rafmacalaba/datause-encoder-data huggingface dataset:
- ii -

differences in the attributes of trainees and the control group. Otherwise, group differences are likely to lead to biased estimates of program effects, because of sample selectivity, or because trainees are overrepresented in the dat:. relative to their true weight in the unemployed population. Both kinds of methodological problems were addressed in our cvaluation. Our evaluation also sought to estimate program effects for specific groups of trainees--by sex, age, schooling completed, prior work experience, and degree of attachmen **t** o the labor force--to provide policymakers with.guidelines on specific groups of unemployed individuals who should be targeted for retraining.

The evaluation was conducted using retrospective data on post-training experiences of the 1990 cohort of PROBECAT trainees, collected by the STPS in February 1992. For a control group, we developed a matched sample of unemployed individuals from the 1990-91 quarterly urban labor force surveys (ENEU). The two datasets permitted an evaluation of the PROBECAT program that substantially improved upon previous evaluation efforts. The earlier studies were constrained by lack of an appropriate comparison group (outcomes were compared for training completers and those who dropped out of training), and by relatively crude wage data (wage information was bracketed, and reported in reference to the minimum wage). Both of these limitations have been largely overcome in our evaluation.

# Our evaluation of the PROBECAT program yielded several main findings:

(a) Compared to the control group, trainees on average tend to find jobs more quickly. Participation in PROBECAT reduces the mean duration of intervening unemployment spell by 2.5 months for males, and by 1.9 months for females. However, these figures hide considerable variations across different groups of trainees. The strongest effects are for trainees over the age of 25 years: training reduces the duration of unemployment spell by 3.7 months for males and 3.7 months for females. Much smaller impacts are found for those age 25 and under. Training does not shorten unemployment duration for those without work experience. They have longer spells of unemployment than the controls.

(b) Male trainees are more likely to be employed three and six months after training than the controls. However, this effect does not persist: at twelve months, the probability of employment is the same for trainees and controls. Taken together with (a), the implication is that PROBECAT training shortens the time to first job, and thus increases the amount of time trainees spend in employment in the first year after training.
||Direct|Partial|Total|
|---|---|---|---|
|Argentina||||
|As % of manufacturing exports in 2006|Kg|59|96|
|As %<br>of all exports in 2006|10|16|27|
|Brazil||||
|As % of manufacturing exports in 2006|20|70|91|
|As %<br>of all exports in 2006|9|30|39|
|Chile||||
|As % of manufacturing exports in 2006|29|SS|82|
|As %<br>of all exports in 2006|2|4|Ss|
|Colombia||||
|As % ofmanufacturing exports in 2006|15|66|81|
|As % of all exports in 2006|a|20|25|
|Costa Rica||||
|As % of manufacturing exports in 2006|36|60|96|
|As% of all exports in 2006|pss|36|58|
|Mexico||||
|As%ofmanufacturingexportsin2006|70|28|99|
|<br>As% of all exports in 2006|52|21|72|
|LAC||||
|As % of manufacturing exports in 2006|62|31|94|
|As%ofallexportsin2006|27|14|40|
_EIGURE 9_ <!-- Start of picture text --> Actual mGEffictent Corfauntton X<br>0.45o<br>0.4s<br>0.25<br>0.2<br>0.2<br>0.1<br>0,05<br>0s<br>75 76 77 70 79 00 Of 02 3 84 85 0 87 8 8<br>eff cmiWs W*<br><!-- End of picture text -->
- 21 -

<!-- Start of picture text --> (2) B*<br>b * -=<br>P*<br><!-- End of picture text --> B* is the stock of external PS debt<sup>denominated in current dollars and P* is the</sup> trade-weighted average international<sup>price index with weights given by trade</sup> currency composition.

Equation (2) indicates that it will<sup>be more expensive for a country to</sup> service its debt if the currency composition<sup>of the debt and trade differ and</sup> if the trade-weighted average international<sup>price index falls. These effects</sup> will undermine the cost of the external<sup>PS debt by making it more expensive in</sup> terms of purchasing power parity of trade<sup>flows. This will be true notwithstanding</sup> the fact that a price fall might<sup>result in an unrealized capital loss because</sup> it will have an immediate effect on:<sup>(i) the amortization and interest payments;</sup> and (ii) the country's creditworthiness.<sup>The effects of this price index (P*,</sup> Int. Cur. Prices) for each of the<sup>six countries is illustrated in Figures 3a and</sup> 3b by comparing its evolution to that<sup>of the trade-weighted average international</sup> price index free of fluctuations<sup>in international exchange rates</sup><sup>_(P*,_Int. Adj.</sup> Prices).10 In 1980-89, it is possible to distinguish<sup>four patterns in terms of the</sup> effect of trade shocks on external<sup>**_k:_**debt for these six countries. First is</sup> the case of Yugoslavia, which was positively<sup>affected, that is, experienced an</sup> increase in its trade-weighted international<sup>price index and thus a terms of</sup>

**10/** The real exchange rates (RERI)<sup>reported in these figures were estimated</sup> using the IMF methodology. An increase<sup>in the index indicates a real c-vervaluation</sup> and a fall a real devaluation.
# **Table 15. Panel 5 years average, Fixed and random effects.**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|
||Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|
|Constant|-3.624|-3.580|-4.288|-5.847|-2.489|-2.503|-4.853|-8.293|3.691|-3.969|
||(2.595)|(1.093)***|(6.055)|(1.477)***|(6.391)|(1.833)|(6.059)|(1.475)***|(6.833)|(1.707)**|
|GDPpc|-0.000|-0.000|-0.000|-0.000|0.000|-0.000|-0.000|-0.000|-0.000|0.000|
||(0.000)***|(0.000)**|(0.000)***|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)***|(0.000)|(0.000)|
|Invt/gdp|0.111|0.145|0.093|0.109|0.147|0.196|0.087|0.134|0.113|0.186|
||(0.030)***|(0.023)***|(0.061)|(0.036)***|(0.069)**|(0.043)***|(0.053)|(0.034)***|(0.075)|(0.040)***|
|lifeexpect|0.091|0.045|0.102|0.099|0.062|0.041|0.120|0.091|-0.006|0.079|
||(0.042)**|(0.020)**|(0.105)|(0.029)***|(0.120)|(0.036)|(0.103)|(0.029)***|(0.129)|(0.032)**|
|m2/gdp|-0.054|-0.006|-0.032|0.015|-0.049|-0.002|-0.020|-0.000|-0.006|0.014|
||(0.014)***|(0.008)|(0.028)|(0.014)|(0.030)|(0.016)|(0.030)|(0.014)|(0.032)|(0.015)|
|imports/gd<br>|0.030|0.007|0.029|0.003|0.040|-0.012|-0.003|0.014|0.063|-0.028|
|p|||||||||||
||(0.019)|(0.009)|(0.042)|(0.013)|(0.040)|(0.015)|(0.035)|(0.012)|(0.047)|(0.017)*|
|inflation|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|main|-0.003|0.001|||||||0.023|0.012|
||(0.003)|(0.003)|||||||(0.011)**|(0.006)*|
|egc|||-2.427|-1.325|||||-6.563|-4.407|
||||(1.516)|(0.709)*|||||(2.014)***|(1.258)***|
|rail|||||-3.314|-1.242|||-4.596|-0.642|
||||||(5.158)|(1.093)|||(5.445)|(1.083)|
|pav|||||||-2.823|0.116|-4.053|-0.851|
||||||||(1.084)***|(0.188)|(1.776)**|(0.448)*|
|Obs|497|497|313|313|237|237|276|276|202|202|
|R-squared|0.23||0.20||0.27||0.24||0.35||
|Haus test<br>FEvsRE|51.06***||12.43||11.48||18.41**||19.79*||

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Full set of period dummies included.

35
Table 2: Alternative measures of import market concentration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable|Share of|Share of|Log number|Log number|Log|Share of|
||the largest|the 4 largest|of|of source|unit|commodity|
||importer|importers|importers|countries|value|exporters|
|Panel A)|||||||
|Commodity export share ∈[0,1]|0.04*|0.02|-0.25*|-0.23***|0.33*|0.00|
||(0.02)|(0.01)|(0.13)|(0.08)|(0.18)|(0.02)|
|R-squared|0.73|0.85|0.94|0.90|0.82|0.56|
|Number of observations|2,176,363|2,176,363|2,176,363|2,176,363|1,818,137|1,969,637|
|Dependent variable mean|0.530|0.807|2.616|1.762|1.739|0.189|
|Panel B)|||||||
|Export commodity price index (1=2012)|0.09*|0.04|-0.40|-0.58***|0.95***|0.01|
||(0.05)|(0.04)|(0.35)|(0.20)|(0.27)|(0.06)|
|Number of observations|2,249,099|2,249,099|2,249,099|2,249,099|1,883,317|2,034,411|
|R-squared|0.73|0.85|0.94|0.90|0.82|0.57|
|Dependent variable mean|0.53|0.81|2.63|1.77|1.74|0.19|
|Country-product fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Product-year fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Dependent variables in columns 1, 2, and 6 range from 0 to 1. Standard errors clustered at the country-year level are in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively.

24
In addition to the losses we also have to account for losses from strongly saline lands that could not be cultivated at all. There are estimated to be about 13 million hectares of agricultural land that cannot be cultivated, either because they are waterlogged or because they are highly saline. If we assume half of this area is saline then annual net losses from land wasted due to salinity are about Rs. 60-135 billion Rs. In total therefore losses due to salinity amount to between Rs. 63 and Rs 148 billion. The middle of that range is 110 billion Rs. (0.17% of GDP in 2010).

The losses due to water logging are estimated in a similar way. Then annual production losses are about 20 billion Rs. or 0.03% of GDP in 2010.

The remaining waterlogged wasteland is estimated by Indiastat.com to be 7.5 million. ha. None of this is deemed to be cultivatable. Given that the loss in annual profits for paddy production on one hectare is in the range 15,000-24,000 Rs/ha The annual net losses from land wasted due to water logging are about 83-143 billion Rs. or 113 billion Rs. on average (0.2% of GDP in 2010).

**_6.2 Soil Erosion_** In addition to soil salinity land degradation caused by wind and water erosion is substantial in India.  Two major impacts of this erosion are sedimentation of dams and loss of nutrients in the soil.

Soil erosion contributes to sedimentation of dams in India. This in turn reduces the capacity of dams and thus irrigation capacity. We do not have reliable data on sedimentation of dams and reduction in the capacity of dams in India. Hence estimates of losses in crop production as a result of sedimentation could not be made.

As far as soil erosion and the loss of soil nutrients are concerned, this can be valued in terms of the costs of replacing the losses.

The estimated cost of soil nutrients (in terms of nitrogen, phosphorus and potassium) substitution is about 320-600 billion Rs. or 460 billion Rs. on average (0.7 % of GDP in 2010). Soil erosion is thus by far the most substantial problem of land degradation in India.

Adding up the three categories of losses arising from land degradation in India we get a total of 715 billion Rs. or 1.1 % of GDP in 2010 (Table 6.2). Another way to express the loss is as a percentage of GDP from agriculture, forestry and fishery, which are sources of income predominantly for the poor. Gundimeda and Sukhdev (2008) refer to this as the “GDP of the poor” and as a percent of that the loss is about 6.4%.

24
- 32 -

benchnmark is 1000, then the benchmark sequence is (1350, 1000, 1000) and, if the benchmark utility (equation (22)) is l/(l-A)(Y +C0 )1-A where A is 2, then the utility would be evaluated as -13501 = -7.407 x 10-4.

In a stochastic simulation, each sequence could be thus evaluated as a utility, averaged over replications (repeated pseudorandom encounters) and thus an expected utility computed. This could be interpreted conveniently as a certainty equivalent by solving equation (22) for the certainty equivalent benchmark and, in turn, the difference between this and the (computed) expected benchmark E[Y +C I ] gives the risk adjustment which might be expressed in proportional terms analogous to the previous Step 6. The decision as to the need for risk accounting can then be taken as before. All this sounds a little tedious, and it surely will be. Such, howevar, are the challenges minimally faced in social risk accounting in project appraisall 4.2 A case study illustration For simplicity, a simple hypothetical project is considered to illustrate the methods proposed in section 4.1. This is first examined with the rough-and-ready method. As it is hypothetical, there is little need to dwell on the context and assumptions except in so far as they have implications for the methods being described.

The assumptions about the economy are based on the recent experience of the Dominican Republic. This country is typical of several efficient producers and exporters of sugar (from cane) in that sugar is a major source of foreign exchange (here about 35 percent of exports) but, since the traded sugar market is so volatile, this source is rather unstable and contributes to significant macroeconomic fluctuations. Gross value of sugar production constitutes about 10 percent of GDP but this varies considerably (e.g. from 27 percent in 1974 to 4 percent in 1978). Another measure of this dependence
_5_ explicitly or implicitly, with a complex policy<sup>networkandpolicychangesthat bringthe rate</sup> to the "equilibrium" level.

**7.** The paper emphasizes the implications for Bank estimates of per<sup>capita,thekinds</sup> of evidence relevant in assessing the underlying conversion factors, and identifies<sup>countries</sup> where review, and adjustment if deemed necessary, of the official<sup>exchangeratemerits</sup> serious consideration. The paper provides illustrative examples in using the<sup>evidenceto</sup> derive alternative conversion factors.

**8.** This paper takes the view that the exchange rate 1,onversion factor) is a pric used in international transactions. Restrictions introduced into the price system in international transactions impact on the domestic relative price level. The reliance, in general,<sup>onthe</sup> officil exchange rate sometimes produces substantial discrepancies in the data.<sup>Thepaper</sup> reviews the main concerns about the adequacy of the official exchange rate as a conversion factor when in effect countries maintain dual or multiple exchange practices,<sup>orthereare</sup> parallel or black market foreign exchange activities, or there is a high incidence<sup>of price</sup> controls and trade-related restrictions. One aspect receiving special attention is the compilation of a general purpose conversion factor from a set of multiple exchange rates for different types of international transactions.

s **A** useful survey of this topic is given in **G.G.** Johnson, "Formulation of Exchange Rate Policies in Adjustment Programs," IMF Occasional **1.** jer, No. **36** (Washington,D.C.: International Monetary Fund, August **1985).**
trade and to other negative outcomes. Freund and Rocha (2011) investigate the impact of three subcomponents of the TAB data on exports, finding that the inland transit time variable was most robustly associated with lower levels of African exports.<sup>9</sup> Hornok and Koren (2011) link subcomponents of the TAB data to larger and less frequent import shipments. Li and Wilson (2009) link the total time-to-export measure to firm-level data and find that producers of time-sensitive goods are less likely to become exporters in countries where the TAB time-to-export measure is larger.

The policy data that we employ as explanatory variables have also been used to explain trade patterns and implied trade costs. Mo ̈ıs ́e et al. (2011) describe the data construction in detail and investigate the effect of policy on trade flows in a gravity regression. Mo ̈ıs ́e and Sorescu (2013) use trade flows to calculate implied trade costs, as in Novy (2013), and find significant effects of the policies on implied trade costs. This gives evidence that policy affects trade flows; but does not necessarily imply that policy affects trading time.

Our purpose is to exploit cross-country variation in policy to understand the empirical relationship between policy and the two subcomponents of the time-to-trade measures: days in import customs and days in export customs. We estimate these links in an appropriate econometric model. Relying on the model’s estimated parameters, we then ask how expected time in customs would change for each country, if that country were to move to best practice in one or all of 12 measures of policy. In order to put the estimated time reductions in context we use parameter estimates from Hummels and Schaur (2013), who calculate a tariff-equivalent measure of a day using U.S. import statistics. These authors estimate that a one-day reduction in trading time is equivalent to 0.6 to 2.1 percentage point tariff equivalent reduction in trade costs. We argue that the smaller of these two values is likely to be more appropriate in this context, and apply it in our back-of-the-envelope calculations.

# **3 Data**

Our estimation exercises employ three types of data: indicators of the _performance_ of customs (and other inspecting) agencies, quantitative measures of countries’ border _policies_ , and other countrylevel variables that control for other potential sources of variation in customs performance. This section describes these indicators, and illustrates some initial properties of the data.

> 9Hornok (2012) uses a measure of time-to-trade that does not come from the TAB data - truck waiting times at European borders - as an explanatory variable for trade outcomes.

5
||Table8.Firstste|p estimations<br>|andlikelihood<br>|of firmbeinga|nexporter<br>|||
|---|---|---|---|---|---|---|---|
|||First step|estimations||Likelihood|of firm being|an exporter|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|VARIABLES|Bribe|Bribe|Exporter|Exporter|Exporter|Exporter|Exporter|
|Log. Stock of capital|-0.254||0.00727||-0.0520|||
||(0.296)||(0.00445)||(0.0406)|||
|Proportion of|3.007*|5.411**|0.0952**|0.138***|-0.148*|-0.0413|-0.131**|
|temporary worker|(1.191)|(1.620)|(0.0244)|(0.0211)|(0.0854)|(0.0726)|(0.0589)|
|Avg. Education of|-1.046***|-1.459***|-0.0180*|-0.0264|0.0194|-0.0195|0.0167|
|Workers|(0.0954)|(0.348)|(0.00800)|(0.0144)|(0.0546)|(0.0341)|(0.0374)|
|Market share|35.28|-19.36|2.840**|3.111**|0.0842|0.197|0.677|
||(33.22)|(14.69)|(1.035)|(1.066)|(1.785)|(1.248)|(1.363)|
|Young|-0.316|-3.001|-0.0310*|-0.0467**|-0.466**|-0.138|-0.527***|
||(0.562)|(1.806)|(0.0142)|(0.0116)|(0.233)|(0.140)|(0.110)|
|Mature|-0.196|-1.206|-0.0140|-0.00943|0.0883|0.260|0.101|
||(0.708)|(1.186)|(0.0253)|(0.0253)|(0.176)|(0.206)|(0.181)|
|Credit line or overdraft|1.256|0.415|0.140*|0.128|0.845***|0.781**|0.845**|
|Facility|(0.631)|(0.604)|(0.0662)|(0.0684)|(0.288)|(0.309)|(0.338)|
|Cost power outage|-0.0294|0.0489|-0.00236**|-0.000995||||
||(0.0294)|(0.0247)|(0.000753)|(0.000963)||||
|Time Tax|<br>0.0444|<br>0.0154|<br>0.00682**|<br>0.00613***||||
||(0.0352)|(0.0283)|(0.00189)|(0.00152)||||
|Cost of insecurity|0.186|0.595*|0.0108**|0.0145***||||
||(0.155)|(0.254)|(0.00310)|(0.00359)||||
|Bribe||||||||
|Average bribe by|0.590**|0.876***|-0.00228|0.00401||||
|Location-sector-size cluster|(0.199)|(0.139)|(0.0123)|(0.0137)||||
|Inputs of foreign origin|-0.0173|0.0480*|0.00406***|0.00526***|0.0156***|0.0153***|0.0155***|
||(0.0199)|(0.0221)|(0.000780)|(0.00103)|(0.00227)|(0.00263)|(0.00219)|
|Foreign Contribution|||||0.893***|1.040***|0.859***|
||||||(0.302)|(0.292)|(0.301)|
|Manufacturing|-26.50|-18.69|-0.387***|-0.334**|-0.493|-0.610|-0.525*|
||(15.37)|(16.62)|(0.0800)|(0.0860)|(0.319)|(0.416)|(0.303)|
|LP per worker|||||0.170**<br>(0.0717)|||
|VA per worker||||||0.109*<br>(0.0649)||
|TFP|||||||0.126*<br>(0.0747)|
|Observations|1277|1327|1277|1327|2699|2789|<br>2693|
|R-squared|0.287|0.419|0.402|0.481||||
|Region FE|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|

Source: Authors based on the data from the Enterprise Surveys 2007/2009 and 2014.

Note: LP=labor productivity per worker; VA=Value added per worker; TFP are obtained using Levinshon and Petrin (2003)’s approach. Robust standard errors clustered at the States level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All the estimates use the weights from the survey.

24
different world regions, except sub-Saharan Africa, is fairly representative. Appendix A<sup>lists the</sup> specific countries included in the analyses.

# Analyses and Findings

This section presents results from cross-national analyses of curricular subject areas and their effects on econornic growth. It begins by briefly surveying descriptive statistics conceming<sup>the</sup> main explanatory variables used in the panel regression models. Table 2 shows that in 1960 nations required, on the average, 870 hours of formal instruction during each year of primary schooling. Overall hours of instruction were higher among developed countries (917.0 hours per<sup>year) than</sup> among developing countries (849.3 hours per year). Furthermnore, the three R's tended to take up about half of all instructional hours: reading and writing (i.e., language education) was allocated over 300 hours of instruction; arithmetic and other math-related subjects about 145 hours<sup>of</sup> instruction. The remaining instructional time was distributed among a series of "core" subjects which were required in almost all national primary curricula and a set of more "peripheral"<sup>subjects</sup> which were required in less than three-fourths of all countries (see Benavot<sup>et al., 19w). Core</sup> subjects included art and music (81.2 hours per year), social sciences (75.6 hrs./year), natural sciences (63.3 hrs./year), and physical education (58.7 hrs./year). Peripheral subjects included religion and moral education (50.5 hrs./year) and pre-vocational education (65.4 hrs./year). Differences in average time allocations by level of development were minimal in science education and religious and moral education, as well as in the combined area of history, geography and social studies. In the remaining subject areas, developed nations placed greater emphasis on aesthetic education and physical education and less emphasis on pre-vocational education than developing nations in the Third World.

Table 3 presents zero-order correlations among all the variables used in the panel regression analyses. By and large, correlations between most curricular emphases and levels of economic development are weak and non-significant. Two subject areas, mathematics and physical<sup>education,</sup> have positive, but not especially strong, associations with economic development; pre-vocational education and economic development are negatively correlated. As expected, associations<sup>between</sup> the other independent variables (rates of educational expansion, fertility rates, export commodity 12