rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLE 2: SAMPLE MEANS OF EMPLOYMENT, HOURS WORKED AND WAGES**

||Observations|Proportion<br>Employed|Mean hours<br>workedper week|Mean weekly income<br> from work(NZ Dollars)|
|---|---|---|---|---|
|APPLICANTS|198|0.723|27.3|108.9|
|Successful Ballots|120|0.662|28.4|194.7|
|Migrants|65|0.754|33.3|424.5|
|Non-migrants|55|0.618|26.0|81.1|
|Unsuccessful Ballots|78|0.731|27.1|104.1|
|NON-APPLICANTS|60|0.672|24.2|69.5|
|All Non-applicants 18-45|180|0.439|16.2|41.4|
|_T-tests of equality of means_|||||
|Successful Ballots vs|||||
|Unsuccessful Ballots||0.349|0.683|0.000|
|Migrants vs Non-migrant|||||
|Successful Ballots||0.111|0.086|0.000|
|Migrants vs unsuccessful<br>ballots||0.754|0.105|0.000|
|**Pure Experimental Estimat**<br>Intention-to-treat effect<br>SEE-TT|**ors of the Gain**|**in Income fro**|**m Migration**<br>90.6<br>320.4||

# Notes:

Average first quarter 2005 exchange rate of 1 Pa'anga = 0.729 NZ Dollars used for comparing mean incomes SEE-TT is the simple experimental estimator of the effect of the treatment on the treated, and compares migrants to unsuccessful ballots.

42
# Adjustment and Income Distortion: A Counterfactual Analysis

by Frangois Bourguignon, William H. Branson, amd Jaime de Melo

## Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.<br>|Model Outline<br> <br>|3|
|2.1|Determination<br>of Income and Wealth Distribution<br>|5|
|2.2|The Financial Sector<br>|10|
|2.3|The Govemment<br>Sector|1 0|
|2.4|Goods and Factor Markets|12|
|3.|Sectoral and Household<br>Disaggregatio**n**<br>a d Elasticity Specificatio<br> <br>|n 1 4|
|3.1<br>|<br>Sectoral and Household<br>Disaggregation<br> <br>|<br>1 4|
|3.2|<br>Elasticity Specification<br>and Calibration|1 7|
|4.|Description<br>of External Shocks and Adjustment<br>Packages|1 7|
|5.|Simulation Results<br>|23|
|5.1|Macro Outcomes|2 3|
|5.2|Income Distribution<br>Outcomes|27|
|6.|Conclusions|31|
|Foot|notes|33|
|Refe|rences|3 5|
|App|endix: Description<br>of the Model|3 7|

This paper builds on work financed by the OECD Development Center in the context of the project "Adjustment Programmes and Equitable Growth." Support from the OECD Development Center and the World Bank is gratefully acknowledged. We thank Akiko Suwa for very helpful research assistance and Maria D. Ameal for logistic support. We also thank seminar participants at Berkeley, EHESS, and the Federal Bank of Govemors for helpful comments.
- Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

# 8. - APPENDIX 1: TYPE OF **OWNERSHIP** OF MAIN 50 WORLD PORTS (1997)

|**Port**|**TEUS**<br>**thous.**|**Public**|**Mixed**|**Private**|<br>**Port**|**TEUS**<br>**thous.**|**Public **|**[Mixed**|**Private**|
|---|---|---|---|---|---|---|---|---|---|
|**I**Hong Kong|14,539||||26 Seattle|1,476||||
|2 Singapore|14,135|*|||27 Gloa Tauro|1,449||||
|3 Kaohsiung|5,693||||28 Manila|1,358|____*|||
|4 Rotterdam|5,445||||29 Hampton R.|1,232||||
|5 Pusan|5,234||||30 Osaka|1,200||____||
|6 Long Beach|3,505||||31 Le Havre|1,1851||||
|7 Hamburg|3,370||||32 Genoa|1,180||||
|8 Los Angeles|3,000||||33 Tacoma|1,159||||
|9 Antwerp|2,969||||34 Charleston|1,151|*|||
|10 Dubai|2,600||||35 Bangkok|1,099||||
|11 Shangai|2,527||||36 L.Chabang|1,036||||
|12 N.York/N.J.|2,457||+||37 Melbourne|1,029|_|||
|13 Tokyo|2,383||||38 Durban|984|*|||
|14 Yokohama|2,328||||39 Barcelona|972||||
|15 Felixstowe|2,251|||0|40 Tianjin|935|*|||
|16 Keelung|1,981|_|||41Jeddah|921||||
|17 Kobe|1,944||||42 Southampton|<br>891|||0|
|18 SanJuan|1,781||*|_|43 Montreal|870||||
|19 Bremen|1,526||||44 Taichung|842||||
|20 Colombo|1,687|*|||45 Valencia|810||||
|21 Kelang|1,684|||0|46 Santos|829*||||
|22 T. Priok|1,671||*||47 Sharjah|815||||
|23 Algeciras|1,538|___||__|48 Houston|798||||
|24 Oakland|1,531||||49 Sidney|765||||
|25 Nagoya|1,498||||50 Miami|761||||

**_SOURCE:_ CASS (1996)**

53
# TABLE 2.1: Breakdown of Secondary Schools by Schooltype in Sample and Guesstimates in Total Population, 1989

||No. in<br>sample||Estimat<br>1989|e|
|---|---|---|---|---|
|Former Group A|4|8|34+|<br>2|
|Former GroupB<br>(urban)<br>Former Group B (rural)|4<br>6|8<br>13|)<br>**_I_**<br>)158@|)<br> <br>)11|
|High Fee-paying|4|8|38|3|
|Hission|6|13|172|11|
|District Council|24|50|1006|67|
|Other|-|-|94|6|
|TOTAL|48|100|1502|+100|

+ Actual Figures

# Variables

The time frame wLthin which the study was conducted produced certain constraints. A decision had to be made as to which variables (from the ideal set of variables one would like to include in a study such as this) could realistically be obtained in the time from mid-February, when the sampling was carried out and the data collection begun, and mid-April, when the schools closed for Baster and the analysis began.

The main prerequisites of the study were that the ZJC English and Mathematics results as well as the Grade 7 English and Mathematics examination scores **were** reported for each student sampled. (All Form I students in 1989 10
# **Table 1a: The Impact of Local Elections (PILKADA) on District Government Expenditures per capita**

Treatment: T (=1 if district held direct election in 2005; = 0 if district did not have direct election during 2005-07)

|_Variable_|_Coeffi-_<br>_cient_|_Administration_|_Public Works_|_Transport_|_Health_|_Education_|_Housing_|_Agriculture_|_Industry_|_Total_<br>_(all sectors)_|
|---|---|---|---|---|---|---|---|---|---|---|
|**Dependent**<br>*|**variable:**|**Per capita total e**<br>*|**xpenditure(re**<br>|**al terms)**<br>||*|*|**||**|
|TD06||193,861|23,151|4,376|13,309|63,327|55,836|18,742|14,225|437,447|
|||(102,882)|(20,306)|(9,235)|(8,841)|(32,642)|(31,746)|(7,379)|(9,376)|(221,435)|
|# obs||823|750|800|814|814|796|814|788|823|
|# districts||174|172|174|174|174|174|174|174|174|
|**Dependent**<br>|**variable:**|**Per capita devel**<br>|**opment expend**<br>|**iture(real ter**<br>|**ms)**<br>||||||
|T*D06||124,110*|26,676|3,692|5,085|4,933|52,524*|12,400***|10,489|233,960**|
|||(63,874)|(20,549)|(8,798)|(3,729)|(5,555)|(30,393)|(4,500)|(8,530)|(117,772)|
|# obs||815|704|791|802|807|791|806|779|816|
|# districts||174|170|174|174|174|174|174|174|174|
|**Dependent**<br>|**variable:**|**Per capita routin**<br>|**e expenditure**<br>|**(real terms)**<br>|||||||
|T*D06||68,987*|533|1,233|7,970|58,950*|3,235*|6,602**|3,581***|201,885*|
|||(40,502)|(1,459)|(900.2)|(5,951)|(30,414)|(1,665)|(3,079)|(1,308)|(105,737)|
|# obs||819|738|772|804|803|752|803|743|821|
|# districts||174|171|173|174|174|172|174|172|174|

Notes: *: 10% level of significance; **: 5% level of significance; ***: 1% level of significance Standard errors in parentheses estimated based on fixed-effects model with an AR(1) disturbance (Baltagi and Wu, 1999) Districts that held direct elections during 2006-07 are dropped from the regression, which implies that the sample consists of districts that held elections in 2005 and those that held no election at all.

31
# **Table** **<u>6. Joining by treatment</u>**

||(1)<br>Burning|(2)<br>Stealing|(3)<br>Giving|
|---|---|---|---|
|Kenya|0.346|**0.467*****|**0.389*****|
||(1.630)|(3.781)|(11.820)|
|Uganda|**-0.378*****|-0.079|-0.026|
||(-3.549)|(-0.809)|(-0.377)|
|Gain from joining|**-0.288****|-0.061|0.080|
||(-2.181)|(-0.505)|(1.184)|
|Kenya x gain from joining|**-0.474*****|-0.091|**-0.446*****|
||(-3.224)|(-0.219)|(-5.921)|
|Uganda x gain from joining|**-0.852****|-0.452|**-0.446*****|
||(-2.310)|(-1.487)|(-6.903)|
|Initial endowment|-0.345|**-0.464*****|**-0.110****|
||(-1.618)|(-4.762)|(-2.356)|
|Kenya x initial endowment|**0.586*****|**0.385****|**0.136****|
||(3.559)|(2.438)|(2.223)|
|Uganda x initial endowment|0.537|**0.353****|0.077|
||(1.546)|(2.512)|(0.827)|
|γ|**4.376***|**-0.251*****|**-0.210*****|
||(1.840)|(-3.108)|(-3.150)|
|Constant|0.487**|1.010***|0.882***|
||(2.100)|(10.760)|(17.530)|
|Observations|504|954|990|
|R-squared|0.161|0.069|0.055|

Source: Authors analysis based on data described in the text. Note: The dependent variable is 1 if subject _i_ joins the group, 0 otherwise. Each regression is estimated using a linear probability model. This ensures that each coefficient can be interpreted as the marginal effect of the regressor on the probability of joining. Gain from joining = (p-1)*e_i. Standard errors are clustered at the session level. _t_ -statistics appear in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Randomization was also used by Angrist et al. ( 2002) to evaluate a Colombian program that allocated schooling vouchers by a lottery. Three years later, the lottery winners had significantly lower incidence of grade repetition and higher test scores.

Another example is Argentina’s _Proempleo_ experiment (Galasso et al., 2004). This was a randomized evaluation of a pilot wage subsidy and training program for assisting workfare participants in Argentina to find regular, private-sector jobs. Eighteen months later, recipients of the voucher for a wage subsidy had a higher probability of employment than the control group. (We will return later in this chapter to examine some lessons from this evaluation more closely.) It has been argued that the World Bank should make greater use of social experiments. While the Bank has supported a number of experiments (including most of the examples for developing countries above), that is not so of the Bank’s Operations Evaluation Department (the semi-independent unit for the _ex-post_ evaluation of its own lending operations). In the 78 evaluations by OED surveyed by Kapoor (2002), only one used randomization;<sup>25</sup> indeed, only 21 used any form of counterfactual analysis. Cook (2001) and Duflo and Kremer (2005) have advocated that OED should do many more social experiments.<sup>26</sup> Before accepting that advice one should be aware of some of the concerns raised by experiments.

A well-crafted social experiment will eliminate selection bias, but that leaves many other concerns about both their internal and external validity. The rest of this chapter turns to the main nonexperimental methods found in practice.

# **5. Propensity-score methods**

As section 3 emphasized, selection bias is to be expected in comparing a random sample from the population of participants with a random sample of non-participants (as in estimating _D_ ( _X_ ) in equation 2). There must be a general presumption that such comparisons misinform policy. How much so is an empirical question. On _a priori_ grounds it is worrying that many NX evaluations in practice provide too little information to assess whether the “comparison group” is likely to be sufficiently similar to the participants in the absence of the intervention.

> 25 From Kapoor’s description it is not clear that even this evaluation was a genuine experiment.

> 26 OED only assesses Bank projects after they are completed, which makes it hard to do proper impact evaluations. Note that other units in the Bank that do evaluations besides OED, including in the research department invariably use counterfactual analysis and sometimes randomization.

20
40 some discussion on legal, political and developmental iseues as well as sharing of the Sathin Ro Kagad, the monthly newsletter. In an average WDP village, nearly one-third of the women of the village would come in contact with the Sathin through the women's groups once in every few months. The activities of the group are reported in a jajam.

5.10 once the group has solidified in the sense of having had some formal sittings and a few group actions, women begin to discuss their problems in a much more self-conscious and aware manner at their usual places of congregation like the hand-pump, well or bathing spot. The consciousness of certain issues as concerning the women of a village as a whole, and the idea that something can be done and help sought, is the strongest evidence that WDP has achieved much in the area of confidence building.

5.11 Although it is impossible to get an idea of the numbers of groups and women participating in them, since they are so informal, the important point is that in times of crisis or during the period of group action, many more women appear. They also know that the group is a sympathetic one and the Sathin has the support of the district administration and access to it. The group therefore is not a fi,-ed entity built around one issue. More likely, it is built around the Sathin. Its main function is
sector also appears to require some education, wealth and social status- perhaps less strongly so in the most recent survey year.

While the picture for India as a whole suggests that there has been no acceleration in the rate of poverty decline or in the rate of diversification out of agriculture, we are able to draw on the marked variation across NSS regions and over time, to pursue the impact of rural diversification on rural poverty. Tentative econometric estimates from a region-level panel dataset covering our reference period indicate that poverty is associated in a complex way with the non-farm sector. Controlling for agricultural wages, agricultural productivity, land availability and consumption levels in nearby urban centers, expansion of non-farm employment is directly associated with poverty decline. We suggest that this is consistent with the idea that, while at early levels the non-poor may have been more successful at “capturing” non-farm income earning opportunities, there is some evidence that over time, further expansion of the sub-sector is pro-poor. We show that the pro-poor impact of the non-farm sector is attenuated in settings where education levels are particularly low. Second, the analysis further shows that expansion of the non-farm sector appears to put pressure on wage rates in agriculture (again, less strongly so in settings where education levels are particularly low). Rising agricultural wages rates are, in turn, shown to be strongly correlated with rural poverty reduction. Thus we have presented evidence consistent with an important indirect, as well as direct, impact of the non-farm sector on rural poverty.

In conclusion, policy makers aiming to alleviate poverty should continue to explore options for promoting the non-farm sector. This study suggests that efforts should focus on the promotion of non-farm opportunities that do not impose barriers to entry. These efforts can be expected not only to directly raise the income levels of the poor who gain access to such jobs. They are also likely to contribute to poverty reduction by raising the wages received by those who remain employed as agricultural laborers.

25
# **4.6 Can we relate the MDM and our endogenous variables by means of the ICSs?**

So far we know that the MDMs in the countries analyzed are, in some way, related with a number of firms’ attributes, such as accountability, corruption, openness, informality or size. However, we are not still able to conclude whether the MDM is determined independently of sales and TFP. The debate would probably end if we were able to construct a model of the probability of having a missing value and productivity (or sales) as RHS variable. Unfortunately, this is not possible because, obviously, we do not observe either productivity or sales when we observe a missing value. However, we can still take advantage of the particular structure of the pattern of missing values to relate it with productivity or sales. Since the number of missing values reported increases when we move backwards in time, we can construct a model relating the probability of having a missing value in any PF variable in period t and productivity (tfp) in period t+1 plus other controls. That is, assuming that information in t+1 is better than in period t—bearing in mind that establishments report _recall_ data—we propose the model below for the probability of having a missing value where _s_<sup>_b_</sup> takes value 0 if we do not observe sales and _y_ is the logarithm of firms’ sales.

The question we are trying to answer with these kinds of models is whether the probability of observing a missing value in period t-1 is correlated with the level of sales (productivity or TFP) in period t. Or, in other words, are more productive/profitable firms more likely to keep track of their input/output accountability? Obviously, these models do not imply contemporaneous correlations but we think they might still be a good indicator of the actual relation between the level of sales/TFP and the MDM. On the other hand, an additional consideration should be noted; there is a selection bias in the models as we are only able to use those observations with observable sales or TFP in t+1, so the resulting sub-sample is likely to be biased toward those responding firms. In order to reduce the degree of the bias, we use those imputed values of sales or TFP in period t+1.<sup>32</sup>

> 31 In addition, if we assume a first order Markov process for productivity, Pr(tfpt+1/ tfpt, tfpt-1,...)= Pr(tfpt+1/ tfpt) and therefore _tfp_ in t+1 is a good proxy of _tfp_ in period t the model is reduced to Pr( _sita_  1/ _tfpit_ , _Dit_ )  ( 0  1 _tfpit_  2  _Dit_  _it_ ) . 32 Although by applying this strategy we reduce the degree of sample bias, the problem remains to some extent. Nonetheless, we still believe that the models can be very informative about the relation of the plausible endogeneity of the MDM.

45
# **1. Introduction**

Irregular migration to Europe has become increasingly visible in recent years. According to the European Border Agency (FRONTEX), almost 3 million irregular migrants were detected crossing European borders between 2015 and 2019. Despite this being a relatively small number compared to the size of the EU population (less than 1%), the images of young African men on crowded rubber boats in the Mediterranean Sea have put the phenomenon under the spotlight in a context of growing electoral support for European political parties running on nationalist anti-immigration platforms. In addition to being a topic of key policy interest in Europe, there are important consequences for economic development in Africa. First and foremost are the potential consequences of this journey for the migrants themselves, who face risks of human rights abuses and loss of life. The most common way for youth to migrate from West Africa to Europe is through what is referred to in The Gambia as the “backway”. This describes an overland journey through West Africa, across the Sahara Desert, and into Libya or Morocco, from which youth attempt to catch boats to Italy and other European destinations, with each stage involving multiple dangers. More broadly, the potential of migration and remittances to help alleviate poverty and drive economic development in sending countries (e.g. Clemens, 2011) will not be realized, or will be greatly reduced, if potential migrants die along the way or face vulnerable conditions in the destination countries that limit their earnings.

The Gambia provides a good setting to test the effectiveness of different programs designed to reduce risky migration, since in 2017 it had the highest incidence of irregular migration relative to total population among all African countries, at an annual rate of 0.38 percent.<sup>1</sup> Income gaps with European destinations are huge, with The Gambia’s GDP in 2018 of $713 representing only two percent of Italy’s $34,319,<sup>2</sup> providing a strong incentive to undertake the perilous journey. We conducted a cluster-randomized experiment with 3,641 young males aged 18-30 living in 391 settlements in The Gambia to test the effectiveness of three different interventions designed to reduce the likelihood of backway migration, spur migration to safer alternative destinations, and improve the well-being of potential migrants.

> 1 Calculated from FRONTEX data on illegal border crossings and World Development Indicators data on population. 2 Source: World Development Indicators (2019).

2
given by the slope of import demand functions in the fi rst equality and elasticity of import demand and import levels in the second equality. These two are equal, except if imports are zero. The TRI after the second equality would give a zero weight to the protection level of a good that is not imported, whereas the TRI after the fi rst equality would give that good a positive weight. In the empirical section we use the de fi nition of TRI after the fi rst equality in (18) to avoid the downward bias associated with the de fi nition of TRI that uses imports and elasticities of import demand. The slope of the import demand functions were obtained from the estimation of import demand elasticities in Kee et al. (2004). Using the notation in Kee et al. (2004) it is easy to show that dmn,c/dpn,c = −annGDPc − m<sup>2</sup> n,c<sup>/GDPc−mn,cwhereannisapriceparameterinatranslog</sup> GDP function, and GDPc is the Gross Domestic Product of country c. Thus with information on the slopes of import demand function and levels of protection at the tariff line level one can easily compute TRIs.

# 3.2 Estimating OTRIs

The OTRI focuses on the distortions imposed by each country’s trade policies on its import bundle. It uses the aggregate import value as the relevant metric. It answers the following question: What is the uniform tariff that if imposed to home imports would leave aggregate imports unchanged? More formally, the OTRI is implicitly de fi ned by: where m<sup>0</sup> c<sup>arecurrentaggregateimportsevaluatedatworldprices(again,wechooseunitssothat</sup> all world prices equal unity). Totally differentiating (19), and solving for OTRIc yields: Thus, the OTRI is de fi ned as the weighted sum of protection levels, where weights are given by the slope of import demand functions in the fi rst equality and elasticity of import demand and import levels in the second equality. Again, in the empirical section we use the de fi nition after the fi rst equality to avoid the downward bias associated with the second de fi nition in the presence of prohibitive tariff barriers.

12
**Figure 6: RDD estimates of Grade 4 teacher subject knowledge test score gap associated with teachers hired pre- and post-FPE (by varying bandwidth around cutoff year): Kenya, Madagascar, Mozambique, Tanzania, Togo, Uganda**

<!-- Start of picture text --> Language Math<br><!-- End of picture text --> Note: Model includes country fixed effects. Black lines with solid symbols show estimates are statistically significant at the 10% level. Gray lines with open symbols show estimates that are not statistically significantly different from 0 at the 10% level. Bandwidth is the number of years of teacher start-dates on each side of the FPE cutoff year that are included in the sample.

30