0.122|
|Lower Income<br>|dropped|dropped|6.090**|57.336***|
|Lower income * Nonrenewable resource<br>Dependence|0.078|-0.362*
: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the PIAAC sample. A higher value indicates that jobs are more amenable to WFH.

Finally, we regress the WFH index for each data set on individual and job characteristics and confirm that even after controlling for observable characteristics, women, college graduates and salaried workers are more likely to have jobs amenable to WFH than men, lower educated, and self-employed workers (Table 3).<sup>8</sup>

> 8 Tables showing country-level findings are available in the Online Appendix

> (http://www.hernanwinkler.com/uploads/5/5/1/1/5511764/appendix_jobs_amenability_to_wfh_v13.xlsx).

17
|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<sup>**†**</sup>|2,098,570|47,698|11,260|2,016,257|45,597|10,878|2,094,821|47,624|11,249|
|Within R-squared<sup>**†**</sup>|0.018|0.016|0.013|0.048|0.034|0.042|0.028|0.023|0.030|

Source: World Bank staff analysis using Romania MoF firm-level data from 2016 to 2020. The sample includes all firms. Growth is calculated as the difference between the values in year t and t-1 divided by the average of the values in year t and t-1. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-24.9%, 25-49.9%, or 50% or more, respectively, in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for age and size groups are Start-ups (0-2) and Small (10-49), respectively. The classification of the 4- digit NACE sectors into competitive, partially contestable, and natural monopoly sectors are based on the SOE sector taxonomy developed by Dall'Olio, Goodwin, Alonso, Patino Pena, & Sanchez-Navarro (2022).<sup>**†**</sup> indicates values are for the state ownership model using 10-100
g. CHARLS in China) have questions on ADL. While not shown in Appendix 1, there is heterogeneity in the ADL questions that are asked across countries with most often questions around physical basic activities such as walking over 100 meters, standing or sitting.

Appendix 2 includes 36 countries, 53 data sets and 76 data set-years with broad activity limitation questions. The most commonly found broad activity question asks about the person being limited in daily activities or in learning or in any economic activity due to a health problem in the past six months (not shown in Appendix 2). Some countries have several questions to get at broad activity limitations, including some recording the number of days the person was unable to carry out usual activities.

Appendix 3 has 50 countries, 81 data sets and 113 data set-years with a general disability question such as “does [Name] have a disability?” Often such questions are followed up with a question on the type of disability or the impairment the person has.

Appendix 4 has 43 countries, 54 data sets and 75 data set-years that have other types of disability questions such as belonging to the so-called disabled social group for Armenia.<sup>4</sup> In addition, some of the questions found in this list can be mixes of different types of questions such as impairments mixed with chronic health conditions in Tunisia, Bosnia, and Bulgaria. We have

> 4 Disability focused surveys (e.g. Philippines Model functioning Survey) were not covered in this table for other disability questions, but were covered in Tables 1 and 2 and Appendices 1, 2 and 3. Disability focused surveys typically have several types of other disability questions not beyond functional ADL, general and broad activity limitation questions.

3
conduct DHSs.

We use DHS data for which we have access to the occupation of the individuals as well as a proxy for their location. In order to construct the shares of employment per sector, we use respondents’ answers to questions about their current occupation. We first compute the shares of nonworking individuals and then group all working individuals into three sectors: agriculture, manufacturing, and services. We aggregate individual responses within each EA and then generate an unbalanced panel of districts that had at least one EA during a survey round. The DHS–provided GPS coordinates for EA locations are not perfectly reliable because of the common random displacement applied to GPS coordinates for anonymity.<sup>10</sup> We aggregate EAs per geographic location.

> 8The World Bank reports access to Internet (percent of population) for most countries for a long time period. See https://data.worldbank.org/indicator/WeT.NET.USER.ZS.

> 9Table A2 shows that increasing the electricity threshold to 80% leaves only one district with electriciy but no paved road, the charter city of Dire Dawa in Ethiopia, which is connected to the road network. Removing the electricity alone indicator reduces the risk of capturing data errors.

> 10DHS coordinates of rural (urban) EAs are randomly displaced within a 0-10 kilometer (0-5 kilometer) radius

8
# **References**

Agence Nationale de la Statistique et de la Démographie (ANSD) (2007) _Enquête de suivi de la pauvreté au Sénégal, ESPS 2005‐2006_ , rapport national, août Agence Nationale de la Statistique et de la Démographie (ANSD) (2013), _Rapport définitif de la deuxième Enquête de Suivi de la Pauvreté au Sénégal (ESPS II),_ rapport national _._ <mark>Araar A. and J.-Y. Duclos (2007), "</mark> _<mark>DASP: Distributive Analysis Stata Package</mark>_ <mark>", PEP, World Bank, UNDP and Université Laval.</mark> Baland, J.-M., C. Guirkinger, and C. Mali (2011),. "Pretending to Be Poor: Borrowing to Escape Forced Solidarity in Cameroon." _Economic Development and Cultural Change_ 60, no. 1 (2011): 1- 16.

Baland, J.-M. and R. Ziparo (2018), « Intra-household bargaining in poor countries”, in _Towards Gender Equity in Development,_ edited by Anderson S., L. Beaman and J.-P. Platteau, Oxford University Press Bargain O., G. Lacroix, and L. Tiberti (2018), “Validating the Collective Model of Household Consumption Using Direct Evidence on Sharing” IZA Discussion Paper No. 11653: Beck, S. _et al_ (2015), « Child fostering in Senegal », _The Journal of Comparative Family Studies,_ vol 46: 57-73 Beegle K. _et al._ (2012), “Methods of household consumption measurement through surveys: Experimental results from Tanzania”, _Journal of Development Economics,_ vol. 98(1), May 2012, pp 3-18.

Beegle, K. _et al._ (2016). _Poverty in a Rising Africa_ . Washington, DC: World Bank. © World Bank. https://
**Figure 1. Foreign direct investment flows**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Foreign direct investment, net inflows (% of GDP)<br>Foreign direct investment, net outflows (% of GDP)<br><!-- End of picture text --> Source: World Development Indicators.

**Figure 2. Use of duty-free imports for export processing by location in China**

<!-- Start of picture text --> Duty‐free processing imports used by firms in different areas of China<br>in 2000<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>SEZs, Other Development Areas, Coastal open cities outside SEZs Rest of China<br>Bonded Areas anywhere in and other development areas<br>China<br>% of total duty‐free processing imports<br><!-- End of picture text --> Source: Authors’ calculations using data from General Administration of Customs of the People’s Republic of China (2000). The coastal open cities include Tianjin, Qinhuangdao, Dalian, Shanghai, Nantong, Lianyungang, Ningbo, Wenzhou, Fuzhou, Qingdao, Yantai, Guangzhou, Zhanjiang, Beihai.

Less than a decade later, in 2000, duty-free processing imports represented 53% of the value of China’s total imports (General Administration of Customs of the People’s Republic of China, 2000). About half of these processing imports were used by firms located in SEZs, around 32% of 7