rafmacalaba/datause-encoder-data huggingface dataset:
Persson, Karl Gunnar. 1999. _Grain Markets in Europe, 1500-1900: Integration and Deregulation_ . Cambridge: Cambridge University Press.

- Porteous, Obie C. 2012. “Empirical Effects of Short-Term Export Bans: The Case of African Maize.”

- Poulton, Colin, Jonathan Kydd, Steve Wiggins, and Andrew Dorward. 2006. “State Intervention for Food Price Stabilisation in Africa: Can It Work?” _Food Policy_ 31 (4) (August): 342–356.

- Rashid, Shahidur, Ralph Cummings Jr., and Ashok Gulati. 2007. “Grain Marketing Parastatals in Asia: Results from Six Case Studies.” _World Development_ 35 (11) (November): 1872–1888.

- Rashid, Shahidur, and Solomon Lemma. 2011. “Strategic Grain Reserves in Ethiopia: Institutional Design and Operational Performance.” _IFPRI Discussion Paper_ 01054.

- Salant, Stephen W. 1983. “The Vulnerability of Price Stabilization Schemes to Speculative Attack.” _The Journal of Political Economy_ 91 (1): 1–38.

- Sandmo, Agnar. 1971. “On the Theory of the Competitive Firm Under Price Uncertainty.” _The American Economic Review_ 61 (1): 65–73.

- Seale Jr., James L, Anita Regmi, and Jason Bernstein. 2003. “International Evidence on Food Consumption Patterns.” _USDA, Technical Bulletin_ 1904 (October).

- Sen, Amartya Kumar. 1981. _Poverty and Famines: An Essay on Entitlement and Deprivation_ . Oxford: Oxford University Press.

- Slayton, Tom. 2009. “Rice Crisis Forensics: How Asian Governments Carelessly Set the World Rice Market on Fire.” _Center for Global Development, Working Paper_ 163 (March).

- Staatz, John M, Niama Nango Dembele, Valerie A Kelly, and Ramziath Adjao. 2008. “Agricultural Globalization in Reverse: The Impact of the Food Crisis in West Africa.” _Michigan State University, Department of Agricultural, Food, and Resource Economics, Food Security Collaborative Working Paper_ 55466 (September).

- Swinnen, Johan, and Pasquamaria Squicciarini. 2012. “Mixed Messages on Prices and Food Security.” _Science_ 335 (6067): 405–406.

- Timmer, C Peter. 1989. “Food Price Policy: The Rationale for Government Intervention.” _Food Policy_ 14 (1) (February): 17–27.

- ———. 2010. “Reflections on Food Crises Past.” _Food Policy_ 35 (1) (February): 1–11.

- ———. 2012. “Behavioral Dimensions of Food Security.” _Proceedings of the National Academy of Sciences_ 109 (31): 12315–12320.

43
-26-

the inflation rate was still positive during the recession, with an average value of 7 percent. The prices in the sectors of construction and manufacturing showed more variation than the average price level in the economy.

At the end of the cycle of boom and bust (around 1983-1984), the relative prices in the industrial sectors with respect to France were at about the same level as at the beginning of the cycle. However, the general price level in Cote d'Ivoire (as measured by the CPI), was still 20 percent higher than in France. This difference was due mainly to the rise - the price of services, which we have already observed in Figure 10.

The concomitance of a large increase of public expenditures and of a rise of the domestic price level in the late seventies raise the important issue of the relation between public expenditures and the domestic price level or the real exchange rate. We have estimated for Cote d'Ivoire the same econometric relation as was presented in other related studiesS. The basic idea is that the domestic price level (with respect to some foreign price), adjusts to clear the market for domestic goods. An exogenous increase of demand raises the demand for non-tradeable goods, and thus their price with respect to that of the tradeable goods. A good example of exogenous demand is the level of public expenditures. Since the domestic price level is some average of the price of non-tradeable and tradeable goods, and the price of tradeable goods is "anchored" by foreign prices, one should observe a positive relation between the exogenous shockE of demand (e.g. public expenditures), and the 5See in particular, Rodriguez, [1989], [19901.
three cases one sees higher concentration for immigrant youth than for native youth, and for immigrant youth compared to older immigrants.

# **8. Age at Return**

While migration offers young adults the opportunity to acquire education and earn higher incomes abroad, sending countries often worry that many of these migrants will never return. The proportion of migrants who return is very difficult to measure with the existing data available in most countries. However, the few studies which have been able to measure return show that a high proportion of migrants do return. For example, in the U.S. Jasso and Rosenzweig (1982) estimate that up to 50 percent of the migrants who entered the U.S. in 1971 had returned by 1979. Dustmann (2005) summarizes several other studies, which find rates of return of 80 percent in Switzerland and two-thirds in Germany. Migration return rates can be quite high when the country of return is close by. In a study of migrants from Western Mexico, Reyes (1997) estimates that about 50 percent of all immigrants return within two years, and by 10 years, almost 70 percent had returned.

Given that many migrants return, a key question is then at what age do they return? Individuals who return while relatively young will have more of their working lives to spend in their home countries, potentially using skills acquired abroad. In contrast, individuals who return to their home countries only after retirement may have greatest impact through the savings they bring back. Several household surveys ask questions on

- 15 -
Policy ReseaRch WoRking PaPeR 4678

# **Abstract**

This paper explores the social capital impacts of a largescale, community-driven development project in the Philippines in which communities competed for block grants for infrastructure investment. The analysis uses a unique data set of about 2,100 households collected before the project started (2003) and after one cycle of sub-project implementation (2006) in 66 treatment and 69 matched control communities. Participation in village assemblies, the frequency with which local officials meet with residents and trust towards strangers increased as a result of the project. However, there is a decline in group membership and participation in informal collective action activities. This may have been because households were time-constrained, so that in order to participate in project activities, they needed to temporarily reduce their participation in informal activities. An alternative explanation is that the project improved the efficiency of formal forms of social capital and thus households needed to rely less on informal forms. Finally, the results indicate that, in the short run, the project might have reduced the number of other investments.

This paper—a product of the Social Development Department, Sustainable Development Network—is part of a larger effort in the department to evaluate the institutional impacts of Community Driven Development projects. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jlabonne@ worldbank.org and rchase@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
|Std. Error|(10.4)|
|---|---|
|Blue Line Introduction (Dec 31, 2005)|-16.3|
|Std. Error|(16.6)|
|Blue Line Second Extension (Nov 11, 2006)|-2.9|
|Std. Error|(14.6)|
|Number of Observations|22,158|

Source: Authors’ analysis based on data described in the text.

The dependent variable is the natural logarithm of hourly CO at ITO. Control variables are the same as used in table 4, with the addition of season fixed effects. Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level, and *** at 1 percent level.

**Table 8. Effect of Delhi Metro on NO2 at ITO: Entire Series, 2004–2006**

||Coeff.|Std. err.|
|---|---|---|
|Yellow Line first extension|40.4|99.1|
|Blue Line introduction|-404.1**|161.6|
|t|-0.0002|0.0002|
|t squared|2.81E-08|3.61E-08|
|t cubed|-1.52E-12|1.78E-12|
|t*YL1E|-0.0085|0.0195|
|t squared*YL1E|5.71E-07|1.27E-06|
|t cubed*YL1E|-1.19E-11|2.72E-11|
|t*BLI|0.0623**|0.0278|
|t squared*BLI|-3.21E-06*|1.63E-06|
|t cubed*BLI|5.53E-11*|3.29E-11|
|Number of observations|20646||

48
# Coding Bootcamps for Female Digital Employment Evidence from an RCT in Argentina and Colombia<sup>_∗_</sup>

Julian Aramburu, Ana Goicoechea<sup>_†‡_</sup> , Ahmed Mushfiq Mobarak<sup>§</sup>

**_JEL Codes_ :** C21, I26, J16, J21, J24, J48, O12.

**_Keywords_ :** STEM, Computer Science, Coding, Technology, Labor Market Policy, Bootcamps, Gender, Skills, Employment, COVID-19, Argentina, Colombia.

> _∗_ We thank The World Bank team –Denis Medvedev, Martha Licetti, Andrew Beath, Sebastian Hurtado-Cano, Sarah Lenoble, and Victor Mulas– for their support and contributions throughout the design and implementation of the program. We also thank Digital House and Bogotá Institute of Technology for implementing the bootcamps in Buenos Aires, Argentina and in Bogotá, Colombia, respectively, and Bogotá Chamber of Commerce (CCB), Invest in Bogotá, BICTIA, and Facebook for their partnership to implement the program in both cities. The authors gratefully acknowledge the financial contributions of infoDev Trust Fund donors and the World Bank’s Gender Umbrella Trust Fund. Isabel Miranda, Agustina Suaya, and Inés Taylor provided excellent field coordination assistance. We thank Berk Ozler, Alaka Holla, Nate Barker, Paula Calvo, Zara Contractor, Lucas Finamor and participants in the Yale Labor/Public Economics Prospectus Workshop for helpful comments. Julian Aramburu thanks The MacMillan Center for International and Area Studies at Yale for funding travel to conduct field work. This randomized control trial was registered in the American Economic Association RCT Registry (ID AEARCTR-0003850) and approved by Yale University Institutional Review Board (IRB ID 2000025881). The views expressed herein are those of the authors and do not necessarily reflect the views of The World Bank nor Yale University.

> _†_ Yale University, julian.aramburu@yale.edu.

> _‡_ World Bank, agoicoechea@worldbank.org.

> §Yale University, ahmed.mobarak@yale.edu.
28 Table 4: Average annual NACs<sup>a</sup> and percentage changes in them, key crops, developing and high-income countries, 1965 to 2008 (1 + NRA/100) <u>(a) Average annual NACs (1 + NRA/100)</u>

|||D<br>|eveloping<br>|countries<br>|<br>|||Hig<br>|h-income<br>|countries<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1965-<br>1972|**_1972-_**<br>**_1976_**|1976-<br>1984|**_1984-_**<br>**_1988_**|1988-<br>2004|**_2004-_**<br>**_2008_**|1965-<br>1972|**_1972-_**<br>**_1976_**|1976-<br>1984|**_1984-_**<br>**_1988_**|1988-<br>2004|**_2004-_**<br>**_2008_**|
|Rice|0.97|**_0.91_**|1.02|**_1.27_**|1.30|**_1.20_**|1.23|**_1.07_**|1.37|**_2.53_**|2.31|**_1.87_**|
|Importers|1.06|**_0.99_**|1.09|**_1.35_**|1.35|**_1.23_**|1.85|**_1.70_**|2.28|**_5.78_**|6.99|**_3.47_**|
|Exporters|0.76|**_0.65_**|0.78|**_1.02_**|1.16|**_0.91_**|0.99|**_0.81_**|1.01|**_1.89_**|1.53|**_1.02_**|
|Wheat|1.10|**_0.90_**|1.10|**_1.18_**|1.19|**_1.00_**|1.39|**_0.92_**|1.41|**_2.00_**|1.58|**_1.41_**|
|Importers|1.01|**_0.94_**|1.24|**_1.36_**|0.91|**_0.71_**|1.20|**_0.97_**|1.08|**_1.46_**|1.27|**_1.02_**|
|Exporters|1.12|**_0.89_**|1.09|**_1.18_**|1.22|**_1.05_**|1.45|**_0.91_**|1.50|**_2.18_**|2.05|**_1.87_**|
|Maize|1.09|**_0.99_**|1.03|**_1.13_**|1.07|na|1.38|**_1.21_**|1.37|**_1.62_**|1.42|na|
|Soybean|1.20|**_0.99_**|1.19|**_1.27_**|1.42|na|0.97|**_1.00_**|1.45|**_1.90_**|1.22|na|
|Sugar|1.39|**_0.78_**|1.10|**_1.49_**|1.37|na|2.95|**_1.17_**|2.19|**_3.29_**|2.58|na|

# (b) Percentage change in NAC from previous non-spike period

||**1972-1976**|**1984-1988**|**2004-2008**|**1972-1976**|**1984-1988**|**2004-2008**|
|---|---|---|---|---|---|---|
|Rice importers|-7|24|-9|-8|154|-50|
|Rice exporters|-14|31|-22|-18|87|-33|
|Wheat importers|-7|10|-22|-19|35|-20|
|Wheat exporters|-21|8|-14|-37|45|-9|
|Maize|-9|10|na|-12|18|na|
|Soybean|-18|7|na|3|31|na|
|Sugar|-44|35|na|-60|50|na|

> <sup>a</sup> Unweighted average of national NACs each year, averaged over the number of years in each period.

Source: Authors’ calculations based on updated NRA estimates from Anderson and Valenzuela (2008).
# **_A comparative overview of wage-setting systems_**

Wage setting systems vary significantly across OECD economies. At one end of the spectrum are Anglo-Saxon countries—such as Australia, Canada, the United Kingdom and the United States—as well as Japan, where wages are determined at the firm level; at the other end of the spectrum are Belgium and Finland, where wages are determined mainly through central collective bargaining. In most OECD countries, including Denmark, Norway and Sweden, wage bargaining takes place predominantly at the sector level, with varying levels of multilevel bargaining, coordination, tripartite concertation and extension rules (Table 1).<sup>5</sup> Among the EU15 countries where sector-level collective bargaining is dominant, Sweden has a greater degree of decentralization than some others, such as Austria, Finland, Germany, the Netherlands and Spain: in Sweden collective agreements do not extend to other parties, and government involvement in collective bargaining is very rare (Kullander, 2012). Moreover, company-level agreements are becoming more common in Sweden than in some other EU15 countries.

Table 1: Characteristics of collective wage bargaining in EU15 countries

|||**Level of**<br>Inter-<br>sectoral|**collective ba**<br>Sectoral|**rgaining**<br>Company|Coordination<br>level|Influence of<br>tripartite<br>concertation|Extensio<br>n rule|
|---|---|---|---|---|---|---|---|
|Intersectoral<br>||||||||
|bargaining|Belgium|**+++**|**+++**|**++**|High|Yes|Yes|
|dominant|Finland|**+++**|**+++**|**++**|High|Yes|Yes|
||Denmark|**++**|**+++**|**++**|Medium|No|Yes|
||Greece|**++**|**+++**|**+**|Low|No|Yes|
||Norway|**+++**|**+++**|**++**|High|Yes|No|
|Sectoral|Italy|**+**|**+++**|**++**|Medium|Yes|No|
|bargaining|Netherlands|**+**|**+++**|**++**|Medium|Yes|Yes|
|dominant|Sweden|**+**|**+++**|**++**|High|No|No|
||Spain|**+**|**+++**|**++**|High|Yes|Yes|
||Austria|**—**|**+++**|**+**|High|No|Yes|
||Germany|**—**|**+++**|**++**|Medium|No|Yes|
|C|Ireland|**++**|**+**|**+++**|Low|Yes|Yes|
|ompany<br>barainin|France|**+**|**++**|**+++**|Low|No|Yes|
|gg<br>dominant|Luxembourg|**+**|**++**|**+++**|Low|—|Yes|
||United Kingdom|**—**|**++**|**+++**|Low|No|No|

<mark>Source: Based on data provided in country profiles in Eurofound 2014 at http://www.eurofound.europa.eu. Note: All information is as of September 2014. +++ = principal or dominant level of collective bargaining; ++ = important but not dominant level; + = existing but marginal level; — = not applicable.</mark>

> 5 In tripartite concertation in addition to trade unions and employers’ association government is also part of the negotiations. Extension rules refer to the cases where decisions taken through collective agreements are also binding for employees who are not members of the unions or employers’ associations.

5
12

**Table 2 (continued)**

||||19<br>|98<br>|||
|---|---|---|---|---|---|---|
||OLS|Q10|Q25|Q50|Q75|Q90|
|Education<br>|0.0410*<br>(0.0038)|0.0197*<br>(0.0060)|0.0175*<br>(0.0054)|0.0254*<br>(0.0047)|0.0552*<br>(0.0045)|0.0749*<br>(0.0053)|
|Education<sup>2</sup>|0.0067*<br>(0.0002)|0.0061*<br>(0.0003)|0.0067*<br>(0.0002)|0.0074*<br>(0.0002)|0.0069*<br>(0.0002)|0.0063*<br>(0.0003)|
|Experience<br>|0.0544*<br>(0.0014)|0.0429*<br>(0.0022)|0.0452*<br>(0.0017)|0.0515*<br>(0.0016)|0.0613*<br>(0.0014)|0.0672*<br>(0.0024)|
|Experience<sup>2</sup>|-0.0007*<br>(0.00002)|-0.0007*<br>(0.00003)|-0.0007*<br>(0.00002)|-0.0007*<br>(0.00002)|-0.0008*<br>(0.00002)|-0.0008*<br>(0.00004)|
|Education*<br>experience|0.00024*<br>(0.00007)|0.00025+<br>(0.00013)|0.00053*<br>(0.00010)|0.00062*<br>(0.00010)|0.00027*<br>(0.00008)|0.00007<br>(0.00013)|
|N<br>|||74|003|||
|R<sup>2</sup>/Pseudo R<sup>2</sup><br>|0.490|0.217|0.264|0.303|0.335|0.341|
|Wald χ<sup>2</sup>(19)||||155.84|||

Notes: All equations also include four regional dummy variables, dummy variables for metropolitan and urban status, dummy variables for racial group (black, mixed raced) and interactions of the race dummies with schooling and with experience. Full results available on request. Simultaneous quintile regression standard errors obtained by bootstrapping (100 replications). Wald is a test for combined equality of the reported schooling and experience coefficients across all quintiles. * denotes significance at 5% or less; + denotes significance at 10%.

Source: authors’ calculations from PNAD 1988, 1992 and 1998
association for some models between firm size and labor growth. The share of foreign ownership in a firm is not strongly associated with labor growth for either sample. Because we split the sample by the level of per capita income, there is less need to include that variable in the labor growth regressions. However, in the regressions that pool all countries, that variable is negative and significant indicating that labor growth is slower in countries with high incomes (Table 5, model 3). For the sub-sample regressions, the coefficient for per capita income is also negative, and it is significant in the low-income sample. Perhaps more importantly, its inclusion does not qualitatively change results for other variables (Table 5, models 6 and 9).

In Appendix B, we present OLS regressions that control for additional country-level variables to assess the robustness of our findings regarding financial development and firm labor growth. Because job growth likely depends on the quality of the institutional environment and the general level of stability, we include two variables: an index of adherence to rule of law and another measuring political stability. Certain industries may have experienced more rapid job growth during the period in which our surveys were conducted, and thus we also include variables that measure the share of GDP attributable to agriculture, manufacturing, and services (the omitted category) in our analysis. While these control variables are often significant, they do not change the coefficients for our indicators of financial development much. Private credit/GDP remains significantly positively linked to labor growth within the low-income sample; stock market capitalization/GDP is positively linked to labor growth in the high-income sample.

# **B. Instrumental Variables Regressions**

Because labor growth is as likely to affect financial structure as the reverse, or some omitted factors could be driving both high labor growth and financial development, it is important to investigate whether the associations between financial structure and firm labor growth in Table 5 are causal. To address these concerns regarding the endogeneity of the financial structure variables, we present Generalized Method of Moments (GMM) estimates in Table 6.<sup>13</sup> As described above, we use the average level of interpersonal trust from 1981 to 2006 and net petroleum exports per worker as instruments. Both are measured at the country level. The first-

> 13 We choose GMM over two-stage least squares because GMM can account for heteroskedasticity of unknown form (Woolridge, 2001). In practice, the two-stage least squares results were qualitatively similar to the GMM results presented here.

19
# **Table 2: Propensity to Pay Bribes: Preliminary Results (OLS)**

||**P**|**ropensity to**|**pay bribe**||**A**|**mount Pai**|**d as bribe**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Per Capita Income**|**-0.043**<br>**(9.16)*****|**-0.038**<br>**(8.25)*****|**-0.017**<br>**(3.51)*****|**-0.023**<br>**(4.95)*****|**0.080**<br>**(2.72)*****|**0.079**<br>**(2.86)*****|**0.077**<br>**(2.72)*****|**0.057**<br>**(3.28)*****|
|Household size||-0.010|-0.008|-0.006||-0.011|-0.014|-0.016|
|||(2.07)**|(1.96)*|(1.13)||(2.12)**|(2.59)**|(2.83)***|
|No. of School age children||0.057|0.050|0.048||0.006|0.024|0.010|
|||(6.21)***|(5.94)***|(5.35)***||(0.21)|(1.60)|(0.37)|
|Age of Head||-0.002|-0.003|-0.002||0.003|0.002|0.002|
|||(3.82)***|(4.00)***|(2.73)***||(3.05)***|(1.96)*|(2.26)**|
|Head Female||-0.011|0.017|-0.016||0.040|-0.008|0.044|
|||(0.40)|(0.65)|(0.60)||(0.93)|(0.23)|(0.97)|
|Head Muslim||0.016|-0.013|0.009||0.004|-0.004|0.029|
|||(0.48)|(0.37)|(0.26)||(0.07)|(0.10)|(0.41)|
|Head's education secondary or above||||-0.141||||0.153|
|||||(7.13)***||||(1.90)*|
|Head's occupation (professional=1)||||-0.086||||0.259|
|||||(3.18)***||||(1.02)|
|Regional Fixed effects|Yes|Yes|No|Yes|Yes|Yes|No|Yes|
|Village Fixed effect|No|No|Yes|No|No|No|Yes|No|
|Observations|3760|3760|3760|3760|1832|1832|1832|1832|

Notes: (1) Standard errors are clustered at the primary sampling unit (PSU) level (2) Robust t statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
# **3.3 The composition of intangible expenditures by industry group**

This section discusses our industry-wise estimates of intangible expenditures in Brazil. As explained in Section 2, we obtained these estimates by aggregating firm-level data to the level of industries, or if this was not possible, by using the sector input-output coefficients in the national accounts tables. Specifically, among major categories, industry-level spending estimates for computerized information, other product development and organizational improvement are based on input-output tables and are therefore less precise than other bottom-up estimates.

We give an overview of our industry level findings again by categories of intangibles, grouping our 55 industries (as classified in the Brazilian national accounts) into six broad categories: agriculture, extractive industries (including petroleum), low tech manufacturing, medium/high tech manufacturing, ICT services (which includes R&D services) and all other services (including construction). Our division of manufacturing into just two groups is admittedly crude but quite effective at capturing key interindustry differences in intangible investment. The category called ̳medium/high tech manufacturing‘ includes industries generally considered to be more technologically sophisticated _on average_ , such as chemical and pharmaceuticals, machinery and equipment, electronics, instruments, motor vehicles and other transport equipment, basic metals and metal products. The category called ̳low tech manufacturing‘ includes industries like food products, textiles, leather, wood, paper, which are considered technologically less sophisticated on average.

As indicated by the share of computer software and database spending in total industry sales, the extractive industries and (unsurprisingly) ICT are by far the most IT-intensive industries in Brazil (Figure 7). All other sectors spend less than 0.75 percent of sales on computerized information, which is quite low by US standards. As expected, firms in low tech manufacturing spend even less than those in medium/high tech manufacturing. However, manufacturing is also seeing considerable growth in computerized information expenditures, with its share in industry sales doubling in the case of low tech manufacturing.

23