rafmacalaba/data-use-mentions-extended huggingface dataset:
World Bank|

# **3. Findings**

We begin by controlling for cross-country differences in income levels using GDP per capita, a proxy for political change, and year and country fixed-effects. We find that richer countries have less incidence of poverty, and that countries that spend more on government services as a share of their GDP also display lower poverty headcount. Government expenditure on health and education is also negatively correlated with the poverty headcount. These results are consistent with the previous literature. When we include health and education expenditures separately, education expenditure is negatively correlated with the poverty headcount and this correlation is always statistically significant. Health expenditure appears with a negative sign but is only sometimes statistically significant (not shown).

The effect of inadequate health care, proxied by adult female mortality, on the poverty headcount is positive and statistically significant (Table 2). Population growth affects negatively the poverty headcount and this association is statistically significant, as in previous studies. The effect of political change is insignificant, while inflation is marginally significant (at the 10% level), with a negative sign.

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neighbors. The result of such neighborhood<br>deterioration only became visible in what the authors call “stifling hot, and ultimately lethal,<br>residences” (675). As Klinenberg (1999) shows, slow medical and political response coupled with<br>the media’s natural disaster narrative contributed their share to rendering invisible the<br><!-- End of picture text --> <!-- Start of picture text --> dimensions of social tragedy, with which the general public still wrestles, even today.<br>The European heat wave of August 2003 was among the worst since in‐depth records started in<br>1976. Table 4 lists reported heat waves in Europe with associated mortality between 1976 and<br>2003. The exceedingly high numbers of 14,802 within two weeks in France alone (other sources<br>speak of up to 14,947) represented an excess mortality of 60%, with +96.5% in the second week<br>(Robine et al., 2008). Similar to the Chicago heat wave, social inequality was the key driver.<br>Unlike more graphic extreme climate events such as floods, heat waves are not only silent killers<br><!-- End of picture text --> The European heat wave of August 2003 was among the worst since in‐depth records started in 1976. Table 4 lists reported heat waves in Europe with associated mortality between 1976 and 2003. The exceedingly high numbers of 14,802 within two weeks in France alone (other sources speak of up to 14,947) represented an excess mortality of 60%, with +96.5% in the second week (Robine et al., 2008). Similar to the Chicago heat wave, social inequality was the key driver. Unlike more graphic extreme climate events such as floods, heat waves are not only silent killers but also a socially attenuated risk
# **4. Governance Mafters**

How much does govemance matter for development outcomes? In Figures 2-4, we document the strong positive association between each of the six aggregate governance indicators and three development outcomes: per capita incomes, infant mortality, and adult literacy. However, these simple correlations cannot be interpreted as evidence of a causal relationship from better governance to better development outcomes, since they do not control for other determinants of development, and may also reflect a reverse causation from development to governance. In this section, we disentangle the direct effects of governance on development outcomes by instrumenting for govemance in a series of cross-sectional regressions of these outcomes on each of our six governance aggregates. We find that improvements in govemance have a very large payoff in terms of development outcomes.

Our approach is motivated by Hall and Jones (1999), who argue that the key determinant of cross-country differences in output per worker is differences in what they call "social infrastructure', and which can be broadly interpreted as some combination of the aspects of governance we discuss above. They measure social infrastructure as a simple average of a number of governance indicators from PRS and a variable measuring trade openness constructed by Sachs and Wamer (1995). We extend the these results by considering a wider range of govemance indicators and development outcomes in a larger sample of countries.

# **Empirical Specification**

To simplify the discussion, we present the empirical specification using the logarithm of per capita GDP as the dependent variable. When we tum to the results, we will consider the identical specifications for the two other development indicators: infant mortality and adult literacy. We follow Hall and Jones (1999) in writing an extremely parsimonious specification in which the logarithm of per capita income, yj, is a linear function of govemance, **gj,** and an error term ej: (1) y 1 =a+03*g +e 1 12
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GR - gearing ratio; total debt divided by total assets.

The decomposition in equation (1) is useful because it helps one identify the real and financial factors that affect profitability. Real factors are captured by EBIT and AU. Movements in EBIT are primarily determined by movements in sales and non-interest costs. EBIT can be further divided into sub-components, the most important of which is gross margin (GM, net sales income minus cost of goods sold divided by net sales income).27 Changes in GM reflect changes in the price of output relative to input prices and therefore is expected to be closely influenced by such economic variables as demand, wages, exchange rate, and policies that affect these variables. The asset utilization ratio, AU, reflects the rate at which assets of the firm generate sales income. It is generally interpreted as a proxy for capacity utilization; it is also analogous to average output-capital ratio. Everything else constant, increases in EBIT, _GM_ and AU effect profitability positively.

Financial factors are captured by FC and GR. FC is influenced by interest rate and monetary/credit policies. Everything else constant, an increase in financial costs reduces profitability. The importance of financial costs for a firm's profits is directly proportional to the firm's level of indebtedness. Gearing ratio (or leverage) is an indicator of the firm's indebtedness. In general GR will be interpreted as firms' response to changes in the other variables.

The analysis of variance model: Once these financial ratios were calculated, each of them were statistically compared across time and groups of 27 Other components of EBIT, namely overhead and other net income (see Appendix 1 for definitions) are small in the CMB dataset. Furthermore, relatively large recording errors have been detected in these variables. See Ersel and Sak (1986), Appendix A.2. Therefore, these components will not be treated individually.
‘s log per capita expenditure.<sup>11</sup> In this and other figures, the vertical line represents the income range beyond which the data is very sparse and therefore, less reliable. There is an obvious difference between the recall periods; the outcome variables measured with weekly recall are always significantly larger than those measured with monthly recall. The slope of the weekly recall curve is very steep, and for the highest income values, approaches the monthly recall curve. While more than two episodes of illness are forgotten per month by the poorest, the wealthiest forget less than half an episode per month on average. When measured using monthly data, the difference between the rich and the poor is slightly less than one acute episode per family per month. The apparent recall effect

> 11 Chronic illnesses are excluded here because the survey methodology guarantees that the chronically ill are recorded as sick (with a chronic illness) once diagnosed regardless of the recall period used. To create monthly measures of reported doctor‘s visits and total expenditures from weekly data, we can just sum the visits and expenditures.  Creating a monthly measure of health events is somewhat more difficult.  For example, an event with duration greater than a week would be reported in multiple surveys in the weekly recall group but only in one survey in the monthly recall group, leading us to overestimate the total number of health events per month using the weekly data.  As a result, we also variously use the total duration of health events in a month or condition on having at least one health event in a given month to ensure that the results on recall and morbidity are not driven by potential ―double-counting‖.

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electricity network is one of the lowest in the word with only 6% of the population having access (the latest data for Lesotho and other groups are for 2002). This is extremely low, even in comparison to low-income (Lesotho’s income group) countries (31%) and less than of the average access rate in Sub-Saharan Africa as a whole (15%). It is also lower than averages in other world regions. Most recent sector data for Lesotho suggest that access has doubled, reaching 11% in 2005 in part due to the World Bank financed Lesotho Utilities Sector Reform; nevertheless, the overall comparative picture has not changed, and  much more progress needs to be made to reach the 35% access targeted for 2015 (de Barros, 2005). Other studies also recognize that electricity is an important constraint on economic activity. This suggests that even for the segment of the economy that does enjoy access to electricity, the quality of service is low. For example, over a third of companies surveyed in a recent investment climate survey report electricity deficiencies as major constraint (World Bank 20005 b). Within SACU, Lesotho’s access to electricity is by far the lowest (Bogetić and Fedderke 2005 b). Unfortunately, no comparative data exist for access to electricity in rural and urban areas, which would probably show even more dramatic shortfalls in access in rural areas of Lesotho.

in the database. Also, efforts to weight the raw data by population could help enhance the quality of the cross-country comparisons.

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’ - <u>Question 5: Can farmers post planting harvest expectations proxy for attainable harvest?</u>

As previously discussed, the LSMS-ISA data contains detailed information on plot, input, and management characteristics as well as agricultural production down to the plot-crop level. This is an important feature as accounting for these differences acknowledges the fact that yields can vary greatly between different plots. For example, different field characteristics and management practices can create variation not only in the realized yield but also the attainable yield. Furthermore, taking Ethiopia as an example, between 35 percent and 44 percent of plots (depending on survey round and crop) are intercropped. This means that we may not have accurate information on the actual share of the plot that was planted with the crop and thus the attainable production (in absolute terms).<sup>37</sup> To potentially address these pitfalls, we can rely on farmers’ ability to somewhat accurately estimate the attainable harvest for each crop on each plot at the post-planting visit. At this point,

> 37 The LSMS-ISA data does contain a question that asks for the share of the plot that was planted with the crop for mixed stand plots. However, the estimate is rather coarse (less than a quarter, 50 percent, 75 percent, more than 75 percent).

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|||
|Fraction of female leaders<br>-0.046|-0.083|0.074|
|(0.051)|(0.054)|(0.100)|
|Observations<br>61,573|43,650|17,923|
|Outcome mean<br>0.117|0.096|0.168|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes_ : Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are indicator variables for whether the woman worked last week (Panel A), whether she worked last 12 months (Panel B), whether she worked in agriculture last week (Panel C), and whether she worked in non-agricultural sector last week (Panel D). The column (1) presents estimates for the full sample, while the columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors * are clustered at the district level. ***, **, and denote significance at the 1, 5, and 10 percent levels.

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Although this figure amounts to only a small proportion of total health spending in the US, the savings are nevertheless impressive for three reasons. First, the savings pertain to only fifteen treatments drawn out of a large universe. Given that patients from the US and UK are traveling to India for major procedures such as cardiac surgery (see e.g., Srinivasan, 2005), the list of tradable procedures is arguably much wider. Identifying the universe of tradable treatments would be a useful exercise but is beyond the scope of this paper. Second, our estimate of cost savings is understated to the extent that we have used Medicare payment rates to calculate US prices, which are lower than those paid by private insurers and the uninsured, as noted above. Third, patient volume data was not available for all procedure categories, and a fuller accounting would certainly magnify the estimate of savings.

# **6. Are there good reasons to deny coverage for treatment abroad?**

Given that there are large gains from trade in health care, it is a puzzle that insurers in countries with expensive health care deny coverage for non-emergency treatment obtained abroad rather than encourage patients to seek care overseas. We consider possible reasons that have emerged from discussions with insurers.

_Insurer concerns about quality of overseas providers and malpractice law_ Insurers may care about the quality of the providers because of a potential negative spillover effect in the consumption of health care. A consumer’s decision to receive care from low-cost overseas providers may prove expensive to the insurer if the treatment worsens health problems and the insurer is obliged to cover the costs of subsequent treatment. The insurer may also be concerned that malpractice may occur in a foreign jurisdiction where the legal regime makes it difficult to sue care providers.

It would make sense to deny the consumer the _option_ of receiving cheaper treatment abroad for these reasons if two conditions hold true. First, the insurer cares more about potential future cost implications of poor provider quality than consumers care about the impact of provider quality on
Our interest is in estimating NPP from cropland, as opposed to natural forests or vegetation. To do so, we make use of a new and unique land cover data set developed by the European Space Agency’s (ESA) Climate Change Initiative. This data set provides information on 37 land cover classes based on the United Nations Land Cover Classification System at a 300m resolution. The data rely on state‐of‐the‐art reprocessing of four different satellite missions (MERIS, SPOT‐VGT, AVHRR, and PROBA‐V).<sup>4</sup> We use a cross‐walking table from Poulter _et al_ . (2015) that <mark>characterizes plant functional types across the ESA classes to guide the grouping of classes into a single cropland category.</mark><sup>5</sup> <mark>Our final data measure changes in NPP for each 0.1‐degree gridcell (approximately 11x11 km at the equator) which contains a minimum level of cropland in the year 2000 (the first year of our data set). There are trade‐offs when choosing the minimum level of cropland for a gridcell to contain to be included the analysis. Too low of a threshold and the analysis will include gridcells which are dominated by natural vegetation or urban land, and thus of no interest to the study, potentially biasing the results. Too high of a threshold and the analysis becomes too restrictive, where only agriculture situated in large farming communities is included. For this reason, we test a range of thresholds, with the main analysis using a 30% cropland minimum threshold to be included in the analysis, and robustness checks using 75% and 90% thresholds.</mark>

# 2.3 Irrigation Data

Gridcells included in this analysis are restricted to those where it is believed that agriculture is irrigated. This ensures that the EC measured in the surface water is like the EC in the water which is feeding the crops. In India
; <u>https://doi.org/10.3390/rs11232833</u> Dasgupta, S., Huq, M., Khan, Z.H., Zahid Ahmed, M.M, Mukherjee, N., Khan, M.F., Pandey, K. (2014). Cyclones in a changing climate: the case of Bangladesh, Climate and Development, 6:2, 96-110, DOI: 10.1080/17565529.2013.868335 Dasgupta, S., Islam, M.S., Huq, M., Khan, Z.H., Hasib, M.R. (2017). Mangroves as Protection from Storm Surges in Bangladesh. Policy Research Working Paper 8251.

Dasgupta, S., Sobhan, I., & Wheeler, D. (2017b). Impact of Climate Change and Aquatic Salinization on Mangrove Species in the Bangladesh Sundarbans. Vol. 46, No. 6, 680-694.

Eckstein, D., Künzel, V., Schäfer, L., Winges, M. (2020). Global Climate Risk Index 2019. Germanwatch e.V.

Etminan, V., Lowe, R., Ghisalberti, M., 2019. Canopy resistance on oscillatory flows. Coast. Eng. 152 https://doi.org/10.1016/j.coastaleng.2019.04.014.

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EV buyers by country by year, (5) non-financial incentives including green plate, free parking, and HOV lane privilege for EVs by country by year, (6) sales of heavy-duty EVs by country by year, (7) unit labor cost index in construction sector by country by year, and (8) socio-economic variables by country by year. The 13 countries include Austria, Canada, China, France, Germany, Japan, Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, and United States, accounting for 95% of world EV cumulative sales. Note that most developing countries are – excluded from the sample due to extremely low EV penetration for example 0.14% for India and 0.1% for Brazil in 2020.<sup>6</sup> We merge these data into a panel data set of 3,980 observations at the level of vehicle model by country by year. Table 1 presents the summary statistics of the data.

**Empirical framework** We define a vehicle model as a brand-model combination (e.g., Telsa Model 3). Let _c_ index a country, _k_ index a model, and _t_ index a year. We specify the following baseline equation for the analysis:

where _qckt_ is the sales of EV model _k_ in country _c_ and year _t_ . _Pckt_ denotes tax-inclusive price of a vehicle. _Sckt_ denotes the total subsidies that consumers are eligible for. The first term measures the actual acquisition cost (price minus subsidies) and _β_ 1 measures consumer sensitivity to the acquisition cost. The implicit assumption is that consumers respond to vehicle purchase price and subsidies similarly on a dollar-to-dollar basis. The assumption could be violated if the subsidies are not as salient as prices or the pass-through of the subsidies is not complete (Busse et al., 2006; Chetty et al., 2009). National-level EV subsidies in our study are of considerable size and widely publicized. Recent research points to complete pass-through in consumer subsidies for alternative
World Bank 2024).

It is thus critical to identify the drivers of and barriers to productivity, formal employment, and formal wage growth in Zambia. Leveraging firm and worker administrative tax data from Zambia, this paper decomposes labor productivity and wage growth among formal firms and workers in Zambia into withinfirm, between-firm, inter-sectoral, and dynamic components. We find that aggregate labor productivity of formal firms declined over the 2014-2021 period, driven by secular within-firm declines in the nonmining industry and service sectors. By contrast, labor productivity grew in agriculture and remained flat in mining over the same period. Real wage trends for formal workers have mostly mirrored labor productivity dynamics, declining 40-50 percent across non-agriculture sectors but growing slightly in agriculture, largely driven by within-firm shifts rather than between-firm or between-sector dynamics. The declines in labor productivity and wages reflect business environment challenges related to access to finance and electricity, as well as burdensome formal compliance requirements and competition with the informal sector. Within-firm labor productivity challenges also reflect low skills and capacity— including low technology adoption—among both firms and workers.

The rest of this paper is structured as follows. Section 2 provides background about the related literature and the private sector in Zambia. Section 3 outlines the data sources we leverage in the paper. Section 4 first outlines the methodology used for productivity analyses and then discusses the results of these analyses. Section 5 outlines the methodology and results for jobs-related analyses. Section 6 discusses the productivity and wage findings in the context of salient business environment and firmlevel constraints in Zambia. Section 7 concludes with the implications the paper’s findings and areas for further research.

2
= standard deviation.

Many other studies estimate the effect of NHIS membership on mean OOP healthcare expenditures, usually based on cross-sectional data such as our sample. Garcia-Mandicó et al. (2021) is unique among these studies as the authors use data from the 2005/06 round of the GLSS, whose fielding straddled the rollout of NHIS at that time. Thus, they use a difference-indifferences strategy comparing survey respondents in the same districts, some of whom gained access to NHIS during the survey-fielding period and some of whom did not. The survey data they use, GLSS 2005/06, are very similar to the GLSS 2016/17 data we use, though it is likely that all OOP expenditures for medicines are reported in one variable in GLSS 2005/06, while they are separated between OOP expenditures in the context of a consultation and other OOP expenditures on medicines in GLSS 2016/17. They find a mean decrease in OOP healthcare expenditures of 18 percent.

Powell-Jackson et al. (2014) investigate a randomized allocation of free healthcare to children ages 6–59 months in one district of Ghana, Dangme West, and find that this comprehensive insurance reduced OOP healthcare expenditures by 27 percent on average. By comparison, we estimate a mean reduction of 16 percent for the overall Ghanaian population, 20 percent for children ages 6–59 months across Ghana, and 46 percent for children ages 6–59 15
born and closing. Empirical benchmarks of a failure to adapt include firm closings, new formal business formation, firm growth and a lower average product of labor in areas facing more weather shocks. Evidence from India using data on both formal and informal firms suggests that floods led to significant decline in employment in the formal sector, especially in the least productive establishments, and towards informal household-run enterprises (Hossain, 2020). This is consistent with the view that such reallocation results from survival strategy of workers who ended up unemployed and suffer from labor market contraction in the formal sector (Tybout, 2000; La Porta and Shleifer, 2014). Given the large productivity gap across formal and informal sectors, such climate-induced reallocation generates a significant reduction in aggregate productivity.

# **Industry dynamics and adaptive capacity**

6