rafmacalaba/datause-encoder-data huggingface dataset:
# Determinants of Global Value Chain Participation: Cross-Country Evidence<sup>�</sup>

Ana Fernandes<sup>y</sup> Hiau Looi Kee<sup>z</sup> Deborah Winkler<sup>x</sup>

## Keywords and JEL Classi

cation: global value chain, factor endowments, trade policy, foreign direct investment, institutions; F13, F14, F23, O2

> �This paper is a substantial revision of the background paper, “Factors A¤ecting Global Value Chain Participation Across Countries,” prepared for the World Development Report 2020, Trading for Development in the Age of Global Value Chains. The authors thank Pol Antràs, Caroline Freund, Penny Goldberg, Aaditya Mattoo, and other colleagues for helpful comments. We are grateful to Alejandro Rojas for research assistance, Farid Toubal, Mary Hallward-Driemeier and Gaurav Nayyar for sharing data on English language and FDI. We acknowledge ...nancial support from the WB’s Multidonor Trust Fund for Trade and Development and the Strategic Research Program on Economic Development. The ...ndings of this paper are those of the authors and do not necessarily represent the views of the World Bank, or its member countries.

> yDevelopment Research Group, The World Bank, Washington, DC 20433, USA; Tel.: (202) 473-3983; e-mail: afernandes@worldbank.org.

> zCorresponding author. Development Research Group, The World Bank, Washington, DC 20433, USA; Tel.: (202) 473-4155; e-mail: hlkee@worldbank.org.

xMacroeconomics, Trade and Investment Global Practice, The World Bank, Washington, DC 20433, USA; e-mail: dwinkler2@worldbank.org.
that, during the growing season for which we collected yield data, rainfall was relatively poor and especially so in the more remote part of our study area.

# Figure 3: Use of Agricultural Techniques by Transport Costs

Separating these influences requires estimating a production function for rice. Let log yield (assuming constant returns to scale) on plot p of household h depend on a vector of inputs xhp according to where ξhp is an observed plot-specific weather shock and the error terms νh and �hp represent, respectively, household and plot-specific unobservables. We assume that Exhp�hp = 0, but that xhp and νh are not necessarily mutually orthogonal. Since there are, on average, two rice plots per household, we can purge νh using household fixed effects to obtain consistent estimates of the production function parameters. After estimating equation 1, we then calculate the residual which is yield net of variation in weather shocks and input use. Finally, we estimate the function bη(τ ) nonparametrically, where τ is transport costs as shown in figure 2.

Figure 4 summarizes the results of these procedures (see Appendix for the production function estimates). The solid curve shows the nonparametric regression of raw rice yield against τ. The decline in yield is fairly substantial, from around 27 to 20 kg/are, though not monotonic. Adjusting yields for plot-level weather shocks alone (rainfall worse/much worse than normal; flooding worse than normal) changes the picture quite dramatically, resulting in somewhat of a u-shape pattern. While rainfall was generally poor during the 2005-06 rice growing season, self-reported drought conditions were much more prevalent on remote plots.<sup>5</sup>

# Figure 4: Rice Yield and Transport Costs

The dashed curve in Figure 4 removes the influence of input use on yields to get at underlying land productivity. This has the effect of flattening out the yield gradient to a considerable extent. If anything, rice productivity rises slightly with transport costs, although this tendency is not statistically significant in a linear regression of bηhp on τ. Thus, the inaccessibility of the most remote corner of our study area does not appear 5 Splitting our sample of rice plots according to whether τ is above or below the sample median, we find that the incidence of worse than normal drought conditions is 65% on remote plots and only 27% on non-remote plots.

5
15 It has been thus far a difficult task to secure LFS data from the Czech Republic which would allow impact analysis of ALMP at the micro-level while Burda and Lubyova (1995) have used regional macro-data to estimate the contribution of ALMPs to outflows from unemployment. Their paper will be discussed, however, in the general Czech context within which ALMPs are embedded. With a very low unemployment rate the most interesting question has to be whether ALMPs are mainly responsible for this rate or whether there are other powerful determinants.

# 3. Evaluation of ALMP for specific countries

# _3. 1. Hungary_

# 3.1.1. Characterisation of the unemployed receivinq benefits

One of the best analyses of the insured unemployed in Hungary can be found in Micklewright and Nagy (1994). The following section is mainly based on their paper but also uses some information from Szeml6r (1994) and from the survey of benefit exhaustees performed by Lazar and Szekely (1994).

The analysis provided by Micklewright and Nagy is based on the cohort of people flowing _from employment_ into unemployment insurance (Ul) receipt in March 1992, giving approximately 40.000 spells. Only 40% of the stock of unemployed is covered by insurance as of August 1994. However, the results of their analysis are extremely interesting insofar as even the most privileged unemployed in Hungary, i.e. those who receive benefits, have on average a poor labour market experience expressing itself in relative to OECD levels very low outflow rates from unemployment (Boeri, 1994 and Micklewright and Nagy, op. cit., p. 10).

Micklewright and Nagy essentially document inflow into and outflow rates from insured unemployed disaggregated by the elements of the following set: {sex, age, education, occupation (manual or non-manual), region). Their results are now briefly presented and discussed.

# Inflow rates into insured unemployment

The monthly inflow rates estimated by Micklewright and Nagy are roughly comparable to those of most European OECD countries during recession and definitely not high by OECD standards. More interesting than this result are the presented relative magnitudes of the inflow rates calculated for the elements of the
**Table 8**

Indicators of Education in Asia and Latin America

||Stock of<br>Human Capital a/<br>1987|Growth Rate of<br> <br>the Stock of<br>Human Capital b/<br>**1960-87**|Social Rates<br>of Return c/<br> <br>1985|Share of<br>Public Education<br>Expenditures d/<br>**1985**|Enrollment<br>Ratio e/<br>1985|Share of<br>Enrollment in<br>Private Sector f/<br>**1980s**|
|---|---|---|---|---|---|---|
|**Asia**|||||||
|Primary|4.4|3.9|19.9||63.0|<br>3.9|
|Secondary|0.7|9.2|13.3||29.4|<br>26.0|
|Tertiary|0.0|3.4|11.7||7.6|<br>28.6|
|_Total_|5.1|4.2||3.1||20.0|
|**Latin America**|||||||
|Primary|4.7|4.7|17.9||61.0|<br>17.7|
|Secondary|0.6|0.6|12.8||29.0|<br>29.1|
|Tertiary|0.3|0.3|12.3||9.9|<br>3.6|
|_Total_|5.5|5.5||3.5||27.0|

a/ Source, Nehru, Swanson and Dubey (1993), in school years of education per person between ages of 15 and 64. b/ Source; Nehru, Swanson and Dubey (1993), OLS growth rate per year over the period 1960-87 c/ Source; Psacharopoulos (1994) d/ Source: Tan and Mingat (1992), p. 10, in percent of GNP.

e/ Source: Tan and Mingat (1992), p.1 1 fl Source: Tan and Mingat (1992), p.<sup>13</sup> .
common between the 2009 HIES and the 2016-18 DHS. Then we estimate the relationship between expenditure and each of those common variables in the 2009 HIES through many permutations based on the statistical significance of each of the common variables. Finally, we choose the empirical specification that has the highest predictive power of monetary poverty, as measured by the smallest absolute difference between actual and imputed poverty rates.<sup>9</sup><sup>_,_10</sup> The model with the best predictive power included information on region, education, dwelling, and household size. Table 1 reports summary statistics using both the 2016-18 DHS and the 2009 HIES for all variables used to impute monetary poverty in the 2016-18 DHS.<sup>11</sup> The figures illustrate patterns consistent with a struggling population. But most importantly, key indicators that are common between the surveys are roughly similar, suggesting that the questions were interpreted similarly across surveys and that there were not large changes that occurred between the surveys that might also change the relationship between monetary poverty and other household variables.<sup>12</sup> The poverty imputation results in a poverty rate of 39.3 percent at the World Bank’s International Poverty Rate of US $ 2.15 (2017 PPP).<sup>13</sup> Figure 2 further illustrates that the imputed poverty rate is higher for groups expected to be poorer and thus the imputed household expenditure is likely capturing the share of the population that is poor. Specifically, Figure 2 illustrates that the severely food-insecure population according to the Food Insecurity Experience Scale (FIES) has a significantly higher imputed poverty rate than those with better food security outcomes; the share of the population that is imputed to be poor is decreasing for higher asset deciles in the DHS; and the share of the

> 9Details on the imputation procedure and for the results from the full regression models are available from the authors upon request.

> 10This is only one of many different ways to potentially impute estimates of monetary poverty. Some alternative methods were compared in the construction of these estimates, but the SWIFT methodology still had the highest predictive power. See Yoshida et al. (2022) for more details.

> 11Summary statistics for all the remaining variables that are common between the two surveys are available from the authors upon request.

> 12The imputation module includes both variables that can adjust quickly and ones that adjust over time, and thus can flexibly capture changes in poverty over the time horizon under analysis (e.g., Yoshida et al. 2022).

> 13The model had a relatively high predictive power relative to other countries where the imputation models have proved to be precise estimates of poverty through subsequent household surveys (e.g., Yoshida et al. 2022).

9
Figure 2: Real Median Family Income and Gap between the Treated and Synthetic Control <!-- Start of picture text --> (a) Real Median Family Income (b) Gap between Treated & Synthetic<br>Pre Post<br>1995 2000 2005 2010 2015 Pre Post<br>Year<br>1995 2000 2005 2010 2015<br>Treated (oil, gas, energy) Synthetic Control Year<br>(c) post/pre RMSPE ratio (d) Gap between Treated & Synthetic<br>0 5 10 15<br>Post/pre RMSPE ratio 1995 2000 2005 2010 2015<br>(e) Placebo Sanctions in the Year 2000<br>Pre Post<br>1995 2000 2005 2010 2015<br>Year<br>Treated (oil, gas, energy) Synthetic Control<br>.4 .1<br>0<br>.2<br>-.1<br>0<br>-.2<br>-.2<br>-.3<br>log (demean) Family Income -.4 Gap in prediction error -.4<br>-.5<br>-.6<br>-.6<br>2 .4<br>.2<br>1.5<br>0<br>1<br>Density<br>-.2<br>.5<br>Gap in family income prediction error<br>-.4<br>0<br>-.6<br>.4<br>.2<br>0<br>-.2<br>log (demean) Family Income -.4<br>-.6<br><!-- End of picture text --> _Source: Iranian Households Income and Expenditure Surveys (HIES)._ Note: The figure reports synthetic control method analyses as well as placebos at industry level in the 1995â2015 period. Panel (a) displays the average real family income for treated households in the oil and gas industry and energy supply (solid line) and the synthetic control (dashed line). Panel (b) shows the gap between actual treated and synthetic control. Panel (c) shows the post/pre-treatment ratios of root mean squared prediction errors (RMSPE) for placebo estimates. The black ones indicate the post/pre-RMSPE ratio using the actual treated industries. Panel (d) shows the gap between treated and synthetic control for placebo estimates, with actual treated industries shown with a black solid line. Treated: oil and gas, energy supply industries. Synthetic control: information (0.148), education (0.169), and health (0.683) industries. Synthetic control for placebo sanctions in the year 2000: education (0.483) and health (0.517) industries.

16
causal effect of additional education on a given climate outcome _Yicy_ , with two-stage least squares, where the first stage is estimated from Equation (1) with educational attainment instrumented by CSL reforms.

This specification mirrors those most common in the economics literature (Acemoglu and Angrist, 2000; Lleras-Muney, 2005; Oreopoulos, 2006). It is important to note that these strategies all identify local treatment effects of education that are applicable to individuals on the margin of dropping out in the absence of the CSL. This is the policy-relevant estimate if the policy in question is to increase minimum schooling requirements.

# **III.B First Stages: the Effect of CSLs on Education**

Compulsory Schooling Laws (CSLs) legally mandate an increase in educational attainment, often by raising the minimum school leaving age. For example, in 1963, Italy increased minimum schooling from 5 years of education to 8 (equivalent to increasing the minimum school leaving age from 11 to 14 years old). We carefully identify reforms for which there is a strong first stage – that is, where an increase in required years of schooling by CSLs substantially increases average educational attainment, net of the time trend, rather than assume all CSLs increase education, or that all individuals are affected by CSLs. While legally enforceable, changes to CSLs will only have a strong first stage if they are enforced, rolled out rapidly, and bind for those who would otherwise not proceed to attain more schooling without the law (e.g., some individuals may attain 8 years of education in Italy even before it was legally required).

We estimate Equation 1 on all rounds of the ESS with standard errors clustered by country _×_ law.<sup>10</sup> We define and analyze binding first stages as those that are positive and statistically significant with a t-statistic greater than 1.96, indicating a robust relationship. Figure 1 shows the 16 countries with relevant reforms (and up to 39 country-reforms, with multiple binding reforms in some countries). Table A3 in the Online Appendix shows all first stages with positive effects, including those that are not statistically significant. Countries in the main analysis include Albania, Austria, Belgium, Bulgaria, Czechia, Germany, Denmark, Hungary, Iceland, Italy, Lithuania, Luxembourg, Latvia, Russia, Slovakia, and Ukraine. We exclude countries, such as the United Kingdom, where reforms occurred at the sub-national level and do not map cleanly to the ESS data.

> 10By using all rounds of the ESS to determine strong first stages, we have more power to estimate the true effect of compulsory schooling laws beyond the country’s time trend.

7
Policy Research Working Paper 5658

# **Abstract**

Institutional features of the African setting—large extended families and imperfect credit and land markets—matter to the equity and efficiency roles played by intergenerational linkages. Using original survey data on Senegal that include an individualized measure of consumption, this paper studies the role played by land inheritance, other bequests and parental background as influences on an adult’s economic welfare and economic activities. Although intergenerational linkages are evident, the analysis finds a seemingly high degree of mobility across generations, associated with the shift from farm to non-farm sectors and the greater economic activity of women. Male-dominated bequests of land and housing bring little gain to mean consumption and play little role in explaining inequality, although they have effects on the sector of activity. Inheritance of non-land assets and the education and occupation of parents (especially the mother) and their choices about children’s schooling are more important to adult welfare than property inheritance. Significant gender inequality in consumption is evident, although it is almost entirely explicable in terms of factors such as education and (non-land) inheritance. There are a number of other pronounced gender differences, with intergenerational linkages coming through the mother rather than the father.

This paper is a product of the Development Research Group and the Gender Group of the Poverty Reduction and Economic Management Network. 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 mravallion@worldbank.org and dvandewalle@ 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
on the land market. Population movements in and out of the city are thus affected both by land grabbing and productivity shocks. Agglomeration and congestion effects are endogenous to migration, affecting wages and land prices.

Our main result is that land property rights institutions, which are very difficult to reform, have a significant effect on the dynamics of urbanization in the long run. Contrary to the developed world experience, leaving urbanization to the market may be suboptimal because of the constraints imposed by dysfunctional property rights systems in developing countries. We also demonstrate that, under certain conditions, informality can be socially optimal, as it makes it possible for low-income residents to contribute to the urban economy. In these situations, strict urban eviction policies can be welfare reducing.

It is important to note that our major insights are conditional on structural fundamentals of prevailing land property rights systems. While our approach is justified given the persistence of land property rights institutions and apparent difficulties in reforming them, our comparative dynamics provides further insights into the political economy of changing land property rights systems. In future research it would be interesting to understand how the parameters of land property rights systems themselves evolve endogenously with urbanization and possibly shed light on the “institutional trap” that seems to be plaguing much of the developing world.

# **References**

- [1] Acemoglu, D., S. Johnson and J. Robinson (2005) The Rise of Europe: Atlantic Trade, Institutional Change and Economic Growth. _American Economic Review_ , 95(3), 546-79.

- [2] Anderson, T. and P. Hill (1975) The Evolution of Property Rights: A Study of the American West, _Journal of Law and Economics_ , 18, 163-79.

- [3] Arru ̃nada, B. (2012) _Institutional Foundations of Impersonal Exchanges. Theory and Policy of Contractual Registries_ . University of Chicago Press: Chicago and London, 299 pages.

- [4] Au, C.-C. and J. V. Henderson (2006) Are Chinese Cities too Small, _Review of Economic Studies_ , 73, 549-576.

- [5] Bairoch (1988) _Cities and Economic Development. From the Dawn of History to the Present_ . The University of Chicago Press.

- [6] Bairoch, P. and G. Goertz (1986) Factors of Urbanization in the Nineteenth Century Developed Countries: A Descriptive and Econometric Analysis, _Urban Studies_ , 23, 285-305.

- [7] Bellman, R. (1957). Dynamic Programming. Princeton University Press. Benabou, R. (1993) Workings of a City: Location, Education and Production, _Quarterly Journal of Economics_ , 108, 619-52.

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# **APPENDIX 1: DATA SOURCES**

**Travel time grid:** Thomas, Timothy S.  2007. _Travel Time Friction Grid for Africa at 500-meter Resolution_ .  Washington: The World Bank, FEU, SLD.

UNEP roads and national roads datasets, CIA World Data Bank II, Waterbodies data, and GRUMP/CIESIN global settlement points were used to create friction grid.

**Roads dataset:** UNEP roads are based on the Digital Chart of the World dataset.  The World Bank added attributes (primary, secondary, and tertiary) from the 2004 Michelin map series.

**Community points:** Uganda National Household Survey, 2005/2006; Uganda Bureau of Statistics, 2006.

**Education:** Uganda Census Uganda Bureau of Statistics, 2002; Population and Housing Census, 2002

**Manufacturing business points:** Uganda Business Registry, UBoS, 2001/2002.; Uganda Business Register, UBoS, 2001/2002.

**Terrain roughness data:** The CGIAR-CSI (Consortium for Spatial Information, http://srtm.csi.cgiar.org/), derived from the USGS/NASA SRTM data, Downloaded March 28, 2007.

**Railroad dataset** : USGS Global GIS Database: Africa CD, VMAP level 0, 2001.

**Powerlines dataset:** UGANDA-AERDP Final Report Annex 1; February 2004.

**Crop Data : IFPRI Spatial Allocation Model; see You, L and Wood, S. (2003) Spatial allocation of agricultural production using a cross-entropy approach. Environment and Production Technology Division Discussion Paper No. 126, International Food Policy Research Institute, Washington, DC.**

26 -
## **I. The pattern of growth**

# _a) Growth and Employment_

Vietnam is a poor country. In 1997, its GDP per capita was only $313. However, in the years before the Asian crisis, Vietnam was one of the 10 fastest growing economies on the planet (Economist, 1998). Table I shows that the average annual growth rate over the 5 years period 1993-1997 was a spectacular 8.9%, led by the industrial sector where value-added increased at an average rate of 13.4% per year. In the same time, services have grown at 9.4% a year, while agriculture has lagged somewhat behind with an annual expansion of 4.3%. This pattern of growth has led to a quick increase in the share of industry in GDP, from an estimated 27.3% in 1992 to 33.1% in 1997. This is a typical and nearly universal characteristic of the structural transformation that accompanies economic development (see, for example, Chenery and Syrquin, 1975).

**Table 1: The industrialization of Vietnam**

||Average annual<br>real GDP<br>growth over<br>1993-97 (%)|Estimated<br>share in total<br>output in<br>1992 (%)|Share in total<br>output in<br>1997 (%)|
|---|---|---|---|
|TOTAL<br>|8.9|100|100|
|Agriculture|4.3|31.6|25.2|
|Industry<br>|13.4|27.3|33.1|
|Industry less construction|13.1|20.7|24.7|
|Services|9.4|41.1|41.7|

_Source:_ Calculations based on GSO, 1997 Yearbook (1998) _Notes:_ Agriculture includes Agriculture, Forestry and Fishery; Industry includes Mining and Quarrying, Manufacturing, Utilities and Construction; Shares in 1992 GDP are estimated backwards using the GSO new 1994 prices series and real growth rates for 1993 and 1994.

In general, such a transformation is accompanied by a corresponding increase in industrial employment. Cross-country evidence indicates that between 1965 and 1983 developing countries experienced on average a growth elasticity of employment in industry of about 0.6. That is, industrial employment expanded at around 60% of the rate of industrial growth (see Gillis et al., 1990). More recently, during the late 1980s and early 1990s, individual countries such as Indonesia were able to raise industrial employment at annual rates above 80% of their industrial growth (see Agrawal, 1996). In Vietnam, however, industrial growth has only had a moderate impact on employment. As can be seen in table 2, the growth rate of industrial employment between 1992/93 and 1997/98 was 4% per year. Although respectable, this represents less than 30% of the rate of industrial output growth. Thus, as table 3 shows, the share of industrial employment in total employment has increased only little, from 11.8% to 13.1%.

4