rafmacalaba/datause-encoder-data huggingface dataset:
_-_ 43 _Low Income and Moderate Setting:_ Countries such as Egypt, India, and Kenya appear to have fewer social constraints inhibiting on women's participation in higher education despite their low level of national income. These countries are socially less conservative, moderately urbanized, have faster growth in their GNP, with medium or high female participation in secondary education. Often the problem in such low income/moderate category countries is the limited supply of school facilities and resources. Therefore, expansion related policies have often been successful in increasing female enrollment rates in higher education. Policies such as increasing student places, reserving places for women, and providing boarding places for girls have successfully increased women's access to higher education.

However, it is also interesting to observe that some of the low income/moderate setting countries have experienced an excess supply of trained personnel in some fields, especially in traditional fields for women, despite a demand for trained workers in non-traditional fields. This is one of the prime reasons of low female participation in technical post-secondary education in India. Establishing a link between labor market and higher education programs, then, is a critical factor in increasing female participation in this category of countries.

_Lower Middle Income and Difficult Setting:_ Lower middle income countries within a difficult setting share constraints similar to low income countries with difficult setting, the greatest constraint being family or societal restriction. In Yemen, for example, higher education is accessible to students who have a respectable secondary education grade point average (World Bank 1992d). Accordingly, those who qualify academically have access to tertiary education. However, family concern about 'protection' of their daughters has been the main barrier on female participation.

Interestingly, in these countries, there has been a relatively high demand for educating women in traditional fields. For example, a recent Bank sector report on Yemen found that teaching is increasingly seen as a socially appropriate profession for females. Policy should focus on professions culturally accepted as suitable for females. Projects expanding places for women in such fields as teaching with appropriate housing for women would doubtless expand
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**9**

**the significance test for Baumol's hypothesis-**<sup>**that the CDP-deflator**</sup> ***lasticity of government-eApenditure deflator is larger than** unizy- -for **the sample countries by stages of development.**

**TABLE 2: TEST RESULTS OF BAUMOL'S HYPOTHESIS**

|**...**<br>----------|-<br>**Countri**|**es ................**||
|---|---|---|---|
|**Statistically**<br>**Significant Value of**a<br>IND|**MIE**|**LIE**|**ALL**|
||**(per**|**cent)**||
|**Less thanorequal to 1.00**<br>**68**|95|100|88|
|**Largerthan 1.00**<br>**32**|**5**|**0**|12|
|Larger than 1.00||||
|<br>**irresract.ve of**<br>**significance**<br>**71**|**18**|21|35|
|**Number of**countries<br>19|38|14|71|
|Median Value ofa<br>1.29|0.81|0.33|0.86|

Note: Significance level used - n.05 Two points ;,re evident from the figures in Table 2. First,<sup>there is</sup> scant statistical evidence for Baumol's hypothesis--only 8 of the 71 countries 8 show an estimated value for d that is significantly<sup>larger than unity'</sup> . One may argue that because of the rmall time<sup>periods over which the coefficients</sup> are estimated the significance tests could<sup>be misleading and one might improve</sup> on these results by using longer series.<sup>However, -ven disregard of the</sup> " While only 5 percent of the coefficients in the<sup>INDs are statistically</sup> insignificant, the frequency of such coefficients<sup>rises to 45 percer.t</sup> in MIEs and 71 percent in LIEs.
62 determine whether relative differences in freight costs between Africa and other countries contributed to the former's poor export performance and what influence these costs have on the location of industrial activity in Africa.26 This chapter also attempts to determine whether the _structure_ of freight costs on African exports has influenced the composition of goods shipped, i.e., are these charges biased (say) against processed goods in favor of primary commodities? Finally, several possible explanations as to why abnormalities in Africa's transport cost profile occur are suggested, and options African governments might utilize for reducing international freight costs are discussed. Before proceeding, it should be noted that analyses of balance of payments statistics (Amjadi and Yeats, 1995) illustrates the importance of this objective. Specifically, this World Bank study showed that sub-Saharan Africa's net freight and insurance payments were about $3.9 billion in 1990/91, which represents approximately 15 percent of total value of the region's exports. However, the individual country's statistics reflect a wide degree of variation around this average. Net transport and insurance payments average more than 25 percent of total exports for 10 of the 30 countries which had available data. The 1990 net payments averaged 42 percent for the 10 landlocked African countries (namely, Burkina Faso, Central African Republic, Chad, Ethiopia, Malawi, Mali, Niger, Uganda, Zambia and Zimbabwe) whose trade must transit neighboring territories and, therefore, incur additional foreign exchange costs. For all developing countries as a group these payments average 5.8 percent, a figure which represents about one-third the corresponding African ratio.

# A. The Evidence from National Customs Statistics

Data compiled by some national customs services can be used to provide some perspectives on the incidence of international transport costs for African exports. Since 1974 the United States has tabulated imports, by-product260ver twenty-five years ago Little, Scitovsky and Scott (1970, p. 310) suggested capital intensive transport technologies (such as containerization) could place some developing countries at an important cost disadvantage. They noted "because competitive pressures in developed countries are stronger, containers, and possibly other innovations in transportation, will be introduced there first, and will not spread to developing countries' trade for a long time. This might be accompanied by a fundamental change in the whole system of rate fixing, since it would seem rational to base the charge on the container rather than on its contents. The result might be a big reduction in freight on exports of manufactures between developed countries, putting exports from developing countries at a severe disadvantage. There are already appreciable economies of scale in shipping, and these may be increased by the introduction of containers. Small volumes of traffic coming from small ports cost more to handle than big volumes from big ports."
make the jobs workers already have more productive. These improvements could allow firms not only to raise the quality of existing jobs, but to create new ones as well.

A theme running through both parts of the analysis is the extent to which market reforms have facilitated productive job creation. Because the main concern of the paper is firm-level evidence, the focus is on policies that have affected firms: privatization, product and labor market liberalization, and obstacles to growth, particularly in the new private sector.<sup>1</sup> Have these policies helped create more productive jobs through between- and within-sector job reallocation, worker flows, and within-firm productivity growth? The evidence on these questions comes from studies employing firm-level data in Hungary, Romania, Russia, and Ukraine. Most of the data, and therefore most of the evidence, pertain to manufacturing, particularly large and medium-size firms inherited from the planning system. These data are comprehensive in coverage, but they contain relatively few labor market variables (principally, employment and wages) for each firm. The analysis is therefore supplemented by survey data, in Russia based on a stratified probability sample of manufacturing firms and in Romania based on a sample of micro and small enterprises in all sectors.

The main messages from a review of available evidence are as follows. Market reform has resulted in a very large increase in the pace of job reallocation. This was mainly in the form of job destruction in the manufacturing sector during the early years of reform, but job creation has also increased in recent years as the economies recovered. The depth of the reallocation process is signified by a relatively large proportion of job reallocation occurring between sectors (compared with OECD economies, see the summary in Davis and Haltiwanger, 1999). But the pace has also been fast and increasing among firms within the same narrow sectors.

Firms appear to have changed their employment levels primarily, however, by adjusting separations rather than hiring rates. Over the course of the transition period, job reallocation became a much larger proportion of worker turnover, again indicating that the flurry of labor market activity involved significant restructuring. Firm turnover has sharply increased from very low levels, and entry and exit are now important contributors to job creation and destruction, respectively.

> 1 On the other hand, labor market policies geared to unemployed workers are not addressed in this paper.

4
|RM1|LMPI<br>DLCPI|RP|LEXCH<br>TREND|
|---|---|---|---|
|�1<br>�0:02<br>[�3:27]|0:02<br>[3:27]<br>1|0|0<br>0|
|�2<br>0|1<br>0|5:18<br>[2:72]|0:54<br>[1:63]<br>�0:0066<br>[�46:41]|
||�1||�2|
|DRM1|�0:52<br>[�0:56]||�0:18<br>[�1:66]|
|DLMPI|�0:78<br>[�0:78]||�0:47<br>[�4:18]|
|DDLCPI|�1:10<br>[�8:87]||0:02<br>[1:75]|
|DRP|�0:05<br>[�1:21]||�0:003<br>[�0:59]|
|DLEXCH|0:06<br>[0:30]||0:05<br>[2:43]|
|Table 4:|An overidentifying|long|-run structure [t-values]|

The ...rst cointegration relationship corresponds to an in‡ation adjustment equation in which in‡ation is reacting to the inverse velocity of money. Formally, In other words, real money in excess of output causes in‡ationary tendencies even though the e¤ect is weak. Looking at the adjustment coe¢ cients �, we observe that the in‡ation rate is highly reacting to the in‡ation adjustment equation, as we would expect. Therefore, it appears that the principal component to determine in‡ation is inverse velocity, and this is consistent with a monetary phenomenon interpretation. Conditional on the inverse velocity, there appears to be no separate Phillips Curve e¤ect because ‡uctuations in excess money are a good explanation for the trajectory of in‡ation. This result is analogous to Bhanthumnavin (2002) who does not ...nd any Phillips Curve in Thailand for the pre-crisis period.

In the second long-run relationship, we obtain a reduced form IS-type equation of the following form: The left-hand side characterizes the output gap which has the conventional negative relationship to the interest rate and is negatively reacting to the exchange rate. A possible explanation for the negative interest rate e¤ect could be that the domestic interest rate proxies foreign interest rate movements. The intuition is that if there was no independent monetary policy, the domestic rate is tight to the foreign rate. Shocks to the foreign interest rates will in‡uence the domestic rate which eventually feed back into output.

Another potential reason could be the ...nancial accelerator argument developed by Bernanke, Gertler and Gilchrist (1996). In their theoretical framework, higher domestic interest rates will put constraints on the balance sheets of manufacturing ...rms from deteriorating credit conditions. Higher agency costs will lead ...nancial intermediaries to increase loan rates which subsequently feeds back into lower output.

12
function of schooling, experience and experience-squared, is not useful; in fact, it is one of the longest lasting specifications in economics, and estimates of returns from this equation have been calculated for more than 100 countries. As a first approximation, it is entirely appropriate (Chiswick 2003). It remains a parsimonious and accurate way of modeling the relationship between education and earnings (Lemieux 2006a).

In this paper we also estimate quantile regression estimates of the returns to education. This allows us to assess the differences in the schooling related pay increment across the wage distribution. Thus, we compare the returns to education for the ―skilled‖ and the ―unskilled‖ workers (conditional on their schooling and experience) in order to shed light on the contribution of schooling upon within-levels wage inequality (the methodology is described in detail in Technical Annex 1). If we assume that education is exogenous then the QR approach tells us the return to education for people with different levels of ability.<sup>§</sup> The few studies that have examined the wage impact of education across the earnings distribution have focused on high-income countries and show education to be more profitable at the top of the distribution. The implication is that education may increase inequality. Higher returns at upper ends of the distribution are documented for all but one European country examined—the exception is Greece.<sup>**</sup> Denmark, Germany and Italy could be considered borderline cases (Martins and Pereira 2004; see Figure 4). The same is true for the United States (Buchinsky 1998), where this phenomenon

> § However, _a priori_ we cannot assume that education is exogenous. Thus, we cannot say that the return to education for, say, the 90th percentile gives the true return to education for high ability people, purged of ability bias. The same caution is given in Arias, Hallock and Sosa-Escudero (2001), who cite QR studies of returns to education (Buchinsky 1994; Machado and Mata 2001; Schultz and Mwabu 1998) and say that the results of these studies should be interpreted with caution since they do not handle the problems of endogeneity bias.

> ** However, the data for Greece do not allow for a straightforward comparison with the other countries, as they are based on net wages.  Progressive taxation is likely to have a stronger impact in eroding the returns to education at the top of the distribution than at its bottom. This may explain the Greek results.

11
Remittances have also been shown to promote entrepreneurship (Massey and Parrado, 1998; Woodruff and Zenteno, 2001; Maimbo and Ratha, 2005; Yang, 2005). Furthermore, a number of studies on infant mortality and birth weight (Kanaiaupuni and Donato, 1999; Hildebrandt and McKenzie, 2005; Duryea et al., 2005; and López-Córdova, 2005) have documented that at least in the Mexican case, migration and remittances help lower infant mortality and are associated with higher birth weight among children in households that receive remittances.

Research on the effect of remittances on economic growth is scant so far and has yielded mixed results. Using a panel of 113 countries over almost three decades, Chami et al. (2003) find that remittances are negatively associated with economic growth. This result is consistent with their model in which remittances weaken recipients’ incentives to work and, therefore, lead to poor economic performance. Solimano (2003), on the other hand, finds a positive association between remittances and growth for a panel of Andean countries, while the IMF’s 2005 World Economic Outlook highlights the lack of correlation between these variables, at least at the country level.

Finally, two recent studies by Giuliano and Ruiz-Arranz (2005) and Mundaca (2005) show that the impact of remittances on growth can depend on the level of financial development in a country. However, these studies reach very different conclusions. Using a panel of more than 100 countries for the period 1975-2003, Giuliano and Ruiz-Arranz (2005) show that remittances help promote growth in less financially developed countries. They argue that this is evidence that agents compensate for the lack of development of local financial markets using remittances to ease liquidity constraints and to channel resources towards productive uses that foster economic growth. Mundaca (2005) analyzes the effect of workers’ remittances on growth in countries in 7
database is scheduled to be updated again in 2009, and so this year we use data from the 2006 database for 2006 and 2007. This dataset is an expert assessment, and the respondents are staff in the country offices of these two agencies in 85 developed and developing countries. Their views on various dimensions of governance and institutional quality are collected through an extensive questionnaire with 365 separate questions. These are combined into a number of sub-indices and indices by the authors of the dataset. Appendix Table A21 reports the variables we take from this source, which appear in all six of the aggregate WGI.

The second data source is the AmericasBarometer survey project of the Latin America Public Opinion Project at Vanderbilt University.<sup>6</sup> This source conducted household surveys in a set of 20 countries in Latin America in 2006. In Governance Matters IV, we used data from the first round of the surveys, covering 7 countries in 2004. We did not include this data source in 2005 because at the time they had not secured funding for subsequent updates of the surveys. Since then they have secured funding for the next several years, and have conducted surveys in 2006 and are fielding surveys for 2008. As detailed in Appendix Table A31 we use various questions from this survey in Voice and Accountability, Rule of Law, and Control of Corruption.

As in past years, we have also made some minor revisions to the underlying data from our existing sources in previous years. In most cases this consisted of ensuring that when we average multiple questions from a single source, we choose questions that are available for as many years as possible so that the composition of these averages changes as little as possible over time. Also, one of our data sources, the Bertelsmann Transformation Index has this year begun to release detailed subcomponents of its broader indices that we have used in the past. For example, they now separately report assessments of corruption, which we now use as a separate source in the Control of Corruption Indicator, while previously this variable was merged with other dimensions of the rule of law. Readers interested in the details of these changes in individual indicators can compare Appendix A of this paper with Appendix A of Kaufmann, Kraay, and Mastruzzi (2007b) describing the 1996-2006 dataset.

> 6 See also Seligson (2008) for further details on AmericasBarometer.

12
in which  is the price of a unit of _h_ , _γ_ is the price of one point in the rule of law index, _g_ ,  _i_ is a country-specific intercept and<sup></sup> _t_<sup>is a time dummy for</sup><sup>_t_=2000 and 2005. We also estimated the</sup> relationship in (10) using a log linear model, and a log-log model, but (11) was our preferred specification.

_4.1 Econometric issues with the estimation of equation (11)_ Intangible capital is, by definition, a broad concept, and our specification in (11) is very parsimonious with only two regressors, _h_ and _g_ . If _h_ and _g_ are correlated with the regression error term, then our estimators would be inconsistent. This potential correlation between regressors and error term can stem from various sources including omitted variables, errors in variables (measurement errors in the regressors) and simultaneous causality.

In order to deal with the first issue, omitted variable bias, we introduce (country- and time-) fixed effects in the regression. The combined time and country fixed effects regression model eliminates omitted variable bias arising from both unobserved variables that are constant over time, and from unobserved variables that are constant across countries. The country dummies (  _i_ ) capture time-invariant country-specific traits while the time dummies (<sup></sup> _t_<sup>) capture common</sup> shocks to the intangible capital across countries.

Measurement error, on the other hand, typically results in attenuation bias, so, provided this were the only source of correlation, we can interpret our coefficients for _h_ and _g_ as lower-bound estimates of their true effects.

The third source of correlation, simultaneous causality, is causality running from our dependent variable, intangible capital, to _h_ and _g_ as well as from _h_ and _g_ to intangible capital. An advantage of our analysis is that intangible capital, by construction, is forward looking. Total wealth is calculated as the present discounted value of future consumption flows (see Section 2.1 for details on its derivation) and by definition cannot determine past educational and institutional outcomes.

The discussion above should go some way to allaying endogeneity concerns. Alternatively, we could use instrumental variables (IV). In practice, finding good instruments, i.e. exogenous sources of variation that affect intangible capital only through their effect on _h_ and _g_ and not directly, is difficult.

Acemoglu, Johnson and Robinson (2001) use differences in European settler mortality rates as an instrument for current institutions. We can use this variable as an instrument for the rule of law index, but we need at least an additional instrument for human capital. Hall and Jones 13