rafmacalaba/datause-encoder-data huggingface dataset:
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE administrative and regulatory reforms are also needed. Reducing delays would significantly help improve utilization of Zimbabwe‘s trucking fleet and lower transport prices.

# Rail

## **Achievements**

Zimbabwe has a functional railway network. The state-controlled National Railways of Zimbabwe (NRZ) rail network has a single-track route length of 3,077 km, of which 2,759 km are operational. The Bulawayo Beitbridge Railway (BBR), a build-operate-transfer (BOT) project, consists of around 317 km and runs from Beitbridge on the border with South Africa to Heany Junction near Bulawayo.

Zimbabwe has the highest rail-traffic density in the region aside from South Africa. As the table below shows, the NRZ has very high freight density at around 902,000 tonne-km/km (table 11), while most other southern African railways are serving substantially less. Passenger density is also comparatively high.

**Table 11. Railway indicators for Zimbabwe and selected other countries, 2000–05**

||**NRZ (Zimbabwe)**|CFM (Angola)|BR (Botswana)|CEAR (Malawi)|Nacala Railroad<br>(Mozambique)|Beira Railroad<br>(Mozambique)|Ressano Garcia Line<br>(Mozambique)|Transnamib (Namibia)|Spoornet (South Africa)|RSZ (Zambia)|
|---|---|---|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state run (0)|**0**|0|0|0|1|1|0|0|0|1|
|Freight density (1,000 tonne-km/km)|**902**|469|827|90|270|663|364|475|2,427|406|
|Passenger density (1,000 passenger-km/km)|**166**|—|—|38|103|44|44|33|60|92|
|Labor productivity (1,000 traffic units per<br>employee)|**390**|580|722|131|710|281|—|484|3,308|502|
|Locomotive productivity (million traffic units<br>per locomotive)|**8**|30|41|3|25|13|—|25|33|25|
|Carriage productivity (1,000 passenger-km per<br>carriage)|**—**|4,046|2,391|1,176|3,333|750|—|—|—|3,286|
|Wagon productivity (1,000 net tonne-km per<br>wagon)|**195**|950|987|82|260|476|—|805|913|377|
|Freight yield (US cents/tonne-km)|**—**|—|—|6|5|3|3|—|—|4|
|Passenger yield (US cents/passenger-km)|**—**|—|—|1|0.9|0.5|1|—|—|1|

_Source:_ Bullock 2009. Derived from AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ * With 2.5 passenger-km equivalent to 1 traffic unit, 1 tonne-km equivalent to 1 traffic unit. — = Not available.

Zimbabwe‘s railways are interconnected with other national networks along the North–South Corridor, allowing for through traffic across Zambia, Zimbabwe, Tanzania, and South Africa. But while the rails are physically connected and of compatible gauge, there is a lack of reciprocal access rights between operators that would allow through train service due to the lack of arrangements for servicing other operators‘ locomotives that may experience technical difficulties. This means that locomotives need 34
WPS 3300

# The Labor Market Effects of Foreign-owned Firms<sup>∗</sup>

Rita Almeida DECRG The World Bank April 21, 2004

## Abstract

Foreign fi rms often have a more educated workforce and pay higher wages than domestic fi rms. This does not necessarily imply that foreign ownership translates into higher demand for educated workers or higher wages, since foreign investment may be guided by unobservable fi rm characteristics correlated with the demand for educated workers or wages. I examine foreign acquisitions of domestic fi rms and fi nd small changes in the workforce skill composition and wages following acquisition. Foreign investors “cherry pick” domestic fi rms that are already very similar to the group of existing foreign fi rms.

Keywords: foreign direct investment, acquisitions, employment, wage structure and panel data.

JEL Classi fi cation codes: C31, F23 and J31.

> ∗I am specially grateful to Antonio Ciccone for raising my interest in the topic and for his guidance and to Pedro Carneiro for several comments. I am also grateful to the Portuguese Ministry of Employment’s Department of Statistics for access to the data and, in particular, to Ermelinda Lopes and Artur Simões for their help. I also thank Bruno Cassiman, Mário Centeno, Koen Debacker, Jaume Garcia, Maia Güell, Sergi Jimenez, Adriana Kugler and José Mata for several suggestions. I bene fi ted from comments made by participants at the 7<sup>th</sup> meeting of the Portuguese Economic Society, Universitat Pompeu Fabra, XXVII Simposio de Analisis Economico(Salamanca), Universidade Nova de Lisboa, CEPR conference on “Globalization and labor markets”(Bergen) and at the DECRG, World Bank. I gratefully acknowledge the fi nancial support of Fundação para a Ciência e Tecnologia. The fi ndings expressed in this paper are those of the author and do not necessarily represent the views of the World Bank. Address: 1818 H Street, NW, MSN MC3-301, Washington, DC 20433, USA. E-mail: ralmeida@worldbank.org
The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals). In addition to the household survey, the KHDS included surveys of communities, prices and facilities.<sup>1</sup> The KHDS 1991-1994 serves as the baseline data for this paper. The household questionnaire was a Living Standards Measurement Study survey instrument which contained numerous indicators of well-being, such as consumption, expenditure, asset holdings, morbidity, health, nutrition, and education. Even though the sample was not specifically designed to be self-weighting, a comparison with the 1991 Household Budget Survey suggests that in terms of basic welfare and other indicators, the baseline data are similar to a representative sample for this period from the Kagera region.

The KHDS 2004 was conducted in the first half of 2004 (Beegle, De Weerdt and Dercon _,_ 2006a). The objective of the KHDS 2004 survey was to reinterview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview. This effectively meant turning the original household survey into an individual longitudinal survey. When a panel respondent was located, the household in which these individuals lived in 2004 was administered the full household questionnaire. The KHDS 2004 used the original questionnaire as the foundation of the survey instrument, to ensure comparability of all main indicators and variables from the earlier survey. For all panel respondents, there is a module on the incidence of economic shocks (both positive and negative) in the last 10 years.

> 1 Information, documentation and the full data set of the KHDS (1991-1994 and 2004) can be found on the Living Standards Measurement Study website: http://www.worldbank.org/lsms/. For further description of the original project and 1991-1994 data see Ainsworth _et al_ . (1992) and World Bank (1993).

8
attitudes and prevalence were significantly different between rural and urban areas.

As one would expect given the literature on the prevalence and causes of IPV worldwide, Figure 2b illustrates that the acceptability of IPV was slightly lower in urban areas than in rural areas, with the difference being between 6 and 9 percentage points lower in urban areas. But as discussed in the background section and contrary to expectations, the reported prevalence of IPV was _higher_ in urban areas than in rural areas for nine out of the 10 types of violence experienced in the past year. Although the confidence intervals of the estimates often overlap and only one of the differences is statistically different at conventional significance levels when including individual-level control variables (e.g., education, etc.), the rural-urban differences in reported IPV incidents are far different than the clear patterns presented in the acceptability of IPV in Figure 2b.<sup>25</sup> However, it is difficult to interpret whether these are actual differences in the prevalence of IPV or if they might represent differences between urban and rural areas in the willingness of victims to report the violence they experience.

# **Section 5a. Empirical Strategy**

As described in the Introduction, we estimate how men’s attitudes about IPV and how the prevalence of IPV incidents reported by women changed following the 2018 earthquake in PNG, which immediately preceded the beginning of the fourth phase of the 2016-2018 DHS. Specifically, we estimate the following difference-in-differences specification: where _Post_ denotes an indicator equaling one if the individual was interviewed after the earthquake in the fourth phase of the survey; _Affected_ denotes an indicator equaling one if the household lived in the Southern Highlands Province, which was the region that was most strongly affected by the earthquake that was able to be included in the post-earthquake phase of the survey;<sup>26</sup> _X_ denotes individual-level control variables; and _t_ denotes month-year, _r_ denotes the four broad regions in the country, and i denotes individuals.<sup>27</sup>

> 25Estimates of the rural-urban difference in the prevalence of reported IPV incidents in the past year when including all control variables included in the baseline empirical specification are available from the authors upon request.

> 26The other significantly affected province- Hela- was unable to conduct any interviews following the disaster. The results are identical if Hela is entirely dropped from the sample, or if the indicator identifies either households from Southern Highlands or Hela as being affected populations.

> 27Treatment in this case is not staggered and there are only two treatment groups, which are the settings in which the validity of the difference-in-difference estimator has been illustrated to be questionable (e.g., Callaway and

12
this calculation, we estimate that 79% of the growth in remittances received by developing countries over the 1990 to 2010 period reflects changes in measurement, with only 21% representing changes that can be attributable to the growth in the migrant stock and to the incomes these migrants are likely to be earning. We then turn to time series micro data on remittances for several countries for which several rounds of data are available, and show that the growth in the macro data greatly exceeds that in the micro data at precisely the times changes in measurement have occurred.

Second, cross-country panel regressions may have too little power to detect the effects of remittances on growth—even if remittances are correctly measured, and even if the true effect is large. Here, we begin by measuring typical changes over time in remittances as a fraction of GDP and in the level of real GDP per capita, and estimate the variance in each. From this information we can estimate the power of tests in cross-country panel regressions of growth on remittances. Even if every dollar remitted adds a dollar directly to GDP—a coefficient of 1, a large effect—the available country-periods of data reveal a power of at most 0.3. This is primarily due to low magnitude and high variance in the remittance data.

Third, much of the variance in remittances across countries is caused by differences in migrant stocks. Those migrant stocks have offsetting impacts on home-country economic product, so that greater remittance flows for a given country are necessarily accompanied by a smaller fraction of the labor force working inside the country. We calibrate a very simple model of migration, remittances, and growth. The net effect of remittances caused by new migration need not be positive for many real-world migration corridors. And where it is positive, the net effect is very unlikely to be large enough to detect in cross-country growth regressions.

It should be no surprise, then, that remittances do not appear to affect short-run economic growth. First, across time, the true amounts remitted seem to have grown much more slowly than the measured remittances used in research. Second, even if increases in remittances over time were correctly measured, there would be too little power in cross-country panel data to detect even substantial growth effects of that rise. And third, across countries, there is little theoretical 3
The analysis also examines the impact of tropical cyclones on fatalities. The analysis finds that almost all deaths from tropical cyclones occur in just two countries: Myanmar and especially Bangladesh. The impact of climate change on tropical cyclone deaths depends on what happens in these two nations. Since the model predicts that tropical cyclones in the Indian Ocean might get more intense but more likely will get less intense, deaths in these two countries are expected to fall. Consequently, global deaths are expected to fall.

There are many uncertainties associated with the forecasts made in this study. The emission path of greenhouse gases is highly uncertain because it depends upon the long term growth of the economy, the long term relationship between GDP and energy, and any government mitigation policies that may be adopted over the next century. The relationship between climate change and greenhouse gas concentrations is also quite uncertain as revealed by the different projections by the four climate models used in this analysis. Exactly how tropical cyclones will react to climate change is also uncertain as it depends upon many factors that are difficult to predict. Finally, the magnitude of the damages that future tropical storms will cause is uncertain. The damages with respect to storm intensity appear to be very sensitive to minimum pressure. The damages are also very dependent on the elasticity of population and especially income. However, the vulnerability parameters are quite different depending upon whether one uses US or international data. There is a clear need for better international data that includes the intensity of storms as well as their location. How damages might change if there is both a change in tropical cyclones and sea level rise has barely been analyzed. Nicholls et al. 2008 suggest an additive effect between sea level damages and storm damages. But the interaction between these two effects may be more than additive. This is clearly a topic that deserves further research.

Finally, how society will adapt to tropical cyclones in the future is not yet clear. Currently, many countries have mal-adaptation policies that make matters worse by encouraging assets to remain in harm’s way. For example, subsidizing flood insurance and capping the cost of catastrophic insurance makes it cheaper to live in risky locations. Even providing federal disaster relief reduces the overall cost of choosing a risky location to develop. Reducing the implicit subsidies in these policies and actively discouraging 28
Table 3.4 presents basic descriptive statistics for this variable. Only 15 percent of the over 5,000 Brazilian cities had local internet service in 1999. Access to digital technologies has grown considerably since then. By 2006, over half of all municipalities had a local internet service provider. Though not all cities have access to the internet, areas of the country with internet services are larger population centers. In 1999, internet access had spread to roughly 60 percent of the Brazilian population. By 2006, almost 90 percent of the population had a local internet service provider.

The map in Figure 3.1 illustrates the spread of the internet across the country over time. The darker areas of the map obtained internet services earlier than the lighter areas on the map. The white municipalities in the center of the country and remote Amazon region are those cities that by 2014 remain without internet service. A casual observation suggests no substantial clustering of the rollout, but this can be difficult to view given the geographic scope of the country. In Figure 3.2, we illustrate the rollout of the internet in the most populous state, São Paulo. Even in this state, we notice considerable over time and across municipality variation. This is exactly the variation that we rely on in our paper.

Returning to Table 3.3, we report the average non-routine-to-routine relative task intensity, conditioning on whether the city has internet service provision. Perhaps unsurprisingly, industries located in cities with internet access consistently employ higher shares of workers in non-routine tasks. All cities—those with and without internet—report shifts toward nonroutine tasks over time.

**Industry-specific information technology intensity** We rely on data from the U.S. Census Bureau’s _Current Population Survey_ , which in an October 2003 supplement asked households whether they use a computer at their current job. Aggregating across all workers within their reported industry categories, we calculate the share of workers in U.S. industries who report using a computer at work. This is our main measure of the industry’s technological intensity following Oldenski (2014). We then rely on publicly-available

16
tends to happen more often, however, it does not point at corruption definitely. It allows for consistent comparisons across time, sectors, regions, and organizations and can be further expanded and related to using additional corruption proxies. For the ease of interpretation, CRI is calculated on the contract level after each individual risk indicator is standardized to 0: non-risky and 1-risky (sometimes an intermediate risk level is added, 0.5). Hence, CRI falls between 0 and 1, with 1 representing the highest observed corruption risk and 0 the lowest.

We match the unique identifiers of all firms awarded with any type of public procurement contract with the same identifiers in the firm administrative dataset. The combined dataset allows to estimate if GPP contracts are allocated to more or less productive firms. First, we estimate if firms awarded with green public procurement contracts are more productive than firms with a comparable size, age, and ownership structure competing in the same 4-digit sector but not awarded with a state contract. We estimate if the productivity differential changes for GPP contracts carrying a lower risk of corruption. Second, we restrict the sample to firms that obtained a public procurement contract to estimate if GPP contracts are more often allocated to more productive firms than other state contracts. We also estimate the impact of corruption risk in public procurement on the productivity of the awarded firm and the interaction effect between green and corruption-risk contracts.

# 3.4 Identifying the impact of green procurement on procurement and firm outcomes

Identifying the causal impact of green procurement on public procurement and firm outcomes, thus testing our hypotheses, is challenging due to reverse causality, omitted variable bias and sample selection. We discuss each of them in detail offering our best strategy for reliable and robust estimation.

First, reverse causality arises where government entities implement GPP in response to a competitive market environment in order to stimulate innovation, attract environmentally conscious suppliers, or improve public perception. In other words, competitive markets would cause the choice of GPP rather than the other way around. We consider this scenario unlikely as GPP is mainly driven by exogenous influences deriving from EU Directives and national sustainability policies and budget allocations.

The second challenge is omitted variable bias, as numerous factors beyond green procurement may influence the level of competition. To mitigate this bias, we employ two methodological approaches: regressions on a matched sample (double robust regression) and fixed effects regression models. First, in order to apply matching we used Coarsened Exact Matching (CEM) methods which groups contracts based on observed 13
Programs (UDB) is intended to cover the poorest 40 percent of the Indonesian population. Over 25 million households have been registered in the UDB using a novel approach based on (i) a pre-listing of households to be surveyed, constructed through census-based poverty mapping (Elbers et al., 2003), and (ii) suggestions from local communities. These households were subsequently ranked by their predicted welfare status estimated using district-specific PMT scores.<sup>5</sup> The government has used the UDB to deliver over US$ 4 billion annually (IDR 43 trillion) in central government social assistance to date. This includes two of the country’s largest social assistance programs that are the focus of this paper: a health insurance program (known by its Indonesian acronym, _Jamkesmas_ ) and an unconditional cash transfer program (known by its Indonesian acronym, BLT).<sup>6</sup> Before the establishment of the UDB, beneficiaries of these programs were selected using ad hoc targeting approaches. In practice, community leaders had a strong influence in determining beneficiaries. Jamkesmas relied on a form of self-targeting through the use of a poverty statement issued by local leaders, and BLT on a PMT-based ranking of households designated by community leaders (World Bank, 2012). This diversity of targeting methods used by programs having similar targeting goals is common in developing countries. We use this variation across programs to assess the expected change in targeting accuracy when moving to a single, unified targeting system. We follow the literature in assessing targeting accuracy based on two key measures: _leakage_ (or inclusion error), when non-intended beneficiaries receive program benefits, and _undercoverage_ (or exclusion error), when intended beneficiaries do not receive program benefits (Cornia and Stewart, 1995).

Our analysis proceeds in two steps. First, we evaluate the targeting performance of the UDB against the performance of past approaches to beneficiary selection. We use data from an auxiliary, independent survey known as SUSETI, which we matched with UDB administrative data. Although limited in its geographic and demographic coverage, this survey allows us to provide internally valid estimates of targeting effectiveness under the old and new targeting regimes. Crucially, SUSETI

> 5 Indonesia’s administrative divisions proceed from province to district to subdistrict to village to hamlet. There were 497 districts at the time of the establishment of the UDB.

> 6 Another major social assistance program is rice subsidy program known as _Raskin_ , which is not considered in the paper because its benefits are typically shared informally across beneficiary and non-beneficiary households. This introduces special challenges in assessing targeting effectiveness that are specific to Indonesia.

3