rafmacalaba/data-use-mentions-extended huggingface dataset:
Working Pape _r_ <mark>, (6007).</mark>

- Calderon, C. (2022). _Boosting productivity in Sub-Saharan Africa: Policies and institutions to promote efficiency_ . Washington, DC: World Bank.

- <mark>Chang, C. C., & Mendy, M. (2012). Economic growth and openness in Africa: What is the empirical relationship?</mark> _<mark>A</mark> pplied Economics Letters_ , _1_ _<mark>9</mark>_ <mark>(18), 1903-1907.</mark>

- Cohen, W., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. _Economic Journal_ , 99(397), 569–596.

- <mark>Dahi, O. S., & Demir, F. (2018). South–south and north–south economic exchanges: Does it matter who is exchanging what and with whom?</mark> _<mark>Analytical Political Economy</mark>_ <mark>, 339-379.</mark>

- <mark>Danquah, M., & Ouattara, B. (2015). What drives national efficiency in sub-Saharan Africa?</mark> _<mark>E</mark> conomic Modelling_ , _44_ <mark>, 171-179.</mark>

- Demir, F., & Duan, Y. (2018). Bilateral FDI flows, productivity growth, and convergence: The north vs. the south. _World Development_ , 101, 235-249.

- Demir, F., & Razmi, A. (2022). The real exchange rate and development theory, evidence, issues and challenges. _Journal of Economic Surveys_ , 36(2), 386-428.

- Didier, L. (2017). South-South trade and geographical diversification of intra-SSA trade: Evidence from BRICs. _African Development Review_
Indicators (WGI, World Bank), and the Fraser Institute’s Economic Freedom of the World project. We obtain data on ethnic fractionalization from Alesina et al. (2003).

Our baseline results for the level of labor productivity are based on a sample of 25,541 manufacturing firms in 84 developing countries for which data are available. The sample for the baseline results for the growth rate of labor productivity includes 23,852 manufacturing firms in 84 countries. Throughout, the sample used is a pure cross-section in that each country and firm is included only once. The latest round of ES in the country is used. In Appendix A, Table A1 provides the list of countries and observations by country, and Table A2 contains the summary statistics of the variables used in the regressions.

# 3.2 _Estimation method_

The main empirical exercise involves estimating the following equation: Yijk = α+ β1Ethnic Fractionalizationk + β2Ethnic Fractionalizationk ∗Firm Sizeijk + β3Firm Sizeijk + IFEj + Firm Controlsijk + Country Controlsk where subscript _i_ denotes firm, _j_ the industry and _k_ the country; Yijk is the dependent variable (labor productivity level and annual growth rate); IFE denotes industry fixed effects, Firm Controls and Country Controls are controls for various firm and country characteristics, respectively, and u is the error term. The interaction term between ethnic fractionalization and firm size captures how the relationship between the dependent variables and ethnic fractionalization varies with the size of the firm.

14
# Appendix 2 – Robustness Checks Tables

_Table A2.1 Lee bounds for the intention‐to‐treat effects on main socio‐economic outcomes_

|(1)<br>Lower bound||(2)<br>Upper bound|
|---|---|---|
|_Main outcomes – Socio‐econo_|_mic_||
|Employment index<br>0.045<br>(0.610)||0.047<br>(0.810)|
|Consumption index<br>0.093||0.098|
|(0.173)||(0.538)|
|Savings, investment and debt<br>0.261**||0.268**|
|index<br>(0.031)||(0.047)|
|Business skills index<br>0.007||0.009|
|(0.942)||(0.926)|
|Observations|2292||

_Note: Outcome variables are listed on the left. Column (1) reports the lower bound. Column (2) reports the upper bound. P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level._ 37
the output gap for the period. Real exchange rate overvaluation – a measure for external imbalances and the risk of balance-of-payments crises – is given by the

> 17 Loayza _et al_ . note that the presence of initial GDP per capita and the governance indicator in regression model (4) help to control for the fact that not all government consumption can be regarded as an obstacle to growth. 18 See Fischer (1993), Easterly, Loayza, and Montiel (1997), Barro (2001), Bekaert, Harvey and Lundblad (2001).

16
, thus allocating lower average values to the households which fulfill the demographic requirements, have a much smaller poverty-reducing effect.

Table 6: Poverty reduction and cost of four CCT scenarios aimed at IDPoor households

|**CCT scenario**|**Design**|**Poverty**<br>**reduction(pp)**|**Cost (millions**<br>**US$/year)**|
|---|---|---|---|
|CCT Version 1|200 US$ per year/child under the age of 3 in<br>households identified bythe IDPoor equitycard|-0.113|16.5|
|CCT Version 2|20 US$ per year/child under the age of 3|-0.062|15.7|
|CCT Version 3|400 US$ per year/child under the age of 3 in<br>households identified bythe IDPoor equitycard|-0.230|32.9|
|CCT Version 4|40 US$ per year/child under the age of 3|-0.113|31.4|

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

<mark>insurance, and salary taxes potentially stems from the high degree of progressivity of tax contributions and lack of highest earners in the data.</mark> 29
and fuel prices at the pump is 80 to 220 percent. Annual increases in pump prices average approximately 26 percent and reached a peak between June 2008 and January 2009. High transport costs clearly restrict the maize trade. Survey data indicate that the average distance between the purchase and sale location of maize transactions is approximately 55 km, with a maximum of 200 km (Fafchamps et al. 2005). Not surprisingly, the problem of high transport costs is particularly acute in remote and isolated areas.

9
prominently in the prevalence of bank accounts in the developing world.<sup>11</sup> While close to 50 percent of adults in cities have an account, the figure is less than 40 percent among individuals in rural areas.

# **_2.3 The Use of an Account to Save_**

In addition to account ownership, we are interested in the use of accounts to save. This information is provided in the question: “ _In the past 12 months, have you saved or set aside any money?”_ If the respondent answered yes, a follow-up question asked, _“In the past 12 months, have you saved or set aside money by: A) Using an account at a bank, credit union, microfinance institution, or another financial institution_<sup>_12_</sup> _; B) Using an informal savings club or person outside the family (e.g., Chit fund or ROSCA)?”_<sup>_13_</sup> Among those individuals who have an account (that is, conditional on having an account), 42 percent of adults, on average, used the account to save in the past year.<sup>14</sup> Unlike what we found in terms of account penetration, we find small differences between the share of individuals, on average, who use an account to save in developed countries (49 percent) and those who do so in low- and middle-income countries (40 percent) (Figure 1). In other words, globally, adults who have a formal account are on average about equally likely to use their account to save.

> 11  Gallup World Poll data contain two variables related to the urban/rural divide: municipality population data that are used to stratify the sample, and interview-coded data on area size category. We classify urban/rural based on the former, but use interview-coded data when this is not available. The correlation between the population-based and interviewer-coded urban
for all individuals. Briefly explained, step 1 changes the demographic structure of the household survey according to exogenous population and education projections. The second step allows for the migration of labor from shrinking to expanding sectors in the economy while changes in skill and sectoral wage premia are modeled in step 3. Step 4 adjusts for

> 4 Another limitation of the model is that it does not incorporate the intended harmonization of labor and environmental standards within the CPTPP. While such harmonization has social and environmental benefits, it may also reduce competitiveness of firms that currently do not meet such standards, reducing the potential economic gains.

> 5 GIDD was developed by the World Bank’s Development Prospects Group and was inspired by previous efforts involving top-down simulation exercises. See Bourguignon, F.,  Bussolo, M., and Pereira da Silva, L. A. (2008); Davies (2009). Earlier versions of the GIDD can be found in Bourguignon and Bussolo (2013); and Bussolo, De Hoyos, and Medvedev (2010a, b). Recent modeling applications include distributional assessments of the effects of demographic change (Ahmed et al., 2014); Africa’s resilience to climate, violence, and global economic stagnation (Devarajan et al., 2015), deeper regional trade integration in Eastern Africa (Balistreti et al., 2016), or the poverty and shared prosperity effects of China’s economic slowdown and rebalancing (Lakatos et al., 2016).

6
paid if they visited other providers than the two partner clinics or did not claim the AfH benefit, or the child received services not covered under AfH.

CSCOMs provide similar value per visit with and without the subsidy (CFA 3652-3807),

> 12In order to facilitate the recording of drugs prescribed, purchased, and taken, mothers were asked to keep the packaging of any medications their child took. The surveyors could search and fill the drug from a database of brand and generic names of about 300 medications commonly sold in Mali.

> 13Value of care is imputed using prices recorded for consultations and medications where the respondent reported having paid the full price themselves. We use these prices combined with provider information, brand name, and point of purchase to construct the median price by source. If the respondent reported another person paying for care or was in the subsidy group, we compare the sum of median prices for the services received with the private expenses the respondent reported and use the higher value. This reflects the cost of care to the family, the NGO, or a third party.

14
are rendered unusable. Consequently, the domestic water supply in rural parts of Bangladesh is disrupted and the water supply becomes unsustainable.

Mean depths to dry-season groundwater levels were estimated from weekly monitoring records from 236 boreholes from 2004 to 2013 (Figure 2c). These monitoring boreholes belong to a network of some 1,250 monitoring wells across the entire country that have been managed by the Bangladesh Water Development Board (BWDB) since the early 1960s. Depth from the well head to groundwater level at each station is referenced to a common horizontal datum known as the Public Works Datum (PWD), originally set approximately at the mean sea level (msl) with a vertical uncertainty of around ±0.45 m.<sup>14</sup> We estimated depth to mean dry-season groundwater levels (i.e., maximum depth below ground level) using the ground surface as a reference level. The map of dry-season groundwater levels shows that the central, north-central, eastern and northwestern parts of Bangladesh have deepest water levels with mean depth acceding 15 mbgl that is twice more than the suction limit of hand pumps. Depths to water levels in much of the southern Bangladesh and along major river floodplains are generally shallower (<8 mbgl) than the maximum suction limit of the No. 6 pump.

# **Demography, access to water supply, and social vulnerabilities**

Demographic datasets on population, poverty, tubewells, and access to pipe water supplies in Bangladesh at the upazila level are collated from a GIS database ( _The Bangladesh Interactive Poverty Maps_ ) published by the World Bank.<sup>15</sup> The country-level demographic database allows one to explore and visualize socioeconomic data at both Zila (district) and Upazila (sub-district) level. The online GIS-based mapping tool enables an easy access to different types of indicators including poverty, demographics of the population, children’s health and nutrition, education, employment, and household access to energy, water, and sanitation services.<sup>
analysis. A globally harmonized comprehensive dataset also allows the quality of jobs to be analyzed across various countries, regions, industries, and sociodemographic groups, such as women and youth.

The results show a strong positive correlation between the JQM and the share of the wage employed in total employment across developing countries, indicating that countries with more opportunities for workers to find paid employment perform better in terms of the availability of good jobs than countries with large self-employed or informal worker populations. Also, there is substantial variation not only in the composite job quality index across countries, but in all its dimensions, with greater differences observed in benefits and stability.

Sectors with the highest JQM include public administration, financial and business services, and utilities, while low-skill sectors, such as agriculture and construction, have the lowest JQMs. The analysis finds that the JQM is, on average, similar between men and women despite differences in individual dimensions. For example, the JQM decomposition reveals that women fare especially well in the working conditions dimension, while men perform better in terms of income. However, the job quality is much lower among other disadvantaged groups, such as rural residents and youth, relative to urban residents and older wage employees. The starkest difference is observed between different educational attainment groups, indicating that the strongest predictors of JQM within countries is educational attainment.

Despite these insightful results, large gaps in the data limit the scope and country coverage of job quality analysis. First, most country surveys lack information on certain dimensions of job quality, which have been discussed in the literature as the desirable characteristics of good jobs, particularly those related to 26
considered promising for timely monitoring of market prices and to support food security assessment.

Generally, the data crowdsourcing approach leverages the so-called “citizen science” principles to engage volunteers (paid or unpaid) to submit data at intervals (defined or undefined) which can be aggregated within or across locations and time. The approach is premised on the notion that the diversity of the crowd volunteers will foster the collection of rich data with minimal or no bias because the data submission is typically decentralized and independent, representing a robust quasi-sampling across the population at scale (Arbia, et al., 2023; Solano-Hermosilla, et al., 2022). Beyond the core value proposition that crowdsourcing is a cost-effective and timely approach to collect price data, it also offers ancillary benefits. For instance, it fosters an inclusive digital and data ecosystem, where citizens are not merely data consumers or data subjects, but they are also empowered as data curators and duly recognized as active stakeholders in data-driven decision making for the food system. This approach was fully developed and tested under an initiative called Food Price Crowdsourcing in Africa (FPCA), which resulted in the submission of food price data by >1,000 volunteer citizens, over a 3-year period (2019 -2021) across a region in Nigeria where food insecurity is prevalent (Thomas, 2023; World Bank, 2023). The outputs provided one of the first near-real-time datasets that revealed the actual impact of the COVID-19 pandemic on food prices, and indirectly, food affordability at the sub-national level (Adewopo, et al., 2021; Adewopo, et al., 2022).

In parallel to data crowdsourcing, the recent leap in the application of AI has revealed new possibilities for food system intelligence, including back-casting, now-casting, and forecasting. By leveraging predictive algorithms, AI systems are now performing complex data operations to spurn new datasets from sparse data points (Nguyen, et al., 2023; Savage, 2023; Lu, et al., 2021; Gavin & Mandal,
13. Bjorvatn, K., and T. Soreide. 2005. “Corruption and Privatization.” _European Journal of Political Economy_ 21: 903–14.

14. Black, B., R. Kraakman, and R.A. Tarassova. 2000. “Russian Privatization and Corporate Governance: What Went Wrong? _Stanford Law Review_ 52: 1731–1808.

15. Blaustein, Albert. 1993. _Constitutions of the World_ : Fred B. Rothman & Company.

16. Boehm, F. 2007. Anti-Corruption Strategies as Safeguard for Public Service Sector Reforms. Working Paper prepared for Anti Corruption Training & Consulting (ACTC) and Research Center in Political Economy (CIEP), Universidad Externado de Colombia 17. Boehm, F., and J. Polanco. 2003. “Corruption and Privatization of Infrastructure in Developing Countries.” Transparency International Working Paper, Berlin.

18. Bolton, P. and M. Dewatripont. 2005. _Contract Theory._ Cambridge, Mass: MIT Press.

19. Bonnet, C.P., D. Martimort and S. Straub. 2009. “Empirical Evidence on Satisfaction with Privatization in Latin America: Welfare Effects and Beliefs.” IDEI Working Paper 566 (March), Toulouse, France 20. Brender, A. and A. Drazen, 2004. “Political Budget Cycles in New versus Established Democracies.” NBER Working Paper 10539. National Bureau of Economic Research.

21. Briceño-Garmendia, C., K. Smits and V. Foster. 2008. “Financing Public Infrastructure in SubSaharan Africa: Patterns and Emerging Issues.” Background Paper 15 (Phase I), Africa Infrastructure Country Diagnostics (AICD). World Bank.

22. Cadot, O., L.H Roller, and S. Andreas, 2006. "Contribution to Productivity or Pork Barrel? The Two Faces of Infrastructure Investment." _Journal of Public Economics._ 90(6–7): 1133–53 23. Candeub, A., B. M
Nepal, M., A. Nepal, Aspara and K. Grimsrud. 2011. “Unbelievable but Improved Cookstoves are not helpful in Reducing Firewood Demand in Nepal. _Environment and Development Economics_ Vol. 16: 1– 23.

Nurse, M. and Malla, Y. 2005. _Advances of Community Forestry in Asia._ Bangkok: Regional Community Forestry Training Center (RECOFTC).

Ojha H, Timsina N and Khanal D.R. 2007. How are Forest Policy Decisions Made in Nepal? _Journal of Forest and Livelihood_ 6(1): 1-17.

Pan, Y, R. Birdsley, J. Fang, R. Houghton, P. Kauppi, W. Kurz, O. Phillips, A. Shvidenko, s. Lewis, J. Canadell, P. Ciais, R. Jackson, S. Pacala, A. D. McGuire, S. Piao, A. Rautiainen, S. Sitch, D. Hayes. 2011. A Large and Persistent Carbon Sink in the World’s Forests, _Science_ 333:988-993.

Pandey, Apoorva, Pankaj Sadavarte, Anand B. Rao, and Chandra Venkataraman. 2014. “Trends in Multi-Pollutant Emissions from a Technology-Linked Inventory for India: II. Residential, Agricultural and Informal Industry Sectors.” _Atmospheric Environment_ 99: 341–52.

Pandit, R, P. R. Neupane, B. H. Wagle. 2017. Economics of Carbon Sequestration in Community Forests: Evidence from REDD+ Piloting in Nepal, _Journal of Forest Economics_ 26: 9-29.

Regmi, M.C. 1984. The State and Economic Surplus: Production, Trade and Resource-Mobilisation in Early 19th Century Nepal. Varanasi: Nath Publishing House.

Revelt D, Train K. 2000. Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier, No E00-274, Economics Working Papers, University of California at Berkeley, <u>http://EconPapers.repec.org/RePEc:ucb:calbwp:e00-274.</u>
**Using household survey data** In most districts, the NSS collected data from four villages. We use data from these four villages to generate districtlevel aggregates. This process can result in sampling error. For example, the sample could contain no households who rent in land, despite such households being present in the district. This would result in missing data for this district/year: 11% of districts do not have any tenant records for either year, and 23% of districts have no tenant records for one out of two years. The latter would imply that rental markets come and go, something which seems unlikely.<sup>13</sup>

**Rural to urban migration** Our district-to-district migration data do not map exactly to the rural/urban distinction implied by the theory. We do not believe this to be a significant limitation. A significant share of migration is not of the traditional rural to urban variant. Aggregate Census data of 2001 indicate that 55% of migration is between rural areas, 15% is between urban areas, 21% is rural to urban migration, and 6% is urban to rural migration. Part of the explanation for this is that areas which have seen a recent surge in population are slow to be re-classified as urban areas. This results in errors in any statistics which rely on this distinction. Ellis and Roberts (2016) note that 30% of the increase in India’s urban population between 2001 and 2011 was due to a of census areas.

# **4 Descriptive statistics**

## **Land tenancy**

Table 1 presents statistics on the land rental market for the years 1992, 2002 and 2012. The top panel presents statistics converted to acreage terms while

> 13We do not have any other reliable data which could serve as a comparison point. We had coded up an alternative data source, the Agricultural Census. This Census is conducted every five years by the Agricultural Census Division of the Ministry of Agriculture since
# **3.3 Panel regression analysis**

Lastly, we estimate a dynamic model to understand the effects of various economic factors on the level of transport-related CO2 emissions. We assume current level of per capita emissions depends on the level of per capita emissions in the previous year, that is, transport activities respond to changes in economic factors with some lags. The model is specified as follows: C 0 1C 2G G 3G G 4 5 + 6 iitt 7B ii,tt−1 iitt (5) iitt iitt iitt 2GGCC = ββ + ββ 2GGCC + ββ CC + ββ CC2 + ββ GGDDeeDDeeTT + ββ GGTTDDT DDllee iitt iitt iitt ii tt iitt Where ββ UUffUUTTllC + + ββ is the natural logarithm of per capita CO2 emissions in country TT + γγXX + αα + λλ + εε _i_ in year _t_ . _GDPPCit_ is per capita GDP. _GDPPC2_ iitt is the square term of _GDPPC_ . _Diesel_ and _Gasoline_ are diesel and gasoline prices, 2GGCC respectively. _Urban_ is the percentage of the total population living in urban areas. _BRT_ represents the number of Bus Rapid Transit (BRT) systems a country has. is a vector of variables, including the valueadded share of agriculture, manufacturing, and service sectors. iitt is an unobserved country fixed effect, XX which includes country-specific characteristics that are fixed over time, such as culture, climate zone, and ii αα government regulation. We also include year fixed effects, , to control for common cyclical components such as a common technology shock. is an idiosyncratic error term. tt 0 7 are parameters to λλ be estimated. We opted for a relatively modest set of explanatory variables because due to limited data iitt εε ββ − ββ TTllaa γγ availability the inclusion of some further explanatory variables would have substantially cut the sample size.

# **4. Data**

Data used in this study come from several sources. The CO2 emissions and energy consumption data are from International Energy Agency (IEA) Fuel Combustion Statistics database and World Energy Statistics and Balances database