rafmacalaba/datause-encoder-data huggingface dataset:
# appear to be in practice in any of the African EPZs.

# <u>Transport and trade facilitation</u>

The second most important criteria noted by all EPZ investors, in both the African and non‐African countries, is access to reliable transport infrastructure. This relates not only to issues of location and hard infrastructure (roads, rail, ports, etc.), but critically also the soft infrastructure of customs and trade facilitation. Indeed, the overall efficiency of the transport environment has a substantial impact on the competitiveness of exporters, as it impacts their ability not only to access markets cost effectively, quickly, and reliably but also to access critical inputs to the production process.

Although EPZ investments should ensure quality transport access between the zones and key trade gateways, in practice this happens only occasionally. Moreover, it is an expensive proposition. Thus, the investment climate performance of an EPZ with respect to transport is likely to be determined, more than anything, by the decision on where the EPZ is to be located. The closer EPZs are to the main trade gateways (ports, airports, borders), the more likely they will be in a position to ensure an effective transport and logistics environment for investors. In general, most of the African countries in the survey have located their main zones either nearby the main trade gateways or major cities. There are, however, a number of zones located in peripheral regions, including in Nigeria, Bangladesh, Vietnam, Dominican Republic, and Lesotho. In all of these cases, the EPZs have struggled to attract more than a handful of investors.

**Table 7: Quality of roads and port infrastructure in selected countries (Country ranking)**

||**Quality of roads**|**Quality of port**<br>**infrastructure**|
|---|---|---|
|**Ghana**|76|69|
|**Kenya**|91|84|
|**Lesotho**|113|114|
|**Nigeria‐**|112|122|
|**Senegal**|78|54|
|**Tanzania**|108|120|
|**Bangladesh**|95|113|
|**Dominican Republic**|70|58|
|**Honduras**|74|36|
|**Vietnam**|102|99|
|_Correlation_||_0.9272_|

Source: Global Competitiveness Index 2009 The importance of access to quality roads and port infrastructure is highlighted once the poor conditions experienced in the overall economy are taken into account. Table 7 shows the ratings from the 2009 Global Competitiveness Index (World Economic Forum, 2009) on the quality of road and port infrastructure in each country in the study. It shows a high correlation between the rankings of road 26
Policy Research Working Paper 7200

# **Abstract**

Bangladesh, with two-thirds of its land area less than five meters above sea level, is one of the most climate-vulnerable countries in the world. Low-lying coastal districts along the Bay of Bengal are particularly vulnerable to sea level rise, tidal flooding, storm surges, and climate-induced increases in soil and water salinity. This paper investigates the impact of drinking water salinity on infant mortality in coastal Bangladesh. It focuses on the salinity of drinking water consumed during pregnancy, which extensive medical research has linked to maternal hypertension, preeclampsia, and post-partum morbidity and mortality. The study combines spatially-formatted salinity measures for 2001–09 provided by Bangladesh with individual and household survey information from the Bangladesh Demographic and Health Surveys for 2004 and 2007. It uses probit and logit analyses to estimate mortality probability for infants less than two months old. Controlling for many other determinants of infant mortality, the analysis finds high significance for salinity exposure during the last month of pregnancy and no significance for exposure during the preceding months. The estimated impact of salinity on infant mortality is comparable in magnitude to the estimated effects of traditionally-cited variables such as maternal age and education, gender of the household head, household wealth, toilet facilities, drinking water sources, and cooking fuels.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Sdasgupta@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
**Table 9: Evolution of Risk Adjusted IGRC by Birth Cohorts Urban vs. Rural and Sons vs. Daughters**

||**CH**<br>|**INA**<br>|**IN**<br>|**DIA**<br>|**INDO**<br>|**NESIA**<br>|
|---|---|---|---|---|---|---|
||Weighted<br>RIGRC|Canonical<br>IGRC|Weighted<br>RIGRC|Canonical<br>IGRC|Weighted<br>RIGRC|Canonical<br>IGRC|
||||**Ur**<br>|**ban**<br>|||
|1980-1989|0.398|0.344|0.630|0.524|0.466|0.427|
|1970-1979|0.396|0.343|0.685|0.537|0.461|0.422|
|1960-1969|0.323|0.273|0.771|0.575|0.617|0.516|
|1950-1959|0.334|0.260|0.788|0.580|0.622|0.554|
||||**Ru**|**ral**|||
|1980-1989|0.453|0.359|0.674|0.512|0.439|0.397|
|1970-1979|0.405|0.300|0.789|0.538|0.512|0.470|
|1960-1969|0.298|0.213|0.913|0.576|0.649|0.555|
|1950-1959|0.308|0.210|0.995|0.620|0.511|0.451|
||||**So**|**ns**|||
|1980-1989|0.469|0.393|0.622|0.505|0.450|0.407|
|1970-1979|0.431|0.357|0.394|0.275|0.499|0.455|
|1960-1969|0.311|0.256|0.906|0.633|0.626|0.529|
|1950-1959|0.310|0.240|1.035|0.704|0.585|0.502|
||||**Daug**|**hters**|||
|1980-1989|0.543|0.447|0.783|0.585|0.482|0.438|
|1970-1979|0.553|0.439|0.913|0.605|0.580|0.529|
|1960-1969|0.425|0.326|1.105|0.653|0.758|0.626|
|1950-1959|0.499|0.349|1.275|0.620|0.612|0.555|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given category. (3) (2) The weights are the proportion of children at each level of father’s education.

33
we referred to destinations as “destination 1”, “destination 2”, etc., randomizing at the individual level the ordering of which destination was labelled with each number in each game. In a final game, we explicitly tested for home bias by telling participants “now the multinational corporation allows you to continue working in your home country, while also giving an option to move to a destination abroad”. We set the cost of moving to be zero, and the monthly wage to be the same in both destinations. If labelling a destination as “home” had no effect on decisions, we would expect individuals to be invariant between the two destinations, and so choices to be equally split between the two.<sup>4</sup> However, we find that 58.4 percent of the Lisbon sample and 63.8 percent of the Nairobi sample chose the home destination, which t-tests show to be significantly different from 50 percent (p=0.0357 and p<0.0001 respectively). Moreover, we find choices in this game to be strongly correlated with their professed desire to work abroad after graduating in the baseline questionnaire: 32.1 percent of those who say they want to work abroad chose the home destination, versus 87.7 percent of those who say they want to remain in Portugal (p<0.0001). Likewise, 40 percent of those who want to work abroad in the Nairobi sample chose the home destination, versus 75 percent of those who say they want to remain in Kenya (p<0.0001). This shows the importance of not using the “home” label in our main game choices, but also suggests that choices in this last game may be predictive of actual future migration decisions – something we test in section 6 using data from a follow-up survey.

# **4. Testing Classic Theories of Income Maximization and Migrant Self-Selection**

We begin by examining how classic migration theories predict migration behavior in the two destination case. For participant _i_ with randomly assigned skill level _si,_ unobserved skill at destination 1 of _e1i_ , and unobserved skill at destination 2 of _e2i,_ the wages available at the two destinations were specified as: (5) 1ii ii 1ii ww = 500 + 300ss + 3ee (6) 2ii ii 2ii As noted, we consider destination 1 as the home destination. There is thus no cost of moving to ww = 1000 + 100ss + ee this destination and the participant always has full information about this destination and no risk of unemployment. Destination 2 has a higher base wage rate, but a lower return to both observed

> 4 Recall that order bias is not a concern here, since participants were randomly assigned to be asked “home or abroad” versus “abroad or home”.

10
Table 10: Aid & bilateral TRADE -CASH – 2<sup>nd</sup> stage

|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Ln(Fatalities)_|-0.035|-0.032|-0.033|-0.033|-0.039|
||(0.026)|(0.027)|(0.028)|(0.027)|(0.029)|
|_Ln(Affected)_|0.019**|0.008|0.007|0.003|-0.002|
||(0.010)|(0.010)|(0.010)|(0.011)|(0.011)|
|_Ln(GDP p.c.)_|-0.171***|-0.149**|-0.152**|-0.189***|-0.170**|
||(0.060)|(0.068)|(0.069)|(0.073)|(0.074)|
|_Ln(Population)_|-0.082|-0.051|-0.051|-0.048|-0.030|
||(0.066)|(0.069)|(0.071)|(0.070)|(0.075)|
|_Trade (% of GDP)_|0.002|0.002|0.002|0.002|0.002|
||(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Distance_|0.018|-0.008|-0.005|-0.007|-0.001|
||(0.012)|(0.014)|(0.014)|(0.014)|(0.015)|
|_Affinity index_|-0.304|-0.307|-0.324|-0.325|-0.321|
||(0.294)|(0.322)|(0.323)|(0.322)|(0.320)|
|_Common religion_|0.249***|0.206**|0.203**|0.201**|0.201**|
||(0.086)|(0.101)|(0.101)|(0.101)|(0.101)|
|_Common language_|-0.134|-0.239|-0.253*|-0.224|-0.282*|
||(0.116)|(0.154)|(0.152)|(0.154)|(0.150)|
|_Former colony_|-0.431***|-0.543***|-0.549***|-0.544***|-0.516***|
||(0.125)|(0.153)|(0.153)|(0.154)|(0.156)|
|_Ln(GDP p.c.) donor_|-0.173**|-0.173**|-0.182**|-0.177**|-0.216***|
||(0.070)|(0.071)|(0.073)|(0.071)|(0.071)|
|_Trade with donor_|0.078***|0.049|0.057*|0.049|0.091**|
||(0.029)|(0.031)|(0.032)|(0.031)|(0.041)|
|_Regulatory quality_||0.009|-0.100|||
|||(0.097)|(0.147)|||
|_Trade with donor_×|||0.027|||
|_Regulatory quality_|||(0.029)|||
|_Corruption control_||||0.170|-0.266|
|||||(0.132)|(0.227)|
|_Trade with donor_×|||||0.094**|
|_Corruption control_|||||(0.038)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|0.052|-0.004|-0.026|-0.009|-0.134|
||(0.197)|(0.198)|(0.206)|(0.199)|(0.210)|
|Constant|1.650*|1.522|1.623|1.978*|1.920*|
||(0.980)|(1.053)|(1.058)|(1.106)|(1.100)|
|Log likelihood|-6908.274|-5846.983|-5846.317|-5784.745|-5784.063|
|N|3284|2757|2757|2757|2757|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

24
# **2.2 Geographical Concentration, Specialization, and Entrepreneurship**

Here, we discuss measures of geographical concentration, specialization and entrepreneurial activity in Cameroon.

## **2.2.1 Geographical Concentration of industries in Cameroon**

The importance of agglomeration varies across industries, hence different industries have been found to exhibit surprisingly different levels of agglomeration (see Ellison and Glaeser, 1997). As pointed out by Kerr and Kominers (2015) “a complete understanding of the pathways by which agglomeration increases firms’ returns requires an effective agglomeration index, a measurement which identifies the concentration of industry and explains the relationship between firm location choice and industry concentration". To get a precise measure and picture of the extent and strength of industrial localization in Cameroon, we compute the well-known Ellison and Glaeser (1997) discrete measure of localization using micro-geographic data from the gec; see Appendix A.1 for details. This allows us to identifies the industries for which localization is potentially the strongest and to examine how this trend varies across different types of industries (formal vs informal, manufacturing, and new entrepreneur).

Table 2 reports our results, and several comments are in order. First, the mean of the eg index is 0.007 and the median is 0.012.<sup>2</sup> Out of the 99 four-digit industries in Cameroon, 60 industries (60%) exhibit a positive value for the eg index. In other words, 60 percent of industries are weakly or strongly concentrated. Within those industries, roughly half of them are considered as strongly agglomerated. Second, formal firms are on average more geographically concentrated than informal firms. Third, new entrants tend to exhibit more geographical concentration than existing firms. Fourth, manufacturing industries are less concentrated than other industries in general. Fifth, few manufacturing and natural-resources related industries are among the most concentrated industries in Cameroon. Sixth, informal firms are on average less concentrated than formal firm in Cameroon. Our results show that around 50 percent of formal firm are geographically concentrated while less than 30% of informal industries are clustered in space. This suggests that informal firms do not tend to cluster in the same locations as formal firms and we should expect a very weak buyersupplier and technology linkages between firms in these two sectors of the economy. Using data from India, Mukim (2015) show that buyer-seller and technology linkages explain much of formal-informal coagglomeration in India manufacturing. Our results

> 2A comparison between our results and the different studies is not reasonable since the eg index should be sensitive to the size and shape of the underlying zoning system as pointed out by Briant et al. (2010).

10
**Table 8:** The effects of AIA and cloud adoption: sample restrictions and additional controls

|Regressions|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dependent variable||||Cloud co|mputing||||
|Restrict/controls|_<_95th<br>percentile<br>investment|_<_90th<br>percentile<br>investment|_>_10th<br>percentile<br>investment|_>_5th<br>percentile<br>investment|Exclude<br>1st-year<br>allowance|Exclude<br>R&D<br>tax credit<br>frms|Employment<br>band-year|Industry<br>2-digit -<br>year|
|AIA Dummy|-0.149***|-0.136***|-0.149***|-0.118***|-0.104**|-0.186***|-0.090**|-0.123***|
||(0.040)|(0.040)|(0.058)|(0.046)|(0.048)|(0.040)|(0.043)|(0.045)|
|Observations|2,090|1,980|1,980|2,090|1,962|2,094|2,202|2,190|

Note: Time period: Cloud regressions use data for 2007 and 2013. The dependent variable cloud adoption is a dummy variable. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. All regressions use a binary AIA eligibility indicator and are estimated via difference-indifferences. Regression 1 excludes largest firms in the top 5% of the investment distribution, while regression 2 excludes those in the top 10%. Regressions 3 and 4 exclude the smallest firms in the bottom 5% and 10% of the investment distribution respectively. Regression 5 restricts firms from the sample which qualified for the first-year allowance while regression 6 excludes those firms which fell under the new EU SME classification in 2008. Regression 7 includes employment size band-year controls while column 8 includes 2-digit industry-year fixed effects. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age, not reported for brevity. Robust standard errors in parentheses are clustered at the firm level. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
for the eectiveness of PTAs.<sup>4</sup> Similar to our results for trade, the estimates of the eects of DTAs on FDI are also heterogeneous. Specically, we do not obtain signicant estimates of the eects of PTAs and DTAs on FDI. However, when we zoom in on the eects of DTAs that include investment provisions, we obtain a positive, sizable, and statistically signicant estimate, which suggests that, on average, the PTAs with investment provisions in our sample have led to a 34.33% (std.err. 14.535) increase in FDI between their members. This result is consistent with earlier ndings from Lesher and Miroudot (2006). We also obtain positive estimates of the eects on FDI of several other DTA provisions including `labor market regulations', `export taxes', `public procurement' and `state owned enterprises'. This analysis reinforces and complements the ndings of Laget et al. (2021) who study the impact of dierent DTA provisions with rm-level data for the period 2003-2015. Finally, our estimates do not reveal a signicant impact of the increase in the depth (number of provisions) on FDI. In fact, our results suggest that an increase in the number/complexity of some investment provisions (e.g., related to `transparency' and `regulations') may actually decrease FDI.

We use the structural model in combination with our estimates of the partial eects of DTAs on trade and FDI in order to quantify the GE impact of DTAs on FDI.<sup>5</sup> We focus the analysis on inward usage of technology FDI per country and outward technology FDI stocks per country used abroad. The main conclusions from this analysis are as follows. DTAs have had large and strongly asymmetric eects on FDI. The DTAs that were in force in 2011 have contributed to about 3% of inward FDI in the world and about 70% of outward FDI. The large average eect of outward FDI is driven by some large outward FDI countries (such as the United States and China), where, consistent with our theoretical model of non-rival

> 4We refer the reader to Fernandes et al., eds (2021), an eBook from the World Bank and CEPR, which is a collection of excellent papers that focus on various aspects of the determinants of DTAs and on the DTA eects on trade and other economic outcomes.

> 5As discussed in more detail in Section 4.2, a caveat with our GE analysis is that the underlying theory is based on the assumption of non-rival technology FDI, while our data includes all/aggregate FDI ows. While this gap, of course, has implications for the quantitative results, our conclusions about the disproportionately large impact of outward FDI will remain qualitatively the same if applied to better suited data.

5
Di Nardo, J., and J.P. Pischke (1997): “The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?," _Quarterly Journal of Economics,_ vol. 112(1), pp. 291—303.

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‐ Krusell, P., L. Ohanian, J. V. Ríos-Rull, G. L. Violante (2000): “Capital Skill Complementarity and Inequality: A Macroeconomic Analysis," _Econometrica_ , vol. 68(5), pp. 1029—1053.

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32
<!-- Start of picture text --> Within-country changes in unemployment and informality rates<br>ESS rounds 3 (2006-2007) vs. 2 (2004-2005); 4 (2008-2009) vs. 3(2006-200<br>LT<br>SI<br>HU<br>IE<br>RU<br>CZNL DEBE FISE FRUA CHES PT<br>CY UK GRRO NO AT<br>DK BG<br>EE<br>PL<br>SK LV<br>-6 -4 -2 0 2 4<br>Change in informality rate, % points<br>Informality rate excludes self-employed<br>Connected points correspond to the same country, with the country label at the earlier point<br>Missing ESS data of round 2 (BG, LV, RO and FR) or round 3 (CZ, LT) replaced<br>with informality rate from the Fourth European Working Condition Survey (2005/Q4)<br>Single points, for countries observed only twice, show 2007 vs. 2005 (AT),<br>2008 vs. 2006 (CY and RU), 2008 vs. 2005 (RO), 2009 vs. 2005 (GR and LT)<br>Base: Labor Force Extended to Include Discouraged Workers<br>10<br>5<br>0<br>-5<br><!-- End of picture text -->

**Figure 3 Change in unemployment and discouragement vs. change in informality rate**

# **3 Informal workers at a glance**

As noticed by Riedmann and Fischer (2008), knowledge of the characteristics of the shadow sector workers is an important prerequisite for designing appropriate political measures to deal with undeclared work. The differences between profiles of informal and formal workers might be country-specific, depending on social norms and corporate culture, on strictness or particular components of employment protection legislation, and on the sectoral composition of the economy. For instance, high firing costs and (relatively) high minimal wage are likely to push young workers disproportionally into informal sector. Lack of flexible working time arrangements in the formal sector might make informal work more attractive for students and married women. Booming construction (respectively, hospitality) sector likely increases proportion of males (respectively, females) among informal workers.

In Section 3.1 we compare (at the country and/or European region level) composition of formal and informal workers in terms of key personal characteristics and [main] job profile. Most of the time, we concentrate on employees with and without contracts. In order to have enough observations on this category for statistical inference at the country level, the ESS data which refer to 2004-2009 have been combined with the data of the Fourth European 22