rafmacalaba/datause-encoder-data huggingface dataset:
|Graduatingthe Poorest into Microfinance: LinkingSafetyNets|with Aude de Montesquiou Fezensac|
|---|---|
|Graduatingthe Poorest into Microfinance: LinkingSafetyNets|with Aude de Montesquiou Fezensac|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|XiningFlood and Watershed Management|XimingZhang|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)<br>|Wendao Cao<br>|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Sustainable Development in Poor Rural Communities(GEF)|Wendao Cao|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Health Insurance Project|Francisca Ayodeji Akala|
|Foreign Debt Front Office CapacityBuilding|Carlos Pinerua|
|Foreign Debt Front Office CapacityBuilding|Carlos Pinerua|
|Foreign Debt Front Office CapacityBuilding|Carlos Pinerua|
|SD - Livestock and Fisheries Development Project - MDTF|Abel Lufafa|
# Table D9. Distribution of Manufacturing Establishments Vehicles for Garbage Disposal by Employment Size, Thailand

<!-- Start of picture text --> VENICLEG (VAW VEHICLES FOR GARBAGE) FIRMSIZE CEMPLOYEES per FIRU)<br>Frequwncy I<br>Percont<br>Row Pet<br>Col Pet 11-19 120-49 150-99 1100-199 1200-499 j500-9m 1'000 A Total<br>I I 41 I I ~~~~~~~~over<br>____.. .. _____+-4--............ .... 4--.--4.--........... ................. ..... +....................<br>YES 1 0 0 1 I 0 1 1 1 0 1 3<br>0:00 0.00 0.33 0.33 0.00 0.33 0.00 I 1.00<br>0.00 0.00 33.33 33.33 1 0.00 33.33 I 0.00 I<br>0.00 0.00 1.47 1.85 0.00 6.25 0.001<br>......+ .. _ . _... . ...... ........... .. .. .. . . .. .. . .. .. . -- -- -<br>No I 33 2 74 ' 67 53 47' 15' 8a 297<br>11.00 24.67! 22.33 1 17.67 15.67 5.00 ! 2.67 99.00<br>1 11.11 1 24.92 1 22.56 I 17.85 1 15.82 1 5.05 1 2.691<br>I 100.00 1 100.00 98.53 1 98.15 1 100.00 1 93.75 1 100.00 1<br>............. ................ - :. .... . .<br>Totat 33 74 68 54 47 16 8 300<br>11.00 24.67 22.67 18.00 15.67 5.33 2.67 100.00<br><!-- End of picture text -->
# **7. References**

Aghion, P., D. Hemous, and E. Kharroubi. 2014. “Cyclical Fiscal Policy, Credit Constraints, and Industry Growth.” _Journal of Monetary Economics 62_ : 41-58.

Afonso, A., and D. Furceri. 2008. “Government Size, Composition, Volatility and Economic Growth.” Working Paper Series 849, European Central Bank.

Aizenman, J., and N. Marion. 1999. “Volatility and Investment: Interpreting Evidence from Developing Countries.” _Economica_ 66 (262): 157–79.

Alesina, A., F. Campante, and G. Tabellini. 2008. ”Why is Fiscal Policy Often Procyclical?” _Journal of the European Economic Association_ 5:1006-1036.

Arroyo Marioli, F., and C. A. Vegh. [Forthcoming]. “Fiscal Procyclicality in Commodity Exporting Countries: How Much Does it Pour and Why?” Mimeo.

Badinger, H., and W. H. Reuter. 2017. "The Case for Fiscal Rules." _Economic Modelling_ 60: 334-343.

Bakas, D., G. Chortareas, and G. Magkonis. 2019. “Volatility and Growth: A Not So Straightforward Relationship.” _Oxford Economic Papers_ 71(4): 874-907.

Barro, R. 1991. “Economic Growth in a Cross Section of Countries.” _Quarterly Journal of Economics_ 106: 407–433.

Bleaney, M., and D. Greenaway. 2001. “The Impact of Terms of Trade and Real Exchange Volatility on Investment and Growth in Sub-Saharan Africa.” _Journal of Development Economics_ 65 (2): 491500.

Bova, E., N. Carcenac, and M. Guerguil. 2014. “Fiscal Rules and the Procyclicality of Fiscal Policy in the Developing World.” IMF Working Paper 14/122, International Monetary Fund, Washington, DC.

Bergman, U. M., and M. Hutchison. 2018. "Fiscal Procyclicality in Developing Economies: The Role of Fiscal Rules, Institutions and Economic Conditions." _Unpublished paper, University of Copenhagen, Copenhagen_ .

Blanchard, O. and R. Perotti. 2002. “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output.” _Quarterly Journal of Economics_ 117: 1329-1368.

<mark>Blattman, C., J. Hwang, and J. G. Williamson. 2007. “Winners and Losers in the Commodity Lottery: the Impact of Terms of Trade Growth and Volatility in the Periphery 1870–1939.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>82(1): 156–179.</mark> 19
proposals were put together quickly, as **is** usual in loans for direct balance of payments support, they often included plans for studies to identify future actions. Sometimes these plans reflected serious intentions to undertake reform, but often they served merely to delay difficult actions. Not much evidence is available for assessing progress on these studies. Proposals to reduce protection for import-substitute: have been cautious. Most programs envisaged that some level of effective protection would continue indefinitely. In some cases, particularly in Sub-Saharan Africa, additional incentives were introduced for import substitution -- for example, higher duties on imported inputs that compete with domestic production. (Increasing the duties on imported inputs reduces the protection provided to finished goods that use them.)

# Implementation Record

For twenty-four of the forty countries receiving trade adjustment loans, detailed implementation data are available. Implementation records were good for the two of the twenty-four countries that had a low level of restrictiveness (Chile) or antiexport bias (Republic of Korea) at the beginning of the 1980s. Success in implementation for the eight countries judged to have a moderate level of restrictiveness covered the range from low (for example, Malawi), through medium (Panama), to high (Mauritius). Among the remaining fourteen countries that had high initial levels of restrictiveness, six of the nine countries with strong commercial policy reform proposals Aad relatively good implementation records (Ghana, Madagascar, Mexico, Philippines. Senegal, and Turkey).
Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Managers Professionals Technicians Clerical Services and Skilled Craft and Plant and Elementary<br>Support Sales Workers Agricultural Related Machine Occupations<br>Workers Workers Trades Operators<br>Workers<br>Argentina Bolivia Brasil Chile El Salvador Mexico Peru Rep. Dominican Uruguay<br><!-- End of picture text --> Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment.

On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment.

Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE.

Figure 3: Prevalence of Part-time employment among salaried employees. (Mid-90s/Mid-2010s) Page 9 of 51
# References

Ackerberg, Daniel A., Kevin Caves, and Garth Frazer (2015), “Identification Properties of Recent Production Function Estimators” _Econometrica_ 83(6):2411-2451.

Allcott, Hunt, Allan Collard-Wexler, and Stephen D. O’Connell (2016), “How Do Electricity Shortages Affect Industry? Evidence from India,” _American Economic Review_ 106(3): 587-624.

Arnold, Jens Matthias, Aaditya Mattoo, and Gaia Narciso (2008), “Services Inputs and Firm Productivity in Sub-Saharan Africa: Evidence from Firm-Level Data,” _Journal of African Economies_ 17(4): 578-599.

Battese, George E., Sumiter S. Broca (1997), “Functional Forms of Stochastic Frontier Production Functions and Models for Technical Inefficiency Effects: A Comparative Study for Wheat Farmers in Pakistan,” _Journal of Productivity Analysis_ 8: 395-414.

Bernard, Andrew B., J. Branford Jensen, Stephen J. Redding, and Peter K. Schott (2007), “Firms in International Trade”, _Journal of Economic Perspectives_ , 21(3): 105-30.

Biesebroeck, Johannes Van (2005), “Firm Size Matters: Growth and Productivity Growth in African Manufacturing,” _Economic Development and Cultural Change_ 53(3): 545-583.

Bigsten, Arne, and Mulu Gebreeyesus (2007), “The small, the young, and the productive: Determinants of manufacturing firm growth in Ethiopia.” _Economic Development and Cultural Change_ , 55: 813–840.

Black, Sandra E., and Lisa M. Lynch (1996), “Human-Capital Investments and Productivity” _American Economic Review_ 86(2): 263-267.

Bloom, Nicholas, Aprajit Mahajan, David McKenzie and John Roberts (2010), “Why Do Firms in Developing Countries Have Low Productivity?” _American Economic Review: Papers and Proceedings_ 100 (2): 619-623.

Bogetic, Zeljko, and Olasupo Olusi (2013), “Drivers of Firm-Level Productivity in Russia’s Manufacturing Sector” World Bank Policy Research Working Paper 6572.

Calderón, César, William Easterly, and Luis Serven. (2003), “Infrastructure Compression and Public-Sector Solvency in Latin America.” In William Easterly and Luis Serven (eds.), Adjustment Undetermined? Public Deficits and Growth in Latin America, 1980–2000. Princetown, N.J.:Princetown University Press.

Canning, David (1998), “A Database of World Stocks of Infrastructure, 1950–1995.” _World Bank Economic Review_ 12(3): 529–47.

Cole, Matthew A., Robert JR Elliott, Giovanni Occhiali, and Eric Strobl. (2018), “Power outages and firm performance in Sub-Saharan Africa.” _Journal of Development Economics_ , 134: 150159.

18
these overall changes mask significant heterogeneity, where there was no change for women who were most likely to underreport IPV incidents and large and precisely estimated declines in IPV incidents reported by other women. Importantly, there were improvements in IPV attitudes among men regardless of the likelihood of their partners to underreport IPV incidents, consistent with reporting issues driving the heterogeneity in IPV prevalence.

We also investigate the hypothesis that these changes in IPV are the result of a potential change in the role of women in the household. Although specific roles and responsibilities assumed by household members are mostly unobservable, the survey utilized here captures the household member that was responsible for specific decisions, such as who was primarily responsible for deciding how to spend household income and whether to make large purchases. Consistent with the conceptual framework that highlighted the possibility of turbulent disasters impacting intrahousehold roles and social norms, we find that the share of husbands solely responsible for several important decisions declined at the same time as the responsibility of the wife increased.

There are three primary contributions of these empirical results. First, these results illustrate that despite a substantial negative income shock and a rise in food insecurity in disaster-affected regions, turbulent natural disasters can decrease the prevalence of IPV given the many other potential impacts that disasters might have on individuals and households. Although the link between disasters and IPV is not uniform across settings, the majority of results indicate either an increase or a null result.<sup>6</sup> These results illustrate an important possibility in a world in which climate hazards are becoming increasingly prevalent (e.g., Hallegatte 2014).

Second, the large differences between changes in IPV attitudes and reported incidents illustrates the importance of accounting for measurement issues when analyzing IPV. Although the possibility has long since been acknowledged, most quantitative studies analyzing the IPV impacts of disasters, income shocks, and other interventions focus only on incidents directly reported by women.<sup>7</sup> The few studies that address the potential mismeasurement of IPV prevalence have primarily focused on list experiments (e.g., Bulte and Lensink 2019; Cullen 2023; etc.). However, the results here also illustrate that triangulating between IPV incidents reported by victims and hypothetical IPV attitudes of potential perpetrators, the latter of which potentially has a lower amount of underreporting than IPV incidents (e.g., Ellsberg and Heise 2005), is also a potentially rich source of information.

> 6 See van Daalan et al. 2022 for a summary of findings across disaster settings.

> 7See van Daalen et al. (2022) for a summary of the IPV measures analyzed in disaster setting and see Tankard and Iyengar (2018) for a summary of IPV measures analyzed when investigating the link between economic shocks and IPV.

4
Policy Research Working Paper 5125

# **Abstract**

The number of national export promotion agencies has tripled over the past two decades. Although more countries made them part of their export strategy, studies criticized their efficacy in developing countries. The agencies were retooled, partly in response to these critiques. This paper studies the impact of today's export promotion agencies and their strategies, based on new survey data covering 103 developing and developed countries. The results suggest that on average they have a statistically significant effect on exports. The identification strategies highlight the importance of EPA services for overcoming foreign trade barriers and solving asymmetric information problems associated with exports of heterogeneous goods. There are also strong diminishing returns, suggesting that as far as export promotion agencies are concerned, small is beautiful.

This paper— is a joint product of the Trade and Integration Team, Development Research Group, and Office of the Chief Economist, Latin America and the Caribbean Region—is part of larger efforts in both departments to study the how the structure of trade affects development. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at dlederman@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
Exporter Dynamics, Firm Size and Growth, and Partial Year Effects example above, the first year size of new entrants is underestimated on average by 80 percent. This will cause the number of small firms to be overestimated and will lead to a corresponding distortion in the firm size distribution. Of course the extent of the bias in the firm size distribution depends on the share of new entrants in the overall number of firms but the potential magnitudes are large: in Peru more than 45 percent of firms are new to exporting each year, while in the US more than 10 percent of firms are new to the domestic market in each year.<sup>8</sup> In addition, the bias can affect attempts to understand the relationship between firm size and firm growth. Entrants are disproportionately small in the unadjusted data and thus the growth rates of the smallest firms is likely to be subject to greater overstatement.<sup>9</sup>

# **3.1 Bias Approximation**

In Table 1, we report the distribution of new Peruvian exporters across months based on their first month of exporting. There is some systematic variation over months; March, April and June have higher shares of entrants while May, July and December have lower shares. However, the monthly averages are reasonably close to what would be expected if entry were uniform across days during the year. This finding suggests both that partial-year bias is likely to be important in the Peruvian export data and that for aggregate or market-specific measures of exporting a relatively simple adjustment might be possible.

The relationship between the firm’s exports in its first full year in the market, _Xs_<sup>_true_</sup> , and the observed first year exports, _Xs_<sup>_obs_</sup> , is given by _Xs_<sup>_true_</sup> = _θsXs_<sup>_obs_</sup> , where _s_ is the month of entry into exporting. The adjustment factor for the levels of exports in the first year for each month of an entering cohort is where _r_ is a common constant compound monthly growth rate and average entry levels across months are assumed to be identical. We can also sign the partial year bias in log difference growth rates, _β_ = ∆ _log_ � _X_ 0<sup>_true_</sup> � _−_ ∆ _log_ � _X_ 0<sup>_obs_</sup> �, for a cohort of firms entering the market in year 0 as where _N_ is the total number of entrants and _N_<sup>_s_</sup> is the number of entrants in month _s_ . If shares of entering firms are constant across months, the bias in log differences can be closely approximated by

> 8The US numbers are the average share of entrants per year from 1994-2007 tabulated by the authors from the Business Dynamics Statistics database, http://www.census.gov/ces/dataproducts/bds/data.html.

> 9In the raw data, more than 76 percent of the bottom decile of Peruvian exporters to the US are new entrants.

9
Page 39 98. The policy issue is a dual one: first, to design programs involving women that represent cost-effective approaches to reach such development goals such as improved economic performance and food security; second, to persuade women and their families that these programs are worth trying, so they take full advantage of them. Among the "eguipRing mechanisms," ggricultural extension and credit for small-scale farmers and entreRreneurs are emerging as key golicy instruments. Research on equipping mechanisms is scarce, however. Others may well emerge in the next few years if research and experimentation expand. And our understanding of extension and credit can also be improved through more experimentation.

99. Agricultural extension. Many women farmers, particularly in Africa, own or manage their own smallholdings. In most regions, women also take responsibility for such key tasks as transplanting, weeding, and post-harvest processing. Programs to supply agricultural extension and credi.t are typically undertaken when existing factor markets provide inadequate information and capital (Schultz 1989). But as designed, these programs often fail to reach women. To do so may require more imagination and effort. Even when women are actively involved in major cash or food crops, for example, agricultural extension programs may focus on male farmers, assuming that men will pass information along to their wives. Studies in Asia, Africa, and Latin America show that such transmission of information is often poor (Berger and others 1988; de Beyer 1988; Lycette 1986; World Bank OED 1988a, 1988b: World Bank 1989a).

100. Examples range from maize production in Kenya to cotton production in Togo, from rubber production in Malaysia to rice production in India (see box). Extension also typically focuses on major crops or on particular cash crops controlled by men, neglecting fruits and vegetables or small livestock controlled by women that play an important role in family nutrition but provide little income (Berger and others 1988; World Bank OED 1988b). Female farmers may thus have more difficulty learning about and instituting new farming methods. This can affect their productivity in traditional types of agriculture and hamper their ability to move toward new lir._s of agricultural activity.