rafmacalaba/data-use-mentions-extended huggingface dataset:
sectors that received these incentives, agglomeration forces led to employment gains in sectors that were not directly incentivized by the policy, and the indirect effects from externalities were more significant through the labor-pooling mechanism than through the input-output linkages.

We caution, however, that our findings do not fully rule out the displacement effect and cannot speak to the national-level welfare impact of the place-based industrial promotion scheme. This will be a useful topic for future research. Additionally, more research is required to assess if agglomeration economies were strong enough for industries to continue co-locating in these areas, once the incentive policies were rolled back. The availability of new data in the coming years makes this a promising research direction.

# References

- Abeberese, A.B. and Chaurey, R., 2019. An unintended consequence of place-based policies: A fall in informality. Economics Letters, 176, pp.23-27.

- Alder, S., L. Shao, and F. Zilibotti. 2016. “Economic Reforms and Industrial Policy in a Panel of Chinese Cities.” Journal of Economic Growth 21 (4): 305–49.

- Amirapu, A., Hasan, R., Jiang, Y. and Klein, A., 2019. Geographic concentration in Indian manufacturing and service industries: Evidence from 1998 to 2013. Asian Economic Policy Review, 14(1), pp.148168.

- Asher, S., Matsuura, R., Lunt, T. and Novosad, P., 2019. The Socioeconomic High-resolution Rural-Urban Geographic Dataset on India (SHRUG).

- Austin, B., E. Glaeser, and L. Summers. 2018. “Saving the Heartland: Place-Based Policies in 21st Century America.” In Brookings Papers on Economic Activity. Conference draft, March.

- Baldwin, R.E., Forslid, R., Martin, P., Ottaviano, G.I.P. and Robert-Nicoud, F. 2003. Economic Geography and Public Policy, Princeton and Oxford:
# <u>Transactions with Partner Institution (from internal records of OIBM):</u>

_Any transfer via direct deposit_ is 1 if the respondent receives any deposit from his or her tobacco club’s account to his or her individual savings account, and 0 if not.

_Deposits into ordinary accounts, pre-planting_ is the sum of (positive) transactions into the respondent’s OIBM ordinary savings accounts during the period of March to October 2009.

_Deposits into commitment accounts, pre-planting_ is the sum of (positive) transactions into the respondent’s OIBM commitment savings accounts during the period of March to October 2009.

_Deposits into other accounts, pre-planting_ is the sum of (positive) transactions into the respondent’s OIBM non-ordinary, non-commitment savings accounts during the period of March to October 2009.

_Sum of deposits into accounts, pre-planting_ is the sum of (positive) transactions into the respondent’s OIBM accounts (sum across all accounts) during the period of March to October 2009.

_Sum of withdrawals from accounts, pre-planting_ is the sum of (negative) transactions out of the respondent’s OIBM accounts (sum across all accounts) during the period of March to October 2009.

_Net of all transactions, pre-planting_ is the difference between the sum of all deposits and withdrawals in the respondent’s OIBM accounts during the period of March to October 2009. _Net of all transactions, planting_ is the difference between the sum of all deposits and withdrawals in the respondent’s OIBM accounts during the period of November 2009 to April 2010.

<u>Agricultural outcomes, household expenditure, and other variables, from endline survey (all</u> - <u>planting and harvest variables refer to the 2009 2010 planting season):</u> _Land under cultivation_ is the total area of land under cultivation, measured in acres. _Total value of inputs_ is the sum of cash spent on agricultural inputs plus the value of inputs included in-kind in loan packages for the 2009-2010 planting season. Input
This paper contributes to the literature on geospatial data and displacement by combining remotely sensed data and IOM administrative data in Afghanistan, where timely, on-the-ground data can be hard to gather. Our study provides a deep dive into Badghis province, hard hit by the 2018 drought, by focusing on dynamics at the settlement level, the smallest administrative unit possible. Geospatial data offers a unique opportunity to track the evolution of indicators through time in hard-to-reach areas (e.g., due to conflicts and natural disasters). This work capitalizes on administrative data from IOM, which provides an exhaustive overview of the overall flow of displaced populations in the region at the settlement levels. Combining administrative data and satellite images illustrates the insights that can be gained in a data-scarce context.

# 3. The Data

## 3.1. Displacement and NTL data

This study exploits a unique set of data from IOM, the Displacement Tracking Matrix (DTM December 2020), to identify detailed displacement patterns in the Badghis region following the 2018 drought. The IOM data for this analysis corresponds to 4 rounds of Baseline Mobility Assessments (BMA) conducted in Badghis between January 2018 and December 2020. The information was collected at the settlement level through focus group interviews with Key Informants and includes information on settlements’ location (GPS) and inflows and outflows of the displaced population. These include both IDPs from other settlements, and returnees, former IDPs returning to their origin settlements. The DTM data is a detailed and unique source of information on displaced patterns in Afghanistan. Compared to Afghanistan Income Expenditure and Labor Force Surveys, the IOM dataset directly focuses on settlements with a mobile population, ensuring a full representation of displacement in the country. Compared to other humanitarian agencies such as the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), it is also the only source of information including IDPs (both from conflict and natural shocks), returnees from abroad and migrants, regardless of their legal status and reasons for displacement.<sup>1<
institutional barriers, product cycles, lower stock of human capital, labor market barriers, and the lack of agglomeration economies. These issues have further been compounded with recent problems in environment and land, as well as regional income disparity.

This paper explores the issue of town development in China by examining town in-migration in three provinces, Zhejiang, Sichuan, and Henan. Specifically, we center around three issues: current status of town in-migration, human capital stock of inmigrants, and labor market barriers. Three provinces represent different macroeconomic conditions in China: Zhejiang is located in the east coast, Henan in the central region, Sichuan in the western region, with Sichuan and Henan legging economically far beyond Zhejiang.

We mainly rely on the three volumes of aggregated statistics from tabulation of 2000 population census data released, supplemented by electronic version of detained tabulations for towns released by the National Bureau of Statistics recently (NBSC 2002). Both short form and long form (9.5% of total population) asked the questions about migration. In the published tables, in-migration into towns is cross-tabulated by some

> 1 For a historical treatment of this issue, see Huang 1990.

2
foreign direct investors, 86 percent find the legal and regulatory environment important or critically important for their investment decisions (World Bank, 2018).

> 9 Note that other areas of the Doing Business database are more sector-specific in nature such as “Dealing with construction permits”, “Getting credit” (i.e. banking services), “Registering property” (i.e. government services), and “Getting electricity”. Other country-broad areas are “Starting a business”, “Paying taxes”, and “Trading across borders”, which are either relevant for trade in goods or setting up a business and not necessarily to trade although some measures under “Starting a business” are also covered under the OECD STRI (see below).

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pursuing the activities of wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods; public administration and defence; private households with and extra-territorial and bodies. As in the employed persons organizations case of the nsso surveys for manufacturing establishments, the sample stratification in services is also includes both the district level and the two-digit nic sectors.<sup>9</sup> The survey covered the whole of the Indian Union except (i) Leh (Ladakh), Kargil, Punch and Rajauri districts of Jammu & Kashmir, (ii) interior villages situated beyond 5 km of a bus route in Nagaland, (iii) villages of Andaman and Nicobar Islands, which remain inaccessible throughout the year. Thus the corresponding State/UT level estimates and the all-India results presented in this report related to the areas covered under survey.

As in the case of manufacturing surveys, the services counterpart of the nsso survey collects analogous establishment-level information on the output, inputs (raw materials and other inputs), fixed assets, value of land and buildings, employment, energy consumption and so on. The sample stratification is also similar to that of the nsso.

Altogether a total of 190,142 service sector enterprises considering both list frame and area frame were surveyed in the 63rd round while the 57th round generated 359,702 establishment level observations. Thus, we have a total of 549,844 observations across the two years. We note that industries with two-digit National classification code 61 (Inland, sea and coastal water transport), 73 (research on natural sciences, engineering, social sciences and humanities) and 90 (sanitation services) have fewer than 400 observations each year and hence not suitable for any industry level computations, such as total factor productivity estimations. Thus, we drop these observations, and are left with 359,050 observations in 2000 and 189,584 in 2005, giving us a total of 548,634. For our calculation of productivity and misallocation metrics, we need to further clean
Only a few of these characteristics are closely correlated with each other.<sup>21</sup>

> 19 We use the IMF’s _de facto_ exchange rate-regime to classify countries by their currency regime. http://www.imf.org/external/np/mfd/er/2006/eng/0706.htm

> 20 Countries are classified as high manufacturing, high export, difficult to fire, and high firing cost countries, based on whether the indicator exceeds the median across countries in the sample. The difficulty of firing index measures whether redundancy is allowed as a basis for terminating workers, and the existence of rendundancy regulations and requirements. The redundancy cost indicator measures the cost of advance notice requirements, severance payments and penalties due when terminating a redundant worker, expressed in weeks of salary Additional information on the redundancy indices is available at http://www.doingbusiness.org/Methodology/Surveys/EmployingWorkers.aspx 21 In only 4 cases do the pair-wise correlations between characteristics exceed 0.25.Latin American countries are most likely to be upper middle income and least likely to be lower middle income. Europe and Central Asian countries and middle income MICS tend to have larger manufacturing sectors, but Latin American countries are less likely to have large manufacturing sectors. Finally, countries with high redundancy costs are far more likely to be in East Asia or Latin America than Europe, and are less likely to be middle income countries. Somewhat surprisingly, the correlation between a high difficulty of redundancy and high redundancy cost is weak and negative.

9
and January-March 2021, average hours worked for refugees remained close to constant at a very low level (7 hours a week) until March 2021 and only improved afterwards. For household incomes, we observe a large and significant difference in income levels at the beginning of the pandemic which narrows in January-March 2021 but widens in April-June 2021. The different rates of recovery lead to a significant and widening difference in the household per capita income and the total number of hours worked towards the end of the panel.

These results cannot be explained by demographic and location differences, previous employment status, living in cities as opposed to camps or sectoral choice. This suggests there is a third, unobservable ‘refugee factor’ which inhibits recovery after a major demand-supply shock and therefore aggravates pre-existing vulnerabilities in times of need. In particular, components of the refugee factor which we cannot control for are the quality of education and training received, the special institutional framework which limits refugees’ rights to move, settle and work freely in Kenya, the restricted access to formal permits, documentation, and social safety-nets or variation in the provision of aid-distributions. Our results indicate that these unobserved characteristics translate to observed effective differences between refugees and nationals, which opens avenues for policy programs to level the playing field through inclusive evidence-based measures.

This study contributes to the emerging literature investigating socioeconomic impacts of the COVID-19 pandemic with special focus on labor-market impacts which remain underinvestigated in the African context as post-COVID-19 data is often scarce (Khamis, et al., 2021; 3
23.6% and 27.6% of male and female employment, respectively (World Bank 2021d). Improving the returns to income-generating activities for women in the farm sector is important given that many of them work as unpaid family workers and because women have fewer opportunities to participate in nonfarm activities due to their household and care responsibilities (World Bank 2021c).

For the purpose of our analysis, ‘farmers’ in this study are defined as those who reported a positive gross crop output value in the 2016 HIES. By this definition, there were 1.7 million farmers in 2016, of whom 1.3 million were male farmers and 0.4 million were female farmers.<sup>3</sup>

> 3 These numbers should not be interpreted as representing the gender breakdown of the workforce in the agriculture sector. Because the definition considers only those farmers with a positive crop output, most unpaid family workers who account for a large share of female workers in Sri Lanka are not included. Also, these figures do not include agricultural wage workers who have different profiles from self-employed farmers (World Bank

4
# **1.  Introduction**

The World Meteorological Organization forecasts that the current greenhouse gas (GHG) emissions trend will increase global temperature 3-5 degrees C by 2100 (Reuters 2018). This would far overshoot the 2-degree limit pledged by the 2015 Paris Climate Accords (COP 21) and might have a catastrophic impact (Steffen et al. 2018; World Bank 2012). In response, several industrial nations pledged very steep emissions reductions at the recent Leaders’ Summit on Climate (April 22-23, 2021).

Unfortunately, these pledges confront a striking information shortfall at the outset: near-total absence of directly-measured local and regional GHG data for problem diagnosis, program design and performance assessment. Recently, the advent of satellite-based GHG measurement has greatly expanded the potential for empirical assessment. High-resolution observations of atmospheric GHG concentrations are now available from several platforms, including NASA’s OCO-2 and OCO-3 instruments, the European Space Agency’s METOP-A and TROPOMI (Sentinel-5P) platforms, China’s TANSAT and the Japan Space Exploration Agency’s GOSAT and GOSAT-2. Detailed technical assessments of measures from these platforms have verified that they provide useful and comprehensive information for global carbon emissions analysis (Weir et al. 2021; Nassar et al. 2021; Pan et al. 2021; Wu et al. 2020; Hakkarainen et al.; Labzovskii et al. 2019).

This paper extends recent research on satellite-based measurement to an easily-updated template for tracking changes in atmospheric CO2 concentrations at local and regional scales. Using observations from NASA’s OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). Our prior work estimates an econometric model that relates satellite-based CO2 measures to georeferenced emissions sources. In this paper, we develop and compare two versions of the CO2 tracking template. The first tracks changes in residuals after fitting the econometric model to satellite-based observations, while the second version simply tracks changes in the observations themselves. Using data from thousands of urban areas,
and bond funds, respectively, with return data.<sup>14</sup> The analyses in the paper that require fundreturn information are restricted to this subset of funds.

Because we do not know the detailed portfolio of each fund within a country, we use country-level indexes to compute returns and assume throughout the paper that all funds investing in a country experience the same return to their investments in that country, disregarding country-return heterogeneity across funds.<sup>15</sup> To this end we collect monthly, dividend-adjusted price indexes in U.S. dollars for stock markets (MSCI Standard Index, S&P Broad Market Index, and local sources for a total of 86 countries) and bond markets (JP Morgan sovereign bond index for 78 countries) in which mutual funds invest.<sup>16</sup> Analyses that require country-return information are restricted to those countries and time periods for which we could gather these data.

Table 1 shows the characteristics of the cleaned mutual fund sample (without constraining by return price availability). Panel A reports sample characteristics for equity/bond funds. There are 965 equity funds (85% of the original sample) with a median number of 47 observations per fund. The total number of bond funds is 111 (92% of the original sample) with a median number of 34 observations per fund. Panel B reports the number of funds and observations by different partitions. Of the total sample, 95% is actively managed and the rest passively managed. Also, almost 65% of the funds have their investment scope in Asia (excluding Japan), global markets, global emerging markets, or Europe. Moreover, Table 1 documents the number of funds and observations by domicile. The funds are primarily domiciled in developed market jurisdictions, in fact, 80% of the funds are domiciled (in order of importance by the number of funds) in Luxembourg, the U.S., the U.K., and Ireland.
-3-

achievement of specified objectives is assessed; and the contrasting goal-free approach, in which all effects of a program are assessed, whether goal-reLated or not.

Models of evaluation are often confused with techniques for design, data collection and analysis. In general, most techniques can be used within a given model. Research evaluation, or evaluation research, is a term most closely identified with the use of the techniques of experimental and quasiexperimental designs, pre-post measures and multi-variate statistical analysis for summative purposes. Monitoring, in. easingly with the aid of management information systems is comprisAd of a sL: of techniques (indicators, surveys, routine reporting, observation) that generate information for formative purposes, as well as a data base useful, in combination with other types of evaluation, for summative judgments.

Student achievement testing and examination systems provide crucially important information for evaluation of all kinds. If implemented routinely and for the education system as a whole, testing systems provide the information that enables administrators, teachers, parents and students to assess educational performance across time, and to compare the performance of different elements of the education systems -- schools, districts and states. Achievement data also provide a framework within which the effectiveness of specific interventions (such as new curricula) can be judged against past practice.

There is a tendency in the Bank, and elsewhere, to combine the terms monitoring and evaluation to form the hybrid "monitoring and evaluation" (M&E). These can be separate activities, each with its own purpose(s), models and techniques. Monitoring alone is essentially a formative management -- process, providing a flow of information for improved implementation including program re-design. Evaluation alone is summative, concerned principally with outcomes and less with processes. Ideally, the two are linked together in an integrated design which provides information on both processes and outcomes, strengthening the ability of decision makers to not only assess outcomes, but trace the causal relationships between programs as actually implemented and achievements.
for example, the returns to education differ between the poor and the non-poor (Appleton, 1995).

Conceptually, this model specification corresponds to modeling the poverty gap, i.e. the poor's expenditure shortfall expressed as a ratio of the poverty line, i.e.

Whereas this ratio is constrained between 0 and 1, the poverty gap itself is constrained between 0 and z. In practice, it ranges between zero and z minus the lowest _E,_ in the sample, which is what equation (2) depicts.

When estimating poverty models on the basis of household survey data, it needs to be recognized that such data are likely to contain a certain amount of measurement error. If the error is limited to the dependent variable, it does not bias the estimated coefficients (so long as the error is not correlated with any of the RHS variables), but it will affect the variance-covariance matrix. A potential concern though is that the measurement error of household expenditure may rise systematically with the level of expenditure. This increases the probability of correlation with RHS variables such as education, which is positively correlated with the level of expenditure. This could lead to biases in the estimation of equations (1) and (2).

The presence of measurement error has led severai authors to substitute limiteddependent variable models for the continuous welfare equation. Gaiha (1988) used a binary logit model to predict the probability that a rural household in India would be 11
170|0.317|0.192|0.242|420|
||Hepatitis B Vaccine (HBV)<br>Tetravalent|0.094|0.202|-0.060|0.876|420|
|Vaccines|HiB vaccine|0.047|0.154|-0.088|0.440|420|
||Measles vaccine|0.094|0.240|0.194|0.615|420|
||Oral polio vaccine|0.330|0.269|-0.024|0.935|420|
||Pentavalent vaccine|0.274|0.356|0.097|0.303|420|
||Tetanus Toxoid (TT)|0.198|0.250|0.118|0.425|420|
||<br>HIV test kit|0.113|-0.212|0.570***|0.000|420|
|Diagnostic|Pregnancy test kit|0.274|0.356|0.097|0.310|420|
|test kits|Syphilis test kit|0.075|0.010|0.396**|0.030|420|
||Urine protein & glucose testing kit|0.009|0.010|0.009|0.268|420|

Note: data from rural health center assessments. All regressions are the difference-in-differences specification with facility fixed effects and controlling for exposure to the collaborative quality improvement and citizen scorecards interventions. Standard errors are clustered at the district and survey round level with wild bootstrapping. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 33