rafmacalaba/datause-encoder-data huggingface dataset:
duties were still applied to 856 tariff lines (837 of which were agricultural products). Of these, 116 lines were excluded from the Cotonou Agreement. An additional 301 tariff lines were eligible for reduced duties, subject to specific quantitative limits (tariff quotas) set for the ACP countries as a group. The remaining 439 products were eligible for reduced duties without quantitative limits. Another important EU program is the Everything But Arms (EBA) initiative, introduced in March 2001. This program grants duty-free access to imports of all products from the LDCs except for three major products where liberalization is delayed: fresh bananas, rice, and sugar. Tariffs on these items will be reduced gradually to zero by 2006 for bananas and by 2009 for rice and sugar, with tariff quotas for rice and sugar increased annually during the transition. A key feature of the EBA is that, in contrast to the GSP, preferences are granted for an unlimited period and are not subject to periodic review.

# **Economic Analysis**

There has been a significant amount of econometric and simulation analysis of the effects of preferences.<sup>13</sup> Most of this has focused on national GSP schemes which can be explained by the fact that for some time this was the predominant scheme used by developed countries. By focusing on the GSP, there is also greater scope for comparisons to be made across programs of different preference-granting countries. However, it must be kept in mind that, in practice, especially during the last 20 years or so, GSP is not the scheme that offers “most preferred” status. That is generally reserved for free trade agreement partners—who liberalize on a reciprocal basis—and subsets of “more preferred” developing countries. In the case of the EU this includes ACP and EBA eligible countries; in the case of the US better than GSP treatment is accorded under AGOA, ANDEAN and the Caribbean Basin Economic Recovery Act (CBERA). Thus, a country that trades under the GSP is actually suffering discrimination, as it is excluded from the better preference schemes available to (a subset of) its competitors. Any analysis that looks only at the GSP will therefore provide a rather distorted picture of the reality. The same is true if account is not taken of other trade policy instruments. In the case of textiles and clothing, the Multi-fiber Arrangement (MFA) for many decades established a

> 13 See Brown (1988) for a survey of the literature up to the mid 1980s.

12
# **<u>Table 8. Firm Efficiency and Government Facilitation: By Ownership Type</u>**

||**State**|**Domestic**<br>**private**|**Foreign**|
|---|---|---|---|
||TFP style|TFP style|TFP style|
|loan help, city-industry-ownership mean|0.729*|1.684***|1.033***|
||(0.436)|(0.496)|(0.321)|
|info facilitation, city-industry-ownership mean|0.738**|0.135|0.453*|
||(0.373)|(0.191)|(0.267)|
|Ln(firm age)|-0.337***|-0.430***|-0.492***|
||(0.065)|(0.107)|(0.073)|
|Ln(L)|0.236*|-0.775|-0.153|
||(0.136)|(0.786)|(0.138)|
|lngdppc|0.373**|0.456***|0.528***|
||(0.157)|(0.110)|(0.105)|
|PS long tenure|0.080|-0.004|0.080|
||(0.185)|(0.147)|(0.137)|
|CEO characteristics, access to finance, institutional<br>environment, efficiency of state machinery,|Yes|Yes|Yes|
|ind dummies * ln(K/L), lnL|Yes|Yes|Yes|
|ind dummies|Yes|Yes|Yes|
|r2_a|0.391|0.368|0.424|
|N|926|1,133|1,753|

Note. *, ** and *** represent statistical significance at the 10, 5 and 1 percent, respectively. Whiteheteroskedasticity-corrected standard errors, clustered at the city-industry-ownership level, in parentheses.

The results based on labor productivity styled regressions are similar (except that market link is statistically significant).

39
In summary, the distance measure is an absolute measure of the difference between the soil properties on a given plot and a specific suitability class, while the membership grade is a relative score, ranging from zero to one, indicating the relative fit of a plot into each suitability class. The membership grades for S1, S2, S3, and N, therefore, sum to one for each plot. Two separate suitability class assignments are constructed for each agricultural plot: one derived from MAPS plot-level soil sample results and one from AFSIS geospatially-derived soil properties.

# **4.2.Econometric Modeling of Production Frontiers**

Aigner, Lovell, and Schmidt (1977) lay out the potential problems in minimizing the sum of squares of a simple production function, such as Cobb Douglas, in estimating the maximum output for a given level of inputs. The authors argue that this method of estimation inadequately explains observed deviations from the maximum output for given levels of inputs. In their proposed stochastic frontier model, they explain the variation in deviations from the modeled maximum output, or the production frontier, and predict an observation-level measure of technical inefficiency.

Much of the literature on stochastic frontier models assumes a translog production function, in which inputs into the production function are also interacted (see Greene (2008), Sherlund et al. (2002), and Ekbom and Sterner (2008)). This can, however, result in an explosion of parameters to be estimated in the case of many inputs, such as in agricultural models. Rather than the translog function, we assume a log-linear Cobb Douglas model, following the seminal work of Aigner, Lovell, and Schmidt (1977) and the agricultural examples set forth by Deininger et al. (2007), Kilic et al. (2009), and others. The estimated stochastic frontier model is as follows: KK ii kk iikk ii (3) ln(YY ) = αα+ � ββ ln(XX ) + εε kk=1 ii ii ii (4) εεwhere = νν is total maize grain output (in kilograms) on plot − μμ _i_ , and parameters to be estimated. _X_ is a vector of traditional economic inputs, including land area, household and hired ii kk YY αα and ββ labor inputs, and inorganic fertilizer usage.<sup>12</sup> As this is a rain-fed agricultural system, rainfall is also controlled for. Plot-specific flowering season rainfall was computed as total rainfall during the 5<sup>th</sup> to 8<sup>th</sup> dekads following the onset of seasonal rainfall, using CHIRPS timeseries precipitation

> 12 Following Sherlund, Barrett, and Adesina (2002), zero values for hired labor and use of inorganic fertilizer were transformed logarithmically as follows: ln(0) = ln(strictly positive sample minimum/10).

12
skilled migrants, which is intuitive as college educated migrants may be better positioned both financially and socially to assist US companies and entrepreneurs in investing abroad. This pattern is confirmed in the last four specifications where both the aggregate migrant stock and the share of skilled migrants have positive and statistically significant coefficients.

The magnitudes of these effects are economically meaningful. A one percent increase in the total migrant stock is associated with a .3 percent increase in the FDI stock. In the case of skilled migrants, the corresponding effect is higher reaching about .4 percent. Using the last two specifications as the basis for inference, we find that a 10 percent rise in the share of tertiary educated migrants in the migrant population increases the FDI stock in their country of origin by an additional .57 percent.

We conclude that there exists a positive correlation between the presence of migrants in the US and the stock of FDI in their country of origin. This correlation is particularly strong in the case of migrants with college education. Our analysis also suggests that ignoring the endogeneity issue tends to underestimate the effect of migration on FDI.

## **_4.3. Robustness checks_**

To check the robustness of our results, we reestimated all regressions excluding Mexico, which has a special status vis-à-vis the US relative to other destination countries included in the analysis, both as a sender of migratory flows and as a recipient of FDI. The special relationship between the US and Mexico is due to their geographic proximity, membership in NAFTA as well as from a large differential in the level of economic development between the two neighbors which prompts a large number of Mexicans to cross the border in search of a better life. The results, presented in Appendix Tables A1 (OLS – aggregate FDI flows), Table A2 (OLS – sectorlevel FDI flows) and Table A3 (IV regressions) do not differ significantly from the results of the orginal model. Therefore, we conclude that our findings are not driven by the figures for Mexico.

# **5. Conclusions**

The purpose of this study was to examine whether migrant networks have a positive effect on flows of foreign direct investment to the migrants’ country of origin. To study this question, we used data on US FDI abroad and the presence of migrants in the US. To take into account the potential simultaneity between FDI and migrant flows, we instrumented for the migrant presence in the US using information on the stock of migrants from the country of origin 10
Table 1: Descriptive Statistics

||**Mean**|**Std.Dev**|**Min**|**Max**|
|---|---|---|---|---|
|Dep. variable<br>|-1.659|2.507|-18.32|9.631|
|EU GSP tarif<br>|.0288|.0359|0|.224|
|EU ACP tarif<br>|.0016|.01|0|.2701|
|EU MFN tarif<br>|.0486|.0369|0|.1672|
|US GSP tarif<br>|.0481|.0862|0|1.189|
|US MFN tarif|.0458|.0471|0|.3185|
|US MEX tarif|.0067|.0189|0|.2624|

Notes: Descriptive Statistics are shown for the entire sample and the manufacturing subsample, respectively, which are pooled over countries. Values shown are in logarithms, tariffs take the form ln(1 + _τ_ ).

Table 2: Between and Within Variation of EC Tariff Rates

||**Std. De**|**viation**|**Ratio**|**Share of**|
|---|---|---|---|---|
||**between**|**within**|**(b/w)**|**constant Obs.**|
|Brazil|.0308|.0089|3.478|.3609|
|China|.032|.0088|3.627|.3832|
|India|.0328|.0082|4.013|.3378|
|Thailand|.0356|.0071|4.987|.3278|
|Vietnam|.0427|.0078|5.489|.2882|
|Colombia|.039|.0062|6.3|.2916|
|Tunisia|.0436|.0048|9.166|.2375|
|ACP|.0617|.0247|2.502|.8375|

Notes: Statistics originate from the manufacturing subsample.

25
# **Section OA6. Tables**

|**Table OA1. Financial Literacy**|||
|---|---|---|
|(1)|(2)|(3)|
|Peru|Mexico|Pooled|
|**_Demographics_**|||
|Male (1 = yes)<br>0.072**|0.145***|0.111***|
|(0.036)|(0.030)|(0.023)|
|Married (1 = yes)<br>0.053|-0.013|0.002|
|(0.035)|(0.029)|(0.022)|
|Age<br>0.000|-0.003*|-0.001|
|(0.002)|(0.002)|(0.001)|
|Monthly household income<br>0.000***|0.000|0.000***|
|(0.000)|(0.000)|(0.000)|
|Reports having no income<br>-0.054|-0.014|-0.020|
|(0.051)|(0.046)|(0.034)|
|**_Socioeconomic level*: (1 = yes)_**|||
|NSE: C<br>0.103*|0.005|-0.040|
|(0.053)|(0.037)|(0.029)|
|NSE: C-<br>0.038|-0.001|0.004|
|(0.080)|(0.035)|(0.033)|
|**_Education: (1 = yes)_**|||
|Post Secundary<br>0.187***|0.073*|0.131***|
|(0.036)|(0.040)|(0.027)|
|**_Occupation: (1 = yes)_**|||
|Employed<br>0.072*|-0.074**|0.011|
|(0.041)|(0.037)|(0.028)|
|Owns business<br>0.066|-0.010|0.048|
|(0.048)|(0.042)|(0.032)|
|**_Internet: (1 = yes)_**|||
|Has internet at home or office<br>0.029|0.071**|0.034|
|(0.047)|(0.033)|(0.028)|
|**_Product Usage: (1 = yes)_**|||
|Has credit<br>-0.008|-0.028|-0.001|
|(0.036)|(0.033)|(0.024)|
|Has savings account<br>0.031|0.117***|0.044*|
|(0.035)|(0.031)|(0.023)|
|Has credit card<br>-0.010|0.006|-0.035|
|(0.035)|(0.039)|(0.026)|
|Number of individuals<br>887|1,071|1,958|
|R-squared<br>0.11|0.057|0.068|
|Country FE<br>N|N|Y|
|Mean Dep. Var.<br>0.481|0.664|0.581|
|Notes: This table reports the estimates of the regression of a dummy variable that indicate|s knowledge of|interest rate|
|_FinLit_ion individual characteristics. Knowledge of interest rate is tested with the followin|g multiple choic|e question:|
|"If you deposit 100 soles in a bank account that charges you nothing and guarantees you a<br>much would there be in the account by the end of the year, if no deposits or withdrawals a<br>answers are: (a) Over 102. (b) Exactly 102. (c) Less than 102. (d) I don’t know. (e) I pref|yield of 2% pe<br>re made?". Pos<br>er not to answ|r year, how<br>sible<br>er. Data|
|come from the initial survey. *In Mexico, C-D socioeconomic groups identify low-to-middl|e income house|hold. When|
|pooling the data from Mexico and Peru in Column 3, we also include country fixed effects.<br>reported in parenthesis under coefficient estimates. Levels of significance * p<0.10 ** p<0|Standard erro<br>.05 *** p<0.01|rs are<br>.|

24
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Policy Research Working Paper 10770

# **Abstract**

Although access to reliable electricity enables manufacturing companies to increase their output, there have been few studies of the distribution of output growth between export and domestic markets. Although some papers have examined the impact of electricity reliability on exports (in volume terms or dummy terms), little is known about the way electricity reliability can push existing manufacturing firms more into the export market. Using the World Bank Enterprise Surveys, this paper examines a sample of 13,025 manufacturing firms surveyed in 39 Sub-Saharan African countries between 2006 and 2022. The paper employs the entropy balancing approach to examine how access to reliable electricity affects the distribution of manufacturing firms’ sales between export and domestic markets. The results show that for medium-sized manufacturing firms, electricity access increases the share of exports in total sales at the expense of the share of domestic sales. However, the results for small and large manufacturing companies are not statistically significant. Among medium-sized manufacturing enterprises, domestic companies improve their exports relative to domestic sales when they have access to electricity, with mixed results for foreign companies. Even in resource-intensive countries, electricity access enhances the share of exports relative to domestic sales. The intra-sectoral structural change induced by power access is not limited to medium-sized companies in the manufacturing sector; the same pattern is observed in the service sector with mixed findings.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at kkaba1@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
increase the concentration of low-skilled migrants compared to the concentration of high-skilled migrants.

Our relative concentration measure is defined as the following: A nice property of this bilateral measure is that its sum across destination countries boils down to the difference between Herfindhal indices for high-skilled and low-skilled migrants. The equation to be estimated for relative concentration can be written as the following: Similar to previous two equations, we have αo and αd as the origin and destination fixed effects, respectively, Dod is a vector of explanatory bilateral variables and ηod is the error term. A negative value for β is expected if existing networks increase the concentration of low-skilled migrants across destinations compared to the concentration of the high-skilled migrants.

# 3 Key empirical issues

Our main empirical goal is to quantify the diaspora effects on the size, skill composition and concentration of migration flows as well as evaluate the robustness of the elasticity based on econometric techniques used. We use the Docquier, Lowell and Marfouk (2009, referred to as DLM from now on) database. Based on census and register information on the size and structure of immigration in all OECD countries, DLM database provides the stock of migrants from any given country to any one of the 30 OECD countries by education level for 1990 and 2000. The dataset covers only the adult population aged 25 and over, and migration is defined on the basis of the country of birth rather than citizenship<sup>1</sup> . We should note that the DLM database does not fully capture undocumented migration for which systematic statistics by education level and country of origin are not available in most destination countries. US census is believed to count most undocumented migrants, however this is not the case in many other OECD countries. By disregarding undocumented migrants (which are disproportionately unskilled), the database probably underestimates bilateral migration stocks/flows and overestimates the average level of education of the immigrant populations in many destination OECD countries.

> 1Even though this is the standard definition of a migrant, especially in the economics literature, the dataset does not include second generation children who are born in the destination country even though they might constitute an important part of a diaspora in the sociological sense. This is simply due to absence of comprehensive administrative data in tracking of the migrants’ children. However, we expect diaspora sizes inclusive and exclusive of second generation to be highly correlated.

6
cite lack of funds as a barrier. This suggests that government policies to promote inclusion may be related to a higher likelihood that individuals perceive that financial services are within their reach.

# **5. Conclusion**

Financial inclusion can bring many welfare benefits to individuals. Yet we know very little about the factors underpinning financial inclusion across individuals and countries. Using data for 123 countries and over 124,000 individuals, this paper tries to understand what factors are correlated with the use of formal accounts and what policies are especially effective among those most likely to be excluded: the poor and rural residents. We find that greater financial inclusion is associated with a better enabling environment to access financial services, such as lower banking costs, greater proximity to branches, and fewer documentation requirements to open an account. Policies targeted to promote inclusion—such as government requirements to offer basic or low-fee accounts, exempting small or rural depositors from onerous documentation requirements, and the use of bank accounts for government payments—are especially effective among rural residents and the poor.

We also study the factors associated with perceived barriers to account use among those who are financially excluded. Overall, those who are financially excluded report lower barriers to account access in countries with lower costs of accounts and greater penetration of financial service providers. We also find that among those who report lack of money as the main barrier to account use, government policies to promote inclusion can increase the likelihood that individuals perceive that financial services are within their reach. Overall, our results suggest a role for policy to expand the pool of eligible account users and increase account use to save and with higher frequency.

34
Baker, David and David Stevenson (1989) "Consequences of Macro Educational Characterstics for Micro Educational Processes: A Comparative Analysis of State Control of Currculum", paper presented at the World Congress of Comparative Educadon, Montreal, Canada, June. Belsley, David A., Edwin Kuh and Roy E. Welsch (1980) Regession Diagoostcs: 1denfifyiog Influential Data and Sources of Collinearty. New York: Wiley-Interscience.

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19