rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5407

# **Abstract**

This paper examines firm entry and survival in exporting, and in products and markets not previously served by any domestic exporters. The authors use data on the nontraditional agriculture sector in Peru, which grew seven-fold from 1994 to 2007. They find tremendous firm entry and exit in the export sector, with exits more likely after one year and among firms that start small. There is also significant entry and exit in new markets. In contrast, such trial and error in new products is rare. New products are typically discovered by large experienced exporters and there is increased entry after products are discovered. The results imply that high sunk costs of entry are of concern for product discovery, especially for products that are not consumed domestically. In contrast, the tremendous entry and exit in exporting and in new markets suggests that initial sunk costs are relatively low. The authors develop a model that explains how entrepreneurs decide to export and to develop new export products and markets when there are sunk costs of discovery and uncertainty about idiosyncratic costs. The model explains many features of the data.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to understand export growth. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The authors may be contacted at cfreund@worldbank.org and mpierola@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 8: Changes in agricultural income: a regional story**

||Centre|East|North|West|
|---|---|---|---|---|
|Log of rainfall (percent of needs measured by WRSI)|-0.335|0.868**|3.826***|0.283|
||(0.825)|(0.370)|(0.578)|(0.524)|
|Log of maize price|0.243|0.657***|1.112***|0.00646|
||(0.219)|(0.114)|(0.166)|(0.132)|
|Log of beans price|0.627*|0.936***|1.348***|1.074***|
||(0.340)|(0.318)|(0.350)|(0.203)|
|Log of number of fatalities|0.129|-0.0721|-0.131**|-0.0521|
||(0.167)|(0.149)|(0.0651)|(0.221)|
|Constant|7.595|-1.921|-21.00***|4.008|
||(5.108)|(2.906)|(3.686)|(3.361)|
|Observations|1,585|2,114|2,253|1,856|
|Number of HHID|504|674|735|626|

Source: Staff calculations using UNPS 2006-2012. Note: Dependent variable is log of real per capita crop income. Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 21
13 somewhat different global and national models is that it is more likely to expose the various determinants of the measured effects in different settings than if only a single type of model was employed.

# **_(a) LINKAGE Model Results_**

Anderson, Valenzuela and van der Mensbrugghe (2010) use the World Bank’s global LINKAGE model (van der Mensbrugghe 2005) to assess the market effects of the world’s agricultural and trade policies as of 2004 on individual countries and country groups, so as to be able to say something about international inequality (in the Milanovic (2005) sense, taking into account the economic size of countries) and about poverty (using a simple elasticities approach). This model also provides the basis for estimating the effects of rest-of-world policies on the import and export prices and demand for the various exports of any one developing country, for use by each of the nine country case studies discussed in the next section.

The LINKAGE model results suggest that developing countries would gain nearly twice as much as high-income countries in welfare terms if 2004 agricultural and trade policies were removed globally (an average welfare increase of 0.9 percent, compared with 0.5 percent for high-income countries – bottom of column 1 of Table 2). Thus in this broad sense of a world of just two large country groups, completing the global reform process would reduce international inequality. The results vary widely across developing countries, however, ranging from slight losses in the case of some South Asian and Sub-Saharan African countries that would suffer exceptionally large adverse terms of trade changes, to an 8 percent increase in the case of Ecuador (whose main export item, bananas, is currently
16 categorization, average annual rainfall and temperature patterns as well as those during the wettest quarter measured at a much higher resolution<sup>30</sup> , were included. The total annual amount of rain and the amount during the wettest quarter of 2007-8 and 2008-9 were also controlled for to reflect preand post-harvest rainfall patterns of the survey year (source: NOAA CPC, resolution 6’ or about 140 km2). Rainfall patterns pre-harvest help proxy for the total harvested quantities and humidity conditions, while rainfall conditions during and after the harvest have a key influence on PHLs at the harvesting and drying stages. They foster early pest infestation and affect the dry matter content before storage, suggesting an increase in PHL when post-harvest rainfall is higher (Hodges 2010).

Finally, while the household sample is nationally-representative, this is not necessarily the case for the sample of maize growers considered in the estimations here. Maize growers may systematically differ from non-maize growers in ways that also affect PHL. Households not growing maize (which is more prone to PHL than sorghum/millet (World Bank, 2011b)) may do so, for example, because they are less able to deal with the challenge of PHL. This would induce a sample selection bias and biased coefficients, when such characteristics are not controlled for. The Heckman technique<sup>31</sup> is used to control for this, whereby the likelihood of growing maize is estimated first using a Probit model (which is applied to the whole national sample of households) and controlled for in the (second stage) PHL regressions through the inclusion of an inverse Mills ratio<sup>32</sup> .

Descriptive statistics on each of the explanatory variables are in appendix Tables A3 and A4. The estimated results are in Table 4. They include Tobit estimates of the proportion of maize harvest lost post-harvest (column 1) as well as Probit estimates of the probability of reporting a harvest loss (column 2). The latter are added to examine the marginal effect of the different determinants on the

> 30 It concerns the average weather conditions during 1960-2000 at a resolution of 30” (or about 1 km2) from the WorldClim database: www.worldclim.org

> 31 See Heckman (1979).

> 32 Successfully doing so requires instruments, i.e. variables that affect the decision of growing or not growing maize, but not the amount of post-harvest loss, beyond their effect on growing maize (i.e. the exclusion restriction). Elevation, slope, terrain roughness, and nutrient availability of the soils, all variables that affect the choice of crop portfolio, but without immediate effects on PHLs, function as our instruments. They prove highly correlated with the choice of growing maize, and also pass the empirical over-identification test (the Hansen-Sargan statistic is equal to 2.5, below the 10% significance levels, indicating that the instruments meet the exclusion restrictions).
# **Vertical and Horizontal Redistribution: The Cases of Western and Eastern Europe**

Maurizio Bussolo, Carla Krolage, Mattia Makovec, Andreas Peichl, Marc Stöckli, Iván Torre and Christian Wittneben<sup>1</sup>

## JEL codes: C63, D31, D63, H22

Keywords: Vertical inequality, Horizontal inequality, Tax and Transfers, Incidence analysis, Europe.

> 1 Maurizio Bussolo (World Bank; mbussolo@worldbank.org), Carla Krolage (University of Munich, ifo Institute; krolage@ifo.de), Mattia Makovec (World Bank; mmakovec@worldbank.org), Andreas Peichl (University of Munich, CESifo, ifo Institute, IZA; peichl@econ.lmu.de), Marc Stöckli (University of Munich, ifo Institute; stoeckli@ifo.de), Iván Torre (World Bank; itorre@worldbank.org) and Christian Wittneben (University of Munich, ifo Institute; wittneben@ifo.de). We are grateful to Florentin Philipp Kerschbaumer, Atul Menon and Yue Zhou for excellent research assistance. We also want to thank Ravi Kanbur who had pushed us to pursue this research and Mathias Dolls for his valuable comments on earlier drafts.

This paper is a background paper produced for the World Bank 2018 report: “Towards a New Social Contract: Taking on Distributional Tensions in Europe and Central Asia”. The results presented here are based on EUROMOD version H1.0+. EUROMOD is maintained, developed and managed by the Institute for Social and Economic Research (ISER) at the University of Essex, in collaboration with national teams from the EU member states. We are indebted to the many people who have contributed to the development of EUROMOD. The process of extending and updating EUROMOD is financially supported by the European Union Programme for Employment and Social Innovation ‘Easi’ (2014-2020). The results and their interpretation are the authors’ responsibility.
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objectives are achieved through a thorough analysis of the flow of public resources in the sector from the central level to the frontline providers, the final intended beneficiaries. The PETS therefore collected data from government agencies, local government bodies, and a nationally representative sample of public health providers.

The sample frame used is a recent census of public providers constructed during the summer of 2006. There are 316 health facilities in the PETS sample (out of a grand total of 2,559) selected using a stratified sampling strategy. In addition to a general facility survey, in each facility seven health workers have been randomly selected for an in-depth interview. For small facilities with less than seven employees, a take-all strategy has been followed. The final sample of health workers interviewed is 1,278. Each selected health worker answered an extensive structured questionnaire covering her work, household, and perception of the health sector. The questionnaire included a question on the amount of gifts in-kind and other direct payments from patients the health worker made. It also asked about moonlighting and the sector in which the health worker held her second job. Finally, to test for the fair wageeffort theory the health workers were also asked about the salary they thought would be fair for the job they provide in the facility.

Because informal payments are a sensitive issue in Tajikistan due to their illegal nature the collection of information was carefully designed. First, the health workers have been firmly reassured of the confidential nature of the questionnaire’s entire content and that it would be impossible to identify them. Second, it was made clear that the firm carrying out the survey was from the private sector and that the data would be the property of the World Bank and not shared with the survey firm or the government except in aggregate form. Finally, the question on informal payments came after other questions on health workers’ income sources including salary and bonuses. Unlike most surveys, however, the question about monthly average intake from informal payments was asked as directly as possible. The question was framed in a way that left no ambiguity whatsoever as to what it referred to.<sup>10</sup> Fortunately, the

> 10 The exact question is the following “How much did you receive on average per month from gifts in-kind and other direct payments from patients?” The same question was asked in a similar survey in Chad (see Gauthier and Wane 2005) but failed because of a high refusal rate. It would be interesting to understand under which

9
Out of the 1,200 households that form the experimental sample, a total of 1,197 were interviewed in July/August 2015, including 405 in the self-reported households and 792 in the proxy-reported households, respectively. As shown in Table 1, the average household head is male, 50 years of age, and has 4 years of formal schooling. On average, household size is 5.6 with 1.5 children aged 6 to 14 per household. These children are 10 years of age on average, with 2.5 years of formal schooling. The average monthly household income was around 1200 birr (about US$52), which was somewhat higher relative to the income of the average farmer in Ethiopia. Nonetheless, our sample participants live in houses with poor infrastructures such as mud-based floors (70 percent), no electricity (78 percent), and no sanitary services (80 percent). On average, the extent of the household plot is close to one hectare, of which 58 percent is used for coffee production while the remaining land is mainly used for cultivation of enset and maize. On average, households reported their plots as being located a 22-min walk from the closest primary school. The p-values of the t- test for the equality of means between the self-reported and proxy groups show statistical balance across all variables for the overall experimental sample and the sub-sample of households that have at least one child in the 6-14 age category. It is worth noting that around 90% of proxy respondents are males.

To address potential concerns about the representativeness of these data concerning nonFairtrade smallholder farmers, we compare key characteristics of our sample with that of a nationwide representative sample of rural households by using the 2015/2016 Ethiopian Socioeconomic Survey (ERSS) that was implemented by the Central Statistics Agency (CSA) as part of the Integrated Surveys on Agriculture program in close collaboration with the World Bank. The ERSS data is a representative survey of 4,954 households living in rural areas, small-town, and medium and large-sized towns.<sup>10</sup> By looking at Appendix Table A.1.1, one observes small differences in 12
# **Annexes**

# **Annex 1. Robustness: GDP Growth Rate**

## **Table A1.1 GDP Growth Regression**

|||Dependent vari<br>|able: log real GD<br>|P<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||FE<sup>a</sup><br>**1996-2013**|RE<br>**1996-2013**|RE<br>**1996-2013**|RE<br>**1996-2013**|RE<br>**1996-2013**|
|**Pillar 1**|0.00744<br>(0.20)|0.0124<br>(0.32)|0.0140<br>(0.36)|0.0140<br>(0.36)|0.0464*<br>(1.56)|
|**Pillar 2**|0.0000765***<br>(4.24)|0.0000785***<br>(4.28)|0.0000938***<br>(5.09)|0.0000938***<br>(5.09)|0.0000298***<br>(2.14)|
|**Pillar 3**|0.000695**<br>(1.95)|0.000708**<br>(1.94)|-0.000935**<br>(-1.80)|-0.000935**<br>(-1.80)|0.000386<br>(0.95)|
|**Pillar 4**||||||
|ToT|0.0849|0.0870|0.109*|0.109*||
|Output gap|(1.20)|(1.20)|(1.51)|(1.51)|0.0107***<br>(13.26)|
|**Pillar 5,6**|0.212**<br>(1.92)|0.195**<br>(1.74)|-0.133<br>(-1.01)|-0.133<br>(-1.01)|-0.126<br>(-1.22)|
|**Pillar 7**||||||
|Tax rate|-0.185***|-0.173**|-0.250***|-0.250***|-0.185***|
||(-2.03)|(-1.85)|(-2.64)|(-2.64)|(-2.65)|
|Reg quality|0.166***|0.163***|0.149***|0.149***|0.0919***|
|Tariffs|(4.28)<br>-0.0606***<br>(-5.04)|(4.08)<br>-0.0622***<br>(-5.04)|(3.76)<br>-0.0502***<br>(-4.01)|(3.76)<br>-0.0502***<br>(-4.01)|(2.77)<br>-0.0448***<br>(-5.03)|
|**Pillar 8**||||||
|Women (%)|0.0294***<br>(2.27)|0.0280***<br>(2.11)|0.0312***<br>(2.38)|0.0312***<br>(2.38)|-0.00947<br>(-0.34)|
|CBR|0.188|0.128|0.161|0.161|0.0105|
||(1.10)|(0.74)|(0.94)|(0.94)|(0.08)|
|**Pillar 9**|-0.0473|-0.0401|-0.0124|-0.0124|0.0209|
||(-1.25)|(-1.03)|(-0.32)|(-0.32)|(0.74)|
|**Pillar 11**|-0.00250|-0.00237|0.000914|0.000914|0.00148|
||(-0.80)|(-0.74)|(0.28)|(0.28)|(0.57)|
|**Pillar 12**|-0.00113|-0.000151|-0.0103|-0.0103|0.0154*|
||(-0.09)|(-0.01)|(-0.83)|(-0.83)|(1.63)|
|EU||0.268|0.387|0.387|-0.540|
|||(0.48)|(0.80)|(0.80)|(-0.97)|
|T2000|No|<br>No|<br>Yes|<br>No|<br>Yes|
|T1990|No|No|No|Yes|No|
|Constant|Yes|Yes|Yes|Yes|Yes|
|Obs|259|259|259|259|237|
|Wald test||125.7|148.8|148.8|431.0|
|P-value|0|0|0|0|0|

_Note_ t statistics in parentheses; * p<0.2, ** p<0.1, *** p<0.05 a In the current specification, our initial hypothesis that the individual-level effects are adequately modelled by a random-effects model cannot be rejected at 99% and 95% CI.

31
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Policy Research Working Paper 9195

# **Abstract**

This paper explores the link between the prevalence of violent conflicts and extremely low female labor force participation rates in South Asia. The Labor Force Surveys from Bangladesh, Sri Lanka, India, and Pakistan are merged with the Global Terrorism Database to estimate the relationship between terrorist attacks and female labor supply. Geographical data on exposure to violence are used to compare administrative units exposed to attacks with those not exposed. The analysis finds that one additional attack reduces female labor force participation rates by about 0.008 percentage point, on average. Violence has less impact on male labor participation, thus widening the gender labor participation gap. The paper tests the added-worker effect theory—which posits that violence might increase female labor force participation as women try to make up for lost household income—and finds mixed evidence: greater prevalence of attacks may encourage married women to work more hours, but when the environment gets more risky, all women work fewer hours. The paper also finds that violence decreases female labor participation less where it was already higher and has a progressively greater impact on lowering female labor participation where the number of attacks is higher.

This paper is a product of the Poverty and Equity 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 gacevedo@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
**<u>Graph 8: Quantity versus Quality of Public Investment</u>**

<!-- Start of picture text --> Who benefits the most from higher public investment efficiency?<br>LI<br>MORE gain from a 1ppt<br>CHN increase in efficiency LMI<br>UMI<br>HI<br>MOZ<br>NERVEN μ=0.05ppts<br>MYS<br>BOLMNG μ=0.10ppts<br>ECU<br>IND μ=0.20ppts<br>BHR NAM<br>MUSJORNZLTUN MDAKGZ SENGAB BEN<br>KORKAZHRV TUR CMR TZA<br>SGP NORBGRUSAPOL LKHUN AME PANXPRYLTUPE R URYKEN HND TGO<br>SVK CRI<br>DNK UKREGYCYP AUS<br>IDNSRB ZAF CIV<br>ARG<br>GBR CHL<br>BEL<br>LESS gain from a 1ppt BRA<br>increase in efficiency ZWE<br>1 .9 .8 .7 .6 .5 .4<br>Efficiency (IEI) [reversed order]<br>Note: μ=Ig/Y/IEI is ppts Ig/Y equivalent to a 1ppt increase in public investment efficiency<br>.25<br>.2<br>.15<br>.1<br>.05<br>0<br>Public Investment-to-Output, I(G)/Y [log scale]<br><!-- End of picture text --> Our calculations so far have involved the _short‐run_ increase in investment equivalent to a 1ppt increase in efficiency ( μ�� ). Instead, one might be interested in the permanent increase in investment that generates the same increase in GDPPC by 2040 as a 1ppt increase in efficiency – what we call μ��. Using numerical simulations we find that the values of μ�� and μ�� are almost identical. This is because the increase in efficiency (and equivalently sized increase in I<sup>�</sup> /Y ) is small, which means that any non‐ linearities are second order.

# **7. Conclusion**

In this paper, we develop a new model of public investment and growth ‐ the Long‐Term Growth Model Public Capital Extension (LTGM‐PC) ‐ which is designed to capture the effect of increases in public infrastructure investment quantity or quality on growth, while at the same time being simple enough to solve in a spreadsheet without macros (the Excel‐based tool is provided as a companion to this paper at the website www.worldbank.org/LTGM). Relative to the Standard Long‐Term Growth Model (LTGM), our extension allows public and private capital to enter the production function separately and for public capital to be of poor quality such that only a fraction can be used in production.

The effects of public and private investment on growth in our model vary substantially across countries depending on whether the country is relatively short of public or private capital ‐ but on average are similar to the effect of aggregate investment in the Standard LTGM. We show analytically and numerically that the effect of public investment on growth is higher when the public capital‐to‐output ratio is lower ‐ that is when public capital is scarce. Conversely, in countries where public capital is abundant _relative_ to other factors – even if it is scarce in absolute terms – public investment will have a 32
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