rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Figure 2: Matched mammal species densities, Marsh et al. (2022) vs. GBIF occurrence reports<br><!-- End of picture text --> <!-- Start of picture text --> ~ a a i re al<br>Rank a ¥ P<br>Ly Group6161 %, | hy .<br>i 2 wi<br>ii 3<br><!-- End of picture text -->

<!-- Start of picture text --> Rank<br>Ly Group6161<br>i 2<br>ii 3<br>i<br>my 66<br>i7<br>=<br><!-- End of picture text --> <!-- Start of picture text --> 66<br>i7<br>=<br>a Se sO nckdj<br>ak :en > ’ athe : ¥s ¢<br>i<br>a<br><<br>=<br>= y<br><!-- End of picture text -->
that this increase in the capital-skill complementarity coefficient (0.0320 or 0.0278, depending on specification) is large and statistically significant. As in Table 6 very little of the acceleration in skill upgrading is explained by capital-skill complementarity – the inclusion of a K/Y term which varies over time reduces the coefficient on the 1990s only slightly, from 0.0073 to 0.0068.

# **_Tests of Robustness_**

We carried out a number of robustness checks on these share equation estimates. Sensitive to the idea that states may have very different experiences, one test is to run the same regression allowing each state a secular trend in the non-manual wage-bill share, perhaps to allow for local changes in trade, infrastructure investment, regulation, business cycles or technological change. Allowing for state fixed effects in growth hardly changes the estimated coefficients, as shown in the rightmost column of Table 7. The 1990s increase in the capital-skill complementarity and output coefficients is reduced only slightly by allowing for state effects. Surprisingly, allowing states a separate trend in changing wage-bill shares of non-manual workers increases the R<sup>2</sup> by only four percentage points.

Table 8 reports tests of robustness to alternative sampling methods. Recall that we split the sample in an unusual way to avoid the 1989-90 industry recoding and that we had some doubts about observations in the early period from West Bengal. Table 8 reports estimated share equations using two different definitions of the periods and excluding West Bengal. We try three approaches: a) redefining the periods as 1984-90 and 1990-98, spanning the industry recoding of 1989-90; b) redefining the periods as 1984-91 and 1991-98 to reflect the pre- and post-reform periods; and c) omitting West Bengal data altogether and using the period definitions 1984-89 and 1990-98 as in the previous tables. Increased capital-skill complementarity is evident in all 24
composite indicator of data restrictiveness. We perform regressions using two types of firm‐level data, namely one at the aggregate cross‐country and industry level using the World Bank Enterprise Survey database, in addition to three country‐specific firm‐level data sets for a small number of East Asian countries, namely Malaysia, Vietnam and China. The results that derive from the two data sets are complementary as they provide us different insights. The World Bank data represents a cross‐ 4
An obvious policy implication of these findings is that government policies that we refer collectively as the business environment have a strong bearing on the spatial distribution of economic activities. We use the term business environment as just another name for what Dollar et al (2004) call investment climate. We define it as something broadly synonymous with institutions in the growth literature (Knack and Keefer 1995), or with what Hall and Jones (2000) call social infrastructure.<sup>3</sup> Our focus on only two items of a possibly long list of elements of business environment is dictated by the prominence that labor deregulation and problems of the power sector have in the current policy reform literature in India. At the same time they seem to be relatively easy to measure, and show significant variation across sub national regions.<sup>4</sup> Our broader result that differences in business environment are a major source of regional gaps in productivity is consistent with Dollar et al (2004) who report similar findings for international gaps in manufacturing productivity based on the World Bank’s Investment Climate Surveys and the same methodology.<sup>5</sup> Our findings on labor regulation are consistent with a recent paper by Besley and Burgess (2004), which also shows that regional differences in labor regulation are part of the reason why some of India’s states have lagged behind others in industrial development.<sup>6</sup> This finding is confirmed by Aghion et al (2004) with data disaggregated to the three-digit ISIC level 3 Accordingly we would include under it government activities relating to regulation of trade and industry and the provision of public goods that affect the cost or scale of production at the firm level by influencing factor productivity, prices or both. Apart from labor market regulations, pertinent regulatory activities include those aimed at financial markets/institutions, environmental externalities and zoning, industrial licensing, bankruptcy laws, the regulation of utilities including power, telecom or transport companies, law and
*

Due in part to strong international conditionality, BiH has made significant progress in achieving macroeconomic stability. On the monetary side, the BiH Central Bank and Currency Board guarantee the most stable currency in South Eastern Europe, which contributed to the near-zero inflation rate of 0.2% in 2002.

## **Table 1: BiH Economy**

||**2000**|**2001**|**2002**|
|---|---|---|---|
|**GNI, Atlas method**<br>**(current US$)**|4.3 billion|5.1 billion|5.2 billion|
|**GNI per capita, Atlas**<br>**method(current US$)**|1,150|1,270|1,270|
|**GDP(current $)**|4.3 billion|4.8 billion|5.2 billion|
|**GDP growth**<br>**(annual %)**|15.6|4.5|3.9|

Source: World Development Indicators database, August 2003 Economic stability is reinforced by a treasury system that is running well and sustained by budgets that are kept within required limits (under the guidance of very tight constraints set by the IMF). The Gross Domestic Product has risen steadily since 1997, and despite unreliable statistical data, it is estimated that the GDP growth rate for 2003 reached 5 percent. Even if this is a considerable slow-down compared to the 25% average of the post-war reconstruction years (1995 – 1997), this rate is now more sustainable and similar to those recorded in neighboring countries.

Nonetheless, eight years into peace building, and despite a heavy reconstruction bill and an ICled agenda of structural reforms, the economy in Bosnia and Herzegovina is still lagging. With a population of
Balma, and A.N. Mukasa, ‘Not a good time’: Economic impact of COVID-19 in Africa. (African Development Bank Group, Working Paper Series #338, 2020).

<mark>7. W.G. Cochran,</mark> _<mark>Sampling Techniques</mark>_ <mark>, 3</mark><sup>rd</sup> <mark>ed. (Wiley, New York, 1977).</mark> <mark>8. K</mark> . Himelein, Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Statistics and Public Policy_ **1** (1): 40 – 45 (2014).

9. Y, Tillé. _Sampling Algorithms_ (Spring, New York, 2006).

10. Greene, W.H. _Econometric Analysis_ , 8<sup>th</sup> ed. (Pearson, New York, 2017).

11. S. Hsiang, _et al_ ., The effects of large-scale anti-contagion policies on the COVID-19 pandemic. _Nature_ **584** , 262-267 (2020).

12. <mark>World Bank, Ethiopia - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3716.</mark> 13. <mark>World Bank, Malawi - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3766</mark> <u>.</u> 14. <mark>World Bank, Nigeria - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/
Community involvement. The communities in 10 villages to be assisted under the project were helped to conduct a "community survey" which consisted of weighing all children and charting their weights on a large community graph. A community meeting was then held to discuss the survey and to ask for solutions to problems it identified.

Guided interviewing. Central-level staff developed open-ended question guides around the problems identified. These were administered by investigators trained in qualitative research and participant observation techniques to a sample of 330 households intentionally biased toward those with malnourished children and limited resources--the hypothesis being that
also changed. To identify the causal effect of changes in the latter on changes in the former, we now turn to multivariate analysis. We work at the industry-year level and take advantage of the panel nature of our data. More precisely, we estimate the following model: where _γi_ , _t_ ( _d_ ) is the _K_ -density cdf for industry _i_ in year _t_ at distance _d_ ; _Ti_ , _t_ is our measure of ad valorem transport costs (4) of industry _i_ in year _t_ ; **X** _i_ , _t_ is a vector of time-varying industry controls measures of international trade and links between (including exposure input-output industries; see Appendices B.2 and B.3 for details on how we construct those measures); _αt_ and _μi_ are year and industry fixed effects, respectively; and _εi_ , _t_ is the error term.<sup>10</sup> The latter is assumed to be independently and identically distributed with the usual properties for consistency of ols. Our main coefficient of interest is _βT_ , which captures the impact of changes in transport costs on changes in the geographic concentration of industries.

# **.2 4 Identification issues**

In order for _βT_ to capture the causal effect of changes in transport costs on geographic concentration, we need to address a number of identification issues. In general, the two main that the of the determinants of concentration are problems plague identification geographic omitted variables and reverse causality (see Combes and Gobillon, 2015, for a recent survey). All studies based on cross-sectional industry data (e.g., Rosenthal and Strange, 2001; Ellison _et al._ , 2010) are potentially prone to these identification problems and use different strategies to overcome them. We first discuss the omitted variables problem and explain the large number of controls that we include in our panel estimations of (5) to deal with it. We then turn to the reverse causality problem and detail our instrumentation strategies. Table 8 in Appendix B.
New estimates of undocumented Mexican migration and the probability of apprehension. _Demography_ , _32_ (2), 203–213.

- OAMDI. (2016). Labor Market Panel Surveys (LMPS). Retrieved from `http:// www.erfdataportal.com/index.php/catalog/161`

- OECD. (2018). _International migration outlook 2018_ . Retrieved from `https:// www.oecd-ilibrary.org/content/publication/migr` ~~`o`~~ `utlook-2018-en` doi: https://doi.org/https://doi.org/10.1787/migr outlook-2018-en

- Orrenius, P. M., & Zavodny, M. (2003). Do amnesty programs reduce undocumented immigration? Evidence from IRCA. _Demography_ , _40_ (3), 437–450.

- Orrenius, P. M., & Zavodny, M. (2005). Self-selection among undocumented immigrants from Mexico. _Journal of Development Economics_ , _78_ (1), 215–240.

- Ortega, F., & Hsin, A. (2018). Occupational Barriers and the Labor Market Penalty from Lack of Legal Status. _IZA Discussion Paper No. 11680_ .

- Patler, C., & Pirtle, W. L. (2018). From undocumented to lawfully present: Do changes to legal status impact psychological wellbeing among latino immigrant young adults? _Social Science & Medicine_ , _199_ , 39–48.

- Phillips, J. A., & Massey, D. S. (1999). The new labor market: Immigrants and wages after IRCA. _Demography_ , _36_ (2), 233–246.

- Reinhold, S., & Thom, K. (2013). Migration experience and earnings in the Mexican labor market. _Journal of Human Resources_ , _48_ (3), 768–820.

- Reyes, B. I. (2001). Immigrant trip duration: The case of immigrants from
# **1 Introduction**

Both roads and electricity play central roles in economic development, yet evidence about whether their expansion is a potential driver of job creation is limited and mixed. Moreover, little is known about the complementary effects that such investments may confer in combination, or the constraints on potential benefits of one kind of infrastructure because of the absence or inadequacy of the other type. For example, greater access to electricity can stimulate local production, but a lack of reliable transport infrastructure can reduce the potential gains due to lack of market access, especially in more isolated areas. On the other hand, greater connections to road networks can increase market access, allowing for better access to customers, but also greater exposure to competition; without access to modern production technology that electricity enables, the competition effect from improved road access may outweigh the benefits of reaching more potential customers, potentially stymieing local economic growth. Empirical evidence on the complementarity between roads and electricity is limited (Moneke, 2020). Most existing studies either analyze the effects of infrastructure investments in isolation, or estimate the aggregate impact of infrastructure in a bundle of many types of infrastructure, calculating the elasticity of output with respect to a synthetic infrastructure index that includes electricity, transport, and telecommunications (Calderon et al., 2015). Thus, questions remain about whether a big push for investments in roads and electricity represents a powerful means of creating more and better jobs.

This paper investigates how infrastructure investments can be coordinated to support economic development by looking at the combined, causal effect of electricity and road expansion on employment in sub-Saharan Africa. The analysis relies on two sets of geo-referenced household survey data: the Living Standards Measurement Study (LSMS) surveys and Demographic and Health Surveys (DHS). We combine these with an extensive array of data on the geo-location of road expansions (Jedwab and Storeygard, 2021) and electric grids in 27 African countries.

The high costs and potentially large benefits of infrastructure investments mean that the placement of electrification projects
**Table 4. Summary of regressions adding one indicator at a time, ICA data**

|Dependent<br>variable|(1)<br>Operating<br>Income to<br>Sales|(2)<br>Any<br>Investment|(3)<br>Any<br>Investment|(4)<br>IK|(5)<br>IK|(6)<br>Ret.<br>Earnings<br>WK|(7)<br>Ret.<br>Earnings<br>WK|(8)<br>Ret.<br>Earnings<br>Invest|(9)<br>Ret.<br>Earnings<br>Invest|
|---|---|---|---|---|---|---|---|---|---|
|Usage of Credit<br>Products|-0.01|0.09***|0.10***|0.09***|0.10***|-101.41***|-97.03***|-193.82***|-195.94***|
||(0.684)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|N Dd|0.04***|-0.00|-0.00|0.00|-0.01|49.53***|48.54***|6.94|6.69|
|o eman|(0.001)|
the length of time required to resolve them -- hence when we refer to "defaults" we will be referring to the first year of a given default episode.

The Tomz-Wright (2010) data on expropriation primarily refer to expropriations of US-owned direct investments abroad. We work with a dummy variable equal to one indicating countryyears in which such an expropriation event occurred. Their dataset is based on primary research into reports by the US Department of Commerce and Bureau of Economic Analysis which provide regular reporting to Congress on expropriations affecting US investors. This is the largest systematic dataset on expropriation events currently available.

A drawback of this data is that it does not cover episodes of default affecting only non-US foreign direct investors. As a partial remedy for this problem, we augment their dataset using data on expropriation and breach of contract claims paid by insurers who are members of the Berne Union, an association of public sector and private sector political risk insurers. This source provides data on the total amount of the two types of claims paid, by country in which the insured event occurred, and by year in which the claim itself was paid. A shortcoming of this data is that it is not possible to identify when precisely the expropriation event occurred, since we only have information on when claims are paid. Absent better information, we assume that expropriations occurred in the year before the Berne Union reports claims were paid, and include any expropriations identified in this way in our dataset if they were not already included in the Tomz-Wright dataset.

Since most of our explanatory variables, which we describe below, are only available starting in 1970 or later, we restrict attention to the period 1970-2004. During this period, the dataset includes 118 episodes of sovereign default, and 282 episodes of expropriations. Of these, 65 episodes are identified using the Berne Union data on claims paid.

# **4.  A First Look at the Data**

**Figure 1** provides a first look at the distribution of default and expropriation events across
between various firm characteristics such as size, age, export status, ownership and so on with the observed management score. Section 4 presents a simple regression framework for assessing the link of management practices with firm performance. Section 5 explores the drivers of better management practices. The last section concludes with policy recommendations.

# **<u>Section 2: Critical Firm Capabilities: Management in Focus</u>**

# _2.1 Why do we need a management survey?_

Although managerial and organizational practices have been conceived to be critical in affecting firm outcome since the time of Adam Smith’s pin factory and Walker’s 1887 paper on business performance, these practices are not well measured in administrative data on firms or the commercial data on companies’ accounts. Some studies have used information on publicly traded firms to link managerial style of senior managers and company performance (e.g., Bertrand and Schoar, 2003), this data is rather limited to a country such as the United States and there is evidence that suggests a company is far more than simply the identity of its most senior employee (Bender et al., 2015). Furthermore, this type of data on its own does not tell much about how firms are managed or organized. Therefore, over the last decade Bloom and his co-authors have attempted to fill this gap in data by collecting comprehensive information on management practices (see Bloom et al., 2014).

In general, three broad management and organizational capabilities that are generally recognized to be important for firm productivity (Bloom et al., 2016b): (i) performance monitoring: collecting and analyzing information, on daily activities of the firm for continuous improvement, (ii) target setting: using and stretching short and long run targets, tracking outcomes, and taking appropriate action, and (iii) performance incentives: rewarding high-performing employees, and retraining or moving underperformers and careful hiring. To collect information on these practices, two alternative strategies could be deployed
- De Mel, Suresh, David McKenzie, and Christopher Woodruff. 2008. “Returns to Capital in Microenterprises: Evidence from a Field Experiment.” _The Quarterly Journal of Economics_ 123(4): 1329–72.

- De Mel, Suresh, David McKenzie, and Christopher Woodruff. 2012. “One‐Time Transfers of Cash or Capital Have Long‐Lasting Effects on Microenterprises in Sri Lanka.” _Science_ 335(6071): 962–66.

- Miller, JD. 1984. “A New Survey Technique for Studying Deviant Behavior.” _Phd thesis. George Washington University._

- Milner, Helen V., Daniel L. Nielson, and Michael G. Findley. 2016. “Citizen Preferences and Public Goods: Comparing Preferences for Foreign Aid and Government Programs in Uganda.” _Review of International Organizations_ 11(2): 219–45. http://dx.doi.org/10.1007/s11558‐016‐9243‐2.

- Ozer, Emily J et al. 2011. “Does Alleviating Poverty Affect Mothers ’ Depressive Symptoms ? A Quasi‐ Experimental Investigation of Mexico ’ s Oportunidades Programme.” _International Journal of Epidemiology_ (July): 1565–76.

- Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, _Journal of Peace Research_ , vol.50, no.4, 523‐532

22
year in all sub-national areas of all five countries (Northern Tanzania is an exception). This simplifies the analysis. Fifth, the countries border each other and are sufficiently similar so that some pooled analysis — i.e. analysis that combines data from all five countries — is reasonable. Sixth, unlike, for example Kenya and Ethiopia, drought-contingent targeting and adaptive safety nets had not been systematically implemented during the period in which the surveys were administered.

The rest of the as follows. In addition to the main vari- paper proceeds describing ables, the next section describes the methodology we use to estimate harvest cycles and droughts. Section 3 provides estimates of the average impacts of droughts across the five countries we study, and then shows that these impacts are more pronounced for the poorest households. Section 4 describes our targeting framework and provides estimates for inclusion and exclusion errors for logistic regressions, decision trees, and decision tree ensembles (random forests and gradient boosted trees). We conclude by summarizing our results and discussing future directions.

# **2 Measuring Shocks and Outcomes in Agrarian Contexts**

## **2.1 Child Anthropometrics as a Proxy for Child Well-Being**

The first two Sustainable Development Goals (SDGs) focus on ending poverty and eliminating hunger across the world.<sup>7</sup> Cognizant of the limitations of defining poverty exclusively in monetary terms (Sen (1999), Deaton (2016)), the first goal also targets poverty in a broader sense — including non-monetary and dynamic measures. While goal 1.5 specifically emphasizes the need to " _build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events_ ", precisely how this target may be measured in practice remains unclear.

Closely related, the second goal concerns ending hunger and malnutrition. Objective measures of hunger rely on caloric intake which may not be strongly correlated with nutrition. Therefore, Deaton (2010, pg. 41) recommends measuring anthropometrics, but
rates on formal bank credit. For example, Cressy and Toivanen (2001) find that collateral provisions and loan size reduce the interest rate paid and that better borrowers get larger loans and lower interest rates. Hernandez-Canovas and Martinez-Solano (2007), using firm-level data from Spain, argue that close relationships with financial institutions may generate advantages such as improved conditions of financing and increased credit availability. Furthermore, Dietrich (2010) argues that the lack of negotiating power of small enterprises has significant explanatory power in explaining differences in lending rates between small and large enterprises.

> 6 There is still an ongoing debate regarding bank size and/or ownership and SME lending. Until recently, a large strand of literature argued that small banks are more likely to finance SMEs as they are better suited to engage in “relationship lending,” ( _see_ Keeton, 1995; Berger and Udell, 1995; and Strahan and Weston, 1996). The strength of bank-borrower relationship is argued to be positively related to various credit terms (Blackwell and Winters, 1997; Harhoff and Korting, 1998; Degryse and Van Cayseele, 2000; Bodenhorn, 2003; Peltoniemi, 2007). However, some recent studies, including Berger and Udell (2006), Berger _et al._ (2007), de la Torre _et al._ (2010) and Beck _et al._ (2008b), have disputed this conviction by arguing that large banks, relative to other institutions, can have a comparative advantage in financing SMEs through arms-length lending technologies, such as asset-based lending, factoring, leasing, fixed-asset lending and credit scoring, as opposed to relationship lending.

> 7 Second OECD Conference of Ministers Responsible for Small and Medium-Sized Enterprises (SMEs), Istanbul, 2004.

4