rafmacalaba/data-use-mentions-extended huggingface dataset:
br>responsible for<br>wastewater<br>management|Department<br>responsible<br>for<br>wastewater<br>management<br>Finance<br>director|Template 7: Governance of<br>public operators|Template 11: Public<br>operators’ financial data|Copies of<br>financial<br>statements/annu<br>al reports for the<br>past five years<br>Water and<br>wastewater tariff<br>schedules|
|**Water supply**|Ministry(ies)<br>responsible for<br>water supply<br>Utility(ies)<br>responsible for<br>water supply|Department<br>responsible<br>for water<br>supply<br>Finance<br>director|Template 7: Governance of<br>public operators|Template 11: Public<br>operators’ financial data|Copies of<br>financial<br>statements/annu<br>al reports for the<br>past five years<br>Water and<br>wastewater tariff<br>schedules|

n.a. = Not applicable Section 1 deals with the pre-field stage. This is the planning phase, and consists of a desk review and interviews with people knowledgeable about the infrastructure sector in the country of interest. The prefield work should draw a comprehensive picture of institutions and their role in providing infrastructure, take stock of available sources (literature and secondary data sources in general), and guide the fieldwork. Effective planning not only enables synergy between sources and interviewees in the field, but also ensuresappropriate country-specific institutional coverage for the data collection.

10
employment have been found in very different contexts, such as in the US (Martin et al. 1995). This suggests that adjusting the phrasing of the question is not enough. Rather, interviewer training is needed to address gender differences in the threshold for reporting an activity.

A critical implication is the necessity of including follow-up questions that ask about specific activities – both those that count towards employment and those that count towards other types of work. Studies have found that the use of lists and screening questions can be very useful for prompting the respondent to identify these activities that may otherwise be considered outside the scope of the survey (for example, Dixon (1982); Anker, Khan and Gupta (1987); Anker and Anker (1989); Anker (1990); Anker and Anker (1995); Martin and Polivka (1995); Schwartz (2002); Langsten and Salen (2008); Bardasi et al. 2011; Koolwal 2019); and survey experiments under the WW-E partnership).

This study was conducted at the same time as the National Institute of Statistics (INE) was updating its household survey to reflect the definitions of work and employment based on the ICLS 19. The results from this study are aligned with the changes that were made to the official survey methodology (Table 6). Specifically, beginning with the 2018 EPHPM, 1) the initial employment question no longer mentions payment – this is asked as a follow-up question; 2) the questions on paid and unpaid work no longer explicitly excluded “household tasks”, and 3) a follow-up question which lists frequently underreported activities was added.<sup>18</sup> Table 6. Labor force questions in the 2015 and 2018 EPHPM

||**2015**|**2018**|
|---|---|---|
||During the past week, did you<br>dedicate an hour or more to any<br>work or activity
# **References:**

Archer, D. W., M. A. Liebig, D. L. Tanaka and K. P. Pokharel. 2020. "Crop Diversity Effects on Productivity and Economics: A Northern Great Plains Case Study." _Renewable Agriculture and Food Systems_ , **_35_** (1), 69-76.

Arnade, C. and L. Hoffman. 2019. "Determination of the International Maize Price: What Is the Growing Role of Brazil and Ukraine?" _Agricultural Economics_ , **_50_** (6), 735-747.

Behnke, G. D., et al. 2018. "Long-Term Crop Rotation and Tillage Effects on Soil Greenhouse Gas Emissions and Crop Production in Illinois, USA." _Agriculture, Ecosystems & Environment_ , **_261_** , 62-70.

Boryan, C., Z. Yang, R. Mueller and M. Craig. 2011. "Monitoring Us Agriculture: The Us Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program." _Geocarto International_ , **_26_** (5), 341-358. Bowles, T. M., et al. 2020. "Long-Term Evidence Shows That Crop-Rotation Diversification Increases Agricultural Resilience to Adverse Growing Conditions in North America." _One Earth_ , **_2_** (3), 284-293. Boyabatli, O., J. Nasiry and Y. Zhou. 2019. "Crop Planning in Sustainable Agriculture: Dynamic Farmland Allocation in the Presence of Crop Rotation Benefits." _Management Science_ , **_65_** (5), 2060-2076.

Brunt, L. 1999. "An Arbitrage Model in Crop Rotation in 18th Century England." University of Oxford, Department of Economics, Economics Series Working Papers: 1999-W32. Castellazzi, M. S., et al. 2008. "A Systematic Representation of Crop Rotations." _Agricultural Systems_ , **_97_** (1), 26-33.

Davis, A. S., et
35 Table 2: Descriptive statistics – household data

||Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Inpatient admissions|96434|0.07|0.40|0.00|24.00|
|Inpatient admission (yes/no)|96434|0.05|0.23|0.00|1.00|
|Expenditure per inpatient admission|96434|82.68|828.70|0.00|79070.13|
|Expenditures on inpatient care|96434|112.27|1172.79|0.00|110000.00|
|Outpatient visits|96434|0.33|1.57|0.00|83.00|
|Outpatient visit (yes/no)|96434|0.12|0.32|0.00|1.00|
|Outpatient expenditure per visit|96434|29.87|496.27|0.00|118605.20|
|Expenditure on outpatient care|96434|67.04|863.98|0.00|118605.20|
|Total out-of-pocket expenditure|96434|179.31|1505.40|0.00|118605.20|
|Autonomy|93937|0.68|0.46|0.00|1.00|
|Health insurance|96434|0.49|0.50|0.00|1.00|
|Per capita consumption|96434|5525.04|4574.99|315.38|132412.40|
|Urban|96434|0.27|0.45|0.00|1.00|
|No.general hospitals in district|96434|1.50|1.28|0.00|12.00|
42

# Conclusions

It is worth highlighting the limitations of this study—in scope and data availability—before summarizing the key findings and possible policy implications:

- With respect to household access, there is much less information on the supply side compared to the demand side. Connection charges tend to increase rapidly with increasing distance from the power line, and yet information on the distance between each household and the nearest distribution line is not available. Grid connection may not be a practical option even for many living in electrified communities.

- Information on income, let alone disposable cash income, is not available and is substituted by household expenditures used to calculate official poverty statistics. Those using kerosene for lighting have some cash‐based expenditures to redirect to electricity purchase, but no survey separates spending on kerosene for lighting from that for cooking. Trying to pinpoint determinants of access beyond affordability based on national household expenditure surveys requires more data collection and analysis in individual countries.

- It would not be appropriate to extrapolate the conclusions and recommendations in this paper to off‐ grid electricity where different factors will be at play. There are many areas in Africa—areas with low population density and low household expenditures, and far from cities and towns—where it would be too costly to supply grid electricity. Where the unit cost of grid electricity supply markedly exceeds what would be considered reasonable to pay for grid electricity, off‐grid solutions are likely to offer more practical solutions.

This paper confirms many widely accepted observations, such as that the percentage of population using electricity remains low in Africa, that electricity use is far more prevalent in urban than in rural areas, and that adoption of electricity and quantities consumed rise sharply with income. There are also relatively new findings. The rate of household access to grid electricity is only weakly correlated with affordability of tariffs applicable to the subsistence level of electricity consumption. There is little evidence of a trade‐off between utilities’ deficits and household access, but there is evidence of trade‐offs between
documented in the U.S., the UK and a number of middle income countries. While the latter result is positive, in the sense that churning contributes to aggregate productivity gains for this category of firms, it must be borne in mind that most firms in Africa are very small. If small and productive firms could grow, this would have dramatic effects on their survival rates and therefore on aggregate productivity.

**Figure 2: Estimated Exit Rates as a Function of Size and Total Factor Productivity**

<!-- Start of picture text --> .35<br>.3<br>Probability<br>of Exit<br>.25<br>.2<br>.15<br>-.25 -.15 0 .15 .25<br>Total Factor Productivity<br>Pr(exit), 10 employees Pr(exit), 50 employees<br>Pr(exit), 200 employees<br><!-- End of picture text --> _Note_ : The horizontal axis is interpretable as the percentage deviation in total factor productivity from the sample mean. The graph thus shows how exit rates change as total factor productivity changes from 75 percent of the sample mean to 125 percent of the sample mean. Source: Söderbom, Teal and Harding, 2004.

Although we have learned a lot from the firm data about several aspects of firm growth, more research is needed in this area. There is still uncertainty as to the nature of the association between firm size and growth in African manufacturing. It may be that growth rates are in fact independent of firm size, in which case policy makers should not expect systematically higher growth rates in certain size segments of the sector. In order to address the methodological challenges that we have discussed it will be necessary to use data in which firms are observed over a relatively 24
conflict in Mali. By making use of a unique data set interviewing the head of household (or another member) for as many as 12 consecutive waves, we managed to gather information on welfare and subjective well-being, as well as on willingness to return. Using mobile phones for interviews allowed us to obtain information about individuals who tend to be neglected in traditional surveys since they are difficult to reach given their high mobility, precarious living conditions and their proximity to conflict zones. In this instance, we managed to follow individuals _while_ they were experiencing a crisis and while on the move, thus avoiding issues common in the literature on violent conflicts (Brück, Justino, Verwimp, Avdeenko, & Tedesco, 2016). Still, the attrition rate was exceptionally low even if respondents changed locations and were difficult to contact in person. We note that the high-frequency, mobile phone based, panel structure used for this research is well-suited for situations where respondents live in highly-volatile environments.

Our goal is to provide as much information as possible on the conditions of people following displacement, whether they are in a refugee camp or in another locality in Mali or outside the country. The place where respondents lived after the conflict might be related to the willingness to return, or 2
<br>5.00<br>4.00<br><!-- End of picture text --> ILO (2018) projects the overall LFPR at 43.0% in 2018 (female at 12.5% and male at 73.0%) down from 44.9% in 2010 (female at 14% and male at 75.6%), giving an average negative growth of ‐0.5% over 2011‐2018 (female LFPR growth at ‐1.4% and male at ‐0.4%). Data in World Bank (2017b) suggest the LFPR in 2015 stood at 47.2%, somewhat higher than the 2010 level of 44.6%.<sup>31</sup> World Bank (2019) meanwhile shows latest/2017 LFPRs for men and women above 15 years of age at 79.1% and 11.9% respectively versus 73.0% and 13.0% in 2010. We use the ILO estimates in our central projection, and for a possible upper estimate, consider an increment in the LFPR by 2018 based on the changes in participation rates of men and women reported in World Bank (2019).

# _3.1.3 Human capital_

Human capital would have been affected by (i) the **interruption of schooling** of the younger population, who represent future entrants into the labor force; and (ii) **migration** and **fatalities** which alter the distribution of years of schooling among the remaining population/existing labor force.

Regarding the **interruption of schooling** , according to SCPR (2016), almost half (45.2%) of all basic education school‐age children residing within the country were not attending school by 2014‐2015. They estimate a decline of 1.5 in the average years of schooling for population aged 15 and above _,_ based on these
days of a child’s life are the most critical for early childhood development and for determining whether a child will grow up stunted. It has also been shown that height at age three strongly predicts adult height (Maccini & Yang, 2009). Lower height‐for‐age scores can lead to severe consequences for cognitive development, overall health, and even socio‐economic conditions that carry into adulthood.

We assign each woman a fractional measure of the share of years exposed to high levels of nitrate‐nitrogen between the year of birth and age 3 in the district where she was born. Since districts have split over time, we use parent districts to allow comparability across time. We then compare later‐life health outcomes among cohorts with more and less pollution exposure after accounting for a rich set of controls.

Even though direct measures of drinking water quality are unavailable, in‐situ monitoring data serve as a reasonable proxy for proximate levels of nitrates in drinking water. This is because nitrates are notoriously expensive and difficult to clean out of water, and cannot be sufficiently treated using conventional methods.<sup>5</sup> Evidence from a slew of countries around the world, including Morocco, Niger, Nigeria, Senegal, India, Pakistan, Japan, Lebanon, Philippines, the Gaza Strip and Turkey, show that nitrates in drinking water often cross conventional safety thresholds (Ward et al., 2018).<sup>6</sup> 2.3 Additional Controls We control for both average rainfall in millimeters and average temperature in degrees Celsius as these have been shown to impact adult outcomes (Maccini and Yang 2009, Fishman et al. 2019, Hyland and Russ 2019) and are known to also interact with nitrate loadings in waterways (Zheng et al. 2016). These are obtained from the Indian Meteorological Department. In some specifications, we also control for fecal coliforms from the CWC data set. They are an oft‐used measure of domestic pollution, and are a
, covering 144 countries. Our second institutional variable is a proxy of creditor rights quality: the credit rights index from Djankov, McLiesh, and Shleifer (2007) for the year 2000. This index measures the strength of legal rights of creditors against defaulting debtors and ranges from 0 (weak creditor rights) to 4 (strong creditor rights). Also from Djankov, McLiesh, and Shleifer, we use dummy variables for the legal origin of a country (French, Scandinavian, German or socialist/transition origin, with British legal origin being the omitted category). We add these variables based on the findings of Djankov, McLiesh, and Shleifer (2007), who find that legal 11
18The NBIC data do not report DWT, though this information is available for most vessels via public web sites like https://www.vesselfinder.com. Our data summary uses DWT data collected by Colin Grabow, who used a collection of public web sites to match DWT data to the names of JA vessels in the NBIC. We cross-validated this information with the NBIC and the sources of DWT data he provided. We thank Mr. Grabow for bringing the NBIC data to our attention, for providing the results of his DWT calculations, and for highlighting the rare examples of JA vessels granted exemptions from the U.S. build requirement.

> 19Because the law allows exceptions to the U.S.-build requirement, we focus on flag-of-registry

12
boys in the three MENA countries for which data are available (Table 2). These findings mirror international trends which indicate a gender gap of around 0.25 of a standard deviation globally. However, in these three MENA countries, the gender gap is almost twice as large as in the rest of the world, with boys being more likely than girls to be the subject of negative comments, humiliation, insults, and harsh discipline. It is to be noted that the fact that boys and girls may be segregated in different classes does not seem to play an obvious role. In Tunisia where boys and girls are sharing classes, the gender gap is indeed higher than in the United Arab Emirates where gender segregation is still the norm.

**Table** 2 _._ **Gender and Teacher’s Hostility index**

||Boys|Girls|Gender gap|
|---|---|---|---|
|United Arab Emirates|0.12|0.55|0.43|
|Qatar|0.13|0.59|0.46|
|Tunisia|-0.03|0.47|0.50|

7 The index is reliable with a Cronbach’s alpha of 0.79 and all items being relevant with satisfactory KMO tests.

7
the contributions of IPAs are likely to be more pronounced in developing countries, where investors may know less about the location, struggle to obtain reliable information, find the regulatory environments more challenging, and encounter further obstacles stemming from institutional and cultural differences between the investors’ home and host markets (Harding and Javorcik 2011, 2012).

Empirical evidence generally shows that IPAs can play a significant role in attracting and growing FDI in developing countries (Charlton and Davis 2007; Cho 2003; Crescenzi, Di Cataldo, and Giua 2019; Morisset and Andrews-Johnson 2004; Bezuidenhout & Pietersen 2015). Relatedly, Harding and Javorcik (2012) found that IPA-promoted sectors experienced 155 percent higher FDI inflows and 68 percent higher employment relative to non-targeted sectors. IPAs can also bolster the quality of FDI by supporting technology transfer, knowledge spillovers, and export growth (Freund and Moran 2017; Moran, Gorg, and Krieger-Boden 2018).

# **_Gaps in the literature_**

While the general importance of countries’ legal and regulatory environments and investment promotion activities is well-explored in the literature, gaps related to specific policy areas and heterogeneity across types of affiliates remain. For example, some researchers argue that factors influencing FDI may vary by sector, especially between manufacturing and services (Erramilli and Rao 1993; Tatoglu and Glaister 1998). Some studies also suggest that younger and older companies behave differently (Jimenez 2011). Nevertheless, heterogeneity remains an understudied topic given the scarcity of reliable data on FDI flows by sector. In addition, existing studies generally examine overall indices of policy and institutional quality and often do not study specific policy levers in detail; more granular approaches would be helpful to aid government policy design.

This paper builds upon the existing literature via a survey-based approach that distinguishes between specific policy areas. High-level survey findings are used to validate or question prevailing findings in the literature regarding the effect of countries’ legal and regulatory environments, and investment promotion on FDI attraction. In addition, by gathering data on respondent profiles, survey data allow
distribution of the localization and urbanization measures that we use in our growth regression analysis and the population and trade data.

## 4.1 Ghanaian firm census data

### **Firm level data**

Table 1 shows the employment and firm numbers and shares of each of the 10 regions in Ghana in the 2003 and 2013 censuses. Greater Accra and Ashanti, which contains the second city of Kumasi, dominated manufacturing employment in 2003, accounting for 58%. The 2000 population census indicates that these two regions had a population share of only 34%, so that employment is heavily concentrated in these two regions, relative to their population shares. By 2014 Greater Accra had increased its share of total employment to 43%, whilst Kumasi’s share shrank by one third. The shares of total employment in the five northern/central/eastern regions increased whilst southern regions’ shares generally declined, other than Greater Accra. These patterns are broadly repeated when looking at the firm numbers and shares of firms. Kumasi’s share of firms again declined whilst the smaller regions increased. Greater Accra’s share stayed constant, suggesting that Greater Accra had a much larger average firm size.

The increasing concentration of employment in the firm censuses in greater Accra shown in Table 1 is important, since agglomeration economies may be a part of the explanation for this pattern. This is not the pattern shown in Lall et al (2009), who used the 1987 and 2003 censuses to show deconcentration away from Accra and Kumasi. Above we noted that the deconcentration result is likely to be due to comparability issues in these two firm censuses, which we have discussed in Kerr and McDougall (2019). We think the 2003 and 2014 firm censuses are more comparable, and in this

> 6 Available at https://wits.worldbank.org/product_concordance.html.

9
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those from the EU-8, the situation subsequently changed. The ratio of EU-8 share in world exports to that of Turkey increased from 3.2 in 1990 and 1993 to 4.3 in 2000 and then fell to 4 in 2004 indicating Turkey’s stronger export growth performance (based on IMF DOT statistics).

Indeed, Turkey’s share in world exports rose 59 percent from 0.4 percent in 2000 to 0.7 percent in 2004 while that of the EU-8 as a group rose 50 percent. Only Romania the top export performer among Central European countries during that period—matched Turkey’s record.

On the other hand, Turkey had the weakest performance among these countries in terms of imports. Its share in world imports, like that in exports, did not budge in the 1990s. It increased