rafmacalaba/data-use-mentions-extended huggingface dataset:
offer formal training (SACE 2014; World Bank Enterprise Survey 2013). Companies in the manufacturing sector and state enterprises are more likely to offer formal training than private companies and the service sector. A few Chinese projects aim specifically to provide skills training for Kenyans, but businesses report that high turnover harms efforts for skills training and promoting Kenyans to higher level management because the high staff turnover raises the cost of training employees (SACE 2014). The BPI suggests that companies engage local employees in formal contracts, regardless of skill level, to build more trust and reduce the incentive to switch jobs and vandalize property. The language barrier also presents a challenge in training, but the growing interest in Chinese langauge study among Kenyans should slowly improve communication

> 10Kenya gained 40,646 jobs and 406 projects from FDI in total between 2003 and 2015.

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1.82|0.93|
|**Less than container load**<br>**2 Model estimates are not significant at 5% level**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|

**Table 13– Observed and estimated Odds ratio using logistic regression model** Source: Cameroonian Customs data- statistical analysis using SAS Software

# **5. CONCLUSION AND POLICY RECOMMENDATIONS**

The variety and thoroughness of databases and statistical elements made available in Cameroon both in customs and with terminal operators for this study is such that only a fraction of what can be analyzed has been summarized above. The main findings of the study comprise both elements related to the specific Douala situation and to the methodology used.

Cargo dwell time in the port of Douala for containerized imports is very significant. An aggregate analysis shows that cargo dwell time exceeds 20 days for a significant proportion of traffic and average dwell time has been consistently about 20 days in the last 10 years. From a customs clearance standpoint, the two main contributors to long dwell times are time between ship arrival and lodging of declaration, and time between payment of customs dues and gate exit. The payment 27
The research collection gathers surveys for a subset of heterogeneous countries. Most of the countries are in the Sub-Saharan Africa region, but there are also 4 countries in the South Asia region, 3 countries in the Europe and Central Asia region, 2 countries in the East Asia Pacific region, 2 countries in the Latin America and the Caribbean region, and 1 country in the Middle East and North Africa region. The coverage of income groups is also of great interest. Thirteen countries are in the low-income group, 11 countries are in the lower middle-income group, and 6 countries are in the upper middle-income group. Table 2 summarizes the selected countries and corresponding surveys.

**Table 2. Selected countries and corresponding surveys for further analysis**

|**Collection**<br>**Country**|**Region**|**Income group**|**Survey name**|**Survey year**|**Community**<br>**questionnaire?**|
|---|---|---|---|---|---|
|Albania|ECA|Upper middle<br>income|Living Standards Measurement<br>Survey|2012|No|
|Burkina Faso|AFR|Low income|Continuous Multisectoral Survey|2014|No|
|Cambodia|EAP|Lower middle<br>income|Socioeconomic Survey|**2019/20**<br>**(LSMS+)**|No|
|Ethiopia|AFR|Low income|Socioeconomic Survey|2011/12,<br>2013/14,<br>2015/16,<br>**2018/19**<br>**(LSMS+)**|Yes|
|Iraq|MENA|Upper middle<br>income|Household Socioeconomic Survey|2012/13|No|
|LSMS<br> <br>Malawi|
and then suppose that a baseline survey is conducted in 2004 and a follow-up survey is conducted in 2010. For firms that enrolled in 2005, the follow-up survey will pick up outcomes four years after the treatment. By then, if the effects are transient, they may have vanished, and the follow-up survey will pick up heterogeneous effects (one year after treatment for firms enrolled in 2009, two years for those enrolled in 2008, and so on). Thus, although costly, it may be necessary to run repeated follow-up surveys year after year.<sup>38</sup> While projects typically have budgets for baseline data collection, these may not always be enough to gather the data needed for a proper IE evaluation after the project is completed. An alternative cost-efficient method is to use official pre-existing sources of data provided that they are collected often enough and provided that they can be closely reconciled with program data. For example, in the IE of an export promotion program, customs records at the transaction/firm level can be used to measure outcomes such as growth in export value (the intensive margin),

> 38 McKenzie (2011a) argues that two advantages of having multiple data points for treatment and control groups are (1) the possibility of studying the trajectory of program impacts and uncovering causal chains and (2) the collection of multiple measurements on possibly noisy and weakly auto-correlated outcomes. By averaging outcomes across multiple data points noise is eliminated and the power to detect genuine effects of a program increases.

29
dependent values of the 8 observations that have visibly the largest residuals in the Q-Q plot with predicted values from Model 5 and re-estimates the specification. This allows comparing directly how the parameter estimates change when these outlier observations are replaced with values that lie closer to the normal range of the data. It is important to note that if these observations are not outliers in an additive sense, but simply reflect the nature of the data-generating process, then these new estimates have in fact an increased bias resulting from further mis-specification. To evaluate whether outliers can be addressed through model-specification, Model 5d first normalizes the data using a power transformation (Johnson) by finding the transformation that minimizes the _p_ -value of a Shapiro test for Normality, and then re-estimates Model 5’s specification on the more Gaussian data. These parameter values cannot be directly compared to the parameter values of Model 5 because the new relationship is nonlinear (logarithmic-type). Nevertheless, Model 5d informs whether the significance of relationships remains intact when the Normality violations are neutralized.

18
as multi-party coalitions may lead to increased cabinet size to accommodate politicians from across the different parties that participate in the government. Next, we use indicators of whether the chief executive or defense minister are military officers. A strong presence of military officers may suggest threats to the stability of the government, which presidents might attempt to manage by enlarging the number of ministers to accommodate important allies and potential rivals. In addition, we use the coding by Cruz and Keefer (2015) to account for the presence of programmatic parties, which can limit clientelism and promote cohesive policies.

In addition, we measure the number of years the chief executive has been in office. A long tenure may suggest that elite clientelism is particularly entrenched and highly personalized. At the time of writing, the longest-serving president in the world is Teodoro Obiang Nguema Mbasogo of Equatorial Guinea, who took office in 1979. Equatorial Guinea is also among the worst countries in the world on several governance indicators – in 2014, for instance, it had the worst score of all countries in our sample on the Control of Corruption measure. Next, to account for the possibility that corruption responds to electoral pressures, we control for the number of years before the end of the current electoral term. Further, it is possible that close links between the

> 8 We also used a second index that captures wider public sector corruption and obtained very similar results. In our sample, the correlation between the _V-Dem_ executive and public sector corruption indices is .82.

> 9 In addition, we worked with the more recent Ibrahim Index of African Governance. The results obtained are very similar to the ones we report, but we do not include them here as the data only start in 2008.

> 10 The units of the WGI are those of a standard normal random variable, with zero mean, unit standard deviation, and ranging approximately from ‐2.5 to 2.5 (Kaufmann et al. 2010: 9). Some extreme scores are outside this range.
Source: Eberhard and others 2009. Derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Achievements**

Despite high tariffs, revenue collection ratios for Liberia’s power utility are very high at 93 percent (table 8). This can largely be attributed to the practice of charging for power up front, one month ahead of actual consumption. By comparison, the utility only collected 50 percent of billed revenues prior to the war. The power utility also seems to be performing relatively well in terms of distribution losses. System 17
Figure 2 presents the share of households with internet access at the subdistrict level. The number of subdistricts with a rate of internet access below 10 percent declined from 57 to 15 between 2010 and 2016; the number of subdistricts with an internet access rate between 10 percent and 50 percent increased from 26 to 55, while those surpassing 50 percent grew from 2 to 15.

Men and women in our sample are of a similar age—approximately 30 years old in 2010— more than half of them were married in 2010 and the percentage increased in 2016. The level of education is low; more than 50 percent of women and men had a basic level of education in 2010, although there was an improvement over time with an important increase in the percentages of women and men with post-secondary education or more.

In our empirical analysis we use the location of 3G cell towers in 2018 and the per capita expenditure on communications in 2010 to construct an instrumental variable. Data on the location of 3G cell towers come from the OpenCellID Project. OpenCellID collects the GPS position of cell towers which is defined as the GPS position (latitude and longitude) where the radio signal of a GSM base station is received. When more than one signal was received, the OpenCellID database provides the average latitude and longitude. The database contains information on different cellular network technologies, such as 2G (or GSM) and 3G (or UMTS), and on the number of measures processed to obtain a particular data point, i.e., the location of a cell tower. Data on per capita expenditure on communications come from the Household Expenditure and Income Survey (HEIS) of 2010. Expenditure in communication captures postal services, telephone and telefax equipment, and telephone and telefax services, including internet connection services.

# **4 Identification strategy**

We estimate a reduced-form specification for men and women separately where we relate the change in labor force participation at the individual level with an indicator variable of 6
public availability of both the complaints petitions themselves and the reasoning behind the dispute resolver‘s response, and the incorporation of other trial-like characteristics (such as adversary legalism) into the procedures to be used.<sup>21</sup> Obviously, courts are more likely to exhibit these legal attributes than other redress procedures; but if venues outside of courts are empowered to handle significant harms, such as sexual assault on the part of a medical provider or a teacher, or a significant loss of earnings from the denial of benefits, they will likely need safeguards to ensure the quality of the adjudication process, even if those safeguards are not identical to the legal ideal. In instances such as sexual assault, redress procedures will need credible guarantees of anonymity for the complainants. On the other hand, making complaints public and accessible to NGOs, journalists, and others, wherever possible, will multiply the ―fire alarms‖ monitoring power of redress procedures. Of course, as legal safeguards increase, so do the costs of providing the redress procedures.

> 17 DWP, 2007, The Pension Service Customer Survey 2007, Research Report no. 532, 140.

> 18 _Id._ , at 143.

> 19 _Id.,_ at 144, Table 8.2: ―Why did not complain after considering.‖

> 20 Will Wiseman, Designing and Implementing a Rural Safety Net in a Low Income Country: Lessons Learned from Ethiopia‘s Productive Safety Net program 2005-2009. World Bank.

> 21 See Robert A. Kagan, _Adversarial Legalism: The American Way of Law_ . Harvard University Press 2003.

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Nevertheless, our findings are subject to a number of caveats. Most importantly, enterprise surveys cover only the formal sector, excluding the informal firms. Hence our results do not speak to informal enterprises. In addition we also do not have data on micro enterprises (less than 5 employees) in our sample. Second, we have data only on surviving firms, which probably overestimates the growth rates for very young firms given they tend to have higher failure rates. While this database is the best available at this point, we recognize these limitations.

Overall, our findings contribute to the emerging literature on what types of firms create jobs. Our findings on size and employment share are consistent with the findings in Haltiwanger, Jarmin and Miranda (2010a, 2010b) who find that in the US, large (and mature firms) have the largest share of employment. However we also find that in developing countries, the SME sector as a whole is comparable to large firms in contributing to employment. On job creation, the US evidence suggests that small mature firms have net job losses whereas in developing countries we find that small firms have the largest share of job creation. Moreover, in countries that have had net job losses in the economy as a whole, it is only the small firms that have net job gains.<sup>4</sup> Our data does not allow us to comment on the contribution of start-ups and young firms that have been shown to be important in the US.

The remainder of the paper is organized as follows. In Section II we describe the data and the indicators used in this paper, and present summary statistics. In Section III, we discuss in

> 4 Haltiwanger et al. (2010a) also find that there is no systematic relation between firm size and growth once age is controlled for. Specifically, they argue that the "systematic inverse relationship between firm size and net growth rates in prior analyses is entirely attributable to most new firms being classified in small size classes
that provisioning requirements reduce both the value of regulatory capital as well as risk-weighted assets (RWA).<sup>9</sup> We calculate a bank’s Break Point (BP) as the NPL ratio that causes a decrease in total regulatory capital (TRC) to the minimum capital threshold (MCR). Using Equation (1), the BP for bank _I_ can be computed as follows: We compute a bank's Distance from Break Point (DBP) by comparing a bank’s BP to its actual NPL ratio: , 0} ii ii ii DDBBPP = max {BBPP −BBccMMBB NNPPNN MMc MMTT We set the DBP to 0 if a bank is already undercapitalized (i.e., when a bank’s BP is lower than its NPL ratio).

To aggregate the individual bank results to the country-level, we calculate the consolidated DBP (CDBP) for a subset of the most vulnerable banks. We do so by sorting banks according to their DBP. We define

> 6 According the Basel Committee on Banking Supervision (2019), the unsecure portion of any loan for which provisions are no less than 20% of the outstanding amount of the loan should carry a weight of 100%.

> 7 See the Bank Regulation Supervision Survey – available at https://www.worldbank.org/en/research/brief/BRSS - for detail on asset classification systems across 161 jurisdictions around the world, see Anginer et al. (2019) for a detailed explanation. 8 In general, provisioning is applied to the outstanding amount of NPLs minus the value of collateral. The value of collateral is in turn reduced by pre-defined haircuts established by supervisors. As we miss key information on the value of collateral and supervisory haircuts, we apply the value of provisioning to the entire NPL exposure.

> 9 This approach is in line with Ong (2014, p. 29).

5
# 4. Estimation Data Set and Methodology

Our main dependent variables are average wages and informality probability. Average wage variable is measured in real _takas_ (normalized with the consumer price index). The informality variable is measured as probabilities at the sub-district level (rates).

To match the trade and labor market data, we use a concordance developed for the Bangladesh Standard Industrial Classification (BSIC). The structure of the BSIC 2001 is similar to the ISIC Rev.3. The structure of BSIC 2009 corresponds to ISIC Rev.4 with an additional division, 6 new groups, and 93 new classes to better correspond to Bangladeshi requirements. We link the BSIC 2001 with the BSIC 2009 and ISIC Rev.3.1 for further merging with the HSO–1988/92 trade classification used by the UN COMTRADE data.

The trade exposure index can be calculated on the basis of regions or industries as explained in the section on methodology. We consider a slightly modified exposure index calculated with only manufacturing industries, rather than all industries, to investigate if nonmanufacturing trade drove

> 9 According to UNDP (2012), Bangladesh’s urbanization rate stood at 3.03 percent over the period from 1975 to 2009—one of the highest in the world. A higher urbanization rate is indicative of greater internal migration or mobility for the country.

> 10 Limited labor mobility in India has been widely documented in the literature. Few key studies include Srivastava and McGee (1998); Singh (1998); Srivastava and Sasikumar (2003); Lusome and Bhagat (2006); and ZL Kone et al. (2018).

20
households’ incomes in rural Malawi are still dominated by agricultural income, shocks that affect crop production also impact on incomes, as does price volatility. And, consumption is highly correlated with agricultural income, though informal insurance mechanisms within a community or extended household may help to smooth consumption in the face of agricultural production and income shocks. In addition, given imperfect labor and credit markets, household labor shocks including illness or death, can affect production and off‐farm income earnings, and subsequently consumption. A priori, we expect harvested quantities to exhibit the greatest variability, followed by income and then consumption. In light of this consideration, this paper is motivated by two key questions: _(1) To what extent is the phenomenon of vulnerability to consumption and income poverty and low crop production per capita present across rural households in Malawi?_ and _(2) What are the determinants of this vulnerability, e.g. the risks they face, assets endowments, and household socio‐demographic characteristics, particularly those expected to proxy for risk management and risk coping capacity?_ To answer these questions, we construct measures of vulnerability to poor realizations of maize harvest per capita, income per capita, and consumption per capita, following the vulnerability measures developed in Christiaensen and Subbarao (2005) and Chauduri et al. (2002). This measure is categorized as a “vulnerability to expected poverty” (VEP) measure, which is comprised of the probability of falling below the poverty line multiplied by a Foster‐Greer‐Thorbecke (FGT) measure of the severity of the expected shortfall below the poverty line. We explicitly consider a number of different shocks that may affect rural households, including rainfall, maize price, health and off‐farm income employment shocks. We use the poverty line developed by the National Statistical Office (NSO, 2012), which is based on minimum subsistence requirements for consumption (see forthcoming Malawi Poverty Assessment), and household level panel data collected by the NSO, in conjunction
on freight expenditures (including insurance) at 10-digit HS product level that are collected using importers’ customs declarations. These data provide a direct measurement of transport costs by mode of transportation together with trade values, quantities, and port of entry (for the US data). Several studies have employed this data to understand the trends in transport costs since the 1950s (Hummels, 2007; Hummels and Schaur, 2010), to model importers’ choice between air and ocean (Hummels and Schaur, 2013), to explore the nature and the determinants of international transport costs and the effect of freight rate on the patterns and quality of trade (Hummels and Skiba, 18
very prevalent, the 19<sup>th</sup> ICLS standards may have a larger effect on the sectoral distribution of the employed population than on the employment-to-population ratio.

To illustrate the effects of the adoption of the 19<sup>th</sup> ICLS standards on headline labor indicators, we rely on nationally representative household surveys from the two early adopters, i.e. the Nigeria GHS Panel Wave 3 (activity-level operationalization) and the Malawi IHS-4 (product-level operationalization).<sup>32</sup> Since the Malawi IHS-4 only collected the intended use of output for crops, we assume that livestock products are for sale, even though we know from the previous section that some livestock (especially chicken) are often reared for own consumption. In Nigeria, the analysis is based on the end-of-season survey, which, as discussed in the previous section, shows a higher share of farmers producing for sale than the start-ofseason survey. The estimates in this section should therefore be seen as a lower bound of the implications of the 19<sup>th</sup> ICLS standards on measures of employment.

Table 5 shows that in both countries, there is a large reduction in male and female employment-topopulation ratios in rural areas under the 19<sup>th</sup> ICLS standards, and a much smaller reduction in urban areas. In Malawi, employment declines disproportionately among women, while in Nigeria, the decline is larger for men. The latter result may be surprising, given that Table 5 is based on the same end-of-season data for Nigeria used in Table 1, which shows women farmers being more likely to be classified as own-use production workers than male farmers. However, there is also a comparatively higher incidence of nonfarm household enterprise work among Nigerian women, while more men than women work on the farm, which mitigates the impact of the 19<sup>th</sup> ICLS standards on female (relative to male) employment. Conversely, in Malawi,