rafmacalaba/datause-encoder-data huggingface dataset:
Only if the growth in wage jobs weighted by its share in the labor force exceeds growth of the labor force will the share of employment in nonwage jobs fall rapidly.

**Figure 1: Employment structure by income group**

<!-- Start of picture text --> 2%<br><!-- End of picture text --> <!-- Start of picture text --> 4%<br><!-- End of picture text --> <!-- Start of picture text --> 34%<br><!-- End of picture text --> <!-- Start of picture text --> 67%<br>26%<br><!-- End of picture text --> <!-- Start of picture text --> 86%<br>9%<br><!-- End of picture text --> <!-- Start of picture text --> 67%<br>53% 86%<br>50%<br>26%<br>26%<br>14% 9%<br>5% 1%<br><!-- End of picture text --> _Source_ : ILO Stat, 2016.

People looking for jobs and firms looking for employees interact in the labor market (Figure 2). The intersection of the supply of labor and firm demand establishes the market wage in the standard or classical model.<sup>5</sup> Wages at any point adjust to equilibrate supply and demand—as shown in Figure 2, where W1 is the equilibrium wage. In a growing, developed economy, most workers will find jobs, allowing for some spatial frictions or temporary joblessness during the search process. Some workers will potentially remain out of the labor force because the equilibrium wage rate is too low to induce entry. As the economy grows, demand for labor shifts out. As the population grows, the labor supply curve shifts out. A new equilibrium results.

Most people enter the labor market in earnest after leaving school. If the labor supply is growing faster than labor demand, the equilibrium wage could fall. For example, where labor markets are differentiated by education, an increase in educated labor could cause that supply curve to shift out faster than the demand curve, causing wages for educated workers to fall. Figure 2 shows this effect as W2.

**Figure 2: A classical model of the labor market**

> 5 Skill differentials could create a number of labor markets, with integration between them.

5
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- 12 -

Table 4 Potential Reallocations Under Law 55 (1985)

|Organization or<br>or Agency|Earmarked Source|Recipients of<br>Reallocation|
|---|---|---|
|1.National Highway Fund<br>and National Fund for<br>Neighborhood Roads|Tax on gasoline<br>and valorization<br>tax on national<br>public works|Construction; maintenance<br>and repair of national<br>niighways and local roads;<br>and improvements in river<br>navigation.|
|2. National Aeronautical<br>Funds|Stamp tax on sales<br>aboard and airport tax,|Ai-port activities and those<br>activities listed in 1 above.|
|3.PROEXPO|62 tax on CIF value<br>of imports|Funding of CERTS (tax refund<br>certificates<br>and other<br>instrmnents<br>to promote<br>exports)|
|4. IFI|Tax on cif value<br>of imports|Small-scale industry, timber<br>industry, and fisheries.|
|5. National Tourism<br>Corporation|Tourism tax|Preservation<br>and restoration<br>of national monuments and<br>parks; prcmotion of cottage<br>industry; and the financing<br>of tourism development<br>certificatee.|
|6. National Coal Fund|Coal production tax|Finance of mining investi-<br>gation and exploration<br>activities.|
|7.Notary and Registration<br>Superintendency|Duties on the registration<br>of public instruments<br>and issuance of notary<br>certificates|Finance of investments for<br>conatruction; irnprovement<br>and equipping of judicial<br>offices and priscn faci-<br>lities.|
|8. Hotion Picture Promotion|Anew 162 tax on cinema<br>ticket prices|8.52 of the tax amount to<br>be paid into the Motion<br>Picture Promotion Fund to<br>be managed by the Motion<br>Picture Development Company<br>and used to finance cultural<br>and artistic programs. The<br>remaining 7.52 shall be paid<br>to producers, distributors,<br>and exhibitors of Colombian<br>films in proportions esta-<br>blished by government with<br>greater amounts going for<br>full length features.|
|9.SENA (National Apprentice-<br>ship Service)|Payroll tax|Training geared to jobs,<br>technical and small industry<br>and agriculture extension.<br>SENA to assume responsibility<br>for financing, in full or in part,<br>industrial schools and technical<br>institutes, colleges, agricultural<br>training schools, and computer and<br>telemetrics programs.|
|10. ICBF (Colombia Family<br>Welfare Institute)|Payroll Taxes|Child and Family protection<br>programs; vaccination and<br>prevention health programs;<br>food assistance programs for<br>India communities; public<br>nursing homes; and also the<br>maintenance of children's<br>hospitals and construction<br>of<br>water supply infrastructure<br>in<br>comnunity with less the 60,000<br>inhabitants (based on 1973 census)|

Source: LEY 55 Por medio de la cual se dictan normas tendientes ai ordenamiento de las Finanzas del Estado y se dictan otras disposiciones; June 18, 1985.
# **Online Appendix**

## **OA.1 Construction of the Market Access Index**

Our Market Access measure is calculated as follows: where, MAit is market access of District _i_ at time _t_

- ttijt is the travel time (in hours) between Districts _i_ and _j_ at time _t_

Pjt _=_ population in destination District _j_ at time _t_ θ= trade elasticity To construct the Market Access measures, population and travel times were aligned as follows:

|MA|Travel Time|Population|
|---|---|---|
|MA1988|1988|**1991**|
|**MA1996**|**1996**||
|MA2004|2004|2001|

Note that for the main analysis, we focus on Market Access calculated using travel time from 1996 and population from 1991. We explore alternative travel times (1988, and 2004) as robustness checks. Following Allen and Atkin (2016) we use trade elasticity equal to 1.5, and report results using trade elasticity equal to 3.8 as well.

## **OA.2 Calculation of the GQ-based instruments**

Our identification strategy relies on two main sets of instrumental variables: one inspired by India’s Golden Quadrilateral (GQ) highways and the other by its colonial railways in the 1880s. Both are calculated using geospatial software. Here, we describe the calculation of the first instrumental variable.

For the first GQ IV, we construct a hypothetical linear network connecting the main cities targeted by the GQ project (see Figure 2). The target cities of the GQ include: New Delhi, Kolkata, Chennai, Mumbai and Bangalore. We construct two networks: one based on Euclidean distance and another “least cost path” derived from elevation and slope data. Our main results rely on the linear, Euclidean distance instrument but are robust to using the least-cost path instrument instead. It is also important to note that though the GQ highway project is a relatively recent investment, parts of it follows historical roads (Figure AF.1).

The least cost path is calculated based on a time cost raster method using the Cost Connectivity algorithm available in ESRI ArcGIS as the minimum time result from off road speeds on land. We construct the
from police reports or focus on household crime data in a specific city or country. While both types of studies are useful, they also face a few challenges. The limitation of using police reports is that given the low level of confidence in police especially in developing countries, crime tends to be under-reported in police reports (Soares, 2004). The limitation of the household crime studies is that they omit crime faced by firms, which tends to be more frequent and cause greater damage. These studies are also limited to the incidence of crime faced by households, which does not account for the financial costs incurred by the victim. Furthermore, although household level crime studies may have a lower degree of under-reporting of criminal activity than police reports, there is still the difficulty of drawing general policy inferences that are based on idiosyncratic country data. This is a particularly difficult obstacle to overcome as even if various household crime surveys in different countries were pooled together, there is still the issue of differences in sampling and methodology of how the surveys were undertaken. Finally, most of these household surveys are unable to address time invariant omitted variables as the data are typically available for one period of time. By using firm-level survey data that are comparable across economies as they use the same sampling methodology, we are able not only to explore the impact of per capita economic growth on crime, but also to overcome most of the obstacles faced by studies based on household surveys.

This study contributes to the literature by making several improvements: (i) This study explores the effect of economic growth defined as real GDP per capita on both the incidence of crime and the burden of crime for firms in developing economies using firm surveys. The focus of this analysis is on small and medium sized firms but we also mention whether the results hold for a full sample of small, medium, and large firms. This is a vast improvement on the literature which 3
The spread between the highest and lowest portfolios remains significant at 1% for risk-adjusted returns, suggesting that the predictive power of the relative ES-implied beta for future returns is robust after controlling for other risk factor loadings.

Table 5 reports the value-weighted returns sorted by the CAPM beta, the relative usual downside beta and the relative ES-implied downside beta for a holding period of one month, respectively. The value-weighted returns exhibit similar pattern as equal-weighted returns. There is still no significant difference between value-weighted returns sorted by the CAPM beta. Returns sorted by the relative usual downside beta decrease first and then increase, and the spread is significant at 5% only for unadjusted returns, while the relative ES-implied beta generates a spread significant for unadjusted and risk-adjusted returns.

I further investigate the predictive power of downside risk over returns in the next 12 months. Each month, I sort stocks into quintile portfolios based on a downside beta computed using daily returns in the previous 12 months and hold the portfolios for 12 months. Panel A and Panel B in Table 6 report equal-weighted and value-weighted monthly returns of portfolios sorted based on the relative usual downside beta "relative _βed_<sup>_−_",respectively.PanelCandPanelDcorrespondto</sup> equal-weighted and value-weighted portfolios sorted by the relative ES-implied beta "relative _βes_<sup>_−_"</sup> in the previous 12 months. Similar to the results for one month holding period, the unadjusted returns of portfolios sorted based on past relative usual downside beta decrease first and then increase. The spread in the risk-adjusted returns, which is significant at 10% for one month holding period portfolios, now becomes trivial and insignificant. In contrast, unadjusted and risk-adjusted returns of portfolios sorted by the relative ES-implied beta increase monotonically from low portfolio to high portfolio. In addition, the spread between the highest and lowest portfolios is much higher in returns sorted by the relative ES-implied beta than in returns sorted by the relative usual downside beta. A trading strategy that buys the portfolio with the highest relative ES-implied beta and sells the portfolio with the lowest relative ES-implied beta on average generates an average return around 6% for a holding period of one year, while the same strategy using the relative usual downside beta leads to an annual return less than 2%.

# **5.4 Out-of-sample performance of Fama-MacBeth regressions**

This section estimates the risk premium of downside betas using the Fama-MacBeth regression. At the beginning of each month, I regress returns in the month on the betas computed using daily returns in the past 12 months, and then compute mean and standard deviation of the estimates.

Table 7 reports the mean and _t_ -statistics of the estimates. Model (1) reports the regression results on the CAPM beta. Consistent with the literature, the CAPM beta implies no significant risk premium to predict future returns. Models (2) and (3) report regression results on the relative 13
there are heterogeneous costs of learning, those most familiar with research findings need not overlap much at all with those who value research most for their work.

But that is not what we see in these survey data. Two-thirds of the responding senior staff put a high value on the Bank’s research for their work. They appear to have a reasonably strong incentive to learn, though stronger for some than others. Operational staff with a stronger incentive to learn tend to be more familiar with Bank research. Only 12% of staff fall into the category of ―independently well-informed‖—those who report high familiarity with research, but attach low value to it for their work. The Bank’s senior staff are mainly found in three other groups. The first are those who are ―happily uninformed‖ about research—they attach low value to it for their own work, and report correspondingly low familiarity with research; 23% of senior staff fall into this group. A second group puts a high value on Bank research but its members are not generally successful in accessing that research or find it of limited relevance. This group is not evenly spread across Bank units and sectors, suggesting that there may be scope for a targeted effort at research and its dissemination. The third and largest group is the ―functionally well-informed,‖ comprising 42% of staff. They put a high value on research for their work (a mean score of almost 8 on a scale of 1-10) and are (by their own assessment) quite familiar with research findings (also a mean score of 8 on a scale of 1-10).

While this paper’s results are consistent with the view that stronger incentives for leaning about research translate into greater knowledge, they are also suggestive of some significant frictions. The slope of the relationship between perceived value and familiarity with research— the effect of an increment to perceived value on familiarity, both on a 1-10 scale—is positive but significantly less than unity, suggesting frictions in how the incentive for learning translates into knowledge. The responsiveness of researchers and the timeliness and accessibility of their outputs are clearly important to how much learning incentives lead to useful knowledge.

There are some marked differences across Bank units. Staff working in the economic policy and human development sectors tend to be both more familiar with the Bank’s research and attach higher value to it than do others, notably in the more traditional areas of the Bank’s lending, such as agriculture, energy and mining, transport and urban development, which account for just under half of Bank lending. For example, while only 7% of senior staff members working on poverty and 10% of those mapped to economic policy are ―happily uninformed,‖ the 22
# **1 Introduction**

This paper explores aid effectiveness in post-conflict situations using project-level data. Post-conflict situations are characterized by economic opportunities for recovery and political opportunities for reform and change (Collier and Hoeffler, 2004; Chauvet and Collier, 2006; Collier, 2007). The opportunities provided by the need to rebuild the economy in post-conflict situations should tend to make aid particularly effective in the first post-war decade. However, this may be offset by the limits to absorptive capacity due to particularly difficult economic and administrative environments. To date the limited quantitative evidence on aid effectiveness post-conflict has come from macroeconomic studies. These suggest that from the perspective of aid effectiveness, the former effect predominates. Collier and Hoeffler (2004) find that during the first few years of peace the absorptive capacity of aid is about double its usual level. Elbadawi et al. (2008) find that the appreciation of the real exchange rate induced by aid is much weaker in post-conflict situations, making aid more effective than normal. Adam et al. (2008) show that post-conflict aid is used to reduce inflation by reigning in the deficit financing typical during conflict. Duponchel (2008) finds that aid stabilizes the post-conflict environment, although she finds limits to absorption: the optimum amount of aid is around 4.8 percent of GDP, around double the average observed in her sample.

This is the first quantitative study of aid effectiveness post-conflict to use project-level data. We use information on the success or failure of World Bank projects and investigate whether performance is systematically different in post-conflict situations. We also investigate whether post-conflict situations need particular sector targeting, enhanced supervision effort or special sequencing of project launching.

World Bank projects are assessed by an independent institution, the IEG ( _Independent Evaluation Group_ ). The IEG has provided the evaluation of all World Bank projects worldwide since the 1960s. Along with its assessment, this database provides information on the characteristics of each project (investment versus budget support; IDA or IBRD projects, financial conditions, NGOs involved, etc.) and on the supervision and preparation efforts of World Bank staff. Although it has not previously been used to investigate post-conflict situations, it is a standard database for the study of aid effectiveness. Using it, Isham and Kaufmann (1999), Dollar and Svensson (2000), Kilby (2000), Dollar and Levin (2005) and Chauvet et al. (2006) analyze the respective importance of donors’ effort and recipients’ macro-economic and institutional characteristics for the success of World Bank projects. From this literature no consensus has emerged as to whether the success or failure of World Bank projects primarily depends upon countries’ political economy or on project characteristics, notably the supervision of projects. In a slightly different framework, Guillaumont and Laajaj (2006) also explore 2
**Figure 1-A. Population growth over time for Taiping Occupied Prefectures**

<!-- Start of picture text -->
1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>1851 1880 1910 1953 1982 2000<br>Control (147 Prefectures) Taiping Occupied (64 Prefectures)<br>Difference 95% Conf. Interval of Difference<br><!-- End of picture text -->

<!-- Start of picture text --> Figure 1-B. Population growth over time for Taiping Regime<br>Prefectures<br>1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>-0.8<br>1851 1880 1910 1953 1982 2000<br>Control (147 Prefectures) Taiping Regime (55 Prefectures)<br>Difference 95% Conf. Interval of Difference<br><!-- End of picture text --> Notes: Population Growth in year t = ln (Population in year t / Population in 1820); Control group covers 147 prefectures which were never occupied by Taiping Army; Taiping Occupied group covers 64 prefectures which were occupied by Taiping Rebellion, but without being in the Taiping territory; Taiping Regime group covers 55 prefectures which were occupied by Taiping Rebellion and in the Taiping territory.

44
accelerating private investment in infrastructure in developing countries. In the late 1980s-early 1990s, developing countries sought to develop their financial markets by implementing structural reforms including removing regulatory bottlenecks and rolling back the interventionist role of the state through privatization of commercial banks or by strengthening the independence of central banks. In parallel, project sponsors have also attempted to increase the use of local currency loans in closing the financing of infrastructure projects in developing countries.<sup>3</sup> These efforts to develop appropriate local financial markets have however faced further difficulties due to the nature and profile of infrastructure projects (high economic stakes, long payback, and exposure to political interferences).

While the need for developing countries to foster investment in infrastructure sectors has been emphasized in the literature, the issue of these countries’ limitations to attract private capital remains relatively weakly explored. This paper seeks to contribute to filling this void by testing that the level of economic and financial development of a country is a good "predictor" of its ability to attract private investment in infrastructure projects. We specify a regression model that we fit to a 19902007 annual data set on the power sector in 37 developing countries.

Our findings suggest that a country’s level of economic development matters to private investors seeking to enter infrastructure sectors in developing countries. Similarly, we find empirical evidence that the level of development of a country financial sector, in particular the extent to which local capital markets are developed, is a key determinant of private investment in the power sector. As can be expected, quality of governance, and risk factors, such as exchange rate fluctuations, also influence private investors’ decisions. In all our regressions, the existence of an autonomous energy sector regulator is found to significantly simulate private investment in power projects. These effects are significant even when we account for the interactions between economic growth and financial sector development.

The paper is organized as follows. The next section provides a review of the literature that discusses the role of infrastructure in development, its financing, and the determinants of private participation in infrastructure projects. Section 3 describes the data used, the main variables of interest, and briefly discusses some simple properties of the data. Section 4 presents the econometric approach used to

> 3 Note, however, that these initiatives have only led to some local currency loans and bond issuances mainly concerned telecom projects.

3