rafmacalaba/datause-encoder-data huggingface dataset:
in each location, our sample calculations required a larger number of individuals in the treatment group. This is why, as specified in the pre-analysis plan, we pooled samples in both locations for our main analysis. This required that both the content as well as the targeting of the bootcamps needed to be designed to be almost the same for both cities, with small variations only to allow for adjustments to the local context.

The bootcamps were part-time high-quality rapid-skills courses that prepared students to qualify for an entry-level coding job shortly after its completion. Courses entailed a mandatory computational training component and an optional soft-skill and career development module. The mandatory computational training component entailed basic programming skills based on labor market demands, including JavaScript, Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and other platforms. Students were also required to complete various practical projects in a workshop setting. The optional softskills component entailed professional development workshops focusing on confidencebuilding, leadership, communication and presentation skills. Workshops on job market strategies were also offered, covering topics like building a LinkedIn profile, resume development, job interviews, and teamwork. Personal mentoring sessions with a coach were included in this optional component as well.

The program was carefully designed drawing on lessons learned from earlier pilots in Colombia, Lebanon and Kenya (World Bank, 2018). The main lessons pointed towards the importance of a strong attraction and outreach campaign to attract enough candidates interested in participating in the bootcamps,<sup>6</sup> the need for a solid understanding of participation constraints that women face, the quality and expertise of the bootcamps providers, the relevance of the curriculum, and the identification of the target of participants who can benefit from the intervention.

First, in order to lead to a high return, bootcamps needed to provide the right set of skills valued by the market. In this regard, the selection of well-established coding schools with a proven track record and experience in providing high value-added bootcamps was key. Second, tuition costs to attend the bootcamps at these high-quality schools are high. In Argentina (Colombia), the monthly tuition costs were equivalent to about 1.2 (0.7) times the mean monthly labor earnings in our sample. The scholarship to cover most of the

> 6The pilots implemented in Lebanon and Kenya counted with only 15 and 18 students respectively, making it unfeasible to implement any quantitative evaluation. The pilot in Colombia included a total number of 281 enrollees, of which 120 were selected as beneficiaries, and 161 as non-beneficiaries. The impact evaluation for this pilot did not find significant results of the program on its main outcomes of interest. The study cannot rule out that this could be due to a sample with an insufficient size (World Bank, 2018).

5
Table 5: FGLS estimation <u>results with alternative defnitions of distance:</u> net FDI inflows

||(1)|(2)<br>(3)|
|---|---|---|
|**Variable**||**Coefcient**|
|GDP|0.899<sup>_∗∗_</sup>|0.900<sup>_∗∗_</sup><br>0.899<sup>_∗∗_</sup>|
||(0.017)|(0.017)<br>(0.017)|
|OPEN|0.941<sup>_∗∗_</sup>|0.922<sup>_∗∗_</sup><br>0.909<sup>_∗∗_</sup>|
||(0.083)|(0.081)<br>(0.080)|
|GDPGRO|0.256<sup>_†_</sup>|0.266<sup>_†_</sup><br>0.261<sup>_†_</sup>|
||(0.142)|(0.143)<br>(0.143)|
|CPIGRO|-0.008<sup>_†_</sup>|-0.008<sup>_†_</sup><br>-0.008<sup>_†_</sup>|
||(0.005)|(0.005)<br>(0.005)|
|RERGRO|-0.336<sup>_∗_</sup>|-0.320<sup>_∗_</sup><br>-0.317<sup>_∗_</sup>|
||(0.146)|(0.147)<br>(0.147)|
|WLDGRO|-0.677<sup>_†_</sup>|-0.653<sup>_†_</sup><br>-0.636|
||(0.391)|(0.394)<br>(0.393)|
|WLDFDIadj|0.373<sup>_∗∗_</sup>|0.405<sup>_∗∗_</sup><br>0.409<sup>_∗∗_</sup>|
||(0.039)|(0.038)<br>(0.037)|
|PTAGDP|0.060<sup>_∗∗_</sup>|0.002|
||(0.018)|(0.016)|
|DISTA|-0.155<sup>_∗∗_</sup><br>(0.058)||
|DISTW||0.042<br>(0.054)|
|PTAGDPw||0.011<sup>_∗∗_</sup><br>(0.004)|
|N|1541|1541<br>1541|
|Log-lhd|-1716.811|-1720.753<br>-1721.06|
|_χ_<sup>2</sup>|3146.643|3186.223<br>3178.957|
|Signifcance level<br>_Note:_<br>Standard|s:<br>_†_ 10%<br> errors are in|_∗_5%<br>_∗∗_1%<br> parentheses.<br>The de-|
|pendent variable|is expressed|in natural logarithms.|
|Column (1) repro|duces the res|ults from Table 4.|

than one. In terms of the model in equation (2), the new _PTAGDPw_ variable replaces both the _PTAGDP_ and _DISTA_ variables. The estimation results are shown in column (3) of Table 5.

The coefficients on the control variables are again similar to the reference specification in column (1), and the new distance-weighted PTA common market variable has the expected positive sign and is significant at the one percent level. Although the world growth variable loses its significance at conventional confidence levels, the actual change in the _p_ -value is quite small, going up from 0.083 to 0.106. In accordance with expectations, the elasticity of net FDI inflows with respect to _PTAGDPw_ is much smaller than the _PTAGDP_ elasticity from column (1), implying that the negative effects of distance from PTA partners have been incorporated into the estimate. Therefore, the results of Table 5 confirm our earlier hypothesis that proximity to PTA partners is an important determinant of net FDI inflows following preferential liberalization. Given our earlier criticisms of the _PTAGDP_ variable, its weighted version is better suited to capture the heterogeneity of the 32
thus suggesting that empirical comparisons between war‐afflicted and non‐conflict economies are needed to ascertain whether debt accumulation is a central tendency around the onset of armed conflicts.

Public debt and inflation are key pillars of the macroeconomics of conflicts. However, the role they play at different stages of conflict remain blurred. This paper thus contributes new evidence to a long‐standing literature on the macroeconomics of armed conflict primarily by exploring differential trends across countries.

# **III. Data and empirical strategy**

The empirical strategy relies on two sets of variables. First, the analyses require data on the timing of the onset of armed conflicts in developing economies from 1950‐2016. Second, it requires data on the three macroeconomic variables of interest.

# **_III. A. Data on conflicts_**

To analyze the macroeconomic behavior of developing countries before, during and after the start of an armed confrontation, this paper relies on the information provided by the UCDP/PRIO Armed Conflict Dataset version 17.2.<sup>2</sup> Given that the main goal is to study the relationship between macroeconomic performance and conflicts in general, all extra‐systemic, interstate, internal and internationalized internal armed conflicts affecting developing countries reported by the database are included in the estimation samples. For conflicts that occurred between two or more states or between a state and a non‐state group outside its own territory, conflicts have been assigned to the developing countries involved in such conflicts. That is, high‐income economies are not included in the estimation samples.<sup>3</sup> We apply an event study methodology, transforming calendar years into event years.<sup>4</sup> Under this approach, year zero corresponds to the onset of a conflict. The negative and positive event years represent the periods before and after the beginning of the crisis, respectively. In total, this study covers 11 years of every conflict; five years before and after the starting date plus the year the conflict was triggered. Considering that our methodology is centered on the starting date of a confrontation, the way

> 2 The data set defines conflict as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle‐related deaths in a calendar year.”

> 3 Since 1950, there are few conflicts that involved high‐income economies to such a degree that they affected their macroeconomic performance significantly. Consequently, the inclusion of high‐income economies would likely attenuate the econometric estimates of the impact of the advent of conflict in developing countries.

> 4 Examples of this approach include Chen, Loayza and Reynal‐Querol (2006) and Elbadawi, Kaltani and Schmidt‐ Hebbel (2008).

5
<!-- Start of picture text --> (0.267,<br>(0.059)<br>(0.195)<br>(0.126)<br>(0.040)<br>(0.161<br>(0.091)<br>(0.034)<br>(0.053)<br>(0.015)<br>(0.004)<br>(0.009)<br>Mean of Dependent Vble. -0.340<br>Mean of Passing Rate (%) 42.29<br>Std. Error of Regression 0.574<br>Pseudo-R? 0.810<br><!-- End of picture text --> Notes: Based on 871 school-year observations corresponding to schools with at least 2 years of data. Passing rate is expressed between 0 and 100. Parameters are 2SLS estimates including campus and year fixed effects. Observations are weighted by total school enrollment. Standard errors are clustered at the school level. Coefficients marked with (*) are significant at the 5% significance level. Estimates and standard errors in Italics were obtained through minimum-distance estimation (regression of campus fixed effects on time-invariant school characteristics; number of observations = 193). Pseudo-R? equal to the squared correlation between observed and predicted values of the dependent variable.
involve an increase in government burden on the economy. Moreover, using Egyptspecific data, the study finds a positive and significant link between infrastructure expenditures and infrastructure development. Based on these results, this study concludes that improving infrastructure in Egypt will have a beneficial effect on economic growth and that, in turn, improving infrastructure will require a combination of larger infrastructure expenditures and more efficient investment.

The analysis provided in this study suggests that a permanent increase in infrastructure expenditures has a gradually rising effect on per capita GDP growth. As the infrastructure stock builds up, it renders higher growth; on the other hand, as per capita GDP rises, it becomes more difficult to grow (due to diminishing capital returns). In addition, the government burden of increased expenditures may reduce any growth improvement. These three forces play against each other dynamically to render the projected growth path of a permanent rise in infrastructure expenditure. The effect of higher infrastructure investment dominates over time, and, thus, economic growth rises gradually to converge to a positive value.

The quantitative estimates obtained in this study allow some tentative growth projections. If infrastructure expenditure in Egypt rose permanently from its current level of 5% of GDP to 6%, the growth returns would be rather small in the first years but would gradually rise. Towards the end of the first decade, the gain in per capita GDP growth would reach almost 0.5 percentage points, by the third decade it would amount to 1 percentage point, and eventually it would converge to 1.6 percentage points. If the increase in infrastructure expenditures were more pronounced, from 5% to 8%, the rise in per capita GDP growth would amount to 1.5, 3, 4.7 percentage points in the corresponding time horizons.

These quantitative estimates can be used to elucidate questions of fiscal sustainability and social welfare. First, on financial fiscal sustainability, the issue is whether the improvement in infrastructure can render a sufficient increase in government revenues to fully finance infrastructure investment. According to the simulations presented in the study, if the proceeds derived from the new infrastructure projects were limited to general government revenues, public infrastructure investment would not pay for itself. Considering the rates of government revenues prevailing in Egypt (25-30% of 50
**What are the causes of the growing trend of excess savings of the corporate sector in developed countries? An empirical analysis of three hypotheses**

_Version: August 2013_

# **I. Introduction**

Aggregated statistics of many major developed countries show that the excess of gross savings over capital formation in the non-financial corporate sector has been increasing at least since 2001 onwards. According to some studies at the aggregate level (IMF, 2006, OECD, 2007), this trend in the excess savings (ES) of non-financial corporations could be due to several factors that have a positive impact on earnings and a negative impact on investment, and also due to the lower propensity to pay dividends in recent years (Fama and French, 2001). Non-financial corporations utilized ES in three ways: debt reduction, cash accumulation, and mergers and acquisitions.

The buildup of liquidity resulting from the ES played a role in the recent global financial crisis that started in 2007. For example, Pozsar (2011) shows how such liquidity helped to meet a significant portion of the demand for assets issued by the deregulated financial system. This demand was positively driven by two factors: on the one hand, the emphasis on safety and liquidity of capital by investment mandates, and on the other, the relative scarcity of safe assets that satisfied such mandates, e.g., guaranteed bank deposits and US Treasuries (Krishnamurthy and Vissing-Jorgensen, 2010).

The need for a better understanding of the ES becomes even more relevant because it is related to the sluggish economic recovery in developed countries. On the one hand, according to recent studies and data, the growing trend of the ES accelerated in 2008, partly due to the credit crunch set off by the financial crisis (IILS, 2011, Kahle and Stulz , 2011, Campello, Graham and Harvey, 2011).<sup>1</sup> On the other hand, the low corporate investment recovery since 2009 has been frequently mentioned as the main culprit responsible for the slow recovery in economic activity and employment.<sup>2</sup> Finally, irrespective of the current economic conditions,

> 1See The Economist,`Why are firms saving so much? ́, 1/7/2010.

> 2Wall Street Journal, `What will it take for companies to unlock their cash hoards?`, 28/05/2011, Financial Times, `Corporate Finance: Rivers of Riches ́, 22/5/2011. See also CNBC `Cash-Hoarding Companies Put Economy, Stock Rally at Risk`, 28/03/2011, The New York Times, `Companies Still Hoarding Tons of Cash ́, 17/09/2010, The Economist, `Show us the money ́, 1/7/2010., Wall Street Journal, `Jittery Companies Stash Cash ́, 3/11/2009

2
**Table 2: Non-Contributory Social Pensions in Latin America and the Caribbean**

|**Country**|**Program**|**Target Population**|**Transfer**<br>**(monthly US$)**|**Number of Beneficiaries**<br>**(latest available)**|**Cost**<br>**(% of GDP)**|
|---|---|---|---|---|---|
|**Argentina**|Pensiones No<br>Contributivas|65+ without contributory<br>pension and inpoverty|151.5|65,900||
|**Bolivia**|Renta Dignidad<br>(former Bonosol)|60+, universal|22-29|782,660|1.30|
|**Brazil**|Rural Pension|65+ in rural areas|342|800,000||
|**Chile**|Pension Basica<br>Solidaria|65+, in 3 lowest quintiles of<br>income distribution|150|407,000|0.50|
|**Costa Rica**|Regimen No<br>Contributivo|65+ in poverty|135|53,492|0.24|
|**Ecuador**|Pension Asistencial|65+ without contributory<br>pension|35|502,828||
|**El Salvador**|Pension Basica<br>Universal|70+ in severe extreme poor<br>municipalities|50|19,534||
|**Mexico**|70 y mas|70+ in selected municipalities,<br>universal|28.5|2,000,000||
||Oportunidades Adulto<br>Mayor|<br>70+ in households receiving<br>CCTs(Oportunidades)|22.7|80,000||
||Pension Alimentaria|70+ in Mexico DF, universal|63.2|470,000||
|**Peru**|Gratitud|75+ without contributory<br>pension and inpoverty|36||0.03|
|**Trinidad and**<br>**Tobago**|Senior Citizen Pension|65+, income means-tested|189-472|73,110|1.30|
|**Uruguay**|Beneficio No<br>Contributivo|70+, Income means-tested|240|31,577||

Source: Murrugarra (2011).

This paper simulates impacts of categorical and poverty targeted cash transfers on poverty and inequality in 13 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, Guatemala, Mexico, Nicaragua, Panama, and Peru. The simulations focus on two programs that tend to be the most widespread in the region: transfers to children up to five years of age, and to elderly people that are older than 65.

While, by their nature, poverty targeted transfers always deliver a higher poverty impact, both the incidence of poverty and the depth of the poverty gap appear to be important factors affecting the relative effectiveness of categorical vs. poverty targeted transfers. The comparison of transfers to children and the elderly also supports the view that choosing carefully categories of beneficiaries is almost as important as targeting the poor for achieving a high poverty impact. Overall, the findings suggest that in the Latin 4
Staff was reshaped as the Operational Policy Staff (OPS) whose mandate was, among other things, to conduct applied policy research in close collaboration with operational staff. Within OPS, the Country Policy Department was to act as the “applied counterpart” of the Development Research Department. Within the Economic Research Staff (ERS), the Development Research Department (DRD) was responsible for macroeconomic research. The Central Projects Staff became known as Operations Policy Staff (OPS), with responsibility for research on sector and micro topics.

The new Vice President and her team reorganized research at the Bank. Prior to the reorganization, the “Economics Department” (DPS) accounted for 70 percent of Bank research expenditures, with the remaining 30 percent being in Operations. The reorganization would change this balance radically: after the reorganization, more than one-half of Bank research would be located in and managed by departments in Operations. By 1983, research resources were shifted toward operations: research resources controlled by ERS dropped to slightly below 50 percent. Research funds were concentrated in a few large cross-country comparative research projects with several small projects. Anne Krueger and the Research Policy Council, which advised her, also established a _Research News Bulletin_ and the two journals, the _World Bank Research Observer_ (WBRO) and the _World Bank Economic Review_ (WBER)—the news bulletin and the WBRO targeting the widest range of readers interested in development, and the WBER intended for economists.

Under Anne Krueger’s leadership, research moved away from what was characterized as “engineering economics” with its emphasis on large-scale quantitative forecasting and planning models, toward studies that emphasized factual behavioral patterns.<sup>31</sup> This shift implied greater emphasis on research oriented toward making policy and less toward understanding what would happen in an optimizing society where all regulations could be exogenously imposed and costlessly enforced; it represented a departure from large modeling enterprises for planning and forecasting and a reorientation toward studies of how individuals, institutions and governments behave in different economic environments. Policy research was primarily carried out through a series of large-scale studies of major development issues, each built on in-depth comparative analysis of 20 or so country experiences. The Comparative Studies Program dominated the course of centrally funded

> 31  This is the language used in the evaluation of World Bank research by Assar Lindbeck, Director of the Institute of International Economics in Stockholm, presented in the 1984 Annual Report on the World Bank Research Program to the Board of Directors.

21
WPS4155

# **How Good a Map? Putting Small Area Estimation to the Test**

Gabriel Demombynes, Chris Elbers, Jean O. Lanjouw, and Peter Lanjouw<sup>1</sup>

# <u>Abstract</u>

This paper examines the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, predicted welfare indicators for a set of target populations are compared with their true values. The target populations are constructed using actual data from a census of households in a set of rural Mexican communities. Estimates are examined along three criteria: accuracy of confidence intervals, bias and correlation with true values. We find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original Elbers et al (2002, 2003) approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. Precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but we show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

**Keywords:** Poverty, Inequality, Small Area Estimation **JEL Classification:** C13, C88, D31, I32, O15, R13

# World Bank Policy Research Working Paper 4155, March 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 World Bank, Free University of Amsterdam, UC Berkeley and World Bank.  We are grateful to Martin Ravallion and Danny Pfeffermann for comments and suggestions.  The views in this paper are the authors’ and should not be interpreted to reflect those of the World Bank or affiliated institutions.