rafmacalaba/datause-encoder-data huggingface dataset:
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Jacobsen, K. (2006) Using microenterprise interventions to support the livelihoods of forcibly displaced people: the impact of a microcredit program in IDP camps in Lira, Northern Uganda. _Refugee Survey Quarterly_ 25(2): 23-39.

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Laczko, F. and Collett, E. (2005) _Assessing the Tsunami’s Effects on Migration_ . Feature story on Migration Information Source, Migration Policy Institute, viewed 4 December 2008, <u><http://www.migrationinformation.org/feature/display.cfm?ID=299></u> 56
The 1985-1994 Global Real Estate Cycle. Page 30 most households until 1989 when interest rates rose.<sup>32</sup> The result has been a sharp increase in median LTV ratios within a very short period of time during the cycle, by some 10% over all outstanding loans in Sweden. Lagging expectations about the eroding impact of inflation on debt which happened in the 1970s also led to underestimating the burden of debt in the new environment of low inflation and high real interest rates since 1985.

Housing price deflation has had a disproportionate impact on first time home buyers and has produced significant cohorts of young households with negative housing equity. The problem has been particularly severe in the most volatile markets were actual defaults have become a serious banking as well as social problem (for the UK see Dorling [1994], Finland see Table I and Kosonen and Timonen [1994], and Norway see Nordvik [1994]). Figure 12 which is derived from Building Society Association data in Dorling shows how rapidly the group of households with negative equity in housing expended in the UK within a short period of 15 month. This data covers people who bought housing during the threeyear 1988-91. A high percentage of this group is technically insolvent. Anyone who bought housing at a loan-to-value ratio above 85% soon was facing severe negative equity problems as early as 1992. Of course, not all households with negative equity will default. many will be able to restore their solvency over time. Rising nominal property values may reverse the situation in due course. It is not clear how long it will take for the housing price level to rise sufficiently in the general environment of debt deflation. Meanwhile this important young population appears to be severely constrained in all its major investment and consumption decisions, including employment relocation if needed.

Figure 12: Sharp **Increase** in Recent Buyers With Negative Equity After 1991, UK.

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Source: Derived from Building Society Association data and Dorlrng [1994] Clearly, financial liberalization has played a role in stimulating excessive lending practices. If it is not an indictment of excessive emphasis on home ownership, the present UK situation and that of several other countries shows that the goal of home ownership is not compatible with high levels of debt in volatile housing markets. 33 The young UK population has been confronted simultaneously with an unusual collection of factors all leading toward greater financial fragility: the lack of an adequate private 32 For a comparative analysis, see for instance BIS [1993], p. 165. Of course, during the boom phase the user cost of housing was even more favorable with rapidly rising housing prices.

> 3 3 In addition to the UK and Nordic countries, similar conditions led to comparable outcomes in Australia. See Gavin Jones [19921 and Judith Yates [1994].
should come as no surprise that this approach does not prove to be fruitful. However, even without such knowledge, we would not necessarily believe that the cutoff is followed strictly (as in a sharp RD design) because there may be differences between the ENCV data and a household’s characteristics at the time of the Sisben interview. (For instance, the program benefit may have lifted a marginal household across the eligibility line.) However, this would not pose any major problems as long as the probability of program receipt were still to change discontinuously at the cutoff (as in a fuzzy RD design). A couple of unpublished working papers on the predecessor of Colombia Mayor, the Programa de Protección Social al Adulto Mayor, attempt an RD approach based on estimated Sisben scores and thresholds, but do not find any results (Rubio 2014; Rubio, Hessel, and Avendano 2015). Analyzing the effect of a different social protection program for which eligibility is based on Sisben scores (Régimen Subsidiado, a public health insurance program), Miller, Pinto, and Vera-Hernández (2013) use a similar strategy, but find only weakly significant results. We are able to observe the actual Sisben scores implied by the ENCV data and find that the official thresholds are not enforced in practice. Moreover, below, we also provide statistics on municipality-specific prioritization, which would be the principal reason to estimate municipality-specific thresholds, and show that, at least for Colombia Mayor, there is not a lot of variance across municipalities.

In table 5, we present the results of a simple RD-inspired spline regression with standard errors clustered at the municipal level. We regress the binary outcome variable of a household receiving Colombia Mayor according to whether it is Sisben eligible (another binary variable) and measure the absolute value of its distance to the cutoff, which we capture using two separate variables depending on whether the household is Sisben eligible (that is, to the left of the cutoff) or not (to the right of the cutoff). In additional specifications, the squared values of these distance measures are included. This analysis is carried out for households with at least one age-eligible member within a neighborhood of either 5 points from the cutoff (columns 1 and 2), or 10 points (columns 3 and 4). This setup corresponds roughly to a first-stage regression in a fuzzy RD design, and, for the approach to be valid, one would need a strong positive effect of the binary variable Sisben eligible (which would be interpreted as showing that the probability of program receipt changes discontinuously at the cutoff). It can be easily seen that the variable of interest is all but insignificant statistically as well as in magnitude. If we add the squared terms, it also becomes clear that the relation between the distance to the threshold and actual program receipt is best captured by a linear trend. Finally, figure 4 gives a graphical impression of the probability of receiving the benefit within the five-point window in the 14
**Table 5. The role of business climate and institutions for economic upgrading**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|
|**Backward links**|||||||
|_FVAX_<br>_FVADP_|0.0245<br>(0.0244)<br>0.271***|0.0623***<br>(0.0097)<br>0.251***|0.0183<br>(0.0217)<br>0.271***|-0.0330<br>(0.0551)<br>0.253***|-0.0798<br>(0.0668)<br>0.252***|-0.0854<br>(0.0604)<br>0.250***|
||(0.0238)|(0.0268)|(0.0237)|(0.0268)|(0.0267)|(0.0269)|
|_FVAX*Property rights_|0.0006*<br>(0.0004)||||||
|_FVAX*Political stability_||0.0178*<br>(0.0095)|||||
|_FVAX*Corruption_|||0.0009**<br>(0.0004)||||
|_FVAX*Doing business_||||0.0015*<br>(0.0008)|||
|_FVAX*Dom. competition_|||||0.0330**<br>(0.0147)||
|_FVAX*For. competition_||||||0.0327**<br>(0.0127)|
|_Constant_|5.741***<br>(0.168)|5.589***<br>(0.166)|5.820***<br>(0.168)|5.780***<br>(0.185)|5.959***<br>(0.188)|5.558***<br>(0.171)|
|Observations|7,177|7,276|7,177|7,156|7,276|7,276|
|R-squared|0.857|0.852|0.858|0.854|0.852|0.853|
|**Forward links**|||||||
|_DVAR_|0.154***|0.211***|0.176***|-0.0287|-0.132|-0.0733|
||(0.0338)|(0.0157)|(0.0306)|(0.0664)|(0.0904)|(0.0754)|
|_FVADP_|0.249***|0.232***|0.249***|0.232***|0.232***|0.231***|
|_DVAR*Property rights_|(0.0226)<br>0.0011**<br>(0.0005)|(0.0258)|(0.0225)|(0.0258)|(0.0255)|(0.0257)|
|_DVAR*Political stability_||0.0154<br>(0.0137)|||||
|_DVAR*Corruption_|||0.0009**<br>(0.0004)||||
|_DVAR*Doing business_||||0.0037***<br>(0.0009)|||
|_DVAR*Dom. competition_|||||0.0776***<br>(0.0195)||
|_DVAR*For. competition_||||||0.0603***<br>(0.0158)|
|_Constant_|4.958***|5.077***|4.701***|5.026***|4.962***|5.187***|
||(0.159)|(0.185)|(0.152)|(0.175)|(0.181)|(0.184)|
|Observations|7,180|7,284|7,180|7,164|7,284|7,284|
|R-squared|0.876|0.870|0.876|0.873|0.871|0.871|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

Table 6 shows that financial markets mediate economic upgrading through GVC integration as a seller in the same way. Once again, both measures are positive and significant (columns 1 and 2), which indicates that this policy area is relevant and independent of the type of GVC integration that a country pursues.

Looking at the results for the role of labor market policy, a very similar picture emerges. The estimates consistently show that, for GVC integration as a buyer, higher unit labor costs can limit economic upgrading through GVCs (table 6, column 3), supporting studies that point to wage differentials as primary drivers of the globalization of production. Labor freedom is equally positive and significant (table 6, 24
> specification than traditional approaches. Our main innovation is to apply this approach to industries

> in countries experiencing financial crises to be able to investigate the real impact of shocks to the financial system in a country over time.

> First, we estimate the basic model in Rajan and Zingales (1998) for our sample of countries

> (model 1).

> _RVAGR, = Ci + INDj +_ A _* SHARE, +_ P2 _* FDi *EDj + -,O_ (1)

where _R VA GR5,_ is the real growth in value added of sectorj in country _i, C,_ is a country dummy for country _i, ii-vD_ is an industry dummy for industryj, **_SHYLIEiJ_** is the snare ot sectorj in the totai value added of country _i, FDi_ is the development of the financial system of country _i, EDj_ is the external dependence ratio of sector ] according to Rajan and Zingales (1998). The specification thus _;nlVude_ ie o.tyrdi.us efcs euevLe at.ltvpUisfru. leveloffnrca **Ui!.UU~lkA~U AJUI_I O.IIU LULUUOUJ** '1 O YY **TV UOV UiILV il~LVLidLiaVCJVA** JLVO **ILU UIV** **_LVI_ U! 1UI1JAM4** development of a country: total credit to GDP, private sector credit to GDP, and M2 to GDP.

The main differences with the Rajan and Zingales (1998) setup is twofold. First, we estimate the model for two sub-periods, namely, before and during a financial crisis. When estimating the model for the crisis period, we use the pre-crisis levels of share in value added and our proxies for financial development to avoid potential endogeneity problems. Second, we estimate the model **for** crLisis **coLU Ucs** or.ly, **UIaL** is, for **lviu.uiv ulaL** arVe liLsteU :... **'aprii 4aU** Illlgvila (20u0.) as **IIdvil,g** experienced a financial crisis (and for which we have data). Note that by including country indicators into the regressions, we control for country-and industry-specifics. By including country 7
is gross motor coordination, for which there is a positive but insignificant effect of preschool participation.

Table 4 reports results on receptive vocabulary as measured by the TVIP. We report effects on the raw score, within sample standardized score, and standardized score as per the test developers. While all three coefficients are positive, the raw score is estimated imprecisely. Effects on the standardized scores are positive and significant, showing a gain of 0.258σ for children who attend preschool.<sup>37</sup> Preschool increased an average participant’s TVIP normed score<sup>38</sup> by 1.8 points (significant at 5%), an increase of about 3% relative to the control group. Figures 3 and 4 plot the raw and the normed TVIP scores by age for treatment and control groups (Changana version of the test shown). Consistent with the regression results, we observe higher scores for children in treatment communities throughout the distribution of ages. One concern with the use of the normed TVIP score is censoring, since for each age there is a minimum raw score that can be normed.<sup>39</sup> About 50% of all children in our sample had censored normed scores, and the probability of censoring increased with age (Figure 5), which could downward bias the estimate of program effect, if positive. There was no effect of the program on the probability of censoring (column 4), suggesting that the sub-sample of uncensored observations is orthogonal to treatment. The program effect on the TVIP normed score for the uncensored sample increases to 3.18 points (column 5).

Results of the EDI, collected on a cross-sectional independent sample of children enrolled in the first grade, are reported in Table 5.<sup>40</sup> In first grade, children who attended preschool have a 0.301σ increase in physical health, a 0.373σ increase on the domain of communication and general knowledge, and a 0.429σ increase in the cognitive domain score. While the estimated impacts on the domains of social competence and emotional maturity are large, results are estimated imprecisely and are not statistically significant at conventional levels. These results suggest that gains in child development achieved in preschool carry over into the first grade, and led to improved cognitive and communication performance in primary school.

> 37 We calculate the z-score of the raw TVIP score for each child’s age in months following Schady et al (2014) and Paxson and Schady (2007).

> 38 Based on the test developer’s tables.

> 39 For example, a raw score of 1 translates into a normed score of 55 for children aged 60 months, which is the same normed score for all children aged 79 months with a raw score below 16.

> 40 See Supplementary Materials Section 6 for item by item responses. Items with statistically significant treatment effects include being interested in mathematics, being able to count, ordering objects, recognizing geometric shapes, writing simple words, the overall social-emotional development, the ability to get along with peers, and the probability of comforting other children. Supplementary Materials Table 28 shows the estimates without the inclusion of controls.

18
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l POLICY RESEARCH WORKING PAPER 2068

# Summary findings

## Combining microeconomic evidence with

macroeconomic theory, Krebs and Maloney present an integrated approach to wage and employment determination in an economy where firms pay above market "efficiency wages" to prevent trained workers from quitting.

The model offers predictions about the behavior of formal employment, labor turnover, and segmentation in response to formal sector productivity shocks (including economic growth and tax reductions), changes in the desirability of self-employment (formal sector tax rates), and the cost of training a new worker.

They use panel data from Mexican labor surveys to estimate the quit function derived from the model and the results support their view that transitions from formal salaried work to informal self-employtnent are quits rather than fires. (Quitting is positively related to the mean self-employment income and the probability of being rehired and negatively related to the mean formal salaried wage.) They then use the parameters estimated from the quit function to calibrate the model economy and simulate the impacts of economic shocks and policy innovations and find the impact on employment, turnover, and segmentation to be substantial.

- This paper a product of the Poverty Reduction and Economic Management Sector Unit, Latin America and Caribbean - Region is part of a larger effort in the region to understand the functioning of developing country labor markets. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Tania Gomez, room 18-102, telephone 202-473-2127, fax 202-522-2119, Internet address tgomez@worldbank.org. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/htmVdec/Publications/WVorkpapers/ home.html. The authors may be contacted at tkrebs@uiuc.edu or wmaloney@worldbank.org. February 1999. (39 pages)

_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 canry 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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
returns to factors of production can only be brought about by increasing productivity or by lowering marketing costs.

Correlations between detrended world commodity prices for the two sub-periods, and for 1961-1987 are found in Table 4. These correlations can be used to design an instability-reducing portfolio of exports for each country. Commodities whose price deviations are negatively correlated with the existing mix are good candidates for inclusion in the portfolio. A cursory review of Table 4 reveals some of the pitfalls associated with designing instability-reducing export portfolios. Because of the large number of potential changes that can take place in a portfolio, each change needs to be evaluated relative to its impact on the overall portfolio variance. Even if the 'best' portfolio variance-reducing changes to the export mix are identified, the structure of international price variability changes over time (Table 4). An example is cotton, whose price instability correlations with tobacco, wheat and sugar changed in sign from period to period. Designing a variance-reducing portfolio based on a short time series of international price variations can lead to faulty policy prescriptions in a changing world environment. More analysis is needed to determine whether longterm international price covariances are stable; long-term stability will enhance the desirability of developing instability-reducing portfolios.

In general, World Bank commodity projections indicate that downward or stagnant world price trends, and year-to-year instability for the "Big-5" export commodities, and most other agricultural commodities will continue (Akiyama and Larson). This means that the case study countries will need to focus their attention on identifying their comparative advantage, and improving productivity and lowering costs by using appropriate policies, and investing in infrastructure and support services.

19
- 38 -

Data on the income of urban wage earners ror both years is from the Economic Planning Board (1964, 1984). The urban wage rate (in terms of efficiency units) is calculated by dividing the urban wage bill by the product of the number of employed persons in the urban sector and the effort level of a typical worker in this sector, which is arbitrarily set equal to 1.0 in the base case equilibrium data. Data on urban employment for both years is also from the Economic Planning Board (1964, 1984). The average farm income per worker is estimated using data on urban/rural differences in earnings taken from Hong (1979). Since the data from Hong are only available up to 1976, we use the 1976 data and assume that they also reflect urban/rural differences in earnings in 1982. The rural wage bill is estimated as the product of average farm income per worker and the number of persons employed in the rural sector. Data on rural employment in each year is from Economic Planning Board (1982, 1986).

The income return to capital in the urban sector is estimated as the residual of the value of production less labor income. To translate this into an observation on the physical quantity of capital used in determining parameters in the model, an estimate of the rate of return on capital in manufacturing is needed. We use estimates on average rates of return on capital during 1954-61 and 1972-'5 (the last period available to us) from Hong (1979) and assume them to be roughly equivalent to the rates in 1962 and 1982.