rafmacalaba/data-use-mentions-extended huggingface dataset:
increased from 18.2 to 46.2 percent (Figure 2.21). Between 2000‐2007 and 2008‐15, these shares have changed on average from 56.3 to 41.3 percent, and from 23 to 40.7 percent (Figure 2.22). For exports of medium‐technology products, this has reflected the increase in foreign sales of phosphate derivatives, cars, and electric equipment and apparel. However, between the same two periods, the share of exports of high‐technology products has actually fallen, from 10.2 to 6.1 percent, reflecting a drop in exports of electronic components. At the same time, the share of low‐technology products in total exports (as well as total value added of the manufacturing sector), despite falling significantly, remains high. By and large, therefore, Morocco’s exports remain insufficiently diversified and concentrated mainly on a limited number of low value‐added products. This lack of diversification is well illustrated also by formal indices such as the Index of Export Quality (IEQ) and the Economic Complexity Index (ECI).<sup>1</sup> In particular, Morocco has the lowest ECI among resource poor countries in MENA. In addition, only about 20 percent of the country’s exports are integrated in global value chains (GVC) compared to around 30 percent for Jordan and Lebanon (Hoekman, 2016. This lack of dynamism acts as a major constraint on the Moroccan economy’s ability to reduce unemployment, improve living standards, and achieve high‐income status.

# _Lack of Geographical Diversification_

Despite some notable changes in the geographical structure of Morocco’s external trade, it remains highly concentrated. Figure 2.23 and Figure 2.24 show changes in that structure between 2013 and 2017. France and Spain, and other European Union (EU) countries, remain the main destination on the export side. The predominance of exports to the EU over a longer period
of computers, and increased decentralization of decision-making. One consequence of such decentralization is the spread of better paid employment opportunities and less inequality of compensation through a production hierarchy, that is, more inclusion in the gains from productive expansion.

In related work on the implications of innovation in management technologies, Bloom et al. (2009) highlight a subtle but important difference between advances in information and communications components. Better information technologies that empower and spur learning by workers such as ERP (Enterprise Resource Planning) for plant managers and CAD/CAM (Computer Aided Design and Manufacturing) for production workers are associated with more autonomy and wider span of control. One key implication is more inclusive growth opportunities by the elevation of local labor productivity and reduction in wage inequality. In contrast, communication technologies like data networks are apt to decrease autonomy for plant managers and workers, substituting away from local knowledge in favor of directives from centralized headquarters, and leading to less inclusive growth by stifling learning and accentuating wage inequality. Despite these differences in types, innovation in management is highly associated with gains in productivity and output-growth opportunities. As such, we hypothesize that these forms of innovation are also, like product and process innovation, likely on average to be inclusive in their overall impacts on enterprise employment.

# **3. Data and empirical specification**

We use establishment-level, cross-section data that are based on the World Bank Enterprise Surveys (ES) collected between 2002 and 2006. We have information on 26,108 manufacturing establishments from 71 countries (15 OECD countries, 6 ̳enhanced engagement‘ developing countries and 50 other developing countries). Most of the establishments represented in the data are registered in the formal/organized sector, and are urban. Sampling is typically stratified by size, sector and location. Any accounts collected in local currency units are converted to constant 2005 US dollars at purchasing-power-parity. Rates of growth are scaled to an annual basis. Table 1a reports sample counts by country and table 1b reports the means and standard deviations
season October-March.<sup>10</sup> We match these EPA-level estimates to households in our analysis sample and then construct the sample-weighted TA-level median of the EPA long-term average mean maize yields, and percent difference from the long-term mean as explanatory variables.

Furthermore, targeting is based on the 2010 district poverty rates provided by the Malawi National Statistical Office (NSO, 2012). Additional explanatory variables that can explain aid distribution across TAs include the TA population density,<sup>11</sup> the median accessibility index in the TA,<sup>12</sup> median slope, and elevation. While not explicitly used by the MVAC, in view of the political economy considerations that have been shown to impact decisions related to humanitarian aid distribution elsewhere in Africa (Francken et al., 2009), we include information on the political party of the member of the parliament (MP) associated with the communities in our sample. Using the publicly-available data on the election results provided on the Malawi Electoral Commission (MEC) website, we match data on the political party composition of MPs elected to TA’s.<sup>13</sup> In some cases, TAs map to more than one constituency, so we construct a simple mean of constituency-level political alignment dummies at the TA level to capture the proportion of political representation in the TA that is part of the president’s party, in opposition to the president’s party, or independent. The 2009 parliamentary election results are applied to observations in 2013, and 2014 parliamentary election results are applied to observations in 2015 and 2016.

Finally, MVAC has constructed livelihood zone categories that also inform the Household Economic Analysis, to capture the extent to which households in different locations are more vulnerable

> 10 Maize yield estimates data had many fewer missing data points.

> 11 TA population estimates are derived from NSO (2008). TA area is derived from publicly available geospatial data. TA population density is calculated by the authors as
696|
|No. of obs. (VCs)|50,010|50,010|50,010|50,010|50,010|50,010|
|R<sup>2</sup>|0.890|0.890|0.890|0.887|0.886|0.885|

_Source:_ Original table for this paper. _Note:_ Conflict indicators and periods for DA or F in different stages of vegetative development are as defined in table 1 in the main text. Standard errors in parentheses: * p < 0.10, ** p < 0.05, and *** p < 0.010. ACLED = <mark>Armed Conflict Location and Events Data; DA = area damaged due to conflict; F = fatalities; GDD = growing degree days; mm = millimeters; VC = village council.</mark> 35
19

# **4. Data and Empirical Results**

## **4.1 Data Description**

We combine household and labor force surveys from each country. The Cambodian data come from the cross-sectional Cambodia Socio-Economic Survey (CSES), which is collected by the National Institute of Statistics. We use data from the 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011 surveys, which contain roughly 12,000 households each. These are crosssectional household surveys that contain detailed household and individual information, including wage, education, age, marital status, gender, location, industry, occupation, some working conditions, and the hours worked.

For Sri Lankan data, we use the 1992–2002, 2008, 2011, and 2012 waves of the crosssectional Sri Lanka Labor Force Surveys (LFS) and 2006 wave of the Sri Lanka Household Income and Expenditure Survey (HIES), which are carried out by the Sri Lankan Department of Census and Statistics. These surveys cover approximately 30,000–60,000 individuals each. They contain information about work-related activities (for example, employment status, occupation, industry, and wages); household characteristics (for example, size and location); and individual demographic characteristics such as age, gender, and education, among others.

Apparel workers in Cambodia and Sri Lanka share a number of characteristics. First, the share of the total labor force employed in the textiles and apparel industry was relatively small and remained stable after the end of the MFA: 5 percent in Cambodia and 6 percent in Sri Lanka (see table 1).
particularly for those youth who are at risk of dropping out in the near future. In many cases, this distinction is not clear-cut. For example, broad programs are often targeted in their implementation, due to budget constraints and need, and in these targeted schools or communities, youth face many risks, including dropout.

## **4.1 Building human capital before youth consider dropping out**

High-quality early childhood interventions have shown promising results for disadvantaged youth.<sup>26</sup> A handful of randomized evaluations from the United States, Jamaica, and other countries demonstrate that early interventions, including nutritional supplementation, psychosocial stimulation, and high-quality preschool can have lasting impacts on at-risk individuals, through increased attainment (and therefore less dropout), higher wages, and better mental health. As discussed earlier, these interventions are particularly appealing because they are preventative, and increase the productivity of later human capital investments.

In Central America, preschool coverage remains limited, particularly among the poor. In Costa Rica, where there is no public spending on preschool, only 25 percent of 3-5 year olds in the bottom quintile are enrolled, compared to 53 percent in the top quintile (World Bank 2014d). In Panama, where an early childhood development plan was adopted in 2009 and is currently waiting

> 25 The authors define rigorous evaluations as those that employ a plausible counterfactual, including randomized control trials, difference-in-difference, regression discontinuity, and other empirical designs.

> 26 All evidence described in this section that is not cited here is cited in detail in Almeida, Fitzsimons, and Rogers (2015).

22
here. Individual probability weights are used to make the sample nationally representative.

The survey was not conducted in North Eastern province due to insecurity in the region. Mobile savings data collected include general financial product usage, savings behavior, mobile phone

> 2 These figures do not include mobile money bill payments or transfers from companies to individuals.

> 3 This figure, based on dividing the overall number of mobile money subscribers in June 2011 by the estimated population age 15 and older, is compatible with the percentage of the population age 18 and older (73 percent) that reported using mobile money in the November-December 2011Afrobarometer survey, as detailed in Section 2.

7
Asian countries, such as Malaysia, Singapore, Taiwan and Hong Kong. The trend is the opposite for men. Although the majority of male migrants find work in Malaysia, they are now increasingly shifting to Saudi Arabia.

About 4 to 5 percent of all sample households in the IFLS survey reported having international migrants. The migrant sending households, however, are concentrated in some provinces such as West Nusa Tenggara, West Java and East Java. In these provinces, the share of sample households with migrants in 2007 was as high as 17 percent (Nusa Tenggara) and 6 percent (West and East Java). While female migrants are predominantly from West Java, male migrants mostly come from West Nusa Tenggara. The shares of migrants from these two main regions are increasing over time.

Although Indonesia migrant workers are increasingly coming from urban areas, the majority of them still come from rural areas. More than 60 percent of migrant workers, during the 2000-2007 period, still came from rural households. The new urbanizing trend is continuing possibly as the Indonesian population is urbanizing, and increasingly more workers from urban areas are finding work abroad. The share of households having migrant workers from urban areas increased from 34 percent in 2000 to 37 percent in 2007. The urbanizing trend is observed stronger for households with only male migrants, with the share from urban area rising from 31 percent in 2000 to 41 percent in 2007.

Households with migrants have, on average, significantly less per capita total expenditure than those without migrants. From 1993 until 2000, almost equal shares of migrant workers came from quintiles 1 to 4. Recently, the highest share of migrant workers came from the poorest quintile of households. Migrant workers from these households look for work outside of Indonesia in order to better financially support their families back home. Households having only male migrants are generally worse off but, interestingly, on average, have higher food expenditure than female migrant households. Meanwhile, households with female migrants tend to have higher expenditure on nonfood items, particularly
40 building’s area, height in stories, and whether it is semi-permanent or permanent. By overlaying district and subcatchment boundaries on the GIS data, we have a mapping between districts and subcatchments. With this mapping we can calculate SemiPermanentDiSj and PermanentDiSj, the total semi-permanent and permanent building areas in each district Di and in each subcatchment Sj.

We assume that the poor live in semi-permanent buildings and non-poor live in permanent buildings. With this assumption, we can calculate PoorSj and NonPoorSj, the poor and non-poor in each subcatchment, respectively, as: Where SemiPermanentDi and PermanentDi are the total areas of semi-permanent and permanent buildings in each district.

# **Economic Exposure**

The distribution of economic assets in the city is also not readily available. We use as a proxy indicator the contribution of each subcatchment to the country’s overall GDP, which can be approximated given available economic data.

The literature provides us with data about Ho Chi Minh City’s contribution to Vietnam’s GDP, GDPHCMC. If we assume that a district’s contribution to Ho Chi Minh City’s GDP is proportional to its contribution to Ho Chi Minh City’s tax revenue, then GDPDi, the GDP contribution of district Di is.

Where RevenueDi and RevenueHCMC are the tax revenues generated in district Di and in Ho Chi Minh City, respectively.

We again GIS data provided by the Steering Center for Flood Control to determine the distribution of economic value in each subcatchment. We use the distribution of permanent buildings across subcatchments as an indicator of the distribution of wealth. Specifically, we assume that subcatchment Sj’s contribution to district Di’s GDP is proportional to the fraction of permanent building area in Di that is also in Sj. Then, we can calculate GDPSj, the economic value of catchment Sj as:

# **Exposure Under Recent Conditions**

We used recent socioeconomic data to calculate poor and non-poor and economic exposure in each subcatchment. Here we describe the basis of those estimates. The Ho
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Power

During the Liberia’s recent civil conflict period, the country’s key generation, transmission, and distribution assets were almost entirely looted or destroyed. Prior to the conflict, Liberia’s installed generation capacity was relatively high at 67 megawatts per million of people. Today the country runs on a single new small diesel plant of 9.6 megawatts. This level of power generation capacity amounts to less than 3 mega-watts per million people—barely a tenth of what is found in neighboring countries. Moreover, access to utility power is close to zero, even in the capital city where only 0.1 percent of households are connected. Due to some prevalence of household generators, the 2007 Demographic and Health Survey reports that 3 percent of households have access to electricity. Liberia’s mining and natural resource concessionaires have significant isolated generation capacity of their own, although the exact amount is not known.

**Table 8. Benchmarking power indicators**

||Unit<br>Res|ource-rich co|untries<br>**Liberia**|Low-income countries|
|---|---|---|---|---|
|Installed power generation capacity|MW/mil. people|43.2|**2.7**|20.2|
|Power consumption|kWH/capita|205.7|**87.0**|107.4|
|Power outages|day/year|14.5|**12.7**|10.4|
|Firms’ reliance on own generator|% consumption|44.9|**97.1**|21.2|
|Firms’ value lost due to power outages|% sales|7.0|**2.9**|6.5|
|Access to electricity (from utility)|% population|46.1
1990s. The Mauro indices, for example, have assessments of Liberia and Nigeria which, given more recent events, seem high. The indices have not been adjusted to reflect these changes in order to preserve the internal consistency of these indices.

# _Adjustments_

Political Freedom: mean shifted to 50. Political Stability, Red Tape, Judicial Efficiency: mean shifted to 50. Corruption: unchanged. Human Development Index: mean shifted to 50. Gini Coefficients: 10 - sqrt(gini), mean shifted to 50.

Outward Orientation: ln(speed of integration index) used as percent change, then applied to initial index, mean shifted to 50.

Central Bank Independence: mean shifted to 50.

Debt to GDP Index: 2 - debt/gdp, mean shifted to 50.

Means were shifted by: Initial Index Number * 50 / Average of Initial Index 22
**<mark>Table 2.  Drivers of cropland losses (pixel level)</mark>**

||Conversion out of<br>cropland|
|---|---|
|(Intercept)|.9886<sup>***</sup><br>(.1648)|
|Distance to city (in km)|.0001497<sup>***</sup><br>(.0000009628)|
|Number of drought months|.0005473<sup>***</sup><br>(.000002151)|
|Land suitability for agriculture|-.001499<sup>***</sup><br>(.000002584)|
|Distance to Coast (in km)<br>Distance to River (in km)|-.00001429<sup>***</sup><br>(.0000001413)<br>.00003348<sup>***</sup><br>(.0000007763)|
|Country fixed effect|Y|
|Observations|48,374,216|
|R<sup>2</sup>|0.06|

_Note: The regression is applied to all 2003 cropland pixels. The dependent variable takes value 1 if the pixel transitioned out of cropland over the 2003-2018 period and 0 otherwise. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ We then recover the country fixed effects from the above regression on the cropland sample and regress them on national level socio-economic and institutional variables as in specification (2). Given the small number of observations, we aim for a parsimonious specification, including cropland per capita—as a proxy for demographic pressure exerted on land utilization—an institutional variable, and controls for the macro-economic and socio-economic contexts. Table 3 below shows the coefficient
11 the small rise in the Gini index in these two countries could have two alternative explanations: that everyone at a given income experienced similar proportionate income increases but those who were better off initially experienced somewhat larger proportionate increases; or that people who were initially _worse off_ experienced larger proportionate increases but this pro-poor vertical redistribution was more than offset by the inequality-increasing redistribution associated with people with similar initial incomes experiencing different income increases (horizontal redistribution) and reranking. Section IV finds evidence in favor of the latter explanation. Without horizontal redistribution and reranking, the Gini coefficient for income in China might have fallen between 1989 and 1997 by as much as 21.6 points. In the event, the horizontal redistribution and reranking involved in the move from the 1989 to 1997 income distributions was so large that the Gini _increased_ —by 2.4 points. A similar picture—albeit less dramatic— emerged from the Vietnam panel for 1993-98.

Do these results matter? If vertical redistribution ( _V_ ), horizontal redistribution ( _H_ ) and reranking ( _R_ ) matter only insofar as they contribute to the change in the Gini ( _∆ G_ ), knowing the relative magnitudes of _V_ , _H_ and _R_ is immaterial. If the policymaker is simply interested in _∆ G_ , she would not care whether a small value of _∆ G_ derives from small values of _V_ , _H_ and _R_ , or from a large negative value of _V_ being offset by a large positive value of _H_ + _R_ . Nonzero values of _H_ and _R_ would be considered bad by an inequality-averse policymaker because— _and only because_ —they reduce _∆ G_ for a given value of _V_ .

There is, in fact, one strand of literature that would regard nonzero values of _H_ and _R_ as a _good_ thing, namely the literature on economic mobility. Mobility is often defined and measured in terms of reranking across income distributions at two points in time (cf. e.g. Schiller 1977, King 1983). Where _w_ in section II can be
the cash deficit, not an accrual measure of the deficit as in the EU, while the definition of general government (which determines the institutional coverage of the rules) is in practice less rigorous than in the EU. Many of the rules come with an escape clause that allows for their suspension in extraordinary circumstances.

> 4 The measurement of debt differs from the ESA2010 or GFS2014 in most of the countries, which makes crosscountry comparisons difficult.

10
.4 lists the variables names and their definitions available in the Registration Document.<sup>7</sup>

# Descriptive statistics

Table 5 shows the mean compliance across all questions and factories without controlling for any other potentially relevant variables. The mean compliance in Table 5 is therefore the simple arithmetic average of the 0/1 compliance variable taken over all questions, all factories and all years. Since 0 indicates compliance (and 1 non‐compliance), higher numbers in Table 5 indicate lower average compliance. Overall, firms are found to be non‐compliant in about 9 percent of all compliance questions in the Compliance Synthesis Report.

**Table 5: Finding of non‐compliance**

|**Finding**|**Frequency**|**Percent**|
|---|---|---|
|No evidence of non‐compliance found|219,154|91.01|
|Evidence of non‐compliance found|21,650|8.99|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Table 6 shows the average non‐compliance rates over time for all compliance questions, as well as for compliance questions that are related and non‐related to public disclosure (“critical issue groups”). It also presents the number of firms that are available in the sample for each year. Overall, compliance tends to be lower for disclosure questions than non‐disclosure questions, across the sample years. On average, 7 We also explored the possibility of merging additional firm‐level data from Better Work Vietnam’s Impact Assessment Survey. The firm identifier needed to merge the data sets is currently not available, though we are exploring the possibility of accessing the appropriate identifier.

14