rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Performance of Students on National Assessment of Knowledge Economy Skills

|Content|Knowledge Economy|Gra|de 5|Gra|de9|Gra|de 11|
|---|---|---|---|---|---|---|---|
|Domain|Skills|2006|2008|2006|2008|2006|2008|
|Math|Communication<br>Information<br>Management|39.1<br>28.0|39.6<br>28.4|39.1<br>36.3|42.5*<br>37.1|38.4<br>17.2|40.9*<br>18.1|
||Using Symbols|22.3|22.9|42.4|45.3*|33.1|35.8*|
||Problem solving|21.4|21.5|28.2|28.9|21.3|21.8|
|Science|Communication<br>Information<br>Management|44.3<br>51.0|44.7<br>51.2|48.3<br>42.1|53.5*<br>46.3*|38.4<br>43.2|40.6*<br>43.4|
||Using Symbols|-|-|-|-|54.1|54.6|
||Problem solving|47.2|47.1|31.2|31.5|33.0|33.7|
|Arabic|Communication<br>Information<br>Management|50.3<br>40.0|53.9*<br>45.3*|51.0<br>49.1|60.1*<br>57.3*|53.1<br>56.0|63.4*<br>65.3*|
||Problem solving|37.2|40.1|38.2|45.9*|55.0|56.1|

*indicates significant improvement

As a result of PISA 2006, the assessment focus was on skills for which the specialized national assessment, NAfKE, was created by NCHRD. The first implementation helped focus on the main issues. Table 1 shows the main results on the different dimensions related to skills with some improvements between 2006 and 2008 in some areas.

# **Methodology and Estimation**

The improvement in TIMSS results in 2007 and 2003 relative to 1999 was the most noticeable. Hence, in this paper we investigate what contributed to the change. For this we use the regression decomposition methodology. The first step is to specify and estimate student achievement in relation to individual, family, school and institutional inputs. We then proceed to decompose the over-time test score gap into an explained component (accounting for student, family, school and institutional characteristics) and an “unexplained” – or returns, or the efficiency by which the country is able to convert characteristics into student learning outcomes as measured by test scores – component, using the traditional Oaxaca (1973)-Blinder (1973) decomposition method. The model specification for the estimation of the achievement function is as follows: where _Tiaj_ is the observed TIMSS score of student _i_ in household _j_ at time _a_ (time of the test), _Aija_ is a vector of individual, student, characteristics; _Fija_ is a vector of parent characteristics, _Sija_ is a vector of school-related inputs, _Iija_ is a vector of institutional characteristics, and _єija_ is an additive 8
18 <!-- Start of picture text --> REAL EXCHANGE RATE, FOREIGN DEBT<br>SERVICE AND INCOLE FROM FOREIGN ASSETS<br>0.3<br>0.2.<br>0.15.<br>0.1 -<br>O..~5<br>-0.05<br>-0.15<br>19801981198219I8S398419859I8651987tSBl2a9gitO<br>ye"<br>R Red Exc. Rate ep -* PtAilc h. ServTce -w Mt. Pv.For.As<br><!-- End of picture text --> Figure 7 <!-- Start of picture text --> DISPOSABLE INCOME AND CONSUMPTION.<br>(in 1980 pesos os % of GOP)<br>0.96<br>0.85-<br>0.8 -~/\<br>80.75-<br>0.7<br>0.65<br>0.6 1980198119821983 841tS5 tS66t1987 tS88 1989 tS90<br>ye"<br>Figur d8*os me C _ l<br><!-- End of picture text --> Yrigure 8
Given Pakistan's apparent commitmen **t** o institutional reform, it is no surprise that the Bank considered that FSAL a success, at least at the PCR stage. The project was rated satisfactory, its outcomes sustainable, and the resulting institutional development substantial.84 The PCR, moreover, viewed these positive institutional developments as "remarkable in light of the disappointing macroeconomic results,""<sup>5</sup> noting that the Pakistani Government had taken reform further than the FSAL had envisaged with respect to commercial bank restructuring and competition from new private financial institutions.86 Although the Bank claimed little financial deepening after the FSAL, the IFS data did indicate a moderate increase in M2/GDP (+2.5%, three years after the loan). At the least, the disintermediation of the pre-FSAL period appeared to have been reversed.87 In short, Pakistan's experience suggests that minor progress can be made towards financial deepening in environments where the initial conditions are found wanting (as viewed by both the Bank and the models of the previous section). Again, however, absent a full audit, it remains a bit unclear whether these institutional reforms are sustainable. If not, the meager financial deepening that was achieved may be reversed.

# (D). **Tanzania**

On the other hand, Tanzania makes it clear that, in countries with relatively favorable initial conditions, post-FSAL financial deepening may occur despite the failure of the institutional reform program. In two respects, Tanzania conformed to the model's 84 PCR, Cover memo.

S5 PCR, p. 12. 86 PCR, p. 5. **87** PCR, p. iv.

51
# **Table 6. Results with Small Interactions**

||**Lgrowth**|||**LPgrowth**||
|---|---|---|---|---|---|
|Corruption obstacle|**0.011**|0.008|**0.019**||0.016|
|Corruption obstacle * Small|**-0.035****|0.007|**-0.003**||0.014|
|Security Cost|**0.404****|0.107|**0.026**||0.202|
|Security Cost * Small|**-0.630****|0.116|**-0.215**||0.240|
|Skilled labor obstacle|**0.018***|0.009|**-0.022**||0.016|
|Skilled labor obstacle * Small|**-0.021***|0.010|**0.018**||0.016|
|Outage|**0.008**|0.008|**0.005**||0.015|
|Outage * Small|**-0.019****|0.007|**-0.010**||0.012|
|Web Intensity|**0.038****|0.009|**0.001**||0.016|
|Web Intensity * Small|**-0.029****|0.008|**0.050****||0.015|
|Land access obstacle|**0.018**|0.011|**0.009**||0.020|
|Land access obstacle * Small|**-0.035****|0.010|**-0.018**||0.017|
|Trade credit|**0.016**|0.013|**0.042***||0.019|
|Trade credit * Small|**-0.027***|0.011|**-0.018**||0.016|
|Labor regulation obstacle|**-0.033****|0.011|**0.012**||0.020|
|Labor regu. obstacle * Small|**0.047****|0.012|**-0.014**||0.019|
|Tax rate obstacle|**0.013**|0.009|**-0.013**||0.018|
|Tax rate obstacle * Small|**-0.036****|0.009|**0.035***||0.015|
|In Big City|**0.016****|0.004|**-0.001**||0.006|
|In Big City * Small|**-0.012****|0.004|**-0.001**||0.007|
|Inf competition|**0.011**|0.008|**0.024***||0.012|
|Inf competition * Small|**-0.036****|0.008|**-0.025**||0.013|
|Capacity agglomeration|**0.075****|0.023|**-0.016**||0.030|
|Capacity agglomeration * Small|**-0.083****|0.019|**0.059***||0.026|
|N|57,455|||44,279||
|Country, industry, year FEs|Yes|||Yes||
|adjusted R-squared|0.225|||0.287||

*, **: significance at the 5 and 1 percent. Heteroskedasticity-corrected standard errors clustered at the country level in columns indicated by “se”.

40
# **Why? Determinants of FDI in Africa**

Although there has been a considerable number of analytical and empirical studies on FDI inflows,' there has been a limited consensus on which factors play an unambiguous role in explaining the location decision of TNCs. It is generally accepted that market size and access to natural resources are crucial determinants in their decision processes.

Not surprisingly, the African countries that have been able to attract most FDI have been those with the largest tangible assets such as natural and mineral resources as well as large domestic markets. About 65 per cent of total FDI inflows to Africa concentrated in South Africa, Nigeria, and Cote d'Ivoire in 1996/1997, which also accounted for about two-third of the sub-continent's GDP during the same period (table 1). The role of market size can be further evidenced by the almost perfect positive correlation between FDI inflows and GDP for a group of 29 African countries during 1996 and 1997 (the correlation coefficient equals 0.99)2.

The role of natural resources in the location decision of TNCs is apparent through the sectoral allocation of FDI inflows within the region. Traditionally, about 60 per cent of FDI in Africa is allocated to oil and natural resources (UNCTAD, 1999). This is corroborated by the coefficient correlation between FDI inflows and the total value of natural resources in each country,<sup>3</sup> which appears close to unity (i.e. 0.94) for the group of 29 African countries during 1996-1997. The Africa region possesses not only large reserves of oil, gold, diamonds and copper but also more than half of the world's cobalt and manganese, one third of bauxite and more than 80 per cent of chromium and platinum. The sub-continent is also among the main exporters of agricultural products such as cocoa, coffee and sugar.

See for example, Wheeler and Mody (1992); Singh and Jun (1995); UNCTAD (1998).

> 2 The link between FDI inflows and size could be further explored, as, for example, one may argue that there may exist a non-linear relationship between these two variables. This goes, however, beyond the scope of this note.

> 3 The total value of natural resources in each country is estimated as the sum of the primary and the secondary sectors, minus manufacturing. Source: World Bank's _World Development Report (1999)._

3
||||(0.655)|(0.656)|(0.682)|
|---|---|---|---|---|---|
|Main income source: Remittances|||0.0547|0.0426|-0.546|
||||(0.544)|(0.546)|(0.605)|
|Main income source: Stable business|||-0.164|-0.174|-0.196|
||||(0.287)|(0.288)|(0.295)|
|Main income source: Other|||-0.186|-0.197|-0.603|
||||(0.605)|(0.605)|(0.645)|
|Does not anyone to borrow money from in c|ase of emer|gency||||
|Can borrow money from at least one or<br>multiple persons in case of emergency|||||0.435*<br>(0.253)|
|Did not receive remittances in the past year||||||
|Received remittances in the past year|||||0.905***<br>(0.298)|
|Constant|0.817***|2.260|1.071***|1.951|1.425|
||(0.133)|(1.595)|(0.267)|(1.708)|(1.815)|
|Observations|393|393|393|393|393|
|Prob > F|0.107|0.164|0.0447|0.0762|0.00335|

_Table 32: Probability of having recovered after two years, as a function of various housing characteristics and magnitude of losses. N.B. Logistic regression is run after matching the data with CEM on a k-to-k match._

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Did not lose more than 10% of household<br>expenditure due to the flood||||||
|Lost more than 10% of household expendi-||||||
|ture due to the flood|-0.951***|-1.011***|-0.998***|-1.037***|-1.094***|
||(0.367)|(0.367)|(0.382)|(0.381)|(0.389)|
|Log of real per capita expenditure||-0.187||-0.119|-0.111|
|||(0.193)||(0.202)|(0.212)|
|Main income source: Monthly salary||||||
|Main income source: Casual labor|||-1.125***|-1.103***|-1.014**|
||||(0.394)|(0.397)|(0.407)|
|Main income source: Hawking|||-0.151|-0.175|-0.274|
||||(0.658)|(0.659)|(0.683)|
|Main income source: Remittances|||0.0870|0.0726|-0.510|
||||(0.553)|(0.555)|(0.614)|
|Main income source: Stable business|||-0.175|-0.186|-0.200|
||||(0.288)|(0.289)|(0.294)|
|Main income source: Other|||-0.214|-0.226|-0.625|
||||(0.614)|(0.614)|(0.654)|

57
that is, if ak is a binary indicator of the ownership of indicator k, then wk is the share of the population who does not own that asset.<sup>17</sup> Next is the index created by simply summing the number of assets owned and housing characteristics, the **count index** . In this index, all of the bs of equation (1) are equal to one: Ai = a1i + a2i + ... + aki (6) The last index we consider captures the value of assets owned divided by the number of household members: **per capita value of durable goods** . Since only durable goods have a readily defined resale value this index includes only consumer durables and excludes the housing characteristic indicators. The value of each asset is defined as the current resale value reported by the household respondent in the questionnaire. The index is therefore: Ai = (p1i·a1i + p2i·a2i + ... + pki·aki )/H (7) where ps are the reported resale value (the “price”) of each asset and H is household size. Note that the resale value is household specific—and subject to the measurement error that it is reported by a respondent who has not actually tried to sell the item at the reported price.

# _b) Data_

The datasets we use are from the Living Standards Measurement Study program (www.worldbank.org/lsms) or from similarly designed large-scale household surveys (details are in Appendix 1). The data are from 11 countries: four datasets are from Sub-Saharan Africa (Ghana, South Africa, Uganda, and Zambia), three are from Latin America (Brazil, Nicaragua, Panama), three are from Asia/Pacific (Nepal, Papua New Guinea, Vietnam), and one is from Europe (Albania). The percentage of the population living on less than $1 a day ranges widely: six of the surveys are from countries with a very high share of the population living in extreme poverty (over 30 percent live under $1 a day in Ghana, Nepal, Nicaragua, Papua New Guinea, Uganda, and Zambia), two are from countries with a high share in extreme poverty (between 10 and 18 percent in South Africa, and Vietnam), and three are from countries with a relatively small share of the population living in extreme poverty (less than ten percent in Albania, Brazil and Panama).

> 17 This index, like the count index, uses the same reduced set of indicators as the IRT index because only binary assets or characteristics whose ownership increases with the index can be included.

16
interpretation or not is at their discretion. Given that significant monetary stakes could be involved, this provides rent seeking opportunities to tax officials. But, even at a more basic service-delivery level, tax corruption from complexity can arise. Complex declaration forms, high costs of compliance, and intricate compliance procedures may provide rent seeking opportunities to tax officials that “facilitate” tax compliance for a “fee.” Both these types of complexity exist in varying degrees in tax administrations around the world, but typically in developing countries with low levels of “maturity” of tax administrations, complex tax administrations abound. And, consequently, corruption in tax administrations is seen as a serious problem in developing countries, with a detrimental impact on tax collections, and on tax morale.

This paper attempts to answer the question of whether or not there is empirical evidence that would link tax complexity and corruption in tax administrations. In the literature there are several studies, investigating the link between tax corruption and taxes<sup>2</sup> and also the link between tax complexity and taxes.<sup>3</sup> But, there are only a very limited number of empirical studies on the relationship between tax corruption and tax complexity which can be considered as an important component of the transmission mechanism between tax complexity and taxes. None of these studies on tax corruption and tax complexity involve a cross-country dimension. For example, Obwona and Muwonge (2002) and Kasimbazi (2003) find tax complexity and lack of transparency leads to tax corruption in Uganda, but focus only on one country in their analysis.

In this paper, tax corruption is measured directly by using firm-level data from 104 different countries. Given data availability, we focus only on business taxes (corporate taxes,

> 2 For example, Tanzi and Davoodi (2002) studies corruption, growth, and public finances, Friedman, Johnson, Kaufmann, and Zoido-Lobaton (2000) studies determinants of unofficial activity in 69 countries, Crandall and Bodin (2005) and Imam and Jacobs (2007) focus on the effect of corruption on tax revenues; and Purohit (2007) studies corruption in tax administration.

> 3 Some papers on the impact of complex tax systems on tax cost: Heyndels and Smolders (1995), Cuccia and Carnes (2001), Evans (2003), Dean (2005), Mulder, Verboon and De Cremer (2009), Saad (2009), Alm (1999), Paul (1997), Oliver and Bartley (2005), Quandt (1983), Alm, Jackson and Mckee (1992), Picciotto (2007). Some studies on how tax complexity may lead to lower taxes: Milliron (1985), Mills (1996), Spilker, Worsham and Prawitt (1999), Forest and Sheffrin (2002), Kirchler, Niemirowski and Wearing (2006), Richardson (2006), and Slemrod (2007). There are some controversial studies, indicating that tax complexity may lead to higher taxes: Scotchmer (1989), White, Curatola and Samson (1990).

3
adapt to anti-drug policies in different and, many times, smart ways. Monitoring their responses using field studies is crucial not only to keep track of the right numbers on the supply side, but also to evaluate the effectiveness of the anti-drug policies implemented.

# **Trends in the composition of the demand for cocaine**

While aggregate figures show that cocaine consumption has, if anything, remained relatively stable in the last few years in the US, and has been on the rise in Europe (although the prevalence rates are still much lower than in the US), the long-term trends in the composition of demand may shed some light on understanding the patterns of illegal drug use. Reuter (1999) argues that “While the general population surveys have shown very stable prevalence figures throughout the 1990s, aggregate stability masks a great deal of change in patterns of drug use.” As was the case with opium in the past, patterns of cocaine consumption may exhibit a life cycle. There are many reasons to expect a life cycle for drug consumption. Among the most obvious ones are fashion and learning. As Levitt and Dubner (2005, p. 109) put it when they analyze the financial structure of drug gangs in the US, “In the 1970s, if you were the sort of person who did drugs, there was no classier drug than cocaine. Beloved by rock stars, moviemakers, ballplayers and even the occasional politician, cocaine was a drug of power and panache.” According to Reuter (2001), the low prices of cocaine have not led to a new epidemic of cocaine consumption because cocaine is no longer a fashionable drug. Cocaine consumption is seen now as a dangerous drug and “there are enough miserable looking cocaine addicts on the streets of bad neighborhoods to make the case for the drug’s perils to any moderately rational youth.” (Reuter, 2001, p. 18). Statistics such as the average age of 24
The Effects of the Intensity, Timing, and Persistence of Personal History of Mobility on Support for Redistribution Andrew Dabalen<sup>*</sup> Rasyad Parinduri<sup>**</sup> Saumik Paul<sup>***</sup> JEL Classification Codes: D31, D63, Keywords: Redistribution, Mobility, Transition

> *Andrew Dabalen, World Bank, Washington, DC; **Rasyad A. Parinduri, Nottingham University Business School, University of Nottingham, Malaysia Campus;<sup>***</sup> Saumik Paul (the corresponding author), Nottingham School of Economics, University of Nottingham, Malaysia Campus, Jalan Broga, Semenyih, 43500, Malaysia, Tel: (+60)3 8725 3567; Email: Saumik.Paul@nottingham.edu.my.
access to infrastructures. Both objective and subjective poverty are under consideration. Information has been collected at both the household and the individual level, but there is no data at the community level.

The National Institute of Statistics of Cameroon (NIS) defined 32 strata according to three modalities, urban (12), semi urban (10) and rural (10) which depend on the number of inhabitants per district: more than 50,000 for urban, between 10,000 and 50,000 for semi urban and less than 10,000 for rural. Rural and semi urban strata are considered as equivalent by the NIS. The ECAM II is based on the 1987 census (Recensement Général de la Population et de l'Habitat) which defined 612 counting zones or clusters. In urban strata, the sampling proceeds in two stages or degrees. The clusters are first sampled according to a single random drawing; then in each urban cluster 18 or 12<sup>9</sup> households are selected according to the same procedure. In rural strata, the sampling selection follows three degrees. First, districts are sampled proportionally to their size in households in 1987. Second in each district, clusters are drawn from a single random drawing, and then 18 households in semi urban strata and 36 or 27<sup>10</sup> households in rural ones are sampled with the same drawing procedure from the selected clusters. Finally 11,533 households from 612 clusters have been sampled and 10,992 were interviewed.

The survey organization was designed to correctly collect the needed variables to calculate final consumption, the living standards indicator. Daily purchases were collected during respectively 10 or 15 days in rural and urban areas and have been completed with data on retrospective expenditures. The survey ran during three months in order to take into account potential seasonal variations.

The access to roads is measured through two questions. Households are asked how far (in kilometers) they are from the nearest tarred road, and how much time (in minutes) is needed to reach it with their usual means of transport. In the paper, we use the time needed to reach the nearest tarred road because it is the more precise measurement of isolation taking into account the main of transport. We also controlled for the use of a motorized transport but our

> and other rural activities, Retrospective non food expenditures of the households, Daily expenditures of the households, Prices.

> 9 Yaounde and Douala.

> 10 Extrême Nord, Ouest and Nord Ouest.

10
Figures 3 and 4<sup>6</sup> clearly show that being a citizen matters: among foreign born, those who became citizen are always more likely to participate in the labor force than those who are not. The only exception is among Hispanic men, among whom there is no statistical difference.

However, the most surprising results in figures 3 and 4, is that for both men and women, among Blacks, immigrants who are not citizen are more likely to participate in the labor force than native Blacks. For all other racial groups, non-citizens are always less likely to be in the labor force than native born.

The ability to speak English is also considered to be an important asset in the American labor market (see Bleakley and Chin, 2004, 2007 and 2008) and might vary across immigrant groups. In the census, individuals are asked about their ability to speak English. This variable has the disadvantage to be self-reported so that there might be a tendency to overestimate English ability. Native born are assumed to speak English. Among the foreign born, I have classified individuals reporting speaking English well and very well as speaking good English and those who reported not speaking English or speaking it, but not well, among those not speaking good English. Table 1 panel B indicates that the ability to speak English is close or above 90 percent for White and Black foreign born, close to 80 percent among Asians born outside the US, but only between 50 and 55 percent for Hispanics born abroad. The ability to speak English well is also, within each racial group, somewhat lower among women than among men.

Figures 5 and 6 clearly indicate that not speaking English well is a disadvantage for labor force participation, since foreign born with difficulties in English always do worse than individuals with a good command of the English language, with the exception of Hispanic males for whom the difference is not significant<sup>7</sup> . It is possible to imagine an explanation under which all immigrants would actually have an intrinsic advantage on the labor market (an “immigrant drive”), but that would be mitigated by their poorer ability to speak English. The fact that most foreign born Blacks speak good English could then

> 6 See also table A2 columns 1 and 2 in the appendix for detailed results.

> 7 See also table A2 columns 3 and 4 in the appendix for detailed results.

8