rafmacalaba/datause-encoder-data huggingface dataset:
other channel such as this incentive effect.<sup>4</sup> However, in addition to the potential incentive effect, migration of a family member may have a number of other effects on child schooling. For example, parental absence as a result of migration may translate into less parental inputs into education acquisition and may also require remaining children to undertake housework or work to help meeting short-term labor and cash shortages. If any of these other channels operate, studies which focus just on the effect of remittances will generally give biased estimates<sup>5</sup> .

This paper therefore focuses on identifying the overall impact of migration on educational attainment in Mexico, estimating the net impact of these various effects. Every fifth household in rural Mexico has at least one member with international migration experience,<sup>6</sup> so the impacts of migration on the next generation are potentially very large. We use historical migration networks formed by 1920 as an instrument for migration seven decades later in order to account for the potential endogeneity of household migration decisions. Our main finding is that living in a household with migration experience depresses the educational attainment of rural Mexican children, with a stronger effect on 16 to 18 year-olds.

We begin with bivariate probit models of school attendance, which reveal large negative effects of being in a migrant household on school attendance of 16 to 18 year-old males and females, and on 12 to 15 year-old males, with insignificant results for 12 to 15 year-old females. Estimation of two-stage least squares models of

> 4 See the appendix in McKenzie (2005) for a methodological discussion of this point.

> 5 Theoretically one could separate the effect of remittances from other effects of migration through the use of a valid instrument which predicts whether or not one migrant will send more remittances than another. Such instruments are uncommon in practice, with the exchange rate shocks used by Yang (2004) coming closest in this regard among the existing literature (although as he acknowledges, these shocks also affect migrant wealth holdings).

> 6 Source: own calculations from ENADID data (see Table 1).

3
# **Introduction**

In 2002, Indonesia made a clear commitment to education – passing a constitutional mandate to allocate at least 20 percent of the total government budget to education. This has led to a large increase in resources, more than doubling education spending in real terms. Despite such substantial financial commitments to education as a whole, a number of challenges persist. In particular, ensuring equitable access to early childhood education and development (ECED) services has historically been a challenge in Indonesia.

To put the nature of the challenge in context, consider that in 2007 the gross enrollment rate in early childhood services for children between the ages of four and six was 23 percent. However, this number masks large differences in the probability of access depending on background. Figure 1 shows differences in the probabilities of access to early childhood services for Indonesian children of various backgrounds using 2007 data from a nationally representative household survey (SUSENAS). On average, a four-year-old child from the richest 25 percent of households in Indonesia had a 33 percent probability of accessing early childhood services while a four-year-old child from the poorest 25 percent of households in Indonesia had an 8 percent probability of accessing such services.

Such inequalities also exist in child development outcomes. **Error! Reference source not found.** shows the average scores of a sample of rural Indonesian children aged 48-60 months on three different measures of child development: the language and cognitive development domain of the Early Development Instrument (EDI), the draw-a-human task, and a test of executive function – the Dimensional Change Card Sort game. In each panel, the performance of children between the ages of 48 and 60 months from richer households (those with a wealth index<sup>1</sup> value above the mean) is compared to that of children from poorer households (those with a wealth index value below the mean). The data shown here suggest that child development among children from poorer households starts to plateau around the age of 54 to 56 months (four and a half years) whereas children from richer households continue to register improvements in their developmental outcomes as they get older.

> 1 The wealth index is derived from a principal components analysis of assets owned by households and is the score of the first principal component normalized to have a mean of zero and a standard deviation of one.

2
economically active population under the age of 15. Based on this definition, it estimates a participation rate among 10-14 years old in Africa at 22%. For West Africa the number is 24.2%. In Ghana the child labor force participation rate was 39% in 1992.

**Table 12: Characteristics of child labor by sector, education level, gender and occupation (Figures in percent).**

||**1987**|**1988**|**1991**|
|---|---|---|---|
|**Sector**||||
|Urban|18.50|16.45|14.84|
|Rural|81.51|83.55|85.16|
|**Education level**||||
|No education|15.50|19.50|27.65|
|Primary|62.83|61.58|52.44|
|Middle|20.32|17.00|18.49|
|Secondary|1.15|1.92|1.41|
|**Gender**||||
|Male|48.00|53.34|53.0|
|Female|52.00|47.00|47.0|
|**Occupation**||||
|Agriculture related|82.55|78.99|67.55|
|Non-agriculture|17.45|16.83|32.45|
|**Industry of work**||||
|Agriculture|82.75|70.61|59.21|
|Mining<br>&Manufact.|5.22|8.43|9.01|
|Elec. const, water|0.00|1.66|0.90|
|Wholesale &trade|9.58|13.98|20.39|
|Finance and<br>transport|0.66|0.65|1.54|
|Services|1.78|4.68|8.95|
|Sample size|1677|1526|3732|
|Participation rate|38.0|32.8|39.0|
|( in percent)||||
|Cote d' Ivoire|||_54-55%_|
|Nigeria|||27-50%|

# **Source: Authors calculations from GLSS 1,2,3.**

**Note: Child labor** is **defined as any child who is below 15 years of age and is working, either as a wage worker or through self employment**

23
9 this set-up strategic considerations inevitably -ome into play. Fortunately, there have been important deverlopments in the area of strategic trade policy in recent years which allow us to address the problem in a reasonable way.<sup>5</sup> The analytic solution for income- and revenue-maximizing export taxes for several countries who compete in the world market is provided in Panagariya and Schiff (1990). Below, we present a brief diagrammatic exposition of the basic model and its application to cocoa. In the following section, we present the simulation results with actual and income-maximizing taxes. Simulation exercises with revenue - maximizing taxes will be taken up at a later stage.

The basic st:ucture of the problem can be explained conveniently with the help of a three-country setup. Denote the three countries by A, B and C and assume that the former two export cocoa to the latter. Assume that exporters do not consume and the importer does not produce cocoa. Individual producers and consumers are perfectly competitive. Each exporting country's governmer.L chooses the export tax so as to maximize the country's profits taking the other exporting country's tax rate as given. This behavioral assumption leads to what is called the Nash equilibrium in the game theory literature.6 Consider country A's problem. For a given export tax by B, say tB, A's government must choose tA so as to maximize profits from exports. We can obtain the excess demand facing A by subtracting B's 5 For a survey of recent game theory models, see Dixit (1986) and the references therein.

6 We assume that the importer, C, does not levy import tariffs.
# Multidimensional Poverty in the Philippines, 2004-13: Do choices for weighting, identification and aggregation matter?

## 1. Introduction

The multidimensional nature of poverty has been widely noted for a long time. Over the last decade or so, however, both the theory and practice of measurement of multidimensional poverty have made rapid advances. Indeed, one measure of the growing recognition of multidimensional poverty is the publication of Multidimensional Poverty Indices (MPI) for more than 100 countries by the United Nations Development Programme (UNDP) in its annual Human Development Report beginning with HDR 2010. Conceptually, the case for measuring multidimensional poverty over and above poverty measured in terms of consumption or income is straightforward. It is rooted in viewing poverty as “capability failure”, the most notable exponent of this view being Sen (1980, 1985, 1999).<sup>1</sup> Viewed as such, multidimensional poverty then invites direct attention to a range of specific capabilities including those relating to health, education, shelter, and access to basic amenities. The key consideration then turns on the question whether consumption or income offers an adequate representation of an acceptable range of capabilities. If it did, there would really not be much of a case for a separate focus on multidimensional poverty.<sup>2</sup> Conversely, the case for a distinctive focus on multidimensional poverty rests in the proposition that there are many aspects of poverty which, for a variety of reasons, may not be adequately captured by income or consumption. It is presently not necessary to dwell on these reasons which for the most part have to do with market failures of one sort or another.<sup>3</sup> In addition, it is

> 1 Sen’s writings on this subject are many; referenced here are only a few examples (including one of the early ones).

> 2 Even from the welfarist perspective of consumption expenditure as a money-metric of utility, consumption may be inadequate as there are other arguments in individual utility functions for which markets (and hence prices) may not exist or if they exist may be distorted.

> 3 Even in the absence of market failures, total consumption or income as a linear combination of prices and quantities of goods and services implies perfect substitutability between these goods and services – an assumption that is questionable from a non-welfarist or human rights perspective that insists on the essentiality of minimum levels of achievement across a range of dimensions.

2
of size and R and D to sales ratio; firms have been also split on the basis of their exchange listing. 18 I/K = 0+ OIQ + **02** (CF/K) + 33(S/K) + **e** (1) where Q is the q ratio, CF is cash flows, S is sales, and K is the replacement cost of capital. In this specification, Q is regarded as a measure of expected profitability, CF is a measure of the availability of funds, and S shows current demand conditions.<sup>1 9</sup> As noted before, this is also a convenient strategy to distinguish between the role of cash flows as a measure of expected profitability and as a measure of the availability of funds. All the coefficients in this specification are expected to be positive.

11

# Results

The empirical results are based on a panel of 603 manufacturing firms from the Standard and Poor's COMPUSTAT database for the 1972-1990 period; the sample excludes firms that were involved in major mergers representing contribution to sales exceeding 50 percent of the acquiring firm's net sales for the year in question. The q ratio has been computed following the methodology outlined in Salinger and Summers (1983).

> Regressions20 were run for eq. 1 after splitting the sample on the basis of median values

's Dividend pay-out ratio was not used to split the sample, since it is not useful in distinguishing between managerial and information-theoretic approaches.

" It should be noted that in addition to sales, Fazzari et al. (1988) also used the desired capital stock term from neoclassical theory to model the demand side and still found the cash flow term to be significant.

20 In general, the relationship is specified as Yi, = _(3_ + XjX + ai + v, + ei, where ai is the individual firm effect and v, is the year effect. The standard approach for sweeping out fixed effects, by transforming variables to deviations from 18
# _Large informal sector_

The size of the informal sector (measured by its share in total employment) varies visibly across sub-regions within ECA, as well as across countries.<sup>3</sup> Still, the informal sector is an important source of jobs in all sub-regions and in virtually all countries (Figure 4). For comparison, its role in ECA is much greater than in OECD countries. In CEE, for example, the informal sector contributes to GDP nearly twice as much as in OECD countries (respectively 29 and 17 percent on average in 2000).

# **Figure 4  Informal sector employment plays an important part in all transition economies, but especially in less developed ones**

## **Employment in the Informal Sector, 1998/1999**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>KazahstanAzerbaijanUkraine _BRUBelarusRussian Fed.Armenia_CIS-7 + KMacedonia, FYRMoldova, Rep. ofKyrgyz RepublicEstoniaGeorgiaUzbekistan _SEEBulgariaLatviaCroatiaRomania _CEESloveniaHungaryPolandLithuaniaSlovakiaCzech Republic<br>percent<br><!-- End of picture text --> Source: Schneidr (2002), Scheider and Klingmair (2004) The size of the informal sector seems to be smaller – although not negligible – in the more advanced CEE countries, and is particularly large in low-income economies of the CIS. In CEE one in five to one in four workers work in the informal sector. In Southeastern Europe (SEE) the proportion goes up to one in three. In the CIS every third to every second worker has an informal sector job.

The cross country variation is substantial. On the one end of the spectrum are the Czech Republic and Slovakia, where the informal sector accounts for around 15 percent of employment, which is close to the OECD average. The percentage is about twice as high in Bulgaria, Estonia and Latvia and reaches 40 percent in Belarus, Russia and Ukraine. At the

> 3   The commonly used definition of the informal sector is: “all currently unregistered economic activities which contribute to the officially calculated (or observed) Gross National Product” (Schneider, 2002).  Specifically, the “shadow labor market includes all cases, where the employees or the employers, or both occupy a “shadow economy” position (ibidem).  Unfortunately, Schneider (2002) does not provide details on how the estimates  of the “shadow economy labor force” (presented below) were obtained.

12
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.2 . Road condition along major transit corridors in East Africa**

|||Co|ndition|||Type||Tr|affic bands (v|ehicles p|er day)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Corridors|Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|>300|300-1000|>1000|Unknown|
|_Mombasa to Nairobi to_<br>_Kampala to Kigali to_||||||||||||
|_Bujumbura (northern_<br>_corridor)_|**_43.8_**|**_34.7_**|**_9.9_**|**_11.6_**|**_94.6_**|**_0_**|**_5.4_**|**_1.2_**|**_10.3_**|**_80.2_**|**_8.3_**|
|Rwanda|66.5|33.5|0|0|100|0|0|14.5|0|85.5|0|
|Uganda|6.9|34.6|10.1|48|77.5|0|22.5|_0_|21.8|78.2|0|
|Kenya|54.3|34.3|11.4|0|100|0|0|_0_|3.9|83.3|12.7|
|Burundi|45.2|4.2|0|0|100|0|0|0|70.3|25.6|4.2|
|_Mombasa to Nairobi to_<br>_Kampala to Juba **_|**_43.7_**|**_47.5_**|**_8.7_**|**_0_**|**_84.1_**|**_15.9_**|**_0_**|**_18.7_**|**_7.9_**|**_64.2_**|**_9.2_**|
|Ethiopia|0|100|0|0|0|100|0|_100_|0|0|0|
|Uganda|21.7|75.4|2.2|1|58.7|41.3|0|_55.5_|25.7|18.8|0|
|Kenya|54.3|34.3|11.4|0|100|0|0|_0_|3.9|83.3|12.7|
|_Dar Es Salaam to_||||||||||||
|_Bujumbura (central_<br>_corridor)_|**_45.3_**|**_36.2_**|**_5.9_**|**_13_**|**_57.3_**|**_42.7_**|**_0_**|**_42.3_**|**_17.6_**|**_39_**|**_1_**|
|Burundi|58.4|27.4|2.5|12|88.3|11.7|0|_2.9_|85.4|0|11.7|
|Tanzania|44|37|6.2|13|54.3|45.7|0|_46.1_|11.1|42.8|0|
|_Addis Ababa to Djibouti*_|**_37.4_**|**_16.5_**|**_16.3_**|**_30_**|**_23.1_**|**_65.8_**|**_11.2_**|**_11.2_**|**_65.8_**|**_0_**|**_23.1_**|
|Ethiopia|48.6|21.5|21.2|9|0|85.5|14.5|_14.5_|85.5|0|0|
|Djibouti|0|0|0|100|100|0|0|_0_|0|0|100|
|_Nairobi to Addis*_|**_36.2_**|**_31.1_**|**_17.1_**|**_16_**|**_63_**|**_37_**|**_0_**|**_47.9_**|**_0.2_**|**_41.8_**|**_10.2_**|
|Kenya|24.9|48.4|26.6|0|42.4|57.6|0|_57.7_|0.2|33.7|8.4|
|Ethiopia|56.4|0|0|44|100|0|0|_30.3_|0|56.4|13.4|
|_Chad border to Djibouti_|**_9.4_**|**_23_**|**_53.1_**|**_15_**|**_43.4_**|**_56.6_**|**_0_**|**_61.3_**|**_24.5_**|**_7.4_**|**_6.9_**|
|Sudan|8.9|18.8|72.3|0|41.1|58.9|0|_74_|15.4|10.6|0|
|Ethiopia|13.9|42.2|11.1|33|33.6|66.4|0|_41.1_|58.9|0|0|
|Djibouti|0|0|0|100|100|0|0|_0_|0|0|100|

Source: AICD calculations. * Denotes portions of the TransAfrica Highway in EAC.

** No data for Sudan available for this route.

Data from 2006 illustrate the significant variation in traffic across key regional corridors. There is heavy traffic around the northern corridor that connects Kigali, Kampala, Nairobi, and Mombasa. This corridor routinely carries more than 1,000 vehicles per day. The central corridor between Bujumbura Dar es Salaam, and the corridor connecting Addis Ababa to Djibouti, see moderate traffic of 300–600 trucks per day. Traffic across the corridor from the Chad-Sudan border through Ethiopia is very low—no more than 300 vehicles a day, which is the traffic threshold required to make paving economically viable. None of the corridors exceeds the threshold of 10,000 vehicles per day needed to make toll road concessions viable.

14
# **9 Tables**

**Table 3.1**

**Changes in the wage premium and the relative supply and demand for skilled-unskilled workers assuming**  **SU=2 and**  **SU=3 (annual log changes multiplied by 100). Summary of results.**

||Wage p|remium|Relative|supply|σs<br>Relative|u=2<br>demand|σs<br>Relative|u=3<br>demand|
|---|---|---|---|---|---|---|---|---|
||1990s|2000s|1990s|2000s|1990s|2000s|1990s|2000s|
|Argentina|3.5|-2.4|4.6|2.4|11.5|-2.3|15.0|-4.7|
|Bolivia|7.9|-4.6|-0.2|5.1|15.6|-4.1|23.5|-8.7|
|Brazil|-0.4|-3.2|1.6|4.4|0.8|-1.9|0.4|-5.1|
|Chile|0.5|-1.9|3.1|1.1|4.1|-2.7|4.6|-4.7|
|Colombia|2.5|-2.0|6.4|6.0|11.5|2.1|14.0|0.1|
|Costa Rica|0.4|-0.2|4.0|3.4|4.9|3.0|5.3|2.8|
|Ecuador||-3.2||3.4||-3.0||-6.3|
|El Salvador|1.7|-0.1|5.5|-0.3|8.9|-0.4|10.6|-0.5|
|Honduras|0.0|-1.9|2.6|2.3|2.6|-1.4|2.6|-3.3|
|Mexico|1.8|-2.8|3.6|2.2|7.2|-3.5|9.0|-6.3|
|Nicaragua|3.5|-6.9|4.6|6.6|11.6|-7.2|15.0|-14.1|
|Panama|0.3|-2.3|2.3|2.4|2.9|-2.2|3.1|-4.4|
|Paraguay|0.8|-5.6|5.3|6.1|6.9|-5.2|7.6|-10.8|
|Peru|0.6|-2.8|0.2|3.8|1.3|-1.8|1.9|-4.6|
|Uruguay|2.3|-0.9|2.9|1.1|7.4|-0.6|9.6|-1.4|
|Venezuela|1.1|-4.8|3.9|4.2|6.2|-5.4|7.3|-10.3|
|Mean|1.8|-2.8|3.4|3.4|6.9|-2.3|8.6|-5.1|

Source: own computations based on SEDLAC (CEDLAS and World Bank).

30
the Yugoslavian governments in exile and this allowed for preliminary agreements on cultivation controls.

In the aftermath of the War, the Lake Success protocol of 1946 transferred administration of the drug conventions from the defunct League of Nations to the newly established United Nations. The U.N. Economic and Social Council (ECOSOC) acquired primary responsibility for overseeing the conventions, and it was supported in this task by the Commission on Narcotic Drugs (C.N.D.), which advised ECOSOC on drug-related matters and prepared draft international agreements. As such, the C.N.D. supplanted the Opium Advisory Committee. In a further innovation to existing control institutions, administrative support that had been provided by the Opium Section was transferred to a new body, the Division of Narcotic Drugs (D.N.D.) The P.C.O.B. and D.S.B. were transferred back to Geneva from Washington, were they continued in their role compiling statistics from national estimates and administering the import / export certification system.

Another new institution, the World Health Organization (W.H.O.) assumed the drug advisory responsibilities formerly exercised by the Health Committee of the League of Nations. The Drug Dependence Expert Committee of the W.H.O. was in turn given the task of determining the addictive potential of drugs and their position on the international schedule of controls (Fazey 2003).

# _The Paris Protocol_

While there had been a collapse in illicit drug trafficking during the war, the international community had to address complex legacies of the conflict, such as stockpiles of medical opium and semi-synthetic drugs and a burgeoning problem of the dependence on new synthetic drugs such as methadone and pethidine, which had been developed during the war but fell outside of the control schedule established by the 1931 Convention. The first post war drugs conference resulted in the 1948 _Paris Protocol_ . This brought any drug liable to cause harm into the schedule of controlled drugs and required states to inform the U.N. secretary-general of any new drug developed that had the potential to produce harmful effects. The progress of the new Convention was not without contention, with the Soviet Union reluctant to acknowledge the authority of the U.N. bodies on the issue, or the existence of a drug problem within its territory. Similarly efforts to restrict opium cultivation proposed by 21
**Table 3 - Budget, leakage, probability threshold for different % of the universal coverage**

||**Budget (as % of**<br>|||
|---|---|---|---|
|**% Coverage**|**universal budget)**|**Leakage**|**Probability threshold**|
|**100%**|100.00|1.00|0.00|
|**80%**|62.25|0.22|0.66|
|**60%**|43.81|0.07|0.83|
|**50%**|3.47|0.00|1.00|

Source: Authors’ estimations based on Jordan UNHCR data.

Summarizing alternative options, Table 4 shows the change in coverage, leakage, probability threshold and poverty rates in correspondence of different budgets (represented as different percentages of the universal budget). The first line shows that, in case of universal budget, every case receives assistance (coverage and leakage are equal to one and cut point is equal to zero) and the poverty rate corresponds to 32.3%. Moreover, 69.2% of the overall budget is used for coverage whereas leakage costs 30.8%. When, instead, only 80% of the universal budget is used, the poverty rate increases to 38.4%, and the budget is split between 81.2% for coverage and 18.8% for leakage. Reducing again the percentage of the universal budget to 50%, the poverty rate raises to 50.1%, coverage absorbs 93.1% of the budget and leakage 6.9%. Therefore, this approach provides a costing of leakage in addition to coverage and the corresponding outcomes in terms of poverty rates. A table like Table 4 is a useful tool for donors and decision makers engaged in taking difficult budget choices.

**Table** 4 **- Coverage, leakage, probability threshold and poverty rate for different % of the universal budget**

|**% Universal**<br>**budget**|**Coverage**<br>**rate**|**Leakage**<br>**rate**|**Probability**<br>**threshold**|**Poverty**<br>**rate(%)**|**% Budget for**<br>**coverage **|**% Budget for**<br>**leakage **|
|---|---|---|---|---|---|---|
|**100%**|1.00|1.00|0.00|32.30|69.2%|30.8%|
|**80%**|0.94|0.49|0.42|38.39|81.2%|18.8%|
|**60%**|0.78|0.20|0.69|45.65|89.8%|10.2%|
|**50%**|0.67|0.11|0.78|50.10|93.1%|6.9%|
|**0%**|0.00|0.00|1.00|69.18|||

Source: Authors’ estimations based on Jordan UNHCR data.

17