rafmacalaba/data-use-mentions-extended huggingface dataset:
procedure potentially solves both of these problems. This is the case if the instruments are correlated with the public sector health spending, but not correlated with under-S mortality except through spending, and the measurement error in the instruments is uncorrelated with that in public sector spending on health. As instruments, we use the average public sector health spending as a share of GDP and the average defense spending as a share of GDP of a country's geographic neighbors.

The two-stage least squares results presented in Table 4, column 3, are similar to the OLS results. The coefficient on public sector health spending is approximately 40 percent larger (in absolute terms), -.192 versus -.135, which is consistent with the OLS estimate being biased towards zero due to measurement error. However, the coefficient is now much more imprecisely measured and statistically insignificant from zero (t-statistic of only .742). This is a typical "good news-bad news" result from instrumental variables estimation as those who would want to argue for a large effect of public spending have a higher point estimate to support their case, whereas skeptics can point to the very low significance level.

A third test for the "robustness" of our results is to use an alternative outcome measure for health status. The results in Table 4, column 4 show that when infant mortality is used (and with our "preferred" two-stage least squares estimation) the results are again similar, although the coefficient on public sector health spending is smaller, which is consistent with the higher "genetic" component of neo-natal deaths (Rutstein, 1984), and statistically not significantly different from zero.

As an additional check we reproduced our results on a different data set constructed directly from the World Bank's Social Indicators of Development 1997 database (World Bank, 22
applying a single poverty line across the two sub-samples, one would conclude that poverty among those covered by the short module is much greater than among those covered by the long module. Because both samples are representative of the same underlying population, this is clearly incorrect'.

_Table 7. Per Capita Monthly Expenditure: Long and Short Questionnaires_

|_Percentiles_|_Long_|_Short_|
|---|---|---|
|_10th_|124.97|94.83|
|_20th_|193.60|153.35|
|_30th_|242.20|204.62|
|_40th_|296.78|255.24|
|_50th_|358.54|315.25|
|_60th_|445.25|382.19|
|_70th_|575.17|483.18|
|_80th_|730.99|627.97|
|_90th_|992.79|864.83|
|Top|2090.5|2225.4|

_Source:_ Republic of El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.

9 The two sub-samples were drawn from the same frame and were explicitly intended to be identical in all respects except for the consumption modules. Scott and Jolliffe (1995) show, in terms of location, household size, income levels, education, etc., that households in the two sub-samples resemble each other closely.

14
used to proxy for the current production and use of inputs. To extend future analysis to include these estimations, the ES has started collecting information on these lagged variables of inputs. This will be used in the future as the data become available. Other, firm-level fixed effects methods have been used to estimate average firm-level productivity over time. The ES has constructed longitudinal/panel data for a large number of countries: TFP estimates based on the panel data will also be provided in the future.

Second, there are issues associated with the fact that often only monetary (as opposed to physical) output and input expenditure are observed in typical firm-level data. Such revenue-based TFP is often referred to as _TFPR_ ( _TFPRi_ = _Pi × TFPQi_ ), where R stands for revenue and Q for quantity, and _Pi_ denotes the firm’s product price. Market dynamics are inseparable within TFPR estimates, which incorporate clearing prices of inputs and revenue-based outputs and can conflate productivity and market (e.g. negotiating) power. As in the case for most firm-level datasets, the ES collect information on revenues and firm-level line item costs (rather than physical inputs and outputs), and hence TFPR is the only measure that can be estimated using the ES data. For a recent discussion of these and other issues in estimation see for instance Foster et al. (2008), Hsieh & Klenow (2009).

A third caveat relates to the importance of the functional form of the production function. The Cobb-Douglas specification in equation (1) assumes a constant elasticity of output, regardless of other output choices. These elasticities are constant and in the form of _βk_ and _βl_ . In other words, a firm’s elasticity of capital output, for example, does not depend on its use of labor: labor-intensive firms expect the same elasticity of output of capital as non-labor-intensive ones. This assumption may be unrealistic in two ways. First, imposing one production function on firms in different industries is almost surely too restrictive. And indeed
19

# <u>Table 3: Health equity: Outcomes</u>

||Indicator|<br>Definition|Main Data Source|
|---|---|---|---|
||Mortality rate, infant (deaths per<br>1,000 live births)|Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years<br>before the survey for full population mortality estimates, and up to 10 years before the survey for<br>wealth quintile specific mortality estimates|DHS|
|**_G_**|Mortality rate, under-5 (deaths<br>per 1,000 live births)|Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years<br>before the survey for full population mortality estimates, and up to 10 years before the survey for<br>wealth quintile specific mortality estimates|DHS|
|**_MD_**|Prevalence of stunting, height for<br>age (% of children under 5)|Percentage of children under 5 with a Height-for-Age z-score <-2 standard deviations from the<br>reference median (z-score calculated using WHO 2006 Child Growth Standards)|DHS, MICS|
||Prevalence of underweight,<br>weight for age (% of children<br>under 5)|Percentage of children under 5 with a Weight-for-Age z-score <-2 standard deviations from the<br>reference median (z-score calculated using WHO 2006 Child Growth Standards)|DHS, MICS|
||Prevalence of HIV, total (% of<br>population ages 15-49)|Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2|DHS|
||Height in meters, adults (age 18<br>and older)|Mean height in meters of population aged 18 and older|ECHP,
# **1. Introduction**

Firm entry and exit – Schumpeterian "creative destruction" – is critical for the continued dynamism of the modern economy. Though evidence has linked entrepreneurship and economic growth in developed countries,<sup>1</sup> we have scarce evidence that such a relationship exists in developing countries. For instance, to what extent does formal sector entrepreneurship contribute to job creation in countries with regulatory, legal, and tax barriers to entry and exit? What factors are related to firm survival in a low-income country? In this paper, we examine job creation and destruction, at both new and incumbent firms, in Cote d'Ivoire, ranked 169 of 181 countries in the 2011 Doing Business "Ease of Doing Business" ranking (where 1 indicates the most favorable business environment) (World Bank, 2010a).

We use a new and unique database that includes the complete census of registered firms in Cote d'Ivoire from 1976 through 1997 to answer these questions.<sup>2</sup> This includes data collected at the time of registration (ownership and sector) as well as annual reporting of total employment. Unlike comparable studies that rely on survey data or manufacturing censuses, we broaden existing studies to compute over time comprehensive formal sector entry and exit rates, as well as growth, contraction, and survival at incumbent firms. Moreover, since we use a census of formal sector firms that extends beyond the manufacturing sector, we can analyze the impact of macroeconomic and other shocks on sector distributions.

First, we examine business dynamics in Cote d'Ivoire. We decompose trends in job creation and growth into entry of new firms and expansion of incumbent firms, and job destruction into exit and contraction. We also examine the distribution of firms by size, sector, and ownership. We find that job creation by new firms is quite high at 8% on average, but the number of jobs added by new firms is small in absolute terms. Next, we
4 treaty-network likely exerts important non-linear effects on a country’s tax revenue. Given the uncertainty about which of these factors drives measured effects, our results need to be interpreted with caution.

Our work adds to a growing, albeit inconclusive, empirical literature on the effect of host country tax regimes and tax treaties on FDI. Egger and others (2006) use propensity score matching and find a negative effect of treaties on outward FDI stocks in the OECD. Lejour (2014) and Barthel and others (2010) find that treaties do increase bilateral FDI, whereas Neumayer (2007) only finds positive effects linked to the conclusion of tax treaties with the United States in middle-income countries. Focusing on Sub-Saharan Africa, our findings suggest that treaties are not associated with increases in foreign direct investment in lowincome economies. Inconclusive empirical findings on the importance of tax treaties for FDI are likely partly a result of the subordinated role of taxation in investor decision making,<sup>8</sup> and of the complexity of FDI decision making in multinational enterprises, which are pursuing different strategies (Carr and others, 2001, Bergstrand and Egger, 2007), with different drivers among those seeking to take over foreign assets as opposed to making greenfield investments (Head and Ries, 2008).

Furthermore, our finding that treaty-conclusion with investment hubs primarily leads to rerouting of investment and income flows, is aligned with earlier work on treaty shopping in the Netherlands and Germany. Using micro-data on Dutch Special Purpose Entities, Weyzig’s (2013) analysis suggests that reduced withholding tax rates on dividend flows are a driver of FDI diversion via the Netherlands. Similarly, Weichenrieder and Mintz (2008) show that an increase in bilateral withholding taxes to and from Germany increases the probability that outward and inward FDI is diverted via a third country. Petkova and others (2018) control for treaty shopping opportunities in their assessment of the relevance of tax treaties for investment when adopting a network approach that builds on van’t Ried and Lejour (2018
per hour)|6.5|18.5|6.5||6.5|
|Container-cargo-handlingcharge(US$ per TEU)|320|220|120|340|140|
|General-cargo-handling charge (US$ per tonne)|8.5|6.5|10|16|5.5|

_Source:_ AICD ports database. _Note:_ TEU = twenty-foot equivalent unit.

23
long-run. Overall, the authors conclude that, while increasing fuel prices improves air quality, the effect is small and other measures—such as more rigorous regulation on vehicle pollution intensity—might prove more effective.

- Similar effects have been documented in Brisbane with respect to the impact of fuel price changes on air quality (Barnett and Knibbs 2014). Their findings indicate that, although higher fuel prices do not lead to a significant change in PM2.5 and PM10 concentrations, increasing diesel prices leads to substantial decreases in nitrogen oxide concentrations. They estimate that increasing the cost of diesel by AUS$0.15 per liter would have tangible health benefits, decreasing daily emergency hospital admissions by 2 percent or approximately 215 people per year.

- A study of 11 regions in China between 2006 and 2015 finds that vehicle volumes, energy consumption, and gross domestic product (GDP) per capita are all significantly correlated with increased PM2.5, PM10, and ozone concentrations (Xu et al. 2019), with increased vehicle volumes in particular consistently increasing pollution levels. However, another study of 21 cities in northern China shows that more than half of the major air pollutants (that is, PM2.5, PM10, and sulfur dioxide) originate from coal-fired heating in autumn and winter (Lin and Ling 2021). But changes in coal prices do not affect air pollution levels, as the government controls heating prices.

# 2.3. Entry points for mitigating air pollution

There are several reasons why the relationship between fuel prices and air pollution may not be as pronounced as that between prices and consumption. One explanation is the effect of available technology on the impact of price changes. For example, vehicle fleets with better fuel efficiency and exhaust filters would register a smaller change in PM2.5 concentration for the same reduction in distance traveled when compared to a “dirtier” fleet. Therefore, depending on fleet composition, PM2.5 concentration in some countries might respond more or less strongly to a price change
these sectoral headings are listed in the methodology note that accompanies the database.<sup>2</sup> Policy interventions by national and sub-national governments, as well as actions taken at the supra-national level by a customs union are included in the data collection exercise.

Trade policy measures are identified by the GTA team using the following methodology. First, automated techniques are used to gather information in multiple languages on relevant trade policy changes from many different sources including websites of relevant government agencies (which can include finance ministries, trade ministries, customs agencies, health ministries or associated agencies, and offices of the head of government or state); websites of relevant international organizations (IMF, ITC, UNCTAD, WCO, WTO); online media sources, including social media; and non-state organizations collecting information on the matters falling within the scope of this initiative, including law firms, consulting companies, research institutes and other academic initiatives.

These sources are searched for key words in major languages relating to the policies and products falling into the two sectors of interest. Such searches create “leads” which are processed to identify those that legitimately characterize a trade policy change falling within the scope of the initiative. Every lead recorded in the GTA’s systems is enriched in a multi-step analytical process. In the first step, an analyst provides an initial assessment of whether the lead is of topical relevance (within scope as defined in the last section), the implementing jurisdiction, the policy instrument category described, the product categories affected, and the direction of the trade policy change. This initial assessment is subsequently validated by a second analyst who confirms, amends, or rejects the first’s choices. Once past this initial assessment, relevant leads then move into the GTA reporting pipeline. Where missing in the first step, an official source is sought and, if found, saved for each lead. Once the official source has been located, the responsible GTA trade policy analyst processes the lead into a GTA database entry that combines
the mean age of study participants. This may be indicative of the cumulative nature of the infection. Also, it alerts us to the importance of controlling for age in the subsequent analysis of the economic impact of OSD.

# _Economic Impact_

In this subsection, we report on the economic impact of OSD. Data on economic variables from plantation records such as gross income and days worked were collected for the latest 12 months available. First, we report on differences of mean gross monthly income, mean days worked per month, and mean daily wage between those with severe-OSD and those classified as non-OSD. Data on income and wages is in the local currency: birr.<sup>13</sup> Subsequently, we conduct a regression

- 12 Clinical classifications of the severity of OSD were based on the criteria in Murdoch _et al._ (1993).

- 13 In 1996, US$1 = 6.33 birr.

_5_
data are reported only for the most used gateway per country, which for coastal countries is generally a port, we assume that unobserved land-border times are a fraction of reported sea-border times.<sup>7</sup> In addition to delays at the maritime border, ships may experience additional delays for loading and unloading, which we calibrate using the median number of days container ships stay in ports, derived from UNCTAD data.<sup>8</sup> Finally, to combine data on land borders and port delays, we rely on the following assumptions. (i) Trucks crossing a border must pass through customs in the country of departure and the country of arrival (total crossing times are equal to the sum of times at the border of departure and the border of arrival). (ii) Export and import procedures at sea borders add up to logistics delays at ports (total times at sea borders are the sum of the country’s sea clearance time plus delays at ports).

In order to reduce border delays, several countries have implemented One-Stop-Border Posts (OSBP) to enable goods, people, and vehicles to stop in a single facility in which they undergo necessary controls to exit one state and enter the adjoining state. We include such facilities using maps and status information from the PIDA dashboard which includes up-to-date implementation status and various sources (World Bank reports

> 4Air transport is likely to be the preferred option for perishable goods, especially flowers (HS06) or vegetables (HS07). These goods represent less than 0.04 percent of an African country’s total exports in 2018. If all chapters from HS01 to HS08 are considered as perishable goods, the share is still less than 0.09 per cent.

> 5The prevalence of roads as a mode of transportation is also reflected in the PIDA action plan, where most of the priority transportation projects concern the road network (or a combination of roads and borders). Railways, on the other hand, are typically associated
candidate covariate at a time, replacing ) in the equation. We evaluate the fit in-sample for reasons of efficiency, since evaluating all models out-of-sample is too computationally intensive. Given that all models are llGG(GGl identical and simple, overfitting is unlikely to be an issue. The left panel of Figure 3 plots the error from these regressions against the availability of each indicator from 1991-2020 for the 218 economies considered by the World Bank. We exclude variables with less than 50% availability given that they have too much missingness to be useful for our application.

We are primarily interested in variables for which no other variable has a lower error and lower missingness. When using the full sample (panel a), this concerns national accounts variables and under-five mortality. The national accounts variables on the frontier are GDP, Household Final Consumption Expenditure (HFCE), and Gross National Income (GNI) (the national accounts variables are expressed in logs and per capita terms throughout). We use versions of these variables expressed in 2017 PPP-adjusted USD even though at times non-PPP adjusted versions perform slightly better (see the unlabeled orange dots in Figure 3). These variables have the advantage of being highly correlated with welfare but the drawback of not always being available. For example, GNI per capita is available only for 58% of the country-year observations. Hence, national accounts data can only offer a partial solution to predict the distribution of welfare. To the contrary, under-five mortality is universally available due to modeling done by the UN Population Division.

9
/0.068) to close the gender gap in earnings.

Looking at individual intrapersonal skills (Panel C), we find that positive self-concept and perseverance are positively associated with earnings for both men and women, although two important gender differences arise in the magnitudes of these correlations. While high levels of positive self-concept are less associated with earnings for women than for men, perseverance has a significantly higher correlation with earnings for women. Strikingly again, for all other individual and aggregated skills, gender differences are statistically insignificant. These results potentially highlight the positive impacts of overcoming gender norms for women.

> 10 We estimate a 33% earnings gap when we include men and women with non-zero earnings, which is similar to estimates from other studies.

> 11 This result holds whether controlling for education levels or not and after controlling for occupation sector, using data from the Ghana and Kenya STEP surveys, which include information on occupation for a random sample drawn from a general urban population.

21
distance to the next settlement of more than 2,000 inhabitants is positively associated with job creation, changes in their proximity to large farms have no significant impact on their propensity to take up permanent or temporary work. This supports the notion that large mechanized farms may be good at increasing the intensity of land use but not at generating local employment. Fourth, for all crops except sorghum, which is often grown because of drought resistance, large farm presence significantly enhanced small farmers’ resilience and ability to withstand climatic shocks. As they were almost exclusively confined to large farms growing the same crop, learning about specific measures to conserve moisture seems a plausible channel underpinning such effects.

Lack of information on non-operational farms implies that our results are valid only for farms operational in 2014. To the extent that this or our inability to check for outmigration does not bias results, this implies that, in Ethiopia, large farm establishment generated modest but positive spillovers. It also suggests that policies affecting investors’ location and crop choices, in addition to those governing levels, use, and destination of lease fees, will affect social and economic impacts from the establishment of large farms. Our finding evidence of positive spillovers even in a setting where neither responses to investor demand nor links to technology transfer or integration with value chains had been well coordinated, suggests that a more integrated approach may well result in higher spillover effects. Experimentation and rigorous monitoring, relying on a wider range of data sources that would include administrative records and remotely sensed imagery, could help to identify ways of enhancing spillovers and making some of the associated data public to facilitate third-party monitoring. Such monitoring could increase investment flows by a growing set of financial players who want to demonstrate compliance with environmental, governance, and social standards.

While we obtain interesting insights, this is only a first step with ample scope for expansion in a number of possible directions. First, it would be worthwhile to explore alternative outcome variables, especially prices. Second, there seems ample potential to deepen