rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.4|
|Socioeconomic background (school)|56.0|2.7|55.7|2.1|
|School safety|6.5|0.8|8.1|1.0|
|N. obs.|80,218||150,382||
|R<sup>2</sup>|0.28||0.48||

Source: Analysis using a linear regression. All coefficients are significant at the 0.1% level. Other control includes country fixed effects, average age of students, share of affluent students, urbanization level. Observations have been clustered at the school level to compute standard errors.

Reading: Pooling data for the MENA countries participating in TIMSS 2015, an increase in one standard deviation in the school safety index is associated, all other things equal, with an increase in math performance amounting to 6.5 points.

The association of classroom disciplinary climate and student performance may be linked with the disciplinary strategies teachers choose to utilize. International research suggests teachers’ disciplinary strategies have influence on student learning behaviors and attitudes (Lewis and others 2005; Roache and Lewis 2011; Shavit and Blank 2012). Classroom management while important may be further examined so that an assessment of teacher strategies to maintain student discipline, focus and motivation can be assessed. Evidence shows that students who are disciplined through more positive approaches (hinting, recognition, involvement, discussion, etc.) versus more coercive approaches (yelling, sarcasm, etc.) tend to be less disruptive and more cooperative with teachers in their class (Lewis and others 2005). This then can improve students’ responsibility toward learning and decrease the emergence of negative attitudes towards learning and teachers. This may explain why some schools are adopting new approaches, that have had positive impacts, to addressing disruptive student behavior including mindfulness programs and mediation in replacement of practices such as detention (Flook and others 2010; Smith and others 2012)
||20_._98|12_._84|77_._88|105_._63|
|Wheat|||0_._05|24_._91|11_._19|80_._74|108_._35|
|Wheat||||24_._91|11_._19|80_._75|108_._36|

Notes. In this table we present the results of the SRHS approach using the groundwater supply or volume measure using the aquifer dataset constructed by BGS. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

32
Beck et al. (2005) use the severity of access to finance as an obstacle to firms’ operations as perceived by the firms and show that the severity level is inversely correlated with firm growth; Djankov et al. (2003) show that a higher degree of judicial formalism is associated with lower perceptions of enterprises of courts’ fairness, honesty and consistency; Beck, Demirguc-Kunt and Levine (2006) and Barth et al. (2009) show that a more market-based supervisory approach and more efficient systems of credit information sharing are associated with lower financing constraints as perceived by the firms. Also see for example, Beck et al. (2010) and Safavian and Sharma (2007).

There are a number of advantages of using micro over macro data. First and at a broad level, using micro data provides an additional layer of robustness check for the existing results based on macro data. Qualitatively, are the findings based on macro data similar to the ones based on micro data? Second, the experience based measure we use is an overall summary measure of the quality of tax administration. That is, by its very nature, the measure covers all possible features of the tax administration system that may affect firms. In contrast, macro-level measures typically capture at best a few aspects of the phenomena under study. For example, the World Bank’s Doing Business project reports on the time it takes to prepare, file and pay three major types of taxes and contributions. We use this measure below to check the robustness of our main results. However, this macro-level measure is at best a partial measure as it does not include for example, the monetary cost or the hassle of complying with tax procedures. It is possible that two agents may take the same amount of time in complying with tax procedures but they may experience different levels of difficulty and costs in doing so. Our experienced based measure picks up any such difference. Third and somewhat related to the previous point, macro datasets have by and
rules for information exchange, shape firm behavior and ensure quality in GVCs. They enable the codification of both product and process specifications to ensure that a wide range of global suppliers can consistently deliver end products that meet the quality requirements of developed-country markets. They are industry-specific and constantly evolving. Failure to comply with these standards can result in exclusion from the GVC.” (Gereffi, Fernandez-Stark and Psilos., 2011, p. 243) According to a recent business survey in the agri-food sector (OECD-WTO, 2013c – NB: 250 lead firms and suppliers in developing countries participated to the survey), about 60% of the firms pointed at the ability to meet quality and safety standards as the main factor influencing sourcing and investment decisions in GVCs. Similarly, 40% of the firms surveyed pointed at the non-compliance with mandatory import requirements as a typical trade problem in dealing with developing country suppliers. About 37% of the firms suggested that the improvement of standards infrastructure and certification capacity would be the most effective way to bring new developing country suppliers into GVCs, and almost half of the firms providing trade technical and capacity building efforts did focus on compliance with safety and quality standards.

Standards of relevance include both process (e.g., the respect of management, labor or environmental standards, often included in a CSR or conduct code) and product (e.g., quality) standards. These need to be respected throughout the value chain since every stage of production could affect the quality of the final product or service, and engage the responsibility and reputation of the lead firm in the country of final consumption of the product or service. In the agri-food business, this translates into “traceability” requirements aimed at protecting the health and increasing the information of consumers.

Standards in GVCs are both public and private, with an increasing prevalence of “voluntary” standards that are imposed by the lead firms (either buyers or producers) to all the input providers and assemblers along the chain (see Lee, Gereffi
C for details).|
|_TFP_OLS_|TFP of the firm calculated using the OLS method (see Appendix C for details).|
|_TFP_IN_|TFP of the firm calculated using the index number method (see Appendix C for<br>details).|
|_TFP_CS_|TFP of the firm calculated using the index function method, with the cost shares<br>generated from the ASIF data (see Appendix C for details).|

``` 30 ```
answering those questions. Nevertheless, it is worth pointing out that when it comes to rigorous evaluation of private sector policies in Africa, there has not been much in the way of either experimental or structural work – so there is plenty of scope for both types going forward – and hopefully this paper will help researchers considering either approach.

I begin section 2 by establishing a number of facts about the private sector and the firms participating in private sector programs in different African countries. Most importantly, the number of medium and large firms is relatively small in most countries, as are the number of firms participating in any given firm-level intervention. Moreover, these firms are quite heterogeneous in terms of employment size, industry, and sales levels. Section 3 examines the implications of these facts for the power of experiments to detect impacts of firm policies, and for the ability of structural models to credibly estimate firm behaviors. The number of firms and heterogeneity of these firms poses challenges for both approaches. In section 4, I then discuss a way forward, which involves focusing attention on a smaller, more homogeneous sample of firms and collecting a lot more data on these firms. Section 5 then concludes.

# **2. Background on Small, Medium and Large Enterprises in Africa**

Before discussing which methods of evaluation and estimation are likely to be most appropriate for assessing policies directed at firms in Africa, it is useful to establish some facts about the likely populations of interest for such studies.

# **2.1 Fact 1: The number of SMEs and large firms in many African countries is relatively small.**

I begin by first asking how many firms there are of different sizes, as collected through industrial censuses in selected countries. These censuses differ somewhat in their coverage: some only cover manufacturing and some exhibit an incomplete coverage of home-based enterprises. Nevertheless, they give a sense of the scale of small, medium, and large scale enterprise activity.

Table 1 shows that there are many firms with fewer
to identify inequalities that exist in every society as well as uneven local to global development processes that create or exacerbate such inequalities. Together, they result in heightened propensity or predisposition to be adversely affected (IPCC 2014a).

Hence, to understand the structural relations between poverty and vulnerability, more explicit attention needs to be paid to the often concealed dynamics that limit the choices poor people — homeless people in urban centers, marginalized castes, disadvantaged female single‐headed households — have to build adaptive capacities while having to deal with climatic and other stressors, simultaneously or cumulatively. Such convergence of stressors can push their already fragile livelihoods over a critical threshold and into poverty traps. Climatic extreme events such as excessive temperatures, droughts, and hurricanes, and subsequent disasters ranging from heat waves and scorched harvests to flooding, have repeatedly revealed poor people’s inability to cope and recover. However, modest shifts in trends and localized small disasters that may not even be reflected in typical meteorological records (e.g. strong winds that destroy a smallholder’s harvest) can also harm the well‐being of the poorest and most vulnerable populations. In combination with other crises, they trigger or cement downward livelihood trajectories from which it becomes exceedingly difficult to escape.

In this paper, I draw on a diverse and growing literature on climate change and poverty to argue that uneven power relations more so than exposure and sensitivity to climatic hazards make the poor and disadvantaged members of society distinctly more vulnerable than more affluent, privileged, and powerful groups and individuals, across the country spectrum of income levels. I further argue that climatic stressors/climate change as well as climate policies, often entangled with social exclusion and 3
Our main dependent variable of interest at any level of aggregation is the level of industrialization defined as the share of manufacturing in economy wide GDP or in economy wide aggregate employment. To adjust for possible resource boom, we also consider the manufacturing’s percentage contribution to non-oil GDP for the restricted set of oil-exporting countries. Given that these variables are bounded (i.e. these are fractional response variables) we perform our analysis using both the linear fixed-effects panel data model - which is subject to pitfalls similar to linear probability models - as well as the “fixed-effects fractional logit” model recently developed by Hardin, Hardin, Hilbe & Hilbe (2007). Our results overwhelmingly do not support the finding that SSA countries have began to deindustrialize prematurely. Moreover, we document meaningful heterogeneity across SSA subregions. The Southern region is found to be the only subregion to have witnessed deindustrialization. However, we are unable to confirm that this deindustrialization in the Southern region of SSA is premature. The rest of the paper is organized as follows. Section 2 presents the data. Section 3 discusses the relevant methodological issues. Section 4 presents the main results. Section 5 explores geographic heterogeneity. Section 6 considers potential hypotheses and Section 7 concludes.

# **2 Data and Descriptive Analysis**

This section describes the data sources used for the analysis and provides summary statistics as well as trend analysis of our main indicators.

## **2.1 Data Sources and Measurements**

The data for this study come from a variety of sources. The data for manufacturing valued added and manufacturing employment come from the Groningen Growth and Development Center 10-sector Database (Timmer, de Vries & de Vries 2015). The GGDC Database collects time series data on value added and employment for ten broad sectors of the economy for 42 countries (both developed and developing) spanning 1950 through 2012.<sup>2</sup> The advantage of the GGDC is that it provides a long-run series of sectoral productivity that is internationally comparable across a
5

# **Infrastructure’s interactions with the Millennium Development Goals**

A large share of foreign aid is now oriented toward achieving the Millennium Development Goals (MDGs).<sup>3</sup> Among other things these targets—endorsed by 189 countries at the September 2000 UN Millennium Summit—aim to, by 2015, halve the proportion of people living in extreme poverty, dramatically improve education and health systems, and protect the environment in developing countries. Although there is considerable policy debate on whether the goals are likely to be achieved (see Ravallion 2003), the academic debate has so far mostly focused on measurement issues.

Both the policy and the academic debates on MDGs have been a source of some frustration for infrastructure practitioners. Except for water and sanitation and to some extent telecommunications, there is a sense that the goals fail to recognize the relevance of transport and to a lesser extent energy (since rural energy was recently added as a priority) in the fight against poverty.<sup>4</sup> This is partly because there is little knowledge about the basic relationship between infrastructure coverage and household income. Only two recent studies establish this relationship: Komives and others (2001) for 15 countries around the world and Estache and others (2002) for Latin American countries.<sup>5</sup> The frustration among practitioners has not been met by academics. Indeed, while there has been a lot of talk about the MDGs in the development community and many publications on the health and education goals, there has been little academic work on the water and sanitation goals or the MDG gaps.

Leipziger and others (2003) provide the first empirical analysis of the determinants of three child health outcomes related to the MDGs: the infant mortality rate, child mortality rate, and prevalence of malnutrition. Using data from Demographic and Health Surveys (DHS), the authors go beyond the traditional cross-country regressions of new growth theory—exploiting the variability in outcomes and explanatory variables
using the DLW and the QMLE methods. The results suggest that none of the methods can recover the precise level of markups. The DLW method is biased upwards relative to the QMLE method. Research that attempts to explain how the markup level changes over time using sales revenue data will be inaccurate. However, the distribution is similar when centered. With enough variation in the data, one would be able to estimate the impact of regulation on the change in markups, implying that policy inference is still possible and hence represents a work-around to the problem raised in Bond et al. (2020).

25
# **7 Practical guidelines**

When adjusting for price differences, welfare analysts are called to make several choices: some are the subject of long-standing debate (which index should be used?), while others have attracted far less attention (should each expenditure component be adjusted by its own price index, or should a single, aggregate index be preferred? When estimating a spatial price index from the survey, should one use data that have been adjusted for inflation, or go with nominal expenditures?). This paper contributes to the second set of issues. The main aim of the analysis has been to clarify the implications of alternative deflation strategies on the welfare aggregate and, based on those implications, to reach some conclusion of which deflation strategy should be preferred. Below is an operational summary of the conclusions.

We start from a scenario that is common in practice, and that occurs after some work has gone into estimating the components of the nominal consumption aggregate. We have _i_ ) all elementary expenditures that make up the consumption aggregate, in nominal terms; _ii_ ) an exogenous set of temporal CPIs, so that there is a question of whether one should use the aggregate or the individual indexes (food and non-food, or even a finer breakdown by categories); _iii_ ) a need to adjust for spatial price differences, and to do so by estimating a price index based on survey data.

A step-by-step deflation strategy is as follows.

- 1) Before doing anything else, adjust all elementary expenditures for inflation. The temporal price adjustment should be performed first in order to be able to estimate the spatial deflator based on inflation-adjusted expenditures. This is especially important if the price dynamics during the survey period is sustained. Omitting this step would imply that the spatial price index would be estimated using distorted unit values and budget shares (section 5).

- 2) To adjust for inflation, the analyst may choose whether to use CPIs specific to each commodity group – food CPI for food expenditures, non-food CPI
change over time based on expert opinions. To obtain a consistent time series, we map all the data to standard admin2 districts using a spatial overlay, assigning values to districts by majority coverage. The FEWS NET data are reported at quarterly frequency from 2009 to 2016, and then every four months afterwards. Our sample of food crisis observations used for estimation and cross-validation purposes extends through February 2019. Data for June and October 2019, and February 2020, are considered purely for temporal holdout validation. We develop predictions up to a full year ahead, through February 2020, using covariates that run through February 2019. Some of the district assessments are missing. However, our approach allows us to generate predictions for all districts where covariates are available, even if food crisis outcomes were not.

The IPC rating scale is intended to reflect the actual on-the-ground conditions inclusive of any humanitarian assistance. Since we are interested in predicting food crises themselves and not the impacts of associated humanitarian response, we need to net out the latter. Since 2012, the FEWS NET data marks locations where humanitarian assistance was significant enough to have reduced the IPC phase by one. These markers are used to reconstruct the relevant minimum IPC phases that would have been issued absent humanitarian presence. Figure A.1 in supplementary Appendix A contains the resulting data and Table 1 summarizes.

> 3FEWS NET is a leading food insecurity information provider funded by USAID that regularly publishes analysis and data on current and future food insecurity outcomes.

> 4Admin2 districts are defined by Global Administrative Unit Layers (GAUL); livelihood zones are available from FEWS NET and define sub-national geographic areas of a country, that may cut through administrative borders, in which people are assessed to share similar options for obtaining food and income and have similar access to markets.

4
sup> the effect is equivalent to a 2.9 percent decrease in local economic activity. ff= 0.2 Table 3 shows that the negative association between nightlights and the hurricane damage index seems to be short-lived. In column 2, it is possible to evidence that, consistent with previous literature (Elliott, Strobl and Sun 2015; Del Valle et al. 2018), hurricanes have a short-lived effect on local economic activity. To evaluate this, we included a lagged term for the hurricane damage index and found that, while the contemporaneous coefficient remains negative and statistically significant, the lagged coefficient is positive and statistically significant. This may be related to implementing rehabilitation and reconstruction stages following a disaster shock.

The effect of windstorms is more negative in storm surge-prone areas, measured as low-elevation coastal zones. Column 3 provides estimates to assess whether hurricane impact is dissimilar in coastal lowland zones. For this purpose, we included dummy variables to identify these areas and create interaction terms with the damage index. The estimated coefficient for non-storm-prone areas is positive and statistically significant, while the coefficient for storm-prone areas is negative and statistically significant as well. This suggests that the effect of hurricanes is considerably greater in low-elevation coastal zones. Specifically, ff using average nightlight levels for 2000 (as in column 1), we found that the negative effect of Category 3 hurricane winds is associated with a 16.1 percent decrease in local economic activity.<sup>22</sup>

> 19 For the regression analysis, we excluded data from Cuba, as they seem to be driven by a positive association between nightlight and the damage index, perhaps due to limited data density but large coverage (see Annex 5). 20 Recall that our specification is a log-linear regression model. Therefore, to find the coefficient that is multiplied by the damage index ( ), the following calculation is performed: _[exp (-0.0177)-1] *100._ 21 Records in nightlight image data are the DN values
# **4 Data and descriptive statistics**

The main data source for this paper is India’s Annual Survey of Industries (ASI), a survey of the formal manufacturing sector conducted by India’s Ministry of Statistics and Program Implementation (MoSPI). The sample frame of the ASI consists of all industrial units (“factories”) registered under Sections 2(m)(i) and 2(m)(ii) of the Factories Act, 1948. This includes all factories employing 10 or more workers if they use power, and those with 20 or more workers if they do not. The ASI has two parts: a census of all factories employing 100 workers or more and a survey that randomly samples about a quarter of all other registered factories.

The ASI collects annual data for the most recent accounting year. For example, ASI 2015 collected data on the accounting year ending March 2015. Since the Rajasthan amendments were passed in November 2014, we consider the accounting year ending in March 2015 as the baseline year of the policy change studied in this paper, and hereafter refer to it as 2015. Thus, 2016 is the first post-treatment year of our study.

## **4.1 State-level panel data for synthetic control analysis**

We use annual state level data on the formal manufacturing sector for the years 1980-2018 in the synthetic control analysis. The data set is a compilation of state level ASI summary data published by the MoSPI.<sup>27</sup> The ASI is stratified by state _X_ 3-digit industry group, and state and industry level aggregates can be estimated by weighting the factory level data by the inverse of the sampling probabilities. The compiled data set includes estimates of total output (revenue), employment and fixed capital by state which match the officially published numbers.

Since officially published state-level ASI summary statistics do not include data on contract workers, we use unit level ASI data from 2001-2018 to estimate state level contract employment.<sup>28