rafmacalaba/datause-encoder-data huggingface dataset:
intervention size through increasing intensity or scope.

We produce three key results. First, we estimate that UCT increase household consumption by 0.35 per unit of transfer. Consistent with the homogeneity of the intervention itself, these estimates are remarkably consistent across contexts compared to other development interventions, and therefore offer a useful benchmark for a broader set of interventions in developing countries. In addition, these impacts are meaningfully more persistent than comparable estimates from richer countries. Second, we find that increasing UCT intensity (through transfer size) reduces cost effectiveness and does not affect persistence. We argue these results are not consistent with predictions from a standard buffer stock model, but can be explained by poverty traps or decreasing returns to scale. Third, we find that increasing UCT scope (through adding complementary graduation program interventions in TUP) reduces cost effectiveness at commonly evaluated time horizons and increases persistence and heterogeneity. This highlights the importance of context-specific estimates of the long run impacts of TUP to inform policy.

These theoretical and empirical results on the impacts of increasing intervention size on cost effectiveness discipline a debate on the need for “big push” interventions. In particular, the presence of poverty traps alone does not justify increasing intervention size. Instead, the distribution of poverty thresholds conditional on targeting is crucial. We note that there are — justifications for increasing intervention size beyond cost effectiveness increasing transfer size or providing complementary programs to the poorest households, for example, can be a powerful tool for poverty reduction.

# **References**

- Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2020). Sampling-based versus design-based uncertainty in regression analysis. _Econometrica_ , 88(1), 265–296.

- Auclert, A., Bardóczy, B., & Rognlie, M. (2020). Mpcs, mpes and multipliers: A trilemma

46
Total 22,824 100 _Un-weighted_

|Status|**stric**|**t_exit**|**weak**|**_exit**|
|---|---|---|---|---|
||n|Percent|N|Percent|
|Surviving|19,244|84.65|17,730|78.01|
|Exiting|3,580|15.35|5,094|21.99|
|_Un-weighted_|||||

In all, data are available for 22,824 firms interviewed in 47 economies<sup>9</sup> over an initial wave of first-round surveys; on average, there is period of just over 4 years between survey rounds. Annex Table 1 includes the descriptive statistics of the survey rounds, including all economies that have included multiple rounds under the globalized ES methodology since 2006. The last column includes the weighted estimate of the number of firms represented, which are collectively almost 684,000. Table 2 presents estimates of exit rate, based on our two measures, for each of the economies in the sample. The table also provides estimates of the share of employment in the base year accounted for by the exiting firms, providing a rough estimate of the job loss associated with firm exit. Based on our conservative measure of exit, strict __exit_ , on average, approximately 16% of the firms exited the market between the two rounds of survey. The values range from as low as 2% in Macedonia to a high of 68% in Ghana.<sup>10</sup> The annualized exit rates, i.e. divided by the number of years between each survey round, range from less than 0.4% for Macedonia to 11% in Ghana, with an average of about 4% per annum for all the countries in our sample.

**Table 2: Estimates of firm Exit and Job loss Rate by Country**

|Country|strict|_exit|Job loss (|strict_exit)|weak|_exit|Job loss|(weak_exit)|
|---|---|---|---|---|---|---|---|---|
||Cumulative|Annualized|Cumulative|Annualized|Cumulative|Annualized|Cumulative|<br>Annualized|
|Argentina|3%|1%|5%|1%|6%|2%|6%|2%|
|Armenia|10%|2%|6%|1%|12%|3%|7%|2%|
|Azerbaijan|18%|5%|13%|3%|30%|8%|19%|5%|

> 9 The initial round for Venezuela (2006) utilized a limited questionnaire and omits certain key covariates. As a result, we remove Venezuela for all analysis (n=500) so as not to bias our results by the inclusion or exclusion of an entire survey round as new variables are introduced. Annex Table 1 provides the list of countries and survey years included in the analysis.

> 10 Noting that a period of six years passed between survey rounds in Ghana.

13
**Table 6. IV Regressions with Corruption**

|**_Dependen_**<br>**_t Var_**||**_Private_**|**_Credit_**|||**_NPL/tota_**|**_l loan_**||
|---|---|---|---|---|---|---|---|---|
||IV1|IV1|IV2|IV2|IV1|IV1|IV2|IV2|
|T_245|29.2**<br>(2.4)|9.6<br>(.56)|32.2***<br>(2.61)|17.2<br>(.98)|-13.8**<br>(-2.06)|-10.2**<br>(-2.03)|-11.0**<br>(-2.2)|-7.5*<br>(-1.64)|
|legor_uk|6.6|6.6|5.88|1.59|3.7|2.95|2.7|1.72|
||(.27)|(.20)|(.24)|(.05)|(1.17)|(.91)|(1.0)|(.56)|
|legor_fr|-15.6|-10.04|-16.4|-18.4|7.01|7.42*|5.6|5.04|
||(-.69)|(-.3)|(-.7)|(-.57)|(1.44)|(1.7)|(1.3)|(1.19)|
|legor_ge|17.2|27.7|15.3|19.4|5.76|5.6|4.7|2.7|
||(.77)|(.87)|(.71)|(.62)|(1.2)|(1.19)|(1.23)|(.62)|
|legor_so|-59.6**|-9.9|-60.2**|-29|15.04*|10.1|11.5|3.42|
||(-2.2)|(-.25)|(-2.38)|(-.82)|(1.7)|(1.49)|(1.63)|(.61)|
|apmindex|-.44||-1.13||2.43*||1.63*||
||(-.14)||(-.35)||(1.8)||(1.73)||
|ospower|.24||.53||-.47||-.55||
||(.16)||(.36)||(-.91)||(-1.33)||
|pmindex_p||17.5***||15.7**||-1.24||-.61|
|c||(2.77)||(2.4)||(-.70)||(-.41)|
|Supoer_pc||-2.38||-.84||-1.19||-.47|
|||(-.39)||(-.16)||(-1.25)||(-.55)|
|Corup0204|6.4|7.6|5.6|4.83|1.1|.36|-.09|-.50|
||(1.17)|(.91)|(1.18)|(.59)|(.49)|(.21)|(-.05)|(--.30)|
|Constant|-99.9**|-11.5|-109**|-36.6|58.6***|59.2***|55.8***|48.7**|
||(-1.9)|(-.14)|(-2.0)|(-.43)|(2.74)|(2.75)|(3.28)|(2.5)|
|# obs|83|61|93|68|64|54|74|61|
|R<sup>2</sup>|.80|.79|.79|.79|.45|.60|.54|.64|

Note: _1) Legor_uk are those countries with English legal system origin, legor_fr – French legal system origin; legor_ge – German legal system origin; legor_so – socialist legal system origin. Latitude is the absolute average latitude of the country (“lat_abg”), ethnic diversity is the “avelf” index. Ospower is the official supervisory power index, apmindex is the adjusted private monitoring index, pmindex_pc is the principal component for the private monitoring index, supower_pc is the principal component of the supervisory power index. Corup0204 is the ICRG corruption index, average over 2002~04 (see Aappendix 2 for the source and variable description) (see the appendix 2 for source and variable description) (2): numbers in parentheses are t statistics._ _(3): Coefficients with * denote significance at the .10 level, ** at the .05 level and *** for the .01 level. (4): For IV1, the instruments for T are avelf, lat_avg, cathshare, and muslshare. For IV2, the instruments are lat_avg, indep, cathshar, and muslshare (see Appendix2 for variable descriptions and sources). (5): The instruments (except the one used in the last column for IV2) passed the over-identification test, with Hansen J statistic quite small (p-value for the χ_<sup>_2_</sup> _larger than .10). We also looked at the first step regression in the 2SLS, to make sure the instruments are correlated with the endogenous explanatory variable ( with the p-value for the F-test smaller than .05)._ 24
# **Reference**

Acosta, Pablo (2006), " Labor supply, school attendance, and remittances from international migration: the case of El Salvador," _Policy Research Working Paper 3903_ , World Bank Adams, Richard H. Jr. (2003), "International migration, remittances, and the brain drain: a study of 24 labor exporting countries," _Policy Research Working Paper 3069_ , World Bank Burgess, Robert and Vikram Haksar (2005), "Migration and Foreign Remittances in the Philippines," _IMF Working Paper_ Bussolo, Maurizio and Denis Medvedev (2006), "Do remittances have a flip side? A general equilibrium analysis of remittances, labor supply responses and policy options for Jamaica," mimeo Chami, Ralph, Connel Fullenkamp, and Samir Jahjah (2003), "Are Immigrant Remittance Flows a Source of Capital for Development?" _IMF Working Paper_ Downes, A., N. Mamingi and R. Antoine (2004), "Labor Market Regulation and Employment in the Caribbean," in _Law and Employment – Lessions from latin America and the Caribbean_ by J. Heckman and C. Pages Funkhouser, Edward (1995), Remittances from International Migration: A Comparison of El Salvador and Nicaragua, _Review of Economics and Statistics_ Goldring, Luin (2004) _Family and Collective Remittances to Mexico: A Multidimensional Typology, Development and Change_ Inter-American Development Bank (IDB, 2003), _Social Panorama of Latin America_ Jacoby, Hanan G. (1997), "Self-Selection and the Redistributive Impact of In-Kind Transfers: An Econometric Analysis," _The Journal of Human Resources_ Kathuria, Sanjay et al (2003), _Jamaica: The Road to Sustained Growth_ , World Bank Matshe, Innocent, and Trevor Young (2004), “Off-Farm Labor Allocation Decisions in Small-Scale Rural Households in Zimbabwe” _Agricultural Economics 30_ , pp. 175-186.

Mishra, Prachi (2006), " Emigration and Brain Drain: Evidence from the Caribbean," _IMF Working Paper_ 17
(2009) and Helpman, Istkhoki and Redding (2010) that hypothesizes a relationship between ...rm-speci...c wages and how the ...rm responds to globalization.

Bustos (2007) posits that an examination of wage inequality that examines the interaction between trade and technology— as opposed to selecting one explanation in preference over the other— will o¤er a better explanation of the relationship between global trade and inequality. Bustos therefore presents a model of the relationship between trade liberalization and demand for technology and skill among ...rms in developing countries that accounts for ...rm heterogeneity. She tests this model using panel data from Argentine manufacturing ...rms. The dataset spans 1992-1996 and therefore captures a period of trade and capital account liberalization in Argentina.

Bustos ...nds a strong relationship between exporting and increases in technology investment and skill upgrading. Speci...cally she ...nds that, prior to trade liberalization, continuing exporters and foreign owned ...rms employed higher skilled labor than those ...rms that were domestically owned and that had never exported. Those ...rms that began exporting after liberalization upgraded worker skill more quickly than those ...rms that remained exclusively in the domestic market; they also upgraded technology more quickly than all other ...rms. Further, after trade liberalization, new and continuing exporters as well as foreign owned ...rms spent 53-69 percent more on technology than their domestic non-exporting counterparts. Those ...rms that invested more in technology upgrading also realized a faster increase in skilled labor. Bustos concludes that, due to the consequences of rising demand for technological investment, trade liberalization can have a strong impact on worker skill upgrading.

While much of the literature (for example, Verhoogen (2008) and Bustos (2007)) looks at the impact of the act of exporting on ...rm behavior, Brambilla, Lederman, and Porto (2010) focus on how the destination of those exports explains ...rm behavior. Brambilla et al develop and then test an integrated theory of export destinations and skills. Exploring the linkages among exports, export destinations, and the use of skilled labor by ...rms, Brambilla 33
Ability to attract FDI is one of the strongest single predictors of the growth of per capita income in federal regions. This result was s obtained from estimating a cross‐sectional model of regional income growth for the period 2001–11. The model is a modified version of that found in Mankiw, Romer, and Weil (1992), in which variables are measured as period averages, either in per capita terms or in shares; it is expressed as follows:

- y�,� �y�,��� � β� �β�y�,��� �β�human_capital�,��� �β�exports�,��� �β�FDI�,��� �β�business_climate�,��� �β�share_mineral�,��� �β�share_urban�,��� �ε�,�

_where_ y denotes real per capita income in region I, y�,��� represents 2001, the initial year, human capital is measured by the percentage of the population enrolled in secondary school, FDI is measured in per capita U.S. dollars, business climate is based on scores from the consulting firm Expert RA for 2003, mineral exports are expressed as a share of total merchandise exports, and urban population as a share of total population. For dependent variables other than income, t‐1 denotes averages for the period.

Along with attracting more FDI, more rapidly growing regions are likely to have higher business climate scores and their populations are more likely to be rural than urban. Other things being equal, poorer regions grow more rapidly than rich regions (“beta convergence”). The other variables (human capital, total merchandise exports, share of mineral exports) are not significant as independent determinants of regional growth; however, regions that are more attractive to FDI also tend to export more.

As an illustration of the importance of FDI, if annual FDI inflows into the Krasnoyarsk Republic ($14.90 per capita in 2011) were to rise to the level of St. Petersburg City ($219.20 per capita), its annual growth in per capita income would go up an estimated 2–2.27 percent, amounting to 30 percent higher income after a decade. In the case of business climate, if the initial business climate score of the Komi Republic (3B2, “Marginal potential‐moderate risk”) were to be raised by one point, to 3B1 (“Reduced potential‐moderate risk”), estimated annual growth would go up from 4.27 percent to 4.55 percent, amounting to a 32 percent increase in per capita income after a decade.

Russia’s poorest regions have on average caught up to its richest regions over time (“sigma convergence”): the coefficient of variation of real log per capita GDP declined from 0.0627 in 2003 to 0.0585 in 2011, with a brief reversal in the global crisis year of 2009.

# **6 Trade Diagnostics for Russia**

Services industries are subject to specific regulatory requirements that are translated into policy. These measures can have two negative effects on trade in services: escalate the costs, and restrict growth and expansion. Services trade costs can be as much as twice as high as costs for trading goods, according to 16
4 the pension administration bureaus. In fact, many of these bureaucrats receive very low explicit salaries and are compensated through substantial income from bribes. In turn bribes are paid by the worker who wants a pension. In such a pension system, it may appear as if the pension administration had very low costs, because it employs people full time for very low explicit wages. This ignores that total charges on the pensioner include payments to a pension advisor which in turn pays the bribes. The risk that the .pension record may remain lost until a ransom is paid is present in basket cases.

Total charges should include the cost of time spent by the pensioner seeking an advisor or pushing paperwork himself. One would also expect the generalized price" or total charges in a pension system with low quality of service to be higher than in a system with speedy attention, although the second may appear as more expensive in the surface.

# Economic Reasons for international differences

We expect administrative charges to be different. across countries for three reasons; a) There may be a minimum efficient scale in pension administration. Smaller countries should exhibit higher charges for this reason alone.

b) International differences in the size distribution of firms are large, and they are associated with different cost levels in pensiop administration. For example, in the U.S., 62.1% of the non-farm civilian labor force worked in firms with 100 or more employees in 1988 (EBRI, 1992, page 69). In Chilean industry, which excludes the service sector, in 1983 only 42.6% (SERCOTEC 1984, Table 1.1) of the labor force worked in firms of that size. The difference is much larger for poor developing countries. It is a well established empirical fact in the United States that smaller firms must pay higher average administrative. charges per worker, so we expect the same correlation to hold across countries. The importance of costs related to the average size of firm may be inferred from the experience of multiemployer pension plans in the US: total costs increase 15% when the number of member firms. double, keeping constant the number of workers (Parsons, 1992).

c) The volume of flows of workers between the formal (covered) and informal sectors, and in and out of the labor force, generate large administrative cost differences. In countries with low coverage the pension system must incur in substantial administrative costs because records must be kept for every past participant to determine benefits, even if the share of
is beneficial in that it decreases the share of people working in low-skilled occupations in rural areas; in these areas, gaining internet access marginally decreases the likelihood of working in a low-skilled occupation by 1.1 percentage points, but the effect is not statistically significant in urban areas.

In the service sector, we can observe that the two infrastructure types complement each other to increase employment, but with a statistically higher effect in urban areas. While roads and internet access complement each other to bring an additional boost to the demand for labor in the services sector in both locations, the boost amounts to 6.3 percentage points in urban areas but only 1.2 percentage points in rural areas.

To summarize, in rural areas, investing in both road and internet infrastructure promotes job migration from low-skilled to high-skilled occupations. In urban areas, bringing the road network closer to communities and also investing in the development of internet infrastructure essentially promotes job creation in the service sector.

# **4.3.3 Gender**

The isolated and combined effects on employment from improvements in roads and internet access differ across genders.<sup>20</sup> Isolated investments in either fast internet or better proximity to a main road benefit female workers the most. The additional impacts from combining road and internet investments are around 23 percent for both male and female workers.

Internet and road investments complement each other, boosting demand for high-skilled labor for both men and women. But the effects are greater for women.<sup>21</sup> However, these two types of infrastructure work together to decrease low-skilled employment, leading to greater impacts on men. In areas with internet access, bringing a main road 1 kilometer closer decreases the probability of having low-skilled employment by 26.1 percentage points for men and by 10.1 percentage

> 20Table (A9) displays the results of regressions undertaken to examine the effect on each gender group.

> 21See table A13 in the Online Appendix.

33
- Roads data [WDI variable name: **is_rrs_totl_km** ] and water data [ WDI variable name: **sh_h2o_safe_zs** ] could not be used as the corresponding time series are too short for almost all of MENA countries.

The TFP growth rates provided by UNIDO’s World Productivity Database (WPD)<sup>4</sup> , which contains information on levels and growth of aggregate total factor productivity (TFP) for as many as 112 countries between 1960 and 2000, are used as the dependent variable, as in (7) (see Isaksson, 2008). UNIDO calculated (not estimated) TFP growth rates following the standard methodology that is, following equation (3)<sup>5</sup> and, in addition, taking into account changes in labor quality. In addition UNIDO provides a series of _estimations_ of TFP growth using (3) as a regression, with _S L_ and _S K_ parameters to be estimated, not observed. They provide results from a variety of techniques, including a non-parametric one using Data Envelopment Analysis with Long Memory.<sup>6</sup> These econometric estimates of TFP have been used in addition to the standard calculated hicks-neutral TFP growth as a test of robustness of the results we get with the latter.

Only 8 countries (see below) in the MENA sample have sufficient data (particularly TFP growth rates) for us to carry out the estimations. This is an area where future research could be developed. In particular, future work could focus on the specific case of Gulf countries (GCC) where the bulk of infrastructure investment in the region has materialized.

# **2.3. Results**

Table 3 reports results from panel regressions with random individual effects. First, we note that telephone and rail variables are not significant in explaining TFP growth, while gross electricity production is (columns 1 and 2). This is robust in the versions of the regression where two different kinds of TFP growth is used (standard Hick-neutral calculated TFP growth and a nonparametric econometric estimate of the same) and where gross electricity production is the sole regressor (columns 5 and 6). While this finding is counter-intuitive regarding

> 4 www.unido.org/data1/wpd

> 5 This is referred to in Isaksson (2008) as Hicks-neutral TFP change.

> 6 See Isaksson (2008). Essentially the LM DEA method relies on linear programming estimators.

9
Policy Research Working Paper 9619

# **Abstract**

This paper examines whether a country’s participation in global value chains supports spatial convergence in the domestic economy. In theory, production disintegration through “unbundling” makes industrial development less lumpy, providing opportunities for smaller cities to plug and play in niche spaces while not having to fight the agglomeration economies offered by large metropolitan areas. Using data on the size distribution of cities within countries and the Organisation for Economic Co-operation and Development’s Trade in Value Added database, the paper finds that integration in global value chains is strongly associated with greater concentration in large urban agglomerations, not less. A unit standard deviation increase in domestic value added in exports of intermediate products is associated with a decline of 0.1 standard deviation in the Zipf coefficient, an index measuring spatial dispersion. Spatial concentration is strongest for global value chains involving knowledge-intensive business services and high-technology manufacturing.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Finance, Competitiveness and Innovation 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 agrover1@worldbank.org and slall1@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