rafmacalaba/datause-encoder-data huggingface dataset:
level. These datasets with large country-sector coverage also enable us to draw conclusions with high general validity.

We find a positive but weak association between the FDI and labor productivity at the countrysector level. Unlike the existing literature, we take a step further. With a battery of robustness checks at both firm and sector level, we argue that GVC participation plays a key role in shaping the effects of FDI. Specifically, a country-sector with lower GVC participation benefits more from FDI. To establish causality, we adopt a shift-share instrumental variable (IV) for sector FDI. In the IV regression, we find that doubling the FDI into a non-tradable country sector will increase the labor productivity of that sector by 15 percent; for a country-sector with an average GVC participation level (= 0.24), doubling the FDI will increase the labor productivity by 8 percent.

To understand the mechanism through which the spillovers take place, we further argue that lower GVC participation may result in higher exposure to domestic competition generated by FDI and thus higher propensity to do research and development (R&D). Our firm-level analysis supports this argument. We find that firms participating less in the GVC have higher propensity to engage in R&D in response to FDI in their sector. We discuss more about this mechanism in Section 5.3 and 5.4.

Moreover, by looking at cross-border FDI, we provide evidence for the existence of positive FDI spillovers through both domestic and foreign backward linkages. That is, FDI into the downstream sector of a home or foreign country also has positive effects on a domestic upstream sector through input-output linkages.

This paper contributes to the literature on the effects of FDI at least in three ways. First, by taking GVC participation into account, this paper reconciles the contradicting effects of FDI in different countries documented in the previous literature. The country variation of the extent of GVC participation explains why the effects of the FDI vary across countries. Second, we identify the existence of cross-border FDI spillovers. To the best of our knowledge, this is the first time that these cross-border effects have been identified. This paper fills the gap by pointing out that the spillovers exist not only through the domestic input and output linkages but also through foreign
Policy ReseaRch WoRking PaPeR 4581

# **Abstract**

Economic development is affected by infrastructure services in both volume and quality terms. However, the quality of infrastructure is relatively difficult to measure and assess. The current paper, using firm-level data collected by a business environment assessment survey in 26 countries in Europe and Central Asia, estimates the marginal impacts on firm costs of infrastructure quality. The results suggest that the reliability or continuity of services is important for business performance. Firm costs significantly increase when electricity outages occur more frequently and the average outage duration becomes longer. Similarly, increased hours of water supply suspensions also reduce firms’ competitiveness. In these countries, it is found that the total benefit for the economy from eliminating the existing electricity outages ranges from 0.5 to 6 percent of gross domestic product. If all water suspensions are removed, the economy could receive a gain of about 0.5 to 2 percent of gross domestic product. By contrast, the quality of telecommunications services seems to have no significant impact.

This paper—a product of the Economics Unit, Finance, Economics and Urban Development Department—is part of a larger effort in the department to explore the linkage between infrastructure quality and economic growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted ataiimi@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
difficult, word problms. Since students from **grades** 4 and 7 were included, raw test scores were converted to standardized scores, and grade-level **_was_** included as **a** control variable in the regression models.

Setting-specific indicators of social class. In the Malawi study, we focused on material aspects and measures of class that are more relevant to Third World settings, moving beyond the global constructs historically used within industrialized nations. For instance, we asked pLpils about seven different work tasks they might engage in after school. Four dealt with farm-related activities (feeding livestock, carrying fodder or water). Other tasks wore more general, taking care of siblings or preparing meals, for instance. We also asked pupils and their headmasters about housing conditions, particularly whether families lived in thatched-roof huts or in Western structures. Students were asked whether their houses had electricity and whether their parents owned a radio. Students also identified their mother tongue. We were especially interested in whether their parents spoke Chichewa, the dominant tribal language, or one of the more than 35 other tribal languages spoken in Malawi. In addition to these situation-specific measures of class, conventional indicators were also used: parents' occupation and schooling levels.

Analysis. Our analysis examined the influence of these indigenous measures of class on math and language achievement. OLS estimates were run for all students, then separately for students in rural and urban schools. Regression models for all students included a control on whether he or she attended an urban or a rural school. In some instances, attendance at an urban school was neaatively related to achievement. Thus, while urban children generally come from families

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fraction of firms that export from i to j. Like Vij, wij implicitly contains the level of fixed trade costs, implying that the fraction of exporting firms will depend on fij. We hence have: where χi = (ε-1)pi + yi is an importer fixed effect and λi = -(ε-1) ln cj + nj is an exporter fixed effect.<sup>15</sup> HMR point out that the exclusion of wij will mean that the effects of trade barriers on firm-level trade would be confounded with their effects on the proportion of exporting firms and γ would hence be biased upwards; in order to correct for this, the selection of firms into the export market, wij, will have to be estimated itself in a first stage using a probit equation. The related latent variable in this probit estimation will be zij and<sup>_z_</sup> ˆ _ij_ * = Φ − 1 (ρ ˆ _ij_ ) . HMR show that _w_ ˆ _ij_ ≡ ln { exp [δ( _z_ ˆ _ij_ * +η ˆ _ij_ * )]− 1 } is a consistent estimate for E[wij¦., Tij=1]. This will correct for unobserved firm-level heterogeneity. There is further the need to correct for Heckman selection bias using the inverse Mills ~~ˆ~~ * ˆ * ˆ * ratio, here expressed as ~~η~~ _ij_ = φ( _zij_ ) / Φ( _zij_ ) .

All of the above implies that we can estimate (3) consistently by transforming it into where βuη ≡ corr (uij, ηij) (σu/ση) and eij is an i.i.d. normally distributed error term.

Since wij (and hence zij) implicitly hinges on the fixed costs faced by firms in their export markets, fixed cost barriers should turn out to be significant in the probit stage of the equation, whereas variable cost barriers are expected to turn out significant in the second stage. As many trade barriers impose both fixed and variable costs at the same time, it is hypothesized that the variables important in the probit stage of the estimation, i.e. those influencing the export decision of a firm, will be similar to those determining export volumes in the actual gravity estimation. It seems likely that harmonization initiatives and mutual recognition agreements affect both fixed and variable costs (Baldwin, 2000; Mattoo and Chen, 2004).

In order to satisfy the exclusion restriction for identification of the second stage of the estimation, the set of variables in the first and second stages cannot be exactly the same. It will therefore be important to identify a trade barrier that affects purely the fixed cost of exporting, such that it can be dropped from the second stage of the estimation.

For the purpose of estimation, dummies will be introduced for mutual recognition agreements and harmonization initiatives initially in both stages. Speculations as to their signs will be discussed below. The following specification will be used (see Table 1 for variable definitions):

> 15 HMR (2006) show that consistency requires the use of separate country fixed effects for exporters and importers as proposed by Feenstra (2002).

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and 2000 employment shares shows a typical manufacturing decentralization process, albeit less than we anticipated. In 1970 the secondary industry share in overall local employment was positively related to agglomeration size. Similarly, the manufacturing share in total nonagricultural employment increases from 29.0 percent among the smallest agglomerations to 34.7 percent in the top group as we move up the urban hierarchy in 1970. But by 2000 there is a dramatic drop in the manufacturing share of the cities in the top two cells to about 25 percent. While the manufacturing share drops in smaller cities as well, the decline is more modest, so that by 2000 smaller cities have more local manufacturing concentration than bigger ones. We therefore observe decentralization of manufacturing industry out of big cities.

**Table 9: Employment Share by Industry in 1970 and 2000**

|Agglomeration size groups<sup>1</sup>|Number<br>of cities|Emp<br>Secondary<br>industry (a)<br>**1970**|loyment sh<br>Tertiary<br>industry<br>(b)|are, %<br>100<br>_a_<br>_a_<br>_b_ <sup>×</sup><br>+|Core area’s<br>secondary<br>industry<br>share<sup>2)</sup>|Core<br>area’s<br>tertiary<br>industry<br>share<sup>2)</sup>|
|---|---|---|---|---|---|---|
|Largest: 1.340 ≤ pop/mean|12|30.9|58.1|34.7|64.2|76.1|
|0.812 ≤ pop/mean < 1.340|12|23.4|53.8|30.3|58.0|72.1|
|0.469 ≤ pop/mean < 0.812|19|24.1|46.1|34.4|69.7|71.9|
|0.256 ≤ pop/mean < 0.469|36|19.7|46.1|30.0|83.8|86.7|
|Smallest: pop/mean < 0.256|44|18.9|46.5|29.0|.|.|
|Total|123|27.8|54.8|33.7|66.6|77.1|
|||**2000**|||||
|Largest: 1.340 ≤ pop/mean|15|24.1|71.2|25.3|47.0|61.3|
|0.812 ≤ pop/mean < 1.340|14|23.0|69.8|24.8|60.0|70.9|
|0.469 ≤ pop/mean < 0.812|17|27.3|63.1|30.2|61.1|70.3|
|0.256 ≤ pop/mean < 0.469|20|24.6|65.3|27.3|79.8|81.4|
|Smallest: pop/mean < 0.256|57|24.0|62.8|27.7|.|.|
|Total|123|24.2|69.4|25.9|55.2|66.5|

1) The relative size cutoff points are calculated for 1970 city size distribution to be divided into 5 cells containing approximately 35%, 30%, 15%, 10% and 10% of all cities starting from the bottom.

2) Core area’s secondary (tertiary) industry share (%) is the ratio of the secondary (tertiary) industry employment in core areas to the total secondary (tertiary) industry employment in an agglomeration. The ratio of the number of suburb to core areas (MCAs) in each cell is 13.7, 4.0, 3.4 and 0.9 for 1970, and 11.7, 4.0, 2.5 and 1.5 from the top cell to cell 4. The last cell with relative population less than 0.256 is not calculated since the core areas has too small suburb areas (on average 0.3).

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<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>All urban areas Potential slum areas Urban expansion areas<br>RP10 RP25 RP50 RP100 RP1000<br>RP10_SLR RP25_SLR RP50_SLR RP100_SLR RP1000_SLR<br>% of area exposed<br><!-- End of picture text --> _Figure 1. Slum areas tend to be more exposed than the average, across all flood scenarios. SLR means the scenario includes a 30cm sea level rise due to climate change. RP denotes the turn period event._

# **6. Limitations**

The findings presented should be interpreted considering a number of caveats.

While we use current and future flood data, we only use current population and poverty data, as reliably projecting these socioeconomic trends spatially into the future is almost impossible. Changes in these trends – among many other factors – can lower socioeconomic vulnerability even as the climate change hazard increases (Hallegatte et al. 2016). Along these lines, while we examine which regions within Vietnam have the highest flood exposure, we do not examine the determinants of vulnerability (other than poverty). Recent analyses suggest that the Northwest, Central Highlands, and Mekong River Delta have the greatest socioeconomic vulnerability (World Bank 2010).

In the flood hazard maps developed for this paper, we assume no protection due to a lack of data and as a result the hazard maps present an upper bound of flood risk. Work is currently ongoing to develop a global database of flood protection, and this information can be mobilized for future work (Scussolini et al. 2015). For the national‐level analysis, flooded areas are defined as any area with inundation higher than 0. We have not yet explored the depth dimension, although the flood hazard maps developed for this study allow for this potential in future work.

For the HCMC analysis, the location of the slum areas in the PUMA data set are mainly restricted to the old town. Furthermore, slum areas are often difficult to define and the data we have likely does not capture all slum areas within HCMC. Finally, the urban expansion data set does not make a distinction between the urban expansion of residential areas or infrastructure (roads, etc.).

19
**Table 1. Size and Type Distribution of Firms and Employment, 2008 (shares)**

||**_Formal and legal_**|**_Mixed_**|**_Informal and legal_**|**_Informal and illegal_**|**_Total_**|
|---|---|---|---|---|---|
|**_Firms_**||||||
|1–5|1.22|3.65|65.84|19.29|90.00|
|6–10|0.69|1.54|1.10|2.43|5.77|
|11–50|0.84|1.20|0.55|0.87|3.46|
|50+|0.25|0.29|0.18|0.05|0.77|
|**Total**|3.00|6.69|67.67|22.64|100.00|
|**_Workers_**||||||
|1–5|0.83|2.65|25.49|11.80|40.77|
|6–10|1.17|2.56|1.73|3.90|9.36|
|11–50|3.98|5.45|2.50|3.49|15.42|
|50+|12.56|13.47|7.41|13.47|34.44|
|**Total**|18.54|24.14|37.12|20.20|100.00|

_Source:_ Author’s computations based on Economic Census data.

# **Misallocation is High and Persistent in Mexico**

Misallocation in Mexico is significant, as evidenced by the fact that the marginal revenue product of capital and the labor of firms that produce similar goods are different. (See appendix S1 for definitions and for descriptions of the data.) In fact, misallocation is higher in Mexico than in the United States or in other Latin American countries with comparable data (Busso, Madrigal, and Pagés 2013). While there is debate over the exact nature of the distortions that induce this phenomenon, three results are robust: distortions result in large productivity losses; they operate in the direction of allocating too much capital and labor to low-productivity firms that are less intensive in more educated workers; and they are persistent through time.

Following Hsieh and Klenow (2009), total revenue productivity, TFPRis, is defined as the value produced by firm _i_ in sector _s_ with one peso of capital and labor (a weighted average of the marginal revenue products of the labor and capital used by the firm). In the absence of any 9
Equation 5 where ∑T�h is the total household expenditure on all food items j included in the index. The index jj is first averaged for each quarter, region, and area combination, and then normalized for each stratum jj EE by the national average.

Equation 6 Housing costs can be analyzed in the same index framework by including imputed rent as a component of the Paache index. Including the cost of rent into Equation 1 as an additional consumption item proceeds by assuming the “quantity” of rent is one (i.e., that the household pays imputed rent on only a single dwelling) and including the resulting unit value as a separate expenditure item for the household. This is conceptually different from most reported food expenditure in the sense that although something of value was consumed (use of the dwelling) no financial transaction took place. The first uses imputed rent directly as reported in the L2CU baseline. This method implicitly assumes that housing is the same “good” for all consumers, regardless of the type of dwelling, and compares the overall differences in the resulting cost-of-living as a partial function of the unmodified imputed housing cost. A second alternative is to use a hedonic housing price measure. The second approach is more common in the literature on measuring the cost-of-living and imposes the requirement that the spatial deflator for housing be based on consumption of a similar “type” of housing, in terms of size and construction. In both cases, the housing costs are included alongside the other components of the Paache index.

There is no estimation for the first option, as self-reported values are included directly. However, the hedonic option is implemented using a separate imputation technique. The process proceeds by imputing the cost of renting the same “standard” dwelling in each locality and including this value in the overall cost-of-living index. The first step involved estimating a simple ordinary least squares (OLS) regression of reported rental values: 52
55

# **2.5.5  Community mobilization**

Many initiatives have aimed to improve the response to violence against women by mobilizing the community through mass media campaigns, community-outreach, community-based awareness raising campaigns, and service provision.

# **_Multi-sectoral initiatives to mobilize a better community response to GBV_**

Based on the recommendations PAHO multi-country study of institutional barriers for abused women, both the PAHO and IDB programs in Latin America placed a great emphasis on the development of community-based networks for coordinating services to victims as well as access to justice and violence prevention. These networks may be made up entirely of governmental agencies such as the criminal justice sector, social welfare, education. Another type of network, such as the Nicaraguan Network of Women against Violence exists for the purpose of coordinating the response of civil society to violence, while a third type integrates public and private agencies addressing GBV. The most promising interventions combine the goals of primary and secondary prevention and help engage governmental and nongovernmental institutions in a joint strategy. These networks can greatly enhance the quality of care provided to survivors and help coordinate the services women receive from multiple institutions. They can also play an important role at a community level in mobilizing public support for survivors of violence and decreasing tolerance of violent behavior. A description of a local community network in Los Altos, Bolivia is presented in Box 2.5.

# **2.5.6  Individual behavior change strategies**

Evaluations of initiatives to change individual behaviors highlight the complexity of violence prevention; may evaluations document that changing attitudes and awareness appears to be much easier than changing violent behaviors.

# **_Mass media campaigns to increase awareness of women’s rights_**

As discussed earlier in this paper, one of the major findings of international research on the causes of GBV is that, although individual risk factors such as witnessing violence as a child or use of alcohol may increase a specific individual’s likelihood to use violence, cultural norms play a large role in overall levels of violence in a community. Community-based educational activities can increase women’s knowledge of legal and social rights and empower them to seek help for abuse. They can also challenge the underlying beliefs that justify women’s subordination and the use of violence for settling conflicts. Promoting nonviolent and equitable relationships between men and women is the key to preventing future violence.Organizations (mostly NGOs) around the world have used mass-media campaigns to raise awareness of GBV, promote nonviolent behavior and to encourage women and men to be more supportive of their friends and family members who experience violence. These
Table 3 Taris Cuts in Textiles and Price Changes

||Tari|Price Change|
|---|---|---|
|Food & Beverages|13.9||
|Textiles|19.4|16.2|
|Minerals|11.6||
|Metals|14.2||
|Other Manufactures|14.1||
|Capital Goods|12.3||
|Non-Tradables<br>short-run||1.36|
|long-run||1.30|

Source: Taris _τj_ (in percent) are from Brambilla, Galiani and Porto (2018). Price changes are computed as _−τj /_ (1 + _τj_ ) . The change in the prices of the non-traded goods comes from the simulations.

Table 4 Responses to Tari Cuts in Textiles Capital, Employment, Real Wages and Welfare <u>(percent)</u>

||Year 1|Year 2|Year 3|Steady State|
|---|---|---|---|---|
|Capital|||||
|Textiles|0.00|-9.41|-14.62|-25.47|
|Other Traded Goods|0.00|0.09|0.24|0.57|
|Non-Traded Goods|0.00|-0.62|-0.83|-1.34|
|Real Wage|||||
|Textiles|-15.04|-17.25|-18.45|-20.32|
|Other Traded Goods|1.45|1.21|1.19|1.20|
|Non-Traded Goods|0.06|-0.19|-0.28|-0.39|
|Employment|||||
|Textiles|0.00|-2.91|-3.90|-4.64|
|Other Traded Goods|0.00|0.59|0.77|0.91|
|Non-Traded Goods|0.00|0.20|0.28|0.34|
|Non-employment|0.00|0.29|0.41|0.53|
|Producer Welfare|||||
|Textiles|-16.91|-20.21|-22.00|-25.73|
|Other Traded Goods|0.82|0.90|0.10|0.11|
|Non-Traded Goods|-1.31|-1.50|-1.57|-1.75|
|Worker Welfare|||||
|Textiles|-1.38|-1.44|-1.47|-1.53|
|Other Traded Goods|-0.89|-0.90|-0.91|-0.92|
|Non-Traded Goods|-0.93|-0.96|-0.97|-0.98|
|Non-employment|-0.94|-0.95|-0.95|-0.96|

Notes: Simulation of the transitional dynamic responses of capital, real wages, employment and producer and worker welfare following the full elimination of taris on the textile sector.

38