rafmacalaba/datause-encoder-data huggingface dataset:
SYRIA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on the 1976-78 Fertility Survey and the 1981 census.

TURKEY: Both the IMR and life expectancy at birth are official estimates, as published in _Social Indicators._ These data are based on the 1988 Population and Health Survey and several other surveys.

UNITED ARAB EMIRATES: Both the IMR and life expectancy at birth are fror.: _World Population Prospects 1990._ The U.N. based its estimates on the 1975 and 1980 censuses. The U.N. medium mortality decline scenario was used to obtain current estimates.

YEMEN. Estimated IMR and life expectancy at birth are weighted averages for former Yemen Arab Republic and Democratic Yemen. For Yemen Arab Republic, the IMR and life expectancy are World Bank estimates based on the 1979 World Fertility Survey and 1986 census. For Democratic Yemen, the estimates are from _World Population Prospects 1990._

# Europe and U.S.S.R.

ALBANIA: The IMR is the average of official vital registration based estimates for 1985 to 1989, as published in _PVSR._ Life expectancy at birth estimates are from _World Population Prospects 1990._ AUSTRIA: The IMR is the average of official vital registration estimates for 1985 to 1989 as published by Institut National d'Etudes Demographiques (1990) and _PVSR._ Life expectancy at birth is from Coal--Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

BELGIUM: The IMR is the average of official vital registration estimates for 1985 to 1989 as published by Eurts,at (1990). Life expectancy at birth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

BULGARIA: The IMR is the average of official vital registration estimates for 1985 to 1988 as published by _PVSR._ Life expectancy at birth estimates are from _World Population Prospects 1990._ CHANNEL ISLANDS: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

CZECHOSLOVAKIA: The IMR is the average of official vital registration estimates for 1985 to 1988 as published by _PVSR._ Life expectancy at birth estimates are from _World Population Prospects 1990._ DENMARK: The IMR is the average of official vital registration estimates for 1985 to 1989 as published by Eurostat (1990). Life expectancy at birth is fromn Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

FINLAND: The IMR is the average of official vital registration estimates for 1985 to 1988 as published by Institut National d'Etudes Demographiques (1990) and _PVSR._ Life expectancy at birth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

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The estimates of the opportunity shares of inequalities in achievements at the TIMSS Math test are shown in **Table 7** . The results for 2007 (column 2) with a similar urban-rural specification give a lower bound for the opportunity share at 18.2 without the corrections for potential selection bias. It is lower than the corresponding figure in 2003 by 3.4 percent; the decrease has a standard error of .85 point, a statistically significant difference. It is worth noting that the 2003 and 2007 estimates are not perfectly comparable for reasons described earlier. However, the ranges obtained with the imputations for selection bias 2003 and 2007 overlap.

The two remaining columns use more detailed information on geography. The lower bound increases to 21.6 percent with the six regions school location variable (and vary between .213 and .266 with the imputations) and to 24.2 percent when accounting for the precise governorates for school locations. This increase suggests that achievement inequalities associated with geography are large. Overall, based on the most complete estimates in the last column, at least a quarter of achievement inequalities at the end of preparatory can be attributed to circumstances. This figure is comparable to those from similar analysis in other countries (e.g., the share of inequality of opportunity was found similar in countries such as UK or the US in Ferreira and Gignoux, 2012). Bu given the high overall learning inequalities, the absolute level of learning inequities at completion of basic education is higher in Egypt than in many other countries.

The estimates of partial shares associated with specific circumstances confirm the importance of geography. Using data on Math test-scores in 2007, 4.5 percent of inequalities are accounted for by area type in the first model, 8.3 percent by the six regions indicators in the second, and 11.2 percent by area type and governorate combined in the third model. In comparison, the next most important circumstances are parental education (3 to 4 percent for father’s and about 2 percent for mother’s education), and immigration status (3 to 4 percent), and ownership of cultural goods which accounts for another 3 percent. Language at home accounts for about 1.5 percent. Gender is not associated with a significant share of inequalities in achievements.

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# **1. Introduction**

Income transfer programs are a common social policy response to macroeconomic crises. Stated aims vary in practice, but the common (explicit or implicit) goal is to help protect the living standards of those families most adversely affected by the crisis. One of the largest such programs in recent times is the Government of Argentina’s _Plan Jefes y Jefas_ (hereafter “ _Jefes_ ”), introduced in January 2002. This was the main public safety net response to the severe economic and political crisis that hit Argentina at the end of 2001. Unemployment and poverty rates reached record levels during the crisis (World Bank, 2003). _Jefes_ aimed to provide direct income support for families with dependents who had lost their main source of earnings due to the crisis. To assure that the program reached those in greatest need, work requirements were imposed. With support from a World Bank loan (and equivalent counterpart funds from the government), the program expanded rapidly to cover about two million households by late 2002.<sup>2</sup> Our knowledge about the impacts of such programs has often been limited by a number of factors, including the speed with which crisis programs have to be scaled up and the paucity of appropriate survey data. Yet opinions still abound, often based on little more than casual anecdotes. In the case of _Jefes_ , critics of the program have made claims about fraudulent participation, such as by pointing to cases of registered participants who do not appear to satisfy the program’s eligibility criteria, or to weakness in the implementation/effectiveness of the program’s counterpart work requirements. At the other extreme, it has been argued that the scheme was a big success in reducing poverty and unemployment in the aftermath of the crisis.

> 2 In 2002, the Government of Argentina spent about US$500 million on _Jefes_ , and about a quarter of that was financed through a World Bank loan. For 2003, the estimate is US$600 million, of which the Bank loan will probably cover about 50%. The loan and counterpart funds cover mainly the payments to beneficiaries. Most costs for materials, supplies, etc.  for the workfare projects are covered by the local governments or NGOs sponsoring the projects.

3
instruments to supplement the internal GMM-style instruments. In a region consisting of n countries, penetration for country i is de...ned as the fraction of countries in the surrounding region that already have an MTEF: for each of the MTEF phases. We use the classi...cation of the world into the twenty-two geographic regions de...ned by the United Nations Statistics Division.

Table 1 reports summary statistics. All variables display considerable variation both between and within countries, justifying the use of panel estimation techniques. An exception is MTPF, which has small within variation due to the few adoptions of this top phase, and OECD membership. Table 3 reports pairwise correlations between the main variables. The correlation coe¢ cients are within plausible ranges and support our choices of regressors in the next subsection.

# 3.2 Empirical Strategy

Estimating the impact of a budget institution on ...scal performance requires that we address several identi...cation challenges: reverse causality, omitted variable bias, and errors-invariables.

First, reverse causality arises because ...scal stress, e.g., a ...nancial crisis, may have prompted a country to restrain spending, adopt an MTEF, or strengthen an existing one. Von Hagen (2006, p. 474) notes that "Historical experience suggests that governments make e¤orts to centralize the budget process to overcome sharp ...scal crises." If MTEFs have positive e¤ects on ...scal performance, and poor ...scal performance increases the probability of adopting an MTEF, then the reverse causality bias is probably negative. In this case, the estimates are still useful as a lower bound for the actual e¤ect.<sup>23</sup> Second, omitted variable bias arises due to the failure to account for a factor that a¤ects both the adoption of an MTEF and ...scal performance. For instance, strong economic growth may reduce the pressure on a government to reform budget institutions, and, at the same time, improve the government’s ...scal outcomes, thus leading to negative omitted variable bias. As suggested by Fabrizio and Mody (2006), a partial solution to this problem is to

> 23The endogeneity of budget institutions with respect to ...scal performance is extensively discussed in the cross-country literature (see Alesina and Perotti 1999, Stein, Talvi, and Grisanti 1999, Perotti and Kontopoulos 2002, and Fabrizio and Mody 2006) yet none of these papers proposes an instrument that in‡uences the probability of ...scal reform while being exogenous to ...scal performance.

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33

|COUNTRY|PROJECT|LEGAL COMPONENT|FINANCIERS|
|---|---|---|---|
|Argentina<br>(Cont.)|Judicial Sector Review<br>IDF Grant No. 28725,<br>December 14, 1992<br>Preparation of the Judicial and Legal<br>Reform Project, Japanese Grant<br>Agreement (proposed)|Upstream diagnostic review of the judicial sector in<br>Argentina focusing on the federal courts and national courts<br>in the city of Buenos Aires. The diagnosis is a<br>comprehensive one including issues related to the operation<br>of the courts, training of court personnel, the procedural<br>codes and other internal judiciary norms governing<br>procedures, alternative dispute resolution methods and legal<br>education of lawyers, judges and the public. The study is<br>planned in four stages and is expected to last about 20<br>months.<br>The Grant will finance consultants' services to assist in (i)<br>defining the new organization, functions, incentives and<br>training plan of the judicial personnel and in preparing a<br>schedule of activities to implement this new organization; and<br>(ii) defining new administrative procedures for case filing,<br>case management, distribution of cases and archives, and<br>developing systems on a pilot basis to automate these<br>pru. edures and test them in two pilot sites.|IDF $426,000<br>Japanese Grant 160,460,000|
||Industrial Modernization Project<br>Japanese Grant Agreement,<br>September 27, 1990|Legal assistance in connection with the preparation of all<br>agreements, charts and regulations required for the<br>functioning of the backstop facility to be financed by a<br>proposed US$500,000,000 loan for the Capital Market<br>Development Project.|Japanese Grant Y243 m.<br>l|
||Public Sector Reform Technical<br>Assistance Project<br>(Loan No. 3362 AR), August 13,<br>1991|Review of existing legislation to support the rationalization of<br>norms and regulations governing economic activities and<br>design of a program to strengthen financial management,<br>budgetary process and control of the public sector including<br>preparation of norms, procedures, guidelines and manuals<br>required as a result of the enactment of legislation of public<br>financial management and performance control.|IBRD $23.1 m.|
<!-- Start of picture text --> To)<br>40* Percentile<br>vt<br>o<br>=<br>O<br>c<br>®<br>Q<br>N<br>fo)<br>-2 0 2 4 6 8<br>Wealth Score<br>————_ Impact evaluation data ———— LSMS data<br><!-- End of picture text --> <!-- Start of picture text --> HWWS exposure Increased Improved practice: Improved<br>(intervention awareness/ HW stations, cleanliness (child<br>recall) kramiletine HWWS at critical and caregiver)<br>~ junctures<br>al Improved health<br>TSSM exposure Strengthened Latrine<br>(intervention supply / demand SETSETE ESE Reduced open<br>recall) for sanitation upgrading defecation<br><!-- End of picture text -->
2.57 (= 20 × 1.00/6.964) years (see Table 2). With N2<sup>a</sup> ,t<sup>=2∀t,CSA(2000)yields</sup> N2<sup>c</sup> ,1<sup>=4.15andN o</sup> 2,1<sup>=0.990.Theadultprematuremortalityrateissetwiththatin</sup> the rural sector, where HIV-prevalence rates are much lower, so as to yield the estimated national average. We obtain q2<sup>a</sup> ,1<sup>= 0.299(weassumeN cp</sup> 2,t<sup>= N cs</sup> 2,t<sup>= N c</sup> 2,t<sup>/2∀t).</sup> We retain the values of γ, β and ρ in the rural sector. Given the much higher educational attainments of town-dwellers, the initial value of λ2,t should be pitched well above its rural counterpart.<sup>16</sup> In the light of the prevailing differences in the quality of schooling and the attainment in vocational and higher education, it seems safe to conclude that in the year 2000, the level of human capital among adults in the cities was at least twice that among those in the country: we set λ2,0 = 2.3 and λ2,1 = 2.8. We are no longer at liberty to choose the efficiency parameter A2,1 as we please; for there are (crude) estimates of the productivity differential between the two sectors, and these must be largely respected. This consideration yields A2,1 = 2.0. Since inputs of labor fully reflect human capital, we choose α2 = 0.667.

Turning to the preferences of young urban adults, let these be Stone-Geary once more, with parameters ωj (j = 1, 2, 3, 4), which relate to current and future consumption and the two levels of education, respectively. The parameters ω3 and ω4 are endogenously determined so that the optimal values of primary and secondary education in period 1 are equal to 0.740 and 0.502, respectively. The remaining ωi parameters are obtained from the normalization ω1 + ω2 + ω3 + ω4 = 1 and the rate of pure impatience in consumption, 1 − ω2/ω1. We are unaware of any attempts to estimate this rate for Ethiopia, but for the U.S., Fullerton and Rogers (1993) find that it lies between 0.25% and 0.5% per annum. With this as a guide, we set ω2/ω1 = 0.85. Finally, e<sup>p</sup> 2,min<sup>=es</sup> 2,min<sup>=ep</sup> 1,min<sup>,andthesubsistenceparameterc2,minisassumedto</sup> about 17% larger than its rural counterpart.<sup>17</sup>

> 16Adult illiteracy rates in urban and rural Ethiopia in 1999 were 28.0% and 77.9%, respectively, (ILO, 2005).

> 17According to the 2000 Welfare Monitoring Survey (World Bank, 2003), the poorest 20% of house-

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percent of the fiscal impoverished are members of households identified as poor according to the official definition. This result is indicative of the additional efforts required to improve the targeting and expand the coverage of direct transfers among the poor population, in particular because direct transfers have a significant large effect in reducing inequality and poverty.

Finally, when put into a regional perspective, the redistributive effect of direct transfers (i.e., the decline in inequality from pre-fiscal income to post-transfers income) is particularly larger in Chile than in other Latin American countries with a comparable fiscal incidence analysis. A remarkably result is that with 1.6 percent of the GDP on direct transfers, the redistributive gains in Chile are as much as 2.7 times larger than in Bolivia and virtually the same as in Brazil, where direct transfers account for 2 and 5.5 percent of the GDP, respectively. In terms of the effect on poverty, as measured by the $4/day poverty line, direct transfers in Chile reduce the incidence by 41.2 percent with respect to prefiscal income, placing the country as a top performer in the region.

As part of its efforts to address persistently high levels of income inequality, the Government of Chile enacted a comprehensive tax reform in 2014 aimed at generating additional tax revenues (amounting to around 3 percent of the GDP) to finance social spending, specially on education; improving neutrality and equity of the tax system; improving the efficiency of tax incentives on savings and investment; and reducing tax evasion and avoidance (Arenas de Mesa, 2016). Even though the effect of such reform is not quantified in this paper, further spending on education could potentially contribute to income inequality decline in the medium and long-term. Moreover, an ex ante evaluation of the reform using the 2013 fiscal records suggested that the tax reform would likely increase the effective tax burden for the top 1 percent of the income distribution by 6 percentage points (equivalent to an increase from 2.4 to 3.5 percent of the GDP, with 80 percent of the latter figure being paid by the top 0.1 percent), while for most of the population the tax burden is expected to remain roughly constant, making the tax system, eventually, more progressive (World Bank, 2016).

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_Algeria_ stays in an intermediate position. _TFP_ calculations thus confirm the productivity gap assessed through Productivity of Labor<sup>27</sup> .

# **4.3- Firm-Level Technical Efficiency**

Firm-level Technical Efficiency ( _TE_ ) is based on the likelihood estimation procedure. As seen in section 2.2., this method allows splitting the error term into two independents factors: the error term _(v),_ which follows a normal distribution, and the Technical Efficiency _(u_ ), which obeys a truncated normal distribution. The technology of production explains the Value Added ( _Y_ ) by the Capital ( _K_ ) and the Labor ( _L_ ). Same hypotheses and definitions as before apply to input and output variables. Equation is as follows: With:

- Y :  Value Added i, j

- L :  Number of Permanent Workers i, j

- K i, j:  Gross Value of Property, Plant and Equipment

- _dum_ j: Country-dummy variables

- _α, β:_ parameters of the equation

- _vi_ , j:  Error term

- _u_ i, j: Technical Efficiency ( _TE_ ).

- i / j:  Enterprise and country index respectively.

Production frontiers have been estimated by industry. As mentioned before, this leads to more homogeneous production frontiers and makes it easier to attribute the residual to differences in efficiency. Differences in coefficients of capital and labor have justified this choice; against an alternative assumption consisting in estimating the same production frontier for all sectors, with specific sector-based dummies (see Table 4).

**Table 4: Estimations of the Stochastic Production Frontiers**

||||**_D_**<br>|**_ependant Va_**<br>|**_riable: Valu_**<br>|**_e Added_**<br>|||
|---|---|---|---|---|---|---|---|---|
|**_Independent_**<br>**_Variables_**|**Textile**|**Garment**|**Leather**|**Agro**<br>**Processing**|**Metal&**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Non Metal**<br>**& Plastic**<br>**Materials**|**Wood**<br>**&**<br>**Furniture**|
|**Log (labor)**|0.659<br>(30.53)***|0.811<br>(42.69)***|0.826<br>(20.20)***|0.695<br>(31.22)***|0.877<br>(33.21)***|0.673<br>(22.21)***|0.886<br>(22.35)***|0.941<br>(29.18)***|
|**Log (capital)**|0.354<br>(24.87)***|0.260<br>(20.96)***|0.277<br>(11.00)***|0.404<br>(28.62)***|0.289<br>(18.52)***|0.444<br>(22.89)***|0.281<br>(13.54)***|0.228<br>(12.79)***|
|**Intercept**|2.007<br>(18.94)***|1.350<br>(9.22)***|1.419<br>(9.81)***|1.863<br>(13.99)***|1.716<br>(15.61)***|2.065<br>(15.39)***|1.419<br>(9.73)***|1.644<br>(11.51)***|
|**σ2u**|0.33|0.22|0.80|0.73|1.12|0.39|1.30|0.79|
|**σ2**|0.99|0.92|1.40|1.47|1.76|1.13|1.86|1.19|
|**σ2u/ σ2**|0.33***|0.24***|0.57***|0.50***|0.64***|0.35***|0.70***|0.66***|
||(6.17)|(3.00)|(6.33)|(8.17)|(12.80)|(5.00)|(10.00)|(13.20)|
|**Observations**|2011|2800|634|2190|1622|1274|907|1033|

_Note_ : * Significance level 10 %; ** 5 %; *** 1 %. Z statistics are into brackets. Regressions include country-dummy variables. _Source_ : Authors’ calculations 12
search costs and develop signaling mechanisms and matching services. Conversely, if the problem is high reservation prestige, the policy response is less clear, since this can reflect a structural mismatch between the types of jobs graduates are willing to work in, and the types of jobs firms are looking to fill.

# **3. Experiment 1: Matching Workers and Firms**

Our first experiment consists of an employment pilot designed to manually match firms and job candidates based on survey data and psychometric assessments. The aim is to reduce search costs and other matching frictions, and thereby increase the employment rates of educated unemployed youth.

# **3.1 The Sample and Randomization Process**

The study required matching unemployed graduates with firms looking to potentially hire workers. This required us to put together separate samples of job candidates and firms.

The _experimental job candidate sample_ consists of 1,354 recent graduates of either community college or university in Jordan, who participated in the program between December 2011 and November 2012. We obtained this sample through advertising the program (called Jordan NOW 2.0) through advertisements in local newspapers, radio stations, cafes, and Facebook, along with a telephone campaign to recent graduates from 14 universities and community colleges in and around Amman. Youth were told this program would be free to participate in, and had the goal of helping match them to jobs. Eligibility was restricted to Jordanians who had graduated from community college or university since May 2009. In the first four months the program was restricted to females only, before also being opened up to males.

Candidates signed up and completed a screening process (described below) on a rolling basis throughout 2012. We then stratified by gender and screening batches (110 strata), and randomly assigned three-quarters of the candidates within each strata to a treatment group that would be attempted to be matched to jobs, and one-quarter to a control group that would not. This continued until Dec 2, 2012, resulting in 343 individuals being assigned to the control group, and 8