rafmacalaba/data-use-mentions-extended huggingface dataset:
and Moretti 2014a). In the absence of such data, it is challenging to separately identify agglomeration economies from the general impact of place-based policies because both could lead to the concentration of firms in the same sector in the targeted area.

We circumvent this issue by investigating spillovers between sectors. Following Ellison, Glaeser and Kerr (2010), we first construct the coagglomeration index measuring the overall tendency that two industries locate close to each other. This measure is related to the covariance of sectors across locations and accounts for both natural advantages and agglomeration economies, after controlling for sectoral differences in firm size. To mitigate the issue of endogeneity, we exclude data from Uttarakhand and Uttar Pradesh when computing the coagglomeration index for all sector pairs.

We use this coagglomeration index to examine whether employment outcomes vary due to sectors’ tendency to collocate, after controlling for the direct effect. The specification is the following: m × T T T × T T T × T T 1 × T T T × E 2 × T T T × E D ;T T T , T T m (4). TT mm mm mm mm mm mm yy = αα+ ββ<sup>CC</sup> + γγ<sup>CC</sup> TT + ρρ + ρρ NNTT E represents the sum of the coagglomeration indices between sector mm + LL[D DDT TTmm mm TTmm] + λλmm + εε _j_ and all other sectors that received positive central excise tax incentives (or treated). TT E represents the sum of the mm NNTT mm 6 Abeberese and Chaurey (2018) also study the impact of tax incentives in the two states on the number of firms using economic census data. However, they continue relying on state-wide comparison without geo-referencing the data and applying the more stringent identification strategy. They do not explore the specific channels, and only capture the impact of the interventions in the early years by using the 2005 Economic Census.

13
Second, a state excess load adds a provision for the excess indemnity excluded from the LCR in the unload rate calculation. It is equal to the state excess indemnity (i.e., the sum of the county excess indemnities at the state level) divided by the state adjusted liability (i.e., the sum of the county adjusted liability), with a minimum of 1% (cupping) and a maximum of 5% (capping). The excess indemnity for each county is thus pooled for the entire state and then spread back to each county. The capping process is actuarially justified by the fact that catastrophic events lack statistical credibility.

Third, a prevented planting load is added as a provision for losses due to crops never planted because of external factors which are not directly related to yield loss.

Finally, the rate loaded by the previous components is loaded by the unit division load, which takes into account that indemnity is not computed for an entire farm but rather for each division of farm.

# _Capping rate changes_

The implied base rate derived above is divided by the current base rate to produce a normalized loss ratio. This loss ratio is compared to a pre-determined schedule of rate changes centered at the mandated target loss ratio of 107.5%. The current base rate is then adjusted to meet this target loss ratio, if necessary. However, public policy considerations force the rate change to range between –5% and 10% of the current base rate. The imposition of such limits, based on political considerations, is to stabilize premiums paid by producers from year to year.

# _Update_

The rates developed to this point have used aggregate data. They are then adjusted to take into account individual characteristics: farming practices, coverage level and average yield. Studies have shown that producers with lower APH yields also have significantly higher variability of yields. For this reason, different Rate Classes are established for producers with different APH yields. Finally, other expenses and capital costs are added.
**Table 3: Probability regressions for new activity specializations, baseline.**

||Dependent<br>comparativ|variable: whether<br>e advantage in a<br>_t+5_(xj,j,t+5=|country_c_has a<br>ctivity_j_at time<br>1)|
|---|---|---|---|
||(1)|(2)|(3)|
|ASj,j,t<br>|0.075***|0.190***|0.055**|
||(0.025)|(0.006)|(0.022)|
|ωj,j,t|0.061***|0.033***|0.063***|
||(0.006)|(0.003)|(0.005)|
|Country-Year FE|X|X|X|
|Industry-Year FE|X|X|X|
|Sample|Total|Advanced|Developing|
|N|137,723|56,919|80,804|
|AdjustedR<sup>2</sup><br>|0.069<br>|0.087<br>|0.065<br>|

_Notes_ : Results from linear probability regression using equation (4). The independent variables are activity specialization and average proximity , defined in equations (3) and (6). is normalized by subtracting the mean and dividing by the standard deviation to ease interpretation. Robust standard errors jj,jj,tt AASS ωω ωω (clustered by country) are reported in parentheses. * p<0.1, ** p<0.05 and *** p<0.01. Own calculations based on annual data for 2000-2018, see section 3.

_Extensive and intensive margins_ We
student will continue in school._ The two outcomes are probably linked: children who perform better are more motivated to continue and their parents may be more inclined to allow them to continue in school. This is clearly the case for rural students, as shown in Table 10. The probability of school desertion is 20% lower amongst students supported by the program, and the effect is just as large for the broader group of students who benefited from only a partial application of the program. Surprisingly, however, the result does not seem to hold for the native population. One-third of the native students who received the full program from 4th grade onward abandoned the _school before completing_ the 6h grade. Their probability of desertion was 12% _greater_ than that of the comparable control group.

**Table 10. Desertion.**

**Percentage of students who quit school by the end of the 6th grade,** 1994.

||**Complete Pro**|**aram***|**Partial oro**|**cram**|
|---|---|---|---|---|
||**Native**|**Rural**|**Native**|**Rural**|
|Experimental|32.9%|28.4%|36.2%|31.1%|
|Control|29.4%|35.7%|36.0%|38.5%|
|Difference|11.7%|-20.5%|0.7%|-19.2%|
|N|698|809|841|1,006|

Source: Own calculations based on PARE's database.

* Students in school that received all PARE components simultaneously.

This result deserves more analysis. An intriguing possibility is that high-achieving students in native communities move to rural schools where they are immersed in
(constant 2015 US$) come from the World Bank’s World Development Indicators database (World Bank 2023).

The data on revenue (percent of GDP), tax revenue (percent of GDP), expenditure (percent of GDP), and subsidies (percent of GDP) are multiplied with real GDP (constant LCU) to derive the respective total values measured in LCU. Following the previous literature (Talvi & Vegh 2005; Frankel et al. 2013), the cyclicality of each of these variables is estimated as the correlation between the cyclical component of the respective variable ( ) and the cyclical component of GDP ( ): XX YY C i i i i )], (1) Cyclicality(XX) = C [(XX −XX<sup>�</sup> ) −(YY −YY<sup>�</sup> where i i ) is the Hodrick-Prescott (HP)-filtered cyclical component of the logarithm of the fiscal policy variable , and i i ) is the HP-filtered cyclical component of the logarithm of real GDP (XX −XX<sup>�</sup> (following Ravn & Uhlig 2002 and Belhaj et al. 2022, we use a smoothing parameter of ). The XX (YY −YY<sup>�</sup> cyclicality of revenue as a share of GDP is computed in the same way but without taking logs. The cyclicality λλ= 6.25 of the overall fiscal balance is computed as the difference between the cyclical component of government revenue and the cyclical component of government expenditure (IMF 2016).

For each fiscal policy variable, a positive correlation indicates procyclical behavior and a negative correlation indicates countercyclical behavior relative to the business cycle. It is important to note that countercyclical fiscal policy requires expenditures to move countercyclically to the business cycle, whereas revenues and fiscal balance should move procyclically to the business cycle; that is, revenues should _increase_ during economic booms (when GDP increases), dampening the cycle,
# _2. Access to Education_

Receiving a good education is arguably the most important factor for the development of a child into a mature and productive member of society. If children receive substandard education and are discriminated against throughout their formative years, they will not develop the skills and the confidence they need to be competitive in the labor market.<sup>48</sup> That is especially true in today’s emerging knowledge-based global economy, and numerous studies demonstrate that “Education is key to a better life and the foundation of every strong society—but far too many children are still being left behind. To realize all our development goals, we need every child in school and learning.” Anthony Lake, executive director, UNICEF (UNICEF 2015) learning and skills play an increasingly important role in shaping prospects for economic growth, shared prosperity, and poverty reduction (Leitch 2006; Sapir 2003). Unfortunately, the United Nations Educational, Scientific, and Cultural Organization (UNESCO 2010, 5) reports, “Around a third of all children in developing countries, or 175 million annually, enter primary school having experienced malnutrition that irreparably damages their cognitive development.” Often, such deprivation is the consequence of circumstances beyond the control of the children and their families. “Marginalization in education is the product of inherited disadvantage, deeply ingrained social processes, uneven power relationships, unfair economic arrangements—and bad policies. The overall effect of marginalization is to restrict opportunity as a result of circumstances over which children have no control, such as parental wealth, gender, ethnicity, and language” (UNESCO 2010, 272).

The access to education indicator examines legislation related to the access of minority groups to education and whether those laws differ from those applicable to the majority groups. In some countries—such as the Netherlands—education is well developed and offers children and their families a variety of educational curricula and the opportunity to choose a school according to one’s ethnic, religious, and cultural preferences.
In the long term, however, our results also point to the inadequacy of existing governance measures. jt is very unsatisfying that existing data, even with favourable assumptions, allows us to identify relatively few statistically significant differences in governance across countries. Moreover, existing data provides at best tenuous links between perceptions of govemance and objective policy interventions that govemments interested in improving the quality of governance can undertake. There is therefore a need to improve the quality and quantity of govemance data, both by improving and extending cross-country survey work of govemance perceptions, as well as employing country-specific in-depth govemance diagnostics.18 Many of the polls and surveys we use suffer from deficiencies, such as poorly-worded questions about ill-defined and excessively broad concepts. There is room to improve these instruments by asking respondents about their direct experiences with well-defined events and using transparent units to measure govemance. However, these are time- and resourceintensive exercises, and intemationally-comparable high-quality data of this sort is years away.

**18** Detailed country diagnostic exercises such as those currently being piloted by the World Bank have the potential to provide much more detailed information on the **specific** institutional failures which contribute to perceptions and the reality of poor govemance. Kaufmann, Pradhan and Ryterman (1998) provide a description of these exercises.

28
_<mark>indicate that a study does use data includes whether a survey or census is described, a statistical model estimated, or a table or means or summary statistics is reported.</mark>_ _<mark>After an article is classified as using data, please note the type of data used. The options are population or business census, survey data, administrative data, geospatial data, private sector data, and other data. If no data is used, then mark "Not applicable". In cases where multiple data types are used, please click multiple options.</mark>_<sup>_3_</sup> _<mark>For task 2, we are looking at the country or countries that are studied in the article. In some cases, no country may be applicable. For instance, if the research is theoretical and has no specific country application. In some cases, the research article may involve multiple countries. In these cases, select all countries that are discussed in the paper</mark>_ <mark>.</mark>

# _<mark>We expect between 10 and 35 percent of all articles to use data.</mark>_

An image of the screen facing the MTurk workers when classifying an article is presented in Figure A.1 in the Appendix. The median amount of time that a worker spent on an article, measured as the time between when the article was accepted to be classified by the worker and when the classification was submitted was 25.4 minutes. If human raters were exclusively used rather than machine learning tools, then the corpus of 1,037,748 articles examined in this study would take around 50 years of human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labeled articles. We use a distilled version
1199814|848350|

Notes: The **Relevant CEFs** imply correct functional form, which are based on Table A.2 in the online appendix. Quadratic model is adopted following Becker et al. (2015). Robust standard errors are in parentheses, clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Sample corresponds to children born between 1957 and 1962 (comparison), or 1968 to 1972 (treatment). Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table. All coefficients are reported in Table A.3. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

40
- 63 -

consistency, we are also mentioning the IISS estimate. The data on the military, should therefore be taken with some caution.

Wages and salaries estimates are from WB's Statistical Handbook 1995: States of the Former USSR, and relate to 1992. GDP data are from Barbara Ossowicka (EC3C1) and relate to 1994. Average Government wage is based on Kazakstan Economic Trends, 2nd quarter 1996, published by the Government of Kazakstan's Center for Economic Reforms and relates to 1994. Estimate is an unweighted average of salary for March June and September data provided by the IMF. Government wage bill for 1994 is estimated at 2.8% of GDP. Source: WB Report Kazakstan: Transition of the State-(draft) February 16, 1995.

# **Kyrgyztan**

Unemployment rate reflects only official unemployment for 1994. Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1994.

Data on Kyrgyz Republic are staff estimates based on information for Education and Health employment are taken from IMF Report SM195/55 and relate to 1995. Central Government employment is a staff estimate based on data found in the same source.

GDP at market prices, wages and salaries are taken from Statistical Handbook 1995: States of the former USSR and relate to 1993. Average Govemment wages are from IMF Report No. SM/95/55 of March 17, 1995 and relate to 1993.

# **Latvia**

Population and labor force figures are from Vera Wilhelm (EC4BS) after consultation with the Latvia Statistical committee and relate to 1995. Total employment figure is also from Vera Wilhelm and refers to 1995. Unemployment figure is estimated on the basis of the above data. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993.

Data on Central Government employment, Education and Health employment is from Vera Wilhelm after consultation with Statistical Office and relates to 1995. Central
3.1 World Bank Enterprise Survey data

Our data are from a recent wave of the World Bank Enterprise Surveys (WBES) that were implemented in seven Latin American economies in 2017—Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru and Uruguay. The WBES are establishment‐level surveys of a representative sample of the non‐agricultural, non‐extractive, formal private economy. The surveys are carried out via face‐to‐face interviews with owners or top managers of establishments in the manufacturing, services, construction, and transport storage and communications sectors. The surveys, which do not cover informal or micro firms, are carried out using a standardized methodology that ensures representativeness at the country level, and comparability across economies and over time. The WBES covers a wide range of topics including access to finance, corruption, crime, infrastructure, as well as measures of establishments’ performance. In recent surveys, several questions have been added to the survey instrument to capture the managerial 7
- J ̈antti, Markus, T ̈orm ̈alehto, Veli-Matti and Marlier, Eric (2013). _The Use of Registers in the Context of EU-SILC: Challenges and Opportunities_ . European Union.

- Kim, Nak Nyeon and Kim, Jongil (2013). Sodok jipyo ui jaegumto [reexamining income distribution indices of korea] (in korean). _Journal of Korean Economic Analysis_ , **19,** number 2, 1–57.

- Korinek, Anton, Mistiaen, Johan A. and Ravallion, Martin (2006). Survey nonresponse and the distribution of income. _The Journal of Economic Inequality_ , **4,** number 1, 33–55.

- Lakner, Christoph and Milanovic, Branko (2015). Global income distribution: From the fall of the berlin wall to the great recession. _World Bank Economic Review_ ; Advance Access published August 12, 2015.

- Larsen, Erling (2014). The Engel curve of owner-occupied housing consumption. _Journal of Applied Economics_ , **17,** number 2, 325–352.

- Morelli, Salvatore, Smeeding, Timothy and Thompson, Jeffrey (2015). Post1970 trends in within-country inequality and poverty: rich and middle-income countries. In _Handbook of Income Distribution_ (eds Anthony B. Atkinson and Franois Bourguignon), volume 2A. Elsevier.

- Sz ́ekely, Miguel and Hilgert, Marianne (1999). What’s behind the inequality we measure: An investigation using latin american data. _Research Department Working Paper Inter-American Development Bank_ .

- van Veelen, Matthijs (2002). An impossibility theorem concerning multilateral international comparison of volumes. _Econometrica_ , **70,** number 1, 369–375.

- van Veelen, Matthijs and van der Weide, Roy (2008). A note on different approaches to index number theory. _American Economic Review_ , **98,** number 4, 1722–1730.

- Verme, P., Milanovic, B., Al-Shawarby
person)<br>person)<br>Internet users<br>Internet users<br>International Internet bandwidth<br>International Internet bandwidth<br>Madagascar Malawi Zambia Lesotho Mozambique Zimbabwe Botswana Angola South Africa Namibia Mauritius<br><!-- End of picture text --> _Source:_ Minges 2008.

Several of the new fixed-line operators have launched WiMAX<sup>8</sup> networks, which, along with Angola Telecom’s ADSL<sup>9</sup> service, provides a degree of fixed broadband competition. Movicel recently launched a GSM network that should enhance intermodal competition in the mobile market.<sup>10</sup> It is likely that it will also launch mobile broadband based on HSDPA<sup>11</sup> technology to complement its high-speed wireless EVDO<sup>12</sup> network. Unitel has also launched mobile broadband services. Given these developments and the availability of international access through up to three undersea fiber-optic cables in the near future, the prognosis for Angola’s broadband infrastructure is positive. The government will need to enhance

> 7 Conselho de Ministros,  ―Programa Executivo do Sector para 2009,‖ Resolução n.o 33/09 de 7 de Maio.

> 8 Worldwide interoperability for microwave access.

> 9 Asymmetric digital subscriber line.

> 10 www.consuladogeral-angola.hk/sub/Press/Press_2010_1201_3.html.

> 11 High-speed downlink packet access.

> 12 1x Evolution-Data Optimized.

34
Glewwe, P. and Jacoby, H. (2000). “Recommendations for collecting panel data”, in M. Grosh and P. Glewwe (eds.), Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 years of the Living Standards Measurement Study. Washington D.C., World Bank.

Horvitz, D.G. and D.L. Thompson. (1952). A generalisation of sampling without replacement from finite-universe. J. Amer. Statist. Assoc., 47,663-685.

Kish, L. (1965), Survey Sampling, New York: Wiley. ISBN 978-0471109495.

Lavallée, P. ( 2007). Indirect Sampling. Springer: Ottawa.

Lynn, P., (2009), Methodology of Panel Surveys, Wyley. Wiley and Sons, Ltd, Publication.

Mecatti, F. ( 2007). A single frame multiplicity estimator for multiple frame surveys. Survey Methodology, 33(2): 151-157.

Narain, R.D. 1951. On sampling without replacement with varying probabilities. J. Ind. Soc. Agril. Statist., 3,169-174.

Särndal C.E., and Lundström S. (2015). Estimation in Surveys with Nonresponse. Wiley. New York.

Singh, A.C. & Mecatti, F. (2011). Generalized Multiplicity-Adjusted Horvitz-Thompson Estimation as a Unified Approach to Multiple Frame Surveys. Journal of Official Statistics, 27(4): 633–650.

Uganda Bureau of Statistics (2011), The Uganda National Panel Survey (UNPS) 2009/10 – Main Report, Kampala, Uganda.

Uganda Bureau of Statistics (2017), The National Population and Housing Census 2014 – Education in the Thematic Report Series, Kampala, Uganda.

Uganda Bureau of Statistics (2016), The National Population and Housing Census 2014 – Main Report, Kampala, Uganda.

University of Essex . (2016), British Household Panel Survey, <u>https://www.
- Donaldson, D. and A. Storeygard (2016). The view from above: Applications of satellite data in economics. _Journal of Economic Perspectives 30_ (4), 171–98.

- Eberhard-Ruiz, A. and A. Moradi (2019). Regional market integration in East Africa: Local but no regional effects? _Journal of Development Economics 140_ , 255–68.

- Flachaire, E. and G. Hollard (2006). Controlling starting-point bias in double-bounded contingent valuation surveys. _Land Economics 82_ (1), 103–11.

- Funk, C., P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, S. Shukla, G. Husak, J. Rowland, L. Harrison, A. Hoell, and J. Michaelsen (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. _Scientific Data 2_ , 150066.

- Garg, T., M. Jagnani, and V. Taraz (2020). Temperature and human capital in India. _Journal of the Association of Environmental and Resource Economists 7_ (6), 1113–50.

- Gollin, D. and C. Udry (2021). Heterogeneity, measurement error, and misallocation: Evidence from African agriculture. _Journal of Political Economy 129_ (1), 1–80.

- Hennermann, K. and P. Berrisford (2020). ERA5: data documentation. Last modified Nov 18, 2020. `https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation` .

- Ito, T. and T. Kurosaki (2009). Weather risk, wages in kind, and the off-farm labor supply of agricultural household in a developing country. _American Journal of Agricultural Economics 91_ (3), 697–710.

- Jagnani, M., C. B. Barrett, Y. Liu, and L. You (2021). Within-season producer response