rafmacalaba/data-use-mentions-extended huggingface dataset:
that about 90 percent

> 13Household living conditions in poor and rich countries differ, and thus it would have been desirable to calibrate country-group-specific within-household transmission rates. Unfortunately, we are not aware of any systematic studies on country heterogeneity in within-household transmission. The value in Lei et al. (2020) is based on various settings, including several estimates from China. A recent paper based on data for Norway (Telle et al., 2021) finds a secondary attack rate (SAR) of 0.24 when the index case is a parent or a child under 6, and a SAR of 0.14 when the index case is an older child. It is thus reassuring that the rates for Norway are similar to those for China, two countries at different levels of development (and with different levels of housing area per resident). There is a feature of our calibration that accommodates the notion that slums or other high-density living arrangements can lead to higher transmission rates. Namely, we calibrate substantially higher community transmission rates in lower- and middle-income countries compared to wealthier ones. This difference can be thought of as capturing the prevalence of high-density living such as slum areas.

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a contract. Self-employed excluded<br>Source: Calculation with ESS data<br>100<br>75<br>50<br>25<br>0<br>100<br>75<br>50<br>25<br>0<br><!-- End of picture text -->

**Figure 13 Formal and informal employees by country and size of establishment**

However, in every country a substantial part (from one fifth to two thirds) of informal employees work in establishments of size exceeding the conventional ̳5 workers informality 33
Gaza, 2017 Panel a: Poverty by location Panel b: Inequality by location <!-- Start of picture text --> 53.0<br>0.3<br>0.27 0.28<br>29.2<br>13.9<br>West Bank Gaza National West Bank Gaza National<br>Gini coefficient (0-1)<br>Below national poverty line (%)<br><!-- End of picture text --> Source: World Bank calculations based on PECS 2016/17.

Note: Poverty calculated based on real consumption per capita. National poverty line establishes different thresholds based on different combinations of numbers of adults and children in the data. Gini calculated based on real consumption per adult equivalent.

**The growing regional poverty disparity reflects geographic, administrative, and economic fragmentation.** East Jerusalem has been under Israeli administration and domestic law since 1967, and it

> 8 Figures and trends in this paragraph drawn from: World Bank (October 2022), World Bank (2022a), Palestinian Central Bureau of Statistics (PCBS) (2018), PCBS (2011), PCBS (2007), PCBS (2006), PCBS (2004).

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booklet. For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically different at the 95% significance level, except FEMALE. Figures in parenthesis represent standard deviations.

The findings regarding PRESCHOOL and REPEAT indicate the possible importance of the trajectory of the student prior to high school. Repetition rates are difficult as comparative indicators of system quality because of the variations across countries in curriculum and standards, but REPEAT is another interesting variable to keep in mind as a possible clue to the mystery of Vietnam’s PISA performance. As in some other East Asian cultures, Vietnamese parents expect their children to study hard. Though Mark Twain, translated into Vietnamese, is quite a best seller for young readers in Vietnam, truancy from school is not perceived benevolently by parents.<sup>2</sup> Table 1 indicates a consistently lower truancy rate

> 2A cultural explanation is possibly quite important in explaining Vietnam’s anomalous PISA results, though the PISA data set may only be able to measure the possible effects of culture rather than measuring cultural differences. Literature from the World Values Survey, that does seek to measure cultural differences,

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all providers with a sign advertising medical care within a 15-minute walking radius around the perimeter of the area of the household survey were noted. This formed a "census" recording the quali fi cations, age, gender and experience of providers in the universe.

From this set, a sample of 20-25 practitioners were drawn from among those who had been visited (with sampling weights proportional to the number of all visits by households) and 10 from the group that had not been visited by anyone in the household sample in each of the seven neighborhoods.<sup>7</sup> From this sample, 205 providers participated in the study after a 15 percent rate of non-compliance.<sup>8</sup> While most practitioners (163) were in clinics staffed by a single person, we also interviewed practitioners in hospitals (public and private) and public clinics with multiple providers. In these cases, we interviewed at least two providers in every facility; for hospitals, interviews were conducted in the out-patient department that serves walk-in patients.

For each of the 205 providers, we completed a two-stage inquiry. In the fi rst part we administered fi ve "vignettes" or hypothetical cases acted out by two interviewers (the provider was aware of the role playing) to every provider in the sample (Das and Hammer 2005). One of the interviewers acted as the "patient" while the other recorded questions the doctor asked and answered those that the patient would not know the answer to, such as the results of an examination or tests recommended. Diagnoses reached and the treatments prescribed were also noted. This large volume of information was summarized by use of item response theory (IRT) that gave each doctor a score derived as a weighted average of the relevant questions asked and treatments prescribed–the weights being determined via the statistical technique. We call this score the “competence” of the provider.<sup>9</sup> The main results of that study relevant for this paper were
higher in the core, respectively. There is also a stronger perception of corruption in the core, measured as the percentage of manufacturing firms expected to pay informal payments or gifts to public officials or tax officials and to give gifts to obtain an operating license. All these indicators are about twice as high as in non-core regions. The perception of corruption is also higher in the core, but to a lesser extent. Regarding finance, the percentage of manufacturing firms in the core with a line of credit or loans in the core is 20 percent higher. In addition, the share of manufacturing firms using banks to finance investments is more than 30 percent higher. Nevertheless, the perception of manufacturing firms regarding access to finance is more pessimistic in the core.

# **2.3 Services Firms**

The analysis of services covers 7,847 firms which represent 19 percent of the overall sample (see Appendix 3). We dropped retail or wholesale trade firms and hotels and restaurants as these do not represent typical firms for which cross-border trade would be significant. Two-thirds of the remaining services firms focus on business services including IT services, telecommunications, accounting & finance, advertising & marketing, and other business services. Another third of the firms is in construction or transportation. On average, 48 percent of services firms are located in the core. Table 2 shows the indicators for services firms. Fewer indicators show a statistically significant difference between core and non-core regions, which might be due to the low number of observations for some indicators.

Regarding firm characteristics, services firms in the core have a higher share of exports in sales than services firms in non-core regions. Export participation, on the other hand, does not show a significant difference. Contrary to the findings in manufacturing, this implies that the threshold of export participation might be lower, but differences in export intensity are more difficult to achieve in non-core regions. Other trade-related indicators show that the average time of inventory of the most important input is more than 20 days
Because the growth of a city is associated with lower consumer prices, surrounding rural consumers who have access to urban markets may benefit through higher real wages (Jacoby 2000). This effect may be due to increased competition among a larger number of producers in the growing urban area as well as thicker market effects in both factor and goods markets (Fujita et al. 1999). However, the increased demand for agricultural goods may also lead to higher prices for these goods, especially if the supply is fairly inelastic. Therefore, the direction of the net effect of urbanization on consumer prices is a priori ambiguous.

This study predicts that the total net effect of urbanization on rural poverty is poverty reducing. Moreover, the bulk of the effect is expected to be felt in rural areas that are relatively close to the growing urban area. This distance decay effect is consistent with recent research on the welfare impact of the expansion of a gold mine in Peru (Aragόn and Rud 2009) and is important for our identification strategy, as explained below. The next sections will detail the methodology used to test these hypotheses by measuring the total net effect in the case of Indian districts.

# **<<A>>Data**

The data for the empirical analysis come from three main sources. For the district-level measures of poverty, we use data from three “thick” rounds of the National Sample Surveys (NSS: the Indian household survey) spanning the 1980s and the 1990s, the 38th (1983–84), 49th (1993–94) and 55th (1999–2000) rounds of the NSS.<sup>7,8</sup> These measures have been adjusted by Topalova (2010) in two ways. First, she uses the poverty lines (based on the state-level prices computed separately for rural and urban areas) proposed by Deaton ( 2003a, 2003b ) instead of the standard Indian Planning Commission poverty lines, which are based on defective price indices. Second, she adjusts the
# **7 Policy Implications**

Our paper studies the effect of weather shocks in 2018 on consumption in Afghanistan, a country affected by conflict that is also witnessing extreme weather events driven by climate change. We combine a geo-coded cross-sectional household survey (IE-LFS 201920) with satellite-based precipitation, temperature, and vegetation measures. The 2018 drought measured by positive temperature anomalies and negative vegetation anomalies decreased consumption and increased poverty rates.

Working in a data scarce environment, we are able to deliver important insights on the relationship between weather shocks and welfare in a conflict affected country, showcasing techniques that can be used in similar context where only cross-sectional data is available. We find evidence that households decreased non-food consumption to save themselves from food insecurity. While both food and non-food consumption expenditures decreased with adverse vegetation anomalies, households were able to maintain their food consumption by reducing non-food monthly per capita consumption when faced with bad temperature anomalies.

In terms of resilience, households owning land could mitigate the adverse effects of droughts, whereas livestock ownership could not protect households against welfare losses due to severe droughts. This confirms that asset ownership may offer a means of insuring against weather shocks by allowing households to either sell or borrow against the asset; however, this coping mechanism is often imperfect (Dercon, 2002; Fafchamps et al., 1998). Given the ongoing conflict at the time in Afghanistan, we also find suggestive evidence that consumption was disproportionately lower in Taliban districts in 2018 that experienced bad vegetation anomalies.

In terms of vulnerability, while 47 percent of households were classified as poor in the survey, a simulation exercise based on historical weather data suggests that 62.5 percent of individuals have a greater than 1 in 4 chance of falling into poverty due to weather shocks. This is likely a lower bound, given that the methodology does not account for displaced households or adaptive strategies. It highlights the country’s heightened vulnerability to drought, which climate change will only exacerbate.

These results highlight the importance of policies to mitigate the impact
Our results are robust to different ways of coding and aggregating policies and to various robustness checks, which include analyzing a more stringent country sample, removing large hydropower from outcome variables, and assessing absolute instead of relative outcome variables, among others.

We see several venues for future research. Understanding of the causal mechanism explaining our results can be further improved. Studies that utilize more granular data at the industry level are necessary to elucidate the effects of unobserved factors in developing countries, such as the extent to which renewable policies are enforced. Another important direction for future research is to study firm-level responses to renewable energy policies, including implications for productivity, entry and exit, the turnaround of capital stock, and other constraints to renewable energy technology adoption.

# REFERENCES

Bacon, R. (2018). _Taking Stock of the Impact of Power Utility Reform in Developing_ _Countries A Literature Review_ World Bank Policy Research Working Paper No. 8460.

Bailey, M. A., Strezhnev, A., Voeten, E., & Walsh, E. A. (2017). Estimating Dynamic State Preferences from United Nations Voting Data. _Journal of Conflict Resolution_ , _61_ (2), 430–456.

Bang, G., Underdal, A., & Andresen, S. (2015). The domestic politics of global climate change: Key actors in international climate cooperation. _The Domestic Politics of Global Climate Change: Key Actors in International Climate Cooperation_ , 1–216. https://doi.org/10.4337/9781784714932 36
hiring and firing workers. This makes production more costly and results in lower operating profits. Lower profits would reduce innovation rates λ according to the first order condition presented in equation 4.

Indian states also show variation in access to external finance. Financial development can be an important facilitator for innovation by allowing firms to spare more resources for investments in R&D. I define a state as financially developed if the average state credit level per capita is above median. The second column of Table 7 shows the result for financial development. Licensing reform has a larger benefit on firms that are located in states that are financially under developed. This finding could be interpreted as follows. Easier access to finance helps to reduce the financial burden on firms caused by license requirements. Thus the states that did not have sufficient financial development might have underperformed in innovation activities due to license requirements. When these requirements were removed, the effect on innovation performance was higher in these states.

Aghion et al. (2008) can only show the impact of licensing reform on firm sales through its differential effect on states with varying levels of labor regulations. They show in Table 2 of their study that without the interaction terms, licensing reform does not have any impact on sales. According to the model presented here, license requirement can directly affect innovation performance and thus sales. The ASI database they use might not reveal this direct relationship as it is not appropriate to for analyzing dynamics of firm evolution. I test the direct relationship between licensing reform, Log(Sales), and sales growth using the data from Prowess. Sales growth is computed below using the formula presented in Davis and Haltiwanger (1992), Sales Growtht-1,t=(Salest-Salest-1)/(( Salest+Salest-1)/2.

Sales values vary a lot across firms and outliers can be a much serious concern than product growth and this formula is robust to outliers. I also computed sales growth as log difference of sales levels which provided quite similar results.

The results are
). Data **consist of** balanced panels, 1993-1996 for Poland and 1994-1998 for Russia. Fraction persistent is the fraction of the shock between period 2 and **3 that** is persistent. **This** fraction is defined by the ratio of **the** variance of **the** persistent shock to **the** variance of **the** total **shock** between period 2 and 3.

# **_Simulations_**

Because the variances of the transitory shocks for the first and last periods are not identified, the paths of persistent and transitory income and expenditure &an only be simulated for periods 2 and 3. While the estimation of the variances of the various 11
Altogether there are 145 countries<sup>3</sup> with at least one observation of public employment in any of the three aggregates, with 60 of the countries classified as high income. On average, countries have 13 years of data when considering the general government aggregate. The sample period starts in 1980, although the number of countries with data changes over time, making it an unbalanced panel. It is important to have in mind the level of agregation (public sector, general government, and central government) when comparing with wage bill data (IMF, 2016).

Total government expenditure as a share of GDP and government expense in compensation to employees as share of GDP are obtained from the IMF’s World Economic Outlook and Government Finance Statistics (October 2018).

# **ii. Expenditure-based measures and public employment: The wage bill**

Most of the literature that explores the size of government uses expenditure-based measures, either of the wage bill or other aggregates. The relationship between the size of the government, measured as expenditure as percentage of GDP, and public employment as percentage of the labor force can differ considerably (Figure _1_ ). On the other hand, the wage bill, although highly correlated with total government expenditure (Figure 2), shows significant heterogeneity across countries, with some having a wage bill that is twice the size of others that have similar government expense levels.

Figure 1: Public employment (% labor force) and general government expenditure (% GDP) (each dot is a country-year obs.)

> government; i.e. all government units, social security funds and non-market nonprofit institutions (NPIs) that are controlled and mainly financed by public authority (Hammouya, 1999).  Finally, the central government aggregate is composed of departments or ministries, of autonomous agencies carrying out special functions, and of all NPIs which are controlled and mainly financed by public authority.  Their fiscal, legislative and executive authority extends over the entire territory of the country.
# **References:**

Ahearne, Alan G., John G. Fernald, Prakash Loungani, John W. Schindler 2006. ―Flying Geese or Sitting Ducks: China‘s Impact on the Trading Fortunes of other Asian Economies‖ Board of Governors of the Federal Reserve System, International Finance Discussion Papers, no. 887. Dec 2006.

Arthukorala, Prema-chandra, 2009 ―Outward Foreign Direct Investment from India‖ Departmental Working Papers 2009-14, Australian National University, Department of Economics.

Barro, R. J., & Lee, J. W. 2001. International data on educational attainment: updates and implications. Oxford Economic Papers , 53, 541-563.

Centre for Chinese Studies (CCS), 2011. ―Assessing China‘s FDI in Southern Africa‖.

Cai, Fang, Du Yang, and Wang Meiyan. 2009. ―Employment and Inequality Outcomes in China‖, _Institute of Population and Labour Economics, Chinese Academy of Social Sciences; paper presented to OECD conference._ Davis, Steven J., R. Jason Faberman and John Haltiwanger 2006. ―The Flow Approach to Labor Markets: New Data Sources and Micro-Macro Links. _Journal of Economic Perspectives_ , 20(3): 3-26.

<u>Depew, Chauncey (ed.),</u> _One Hundred Years of American Commerce 1795-1895_ p 111.

Farole, T., 2011. _Special Economic Zones in Africa: Comparing Performance and Learning from Global Experiences_ , Washington D.C., World Bank.

FIAS, 2008. _Special Economic zones: Performance, Lessons Learned, and Implications for zone Development_ , Washington D.C., IFC-World Bank.

Gerschenkron, Alexander, 1962. _Economic Backwardness in Historical Perspective, a book of essays_ . Cambridge, Mass.: Belknap Press of Harvard University Press.

Gelb, Stephen, 2006. ―South-South Investment: The Case of Africa‖ chapter 16, in _Africa in the World Economy - The National, Regional and International Challenges Fondad, The Hague, December 2005
almost all of the countries (the kernel distribution is completely to the left of the thresholds, except for CL in a balanced sample).<sup>27</sup> However, when we calculate 90 percent con...dence intervals (dashed lines) for the vertical lines at values of<sup><u>�2</u>eachofthe4indicators(bothinthebalancedand</sup> �1<sup>for</sup> unbalanced samples<sup>28</sup> ),<sup>29</sup> this result turns out to be not signi...cant since the bulk of the kernel distribution lies between the upper and lower bounds of the con...dence interval.

> 25For the simulations, we consider the unbalanced sample of 91 countries as in column (3) of tables 1 and 2. Data considered refer to year 2000 from Barro and Lee (2001) and Defoort (2008). When Barro and Lee (2001) data on human capital are missing for year 2000, the human capital data from Defoort (2008) are considered (this happens for 11 countries).

> 26 They are available from the authors upon request.

> 27The few "losers" (negative marginal e¤ect) are all with extremely high brain drain rates.

> 28For EFW indicator, we consider only the unbalanced sample as in the balanced sample both �1; �2 are not signi...cant and there are too few observations.

> 29 <u>2</u> Con...dence intervals of<sup><u>�</u>computedusingtheStatacommandnlcom.Calculationsfollows</sup> �1<sup>are</sup> the delta method, a method based on Taylor series expansions used for deriving variance approximations and con...dence intervals for transformed variables.

25
), a financial inclusion index (composed of access to banking and credit, knowledge of banking and credit, and savings), and a psychological well-being index for the primary woman (standardized weighted scores on the Center for Epidemiological Studies Depression scale, the World Values Survey – happiness and satisfaction questions, and Cohen’s Stress Scale) see Bedoya et al. (2019). The survey also collected data from each household on mobile phone ownership. Nearly all (99%) households with a cell phone provided their phone numbers and consented to the use of their call detail records for this study.

**Sample Representativity** Portions of our analysis are restricted to the 535 households from the TUP survey with phone numbers that match to our CDR (see Section 2.3). Table 1 and S2 compare characteristics of these households to the full survey population. There

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