rafmacalaba/data-use-mentions-extended huggingface dataset:
are not necessarily poorer.<sup>16</sup> For these reasons, the impact on the overall distribution is muted as shown by the GIC 1, the dashed line in Figure 3.<sup>17</sup>

> 14 Note, however, that this effect cannot be currently assessed given the data limitations of the available household survey where income from capital is not accurately measured.

> 15 Because of the segmentation, two separate wages are clearing the rural (agriculture) and urban (non‐agriculture) segments. The urban premium, i.e. the ratio of the wage of unskilled workers in non‐agriculture sectors over that of unskilled workers in agriculture is initially equal to 1.3. A labor migration function allows for some imperfect mobility across segments and this, together with sectoral demand for labor, determines the shift in the urban premium shown in Table 1.

> 16 Moving workers are identified using a probit estimation, as explained in the methodological section, see the Appendix for more details.

> 17 The line ‘GIC 1’ in the figure almost overlaps with the horizontal straight line that intercepts the vertical axis at ‐6.9. This horizontal line represents the average percentage welfare loss, i.e. the loss that everyone would incur were the shock uniformly distributed. The case represented by GIC 1 takes into account the losses of displaced unskilled workers, but the impact is minor, visible only because of the small blips on the GIC 1 line.

16
Bihar’s institutions can also be understood within a broader macroeconomic story of what explains why some countries are rich and others persistently poor. One body of work has argued that European colonization established "extractive" institutions in the history of currently poor countries, and those historical institutions have persistent effects till today, explaining why some places are not able to sufficiently grow out of poverty or develop "inclusive" political institutions that assure health and education for all (Acemoglu, Johnson and Robinson, 2001 and 2002; ). These ideas are tested by Banerjee and Iyer (2005) and Iyer (2010) by examining variation in institutions within India. They find that colonial institutions have persistent effects on agricultural productivity and the delivery of public services. Bihar happens to be the state where the historical institutions used by Banerjee and Iyer (2005) and Iyer (2010) show no within-state variation. All of Bihar was governed directly by the British and through feudal land institutions during the colonial era, which are the institutions these authors find correlated with low agricultural productivity and fewer public good provision in current times (respectively), long after independence and land reforms.

However, the mechanisms of persistent impact remain shrouded in mystery. Research is also lacking on why current formal institutions, such as competitive electoral institutions, courts, police, which most states, including Bihar, possess, are not sufficient to overcome the burden of history. Further, these explanations may be unsatisfying because they are so abstract, and provide such little guidance of ways out of the problem. What precisely are "extractive" institutions, and why is to so hard to reverse them after the colonial powers are removed? It is difficult to argue that Bihar’s political institutions are not "inclusive", after decades of competitive elections, turnover of political parties in office, and rise of lower-caste political leaders, on platforms of empowering the lower castes. Indeed, our survey data are consistent with inclusive politics, uncovering quite astonishing rates of political aspiration, and sophisticated
in arrears were equal to about 7.4 percent of outstanding credits at the end of 1995. Figures for the first two months of 1996 indicate that arrears were likely be significantly higher that year. Disturbingly, arrears have increased every year since 1992, when groups with arrears accounted for 4.7 percent of total groups and payments in arrears were equal to 1.4 percent of outstanding credits (Table 26). The 1995 payment arrears rate is only slightly higher than that of PHBK. However, PHBK's rate (which also includes loans that are actually in default) has declined every year since fiscal 1993/94. Project data indicate that P4K's cumulative default rate since inception is probably about equal to that of PHBK-approximately 2.5 percent.

P4K's rising arrears rate is of considerable concern to program managers who are looking at ways of addressing the issue. This issue also appears to have contributed to a delay in plans to expand the program to fill-scale in 10 provinces currently participating in a pilot.

P4K is an ambitious program that provides a variety of services to borrower groups in addition to those that relate specifically to financial services. For this report, program expenditures have been allocated between financial services and all other activities. Financial services account for about 69 percent of total program costs.<sup>47</sup> The cost figures presented below are estimates of costs for financial services only. They include costs associated with program management, and with the Ministry of Agriculture's involvement in the project. They also include the implicit cost of the subsidy embodied in the low-interest loan IFAD provided to 13RI. To facilitate comparison with the PHBK program, they do not include BRI's direct costs of undertaking P4K lending activities.

> _47_ Financial services costs were estimated based on program organizers' estimates of the amount of time that management and field staff spent on a variety of tasks. Spending on general program-related overhead that could not be directly allocated was divided between financial services and non-financial services
<!-- Start of picture text --> 46% 450,<br>38% 37%<br>32% 32%<br>14%<br>72% 11% 11% 11%<br>Not literate+primary middle + secondary higher secondary graduate +<br>mReweightedCPHS NSS 2017-18 Reported CPHS<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br>38% 37% 36% 36%<br>30%<br>17%<br>13% 14% 13%a0 13% 14 0%<br>Not literate-+primary middle + secondary higher secondary graduate +<br>mReweightedCPHS Mm NSS 2017-18 Reported CPHS<br><!-- End of picture text --> <!-- Start of picture text --> 54% 530%<br>47%<br>34%<br>29% 28%<br>12%<br>Not literate-+primary middle + secondary 9%highero, lI10%secondary 8%graduate8% +g%<br>mReweightedCPHS Mm NSS 2017-18 Reported CPHS<br><!-- End of picture text -->
in panel A suggest that members of (co)-owners’ households can make cultivation decisions for 87 percent of parcels, followed by rights to invest (84 percent), to transfer/mortgage (75 percent), to bequeath (73 percent), and to sell (66 percent).<sup>12</sup> The same general pattern is observed for IHS4 data (panel B) that point to a similar share of parcels that can be sold (66%) and a slightly higher one (78%) that can be transferred via bequest.

Second, compared to individually reported information (from the IHPS), data provided by proxyrespondents (IHS4) overestimate the extent of (co)-ownership for men and underestimate it for women. The share of agricultural parcels reported to be (co)-owned by females in the IHPS is 4 percentage points lower than the comparable figure from the IHS4. In contrast, the share of agricultural parcels reported to be (co)-owned by males in the IHPS is 9 percentage points higher than the comparable figure from the IHS4.

Finally, conditional on female (male) (co)-ownership, proxy reporting in the IHS4 seems to overstate the specific rights that can be exercised by female owners while understating the rights that can be exercised by male owners. For agricultural parcels that are (co)-owned by females, the IHS4 data indicate a much lower incidence of bequest rights (60 percent) vis-a-vis the IHPS (69 percent). This pattern is less pronounced for the right to sell. The opposite is true for parcels that are (co)-owned by males. The IHPS average parcel-level incidence of male rights to bequest and sell are 78 percent and 70 percent, respectively. The comparable estimates based on the IHS4 stand at 74 percent and 64 percent, respectively.

# **3.2 Empirical approach**

We use the parcel-level incidence of organic fertilizer application and cultivation of cash crops (tobacco
receiving transfers. We describe in the Appendix how this problem is solved given that we are working with a database of incomes for every household in the population census.

# **_7.5. Results of spatial targeting simulation_**

Table 2 presents the basic results from our simulations. There are a number of conclusions to be drawn. First, the availability of disaggregated data on poverty can help to improve on a uniform lump-sum transfer across the entire population. Targeting transfers to poor localities, in accordance with the optimization scheme outlined above, yields lower values of the national FGT2 than when the budget is transferred as a uniform lump-sum transfer to the entire population. Second, the more disaggregated the poverty map, the greater the improvement over the uniform lump-sum transfer. The simulations here suggest that with estimates of poverty at the province, district and commune levels, further improvements in terms of impact on the FGT2 with a given budget are attainable, and are non-negligible. Third, while the general patterns we observe are similar across our two poverty maps for 1999 and 2009 respectively, they are not identical. Notably, we see in Table 2 that while communelevel targeting in 1999 would reduce the FGT2 from a level of 0.0110, following a uniform transfer, to 0.0058 with commune level targeting (a 43 percentage point reduction), the improvement from commune level targeting in 2009 would be 66 percentage points – the FGT2 declining from 0.0166 to 0.0057 (Table 3). With district level targeting rather than commune-level targeting, the gains are slightly less marked but nonetheless a striking 58 percentage point reduction compared to uniform transfers.

Table 3 repeats the simulations presented in Table 2 but focuses now on the headcount, or FGT0, measure of poverty. As mentioned above the optimization procedure outlined in Kanbur (1987) applies to the squared poverty gap or FGT2 measure. There is no analogous optimization algorithm for the FGT0 measure. We report in Table 3, however, the resulting FGT0 estimates from having applied
- Develop a robust empirical method to identify the subset of indicators (from a large set) which are significantly associated with measures of trade and investment outcomes at the sector level (trade and investment sector “success”).

- Use the model estimates to construct robust benchmark scores of country/sector performance against the rest of the world for the identified subset of indicators.

In this way, econometric analysis complements the existing ordinal-ranking based CPSD methodology by: i) providing statistical assessment of the strength of association between the individual indicators and the sector specific performance and by ii) explicitly taking into account indicator’s forecasting performance as well as the effects of other indicators and potential confounding factors in the process of constraints benchmarking. The produced benchmarking heatmap empowered by the empirical model estimates provides actionable and refreshable signals of the identified sector level constraints which can be used to facilitate efficient data-driven economic policy decision making.

In the remaining parts of this section we first discuss data which is used in the empirical analysis before discussing in detail the empirical design and econometric approach. We then present the baseline results together with conducted specification checks.

# Data

We use three general types of data in the analysis:

## I. <u>Foreign direct investment data</u>

We use the Financial Times’s _fDi Markets_ database for FDI data. The database provides a comprehensive source of information on the firm-level greenfield investment, containing reported deals/projects worldwide over the 2003-2019 period for 206 countries. The data set contains information on the recipient and the source country, principal industry sector to which the foreign direct investment type can be mapped, the year/month of the transaction, the name of the investing firm as well as various other information. The _fDi Markets_ database focuses on the _new_ FDI projects and provides cleaner information on the FDI sector level patterns relative to the balance of payments (BOP) based FDI data. The latter contains sector level information on FDI flows only for a limited number of countries, does not distinguish between
anomalies. Although these relationships are monotonic, they are significantly nonlinear across the range of weather anomalies. The relationship between temperature anomalies and the log monthly per capita consumption expenditure (MPCE) is close to a quadratic fit. There is a sharp increase in consumption with more negative temperature anomalies (z-score<-2). However, consumption does not decrease much as positive anomalies become more extreme (z-score>0). On the other hand, the decrease in consumption is steep with adverse vegetation anomalies (z-score<-1). Moreover, we find some evidence of households decreasing non-food consumption to save themselves from food insecurity. While both food and non-food consumption decrease with adverse vegetation shocks (zscore,-1), households were able to maintain their food consumption by reducing non-food consumption when temperature anomalies were positive in 2018 (z-scores).

We also show that the distribution of rainfall anomalies has little variation across clusters in 2018. Moreover, we observe less variation in the distributions of rainfall shocks over time: rainfall distributions are tight, with a significant overlap between 2018 (a declared drought year) and 2019 (a normal weather year). This finding is not surprising given the scarcity of rainfall in most parts of the country due to its arid climate. Hence, we use NDVI and temperature anomalies as measures of drought in our analysis.

We explore several potential sources of heterogeneity. Given the complex security situation at the time in Afghanistan, we find suggestive evidence that adverse vegetation shocks disproportionally affected the consumption of households in districts under Taliban control. This may be because these districts had limited access to humanitarian assistance. We find that households who owned agricultural land could mitigate the adverse effects of these weather shocks. This aligns well with extensive evidence that these assets allow households to ‘self-insure’ and smooth their consumption (see, for instance, Dercon 2002). We find no evidence that livestock owners are better insulated from shocks, in line with evidence that livestock ownership is an imperfect coping mechanisms as their sale
they had an opportunity to enroll in playgroups at the appropriate age, starting at age 3 and continuing through age 4. Thus, the two cohorts differed in their likelihood of being exposed to playgroups at the appropriate age.

Our data allow us to track the development outcomes for both cohorts at ages 5 and 8. To control for children’s baseline development, we use measures of child development before exposure to playgroups. For the younger cohort, we have data on child development measures from when they were 1 and 2 years old. For the older cohort, we have data on child development measures from when they were 4 years old. We use a comprehensive set of child development outcomes that measure both cognitive and socio-emotional development. In addition, we capture children’s performance on a test of language, mathematics and abstract reasoning in primary school. Together, these measures allow us to trace out early development on a variety of dimensions.

We build upon previous work by Brinkman et al. (2017b), which analyzed the impact of the project on the older cohort. In this paper, we conduct three new analyses. First, we present child development outcomes for the younger cohort at ages 5 and 8. Second, we present new results for the older cohort using primary school test scores. Third, we contrast the developmental trajectories between the two cohorts. We explore how features of project implementation as well as different rates of exposure to playgroups at the appropriate age may have contributed to the contrasting results between the two cohorts.

This paper also contributes to research on the generalizability of impact evaluations in early childhood education. Preschool interventions in developing countries have produced a wide range of effect sizes, prompting both researchers and policy makers to question the generalizability of these findings (Tanner et al., 2015). In particular, it is difficult to understand what causes these variations since each preschool program is different in terms of what the program provides, how the program is implemented and where the program is carried out.
# **1. Introduction**

The COVID-19 pandemic exposed limitations to economic measurement. Across the world, governments enacted lockdowns and other containment measures to mitigate the public health crisis, with strong implications for economic activity. Rational policy making requires evaluating economic impacts and assessing the economic situation in real-time. However, in many countries, traditional indicators of economic activity do not provide that. First, national accounts estimates are available with substantial lags and often only at the national level. In addition, national accounts estimates in many countries, including in Bangladesh, are only available at annual but not quarterly frequency. Second, data collection through surveys, which are fundamental for the traditional estimation of gross value added, became more difficult during the pandemic due to severe containment measures. In Bangladesh, for example, it took more than a year to finalize the gross value-added estimates for fiscal year 2020 (FY2020) (from July 2019 to June 2020). In such a situation, high-frequency indicators of economic activity can provide crucial insights.

Many new approaches to tracking economic activity have been introduced since the outbreak of the COVID-19 pandemic. For example, high-frequency indicators measuring mobility based on phone locations provided by Google and Facebook have been used to assess the economic situation in real-time (Spelta and Pagnottoni 2021). Beyer, Franco-Bedoya, and Galdo (2021) use daily electricity consumption and monthly nighttime light intensity to analyze subnational economic activity during the pandemic in India. Few comparable indicators for tracking the economy in real-time are available in Bangladesh.

In this paper, we establish daily electricity consumption as a useful indicator for tracking economic fluctuations in Bangladesh. First, we confirm a meaningful relationship between electricity consumption and short-term economic fluctuations. Second, we estimate a daily electricity consumption model that explains over 90 percent of the daily variation in electricity consumption. Deviations of actual electricity consumption from the model prediction can then be interpreted as a real-time indicator of economic activity and can be used to assess economic shocks. We provide this measure not just at the national
Additional socioeconomic controls included in model variants were drawn from the same source, including domestic credit to the private sector, expressed as a percentage of GDP. Data on the Gini coefficient were drawn from a more complete World Bank source (Milanovic, 2005).

The governance indicators come from the International Country Risk Guide (ICRG) of Political Risk Services (PRS 2010). The ICRG is a popular source of governance indicators used in cross-country studies. It includes indicators for corruption, bureaucratic quality, law and order, democratic accountability, government stability, ethnic tensions, and religious tensions. It offers broad country coverage, which reduces the risk of selection bias (Kaufmann et al. 1999; Johnston 2001), and is available for a relatively long time period (1984 to the present), which conveniently covers the entire period that the DFO data are available, unlike governance indicators from all other sources. Indicators in the ICRG database take values between 0 and 6 (except bureaucratic quality and government stability, which have maximum values of 4 and 12, respectively), with higher values denoting better governance. We converted each to a 0-10 scale. A detailed description of the indicators along with their correlation coefficients is given in Appendix Table A1.

The benchmark regression specification included an aggregate governance index formed by averaging all seven indicators. Many studies have averaged or, equivalently, summed governance indicators, with the rationale being that averaging reduces measurement error if the indicators pertain to similar underlying concepts of governance and have independent errors (Knack and Keefer 1995; Mauro 1995, 1997; Ferreira and Vincent 2010). We also included the indicators individually, with particular attention paid to three of them: corruption, democratic accountability, and ethnic tensions. Escaleras et al 12
real wage, and the capital stock. Moreover, an energy efficiency enhancement also results in an increase in energy use, which is known as the “rebound effect”.

A few empirical studies have investigated the macroeconomic effects of changes in energy efficiency.<sup>7</sup> Two notable exceptions are Rajbhandari & Zhang (2018) and Bruns, Moneta, & Stern (2021). Rajbhandari & Zhang (2018) use a panel VAR model to examine the relationship between energy efficiency and economic growth for 56 high- and middle-income economies. A particular advantage of analyzing energy efficiency in this framework is that it allows for an economy-wide assessment of the impact of energy efficiency improvements. However, Rajbhandari & Zhang (2018) confine the interpretation of causality to Granger’s sense. Bruns et al. (2021), on the other hand, estimate a three-variable structural VAR model for the United States with energy consumption, energy price, and GDP data to evaluate the impact of energy efficiency shocks on U.S. macroeconomic aggregates and energy price. The identification of energy efficiency shocks is based on a lower triangular ordering of the variables (Cholesky factorization).

> 6In Angelopoulos et al. (2010), “Kyoto-like rules” are defined as how fast pollution decreases from one period to the next.

> 7The macroeconomic linkages between total energy consumption and economic growth have been studied extensively using vector autoregression (VAR) models in the empirical literature.

8