rafmacalaba/data-use-mentions-extended huggingface dataset:
program. The survey for the first wave of the dataset covered 3,247 households (hereafter baseline households) in the 2009/2010 agricultural year. The sampling was representative at the national, regional and urban/rural levels. The survey for the second wave of the dataset was conducted in the 2012/2013 agricultural year and attempted to track and resample all the baseline households as well as individuals (projected to be at least 12 years) that split-off from the baseline households between 2010 and 2013 as long as they were neither guests nor servants and are still living in mainland Malawi. Once a split-off individual was located, the new household that he/she formed or joined was also brought into the second wave. In all, a total of 4,000 households were traced back to 3,104 baseline households. An overwhelming majority, 76.80%, of the 3,104 baseline households did not split over time; 18.49% split into two households; and rest (4.70%) split into 3-6 households. Considering the 20 baseline household that died in their entirety between 2010 and 2013 and the fact that 4,000 households could be traced back to 3,104 baseline households, the dataset has an overall household attrition rate of only 3.78%.

The study dropped all non-agricultural households (580 and 845 households in the first and second waves respectively), as well as urban agricultural households (370 and 438 households in the first and second waves respectively). The urban agricultural households were dropped because farming in Malawi is predominantly rural. In order to avoid reverse causality in the maize production function, all the households for which questions about their food and non-food consumption were asked after the harvesting of agricultural products were also dropped. In the end, a panel of 1,667 households (2,472 maize plots), 771 households (1,127 maize plots) in the first wave and 896 household (1,347 maize plots) in the second
** of employment has decreased, and the _Source: SSO_ number of people not-employed in the annual censuses has increased, the number of registrations, upon which the official unemployment rate is calculated, have actually fallen slightly since 1991. Unemployment as registered at Ulaanbaatar's EROs stood at about 10,600 at the end of 1996, whereas the census of December 1996 counted 83,100 as notemployed, 63,400 of which "without any reason."'<sup>7</sup> 22. The story being told by Mongolia's employment, unemployment, and not-employment statistics, is that a large portion of the able-bodied, working age, population (particularly in Ulaanbaatar) has fallen out of the calculations of official labor statistics, counted as neither employed nor unemployed. One may
Table A.5: Productivity Decompositions and Policy Changes: 1986- 1998

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.039|-.041|.001|
||(.021)<sup>_∗_</sup>|(.023)<sup>_∗_</sup>|(.015)|
|Input Tariff|-.250|-.268|.018|
||(.109)<sup>_∗∗_</sup>|(.104)<sup>_∗∗∗_</sup>|(.074)|
|FDI Reform|.022|.034|-.012|
||(.014)|(.011)<sup>_∗∗∗_</sup>|(.012)|
|Delicensed|-.0001|-.003|.003|
||(.014)|(.011)|(.012)|
|Obs.|10666|10666|10666|
|_R_<sup>2</sup>|.034|.045|.019|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. Only firm-level data from 1986 to 1998 (policy variables from 1985 to 1997) are included.

57
organizational psychology (e.g. Heslin, Latham and VandeWalle 2005) have revealed how fixed mindsets can be shifted and how this can help improve motivation and performance. This paper also highlights the importance of further exploring this line of work.

Our paper is organized as follows. Section 2 provides information on data, estimation strategy (including a conceptual introduction to the CIF and LASSO algorithms), and limitations; Section 3 provides a descriptive analysis of teacher-level and other covariates, Section 4 presents the main results, and Section 5 concludes.

# **2. Data, Methodology, and Limitations**

## **2.1 Data**

This study is a part of the Research on Improving Systems of Education (RISE) program for Tanzania, wherein several researchers are using the same data for different studies looking at various aspects of the Tanzanian education system and reform.<sup>5</sup> The data for this paper comes from 392 schools randomly sampled from 392 wards across 22 districts in 6 representative regions across Tanzania. Our final sample includes 436 teachers and 3,019 students. The baseline survey was conducted between February-May 2019 and the follow-up survey between January-April 2020, targeting 748 teachers and 6,586 students from Grades 2 and 3.

Three instruments were used to collect data on teacher covariates. These include a detailed teacher survey which also has a dedicated module on teacher mindsets; a teacher subject content knowledge assessment; and classroom observation of teachers using the _Teach Classroom Observation_ tool.<sup>6</sup> For the detailed teacher survey, 10 teachers were randomly selected from the complete teacher roster for the school provided by the head-teacher. To the extent possible, the survey was targeted at teachers teaching Math and Kiswahili in Grades 2 and 3. After the survey, only the Grade 2 and 3 teachers were invited to take the teacher assessment. Teacher assessments were subject-based and linked to the curriculum. For Kiswahili, teachers were expected to read a short text and answer 8
post-harvest. The trends for the post-planting (rainy season) and post-harvest (dry season) rounds are separated for agricultural, (e.g., farming) activity. While there is no notable trend for the post-harvest/dry season, there is a distinctly upward trend for the post-planting/rainy season. This is consistent with either a larger proportion of workers engaged in agricultural activity or, also, a greater number of engagements later and into the dry season, as cited in several accounts of the herder-farmer conflict. We also observe a notable peak in self-employment between 2016 and 2018, which corresponds to the intensification of herder-involved conflict events documented in Figure 1.

The role of informal work, in relation to economic development in general, and more specifically as a means to a ‘safety net’ in response to adverse shocks remains an open debate among researchers and policymakers. Specifically, while some argue that the informal sector is the result of competitive market forces, others contend that informal work is the result of market segmentation, and more recently, some argue that the informal sector offers either attractive employment opportunities or a coping strategy of last resort (Gunther and Launov, 2012). Although the extent to which informal work acts as a ‘safety net’ is an empirical question, existing work demonstrates the potential of informal work to buffer individuals from the consequences of adverse shocks (Loayza and Rigolini, 2011), such as exposure to conflict.

# **3 Empirical Framework**

This section includes four sub-sections. First, we introduce the two primary sets of data we use to construct an individual-level panel data set of exposure to violent conflict events and labor allocation. Second, we discuss some descriptive statistics which characterize our study sample. Third, we specify our core identification strategy and discuss the interpretation of our preferred estimates. Finally, we discuss the potential consequences of sample attrition and discuss how we address these concerns.

## **3.1 Data**

We combine two detailed panel datasets. The
baseline specification allows us to evaluate whether, for example, the districts with active or closed mines experience higher or lower inequality compared with districts with no mines. The same logic also applies to the other sources of exogenous variation mentioned. The coefficient of interest, which disentangles α in equation [2], is θ<sup>j</sup> , with the underlying hypothesis, still following the resource curse literature, being θj > 0. ν is an error term.

One advantage of equation [4] is that it brings into the analysis the mentioned exogenous variation in mining activities, which is useful for explaining the mechanisms through which mining activities can affect district inequality in Africa. However, the impact of mining activities on district inequality is likely to be driven by the specific minerals commodities available (Boschini et al. 2007). This latent effect cannot be captured by Pricem,t in equations [2] and [4].

Obviously, controlling for minerals’ values is important to obtain unbiased estimates and explain the mining-spatial inequality nexus. As noted in Section 3, our analysis, which relies on USGS mineral data, suffers from the lack of data on mineral production at mining sites in Africa. However, the available mineral prices data offer a possibility to circumvent this limitation: the use of minerals unit prices as numeraires for minerals across districts where mineral deposits and mining activities are available. For districts where mineral deposits and mining activities are absent, we assign zero values in unit mineral prices. We therefore update equation [4] slightly differently to take into account the idea of using mineral prices as numeraires. The updated specification takes the following form: where Mineralc,r,d,t denotes the dummies for the major mineral commodities types (see Table 2) available in our sample, and ωc,r,d,t is an error term. A unique feature of equation [5] is the interaction term Mineralc,r,d,t � Pricem,t, which underlies
Park (2011) empirically showed a significant correlation between a program’s rating and the probability of a budget cut, although the significance and strength of this correlation varies over time. Second, performance information is reflected at every stage of the budget process. It is used by the line ministries, the central budget authority, and the legislature. The patterns of interaction may be changed in the future as the legislature increases its engagement, but the link between performance information and budget allocation will likely be maintained. Third, there is a tendency to move away from incremental budgeting. Park (2011) also found that once programs are subject to the review process, the sizes of their budget changes are bigger, compared to changes for programs that did not go through the review process. This suggests that budget officials become more aggressive in budget adjustments when they have new performance information at hand.

Notwithstanding their initial skepticism, budget officials within the line ministries are beginning to rely on the new performance information. According to a survey conducted in Korea by the National Assembly Budget Office, 70 percent of budgeting personnel within line ministries responded with neutral or positive answers to the question, “Are assessment results from Self-Assessments of Budgetary Programs useful in the preparation of budget

> 15 For more detailed discussions and empirical evidence, see Park (2011).

22
_Note_ : Data correspond to state-owned installed capacity on commissioned generation plants, excluding regional projects. Dual fuel and cogeneration are recorded under natural gas. Other renewable energy includes geothermal, solar, wind, and biomass.

# **Table A4.2: State-owned transmission and distribution line data (km)  in the reference year or latest year with available data**

|**Country**|**HV (110 kV and**|**MV to HV (66**|**MV (1–66 kV)**|**Total above 1kV**|
|---|---|---|---|---|
||**above)**|**kV–109 kV)**|||
|Angola_2011|2,094|0|738|**2,832**|
|Benin_2013|136|0|3,408|**3,544**|
|Botswana_2013|3,023|0|—|**3,023**|
|Burkina Faso_2014|3,452|0|471|**3,923**|
|Burundi_2014|0|322|1,779|**2,101**|
|Cameroon_2014|0|2,232|16,785|**19,017**|
|Cape Verde_2012|0|0|859|**859**|
|Central African Republic_2014|0|0|225|**225**|
|Chad_2012|0|0|0|**0**|
|Comoros_2012|0|0|588|**588**|
|Congo,Dem. Rep._2013|30|0|4,483|**4,513**|
|Congo,Rep._2012|1,479|0|1,070|**2,
Finally, we examine whether the relationship between tax compliance costs, public procurement competitiveness, and the adoption of e-government systems depends on countries’ level of development. We expect that countries with higher income, stronger institutions, better technology, and more skilled workers experience greater returns from e-government. Our main proxy for development is GDP per capita (PPP); we also consider other measures such as rates of gross secondary school enrollment and Internet use and measures of the rule of law, government effectiveness, and business freedom. The higher values of these measures correspond to higher level of development.

# _4.3.1. Country-level evidence_

Table 3, columns I and II, presents the results of estimating specification (3) for e-filing at the country level. The coefficient on the interaction term between the e-filing indicator and GDP per capita (PPP) is negative and significant for the number of tax payments, but is not significant for the time required to prepare and pay taxes. For the other measures of development we again obtain negative signs on the interaction terms (Table B.3 in the Online Appendix). The effects are particularly strong for the number of Internet users per capita, government effectiveness, and business freedom index. These results confirm our expectations and suggest that a country needs to have the necessary human capital, regulatory environment, and institutions to fully exploit investments in ICT. More developed countries also tend to have greater resources to design and implement advanced e-filing systems with e-payment functionality, which contributes to greater reductions in tax compliance costs. The estimates also allow us to calculate, for example, the level of GDP per capita at which e-filing implementation reduces tax compliance costs. When GDP per capita (PPP) exceeds approximately $3,200, the adoption of e-filing leads to the reduction of the number of tax payments and the time to prepare and pay taxes.

# _4.3.2. Firm-level evidence_

Table 3, columns III and VI, and Table B.4 in the Online Appendix present
Policy Research Working Paper 7687

# **Abstract**

The relationship between bank competition, firm access to finance, and economic growth is a much debated topic in the economic literature and in policy circles. This paper uses a panel of 23 manufacturing sectors over 2002–10 to investigate the impact of bank competition on industry growth in the Gulf Cooperation Council economies. The results show that greater competition allows financially dependent firms to grow faster. In addition, the results show that lower restrictions on banks’ permissible activities, better credit information, and greater institutional effectiveness mitigate the damaging impact of low competition. These results are robust to a variety of checks. The findings suggest that improving bank competition should be an important aspect of the financial sector development agenda in the Gulf Cooperation Council.

This paper is a product of the Finance and Markets Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pcalice@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|.<br>(0.0919)|.<br>(0.121)|.<br>(0.0772)|.<br>(0.0933)|.<br>(0.0858)|
|_Policy Response_<br>|||||||||||||
|Government Response (GR)<br>_(Oxford tracker)_|-0.375**<br>(0185)|..|..|..|..|..|..|..|..|..|..|..|
|<br>Stringency Index<br>_(Oxford tracker)_|.<br>..|-0.528<br>(0.334)|-0.436<br>(0.283)|-0.393*<br>(0.222)|-0.587<br>(0.367)|-0.420*<br>(0.243)|-0.326**<br>(0.158)|-0.370**<br>(0.185)|-0.424*<br>(0.223)|-0.355*<br>(0.194)|-0.397*<br>(0.235)|-0.426<br>(0.279)|
|_Regional Dummies_<br>Dummy: Sub-Saharan|2.833|-0.259|0.987|1.979|0.0226|2.492|3.314*|2.975|1.653|3.136|0.948|2.103|
|<br> Africa (SSA)|(1.836)|(3
local labor market analysis exploits labor force survey data to calculate the shares of workers at high risk of automation in each industry of a local market in the base year. The shares are then used to aggregate robot import by industry and year within the local labor market.<sup>3</sup> We study long-run trends and account for the

> 2Rodrik (2018) argues that GVC integration requires strict production requirements, which are hard to satisfy using manual work. Similarly, World Bank (2020b) and De Backer et al. (2018) suggest that by adopting technologies used in advanced economies, developing countries could boost trade relationships. Maloney and Molina (2019) discuss how automation in developing countries can improve the quality of inputs demanded by advanced economies.

> 3The methodology in this part of the analysis is similar to Acemoglu and Restrepo (2017) and Dauth

3
<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: The World Bank TPP Database, 2016 Malaysia’s TPP commitment enhances transparency and predictability of applied policies. Although the commitment states that the license is subject to “best interest” of Malaysia, it spells out the criteria that the Minister or the Bank will use to determine whether the investment will meet the “best interest” criteria.<sup>14</sup> Furthermore, the Central bank is mandated to make a decision

> 14 The criteria include (a) the effect of the investment on the level and nature of economic activity in Malaysia, including on productivity, efficiency and quality of financial services; (b) the contribution towards enhancing

13
- **Berenzin, EV, IB Konovalov, P Ciais, A Richter, S Tao, G Janssens-Maenhout, M Beekmann, and Ernst Detlef Schulze** , “Multiannual changes of CO2 emissions in China: Indirect estimates derived from satellite measurements of tropospheric NO2 columns,” _Atmospheric Chemistry and Physics_ , 2013, _13_ , 9415–9438.

- **Beyer, Robert CM, Esha Chhabra, Virgilio Galdo, and Martin Rama** , _Measuring districts’ monthly economic activity from outer space_ , The World Bank, 2018.

**, Sebastian Franco-Bedoya, and Virgilio Galdo** , “Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity,” _World Development_ , 2021, _140_ , 105287.

- **Blattman, Christopher and Edward Miguel** , “Civil war,” _Journal of Economic Literature_ , 2010, _48_ (1), 3–57.

- **Buell, Brandon, Reda Cherif, Carissa Chen, Hyeon-Jae Seo, Jiawen Tang, and Nils Wendt** , “Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa,” _IMF Working Paper_ , 2021.

- **Carlton, Jim** , “Coronavirus Offers a Clear View of What Causes Air Pollution,” _The Wall Street Journal_ , 2020.

- **Castellanos, Patricia and K Folkert Boersma** , “Reductions in Nitrogen Oxides Over Europe Driven by Environmental Policy and Economic Recession,” _Scientific Reports_ , 2012, _2_ (1), 1–7.

- **Chen, Shaohua and Martin Ravallion** , “The developing world is poorer than we thought, but no less successful in the fight against poverty,” _The Quarterly Journal of Economics_ , 2010, _125_ (4), 1577–1625.

- **, , and Prem Sangraula** , “PovcalNet,” _Available at_ _`http: // iresearch. worldbank. org/ PovcalNet/ home. aspx` ._

-
*87%**|**63%**|**37%**|**37%**|**36%**|3%|
|Richest|99%|94%|**83%**|70%|60%|32%|
|Total|94%|82%|**64%**|58%|48%|14%|
|**Nicaragua**|||||||
|Lowest|**86%**|**79%**|**84%**|**50%**|49%|**14%**|
|Richest|97%|**96%**|95%|**77%**|72%|**49%**|
|Total|**95%**|**89%**|**88%**|**63%**|60%|**28%**|
|Note: numbers in<br>percent.<br>2Conditional on a|bold (green<br>chievement|) denote an incre<br>of theprevious th|ase of 5 percent o<br>reshold.|ver 2001 level, h|ighlighted (red) numb|ers indicate a decline in 5|
|Source: Authors’|calculations|based on several h|ousehold survey|s circa 2001 and|circa 2009||

28
protected areas currently managed by AP have METT data for multiple years (treatment group), as do 27 protected areas identified by AP as anchor areas meeting their criteria for future management (control group). The six treatment group protected areas with multiple observations are Pendjari National Park (Benin), W National Park (Benin), Odzala-Kokoua National Park (Republic of Congo), Garamba National Park (Democratic Republic of the Congo), Bangweulu (Zambia), and Kafue National Park (Zambia). The data are so sparse that applying the dynamic difference-in-differences estimator we use for all other analyses results in an error. Consequently, we estimate a two-way fixed effects differences-indifferences regression instead (equivalently, a before-after-control-impact analysis). For a measure of management effectiveness _Y_ in protected area _i_ in calendar year _t_ , we estimate the following equation with ordinary least squares regression: where _APit_ equals 1 if protected area _i_ was managed by AP on or after year _t_ , _γi_ are protected area fixed effects, _δt_ are year fixed effects, _Xit_ is a matrix of other control variables, and _εit_ is the error term. The protected area fixed effects control for all timeinvariant characteristics of each protected area, such as physical geography, while the calendar year fixed effects account for time-varying factors that affect all protected 14