rafmacalaba/datause-encoder-data huggingface dataset:
# **_III.C: City-Level Model_**

In this section, the econometric model described in Section III.A, is tested at the level of cities. Employment growth at the city-level is modelled as a function of economic geography (i.e. agglomeration), educational attainment and infrastructure. The main estimating equation is as follows: ������_�������� ������������������, ����������, ������������������� ��� ���

> Where, the dependent variable is the log employment growth for city _k_ between year � and ��1. Agglomeration includes localization and industrial diversity, education includes proxies for traditional educational attainment and vocational training,  and infrastructure includes proxies for access to electricity transport and water.

Testing an econometric model at the city-level serves many important functions: first, it serves as a robustness check to the results of the firm-level model; second, and importantly, while firms are the main agents of job creation, a city-level model helps make linkages between different industries (agriculture, manufacturing, services) and sectors (formal, informal); and third, a city-level model gives equal weight to cities, while the firm-level model is biased towards cities with the most observations.

**-** **<u>Table 9: What explains employment growth (at the city level)?</u>**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Licensed|-0.0001|0.0001|-0.0007|-0.0009|-0.0003|-0.0004|
||[0.000]|[0.000]|[0.001]|[0.001]|[0.001]|[0.001]|
|Localization|0.0213**|0.0232**|0.0230*|0.0211|0.0706***|0.0742**|
||[0.009]|[0.009]|[0.013]|[0.014]|[0.025]|[0.029]|
|Herfindal|-0.0229*|-0.0234**|-0.0398**|-0.0379**|-0.0971***|-0.0944**|
||[0.012]|[0.012]|[0.016]|[0.016]|[0.033]|[0.035]|
|Education||0.0005|-0.0064|-0.0022|-0.0181**|-0.0154|
|||[0.004]|[0.006]|[0.006]|[0.009]|[0.013]|
|Vocational Training||-0.0010**|-0.0001|-0.0001|-0.0017|-0.0016|
|||[0.000]|[0.001]|[0.001]|[0.001]|[0.001]|
|Electricity|||0.0002|0.0002|||
||||[0.001]|[0.001]|||
|Transport|||-0.0351**<br>[0.013]|-0.0350**<br>[0.013]|||
|Water|||0.0170*<br>[0.009]|0.0168*<br>[0.009]|||
|Constant|1.0252***|1.0371***|1.0696***|1.0558***|1.1717***|1.1632***|
||[0.033]|[0.039]|[0.086]|[0.087]|[0.116]|[0.143]|
|City|**×**|**×**|**×**|**×**|||
|Year|**×**|**×**|**×**||**×**||
|#|74|74|49|49|49|49|
|�<sup>�</sup>|0.106|0.167|0.324|0.356|0.560|0.573|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: 1) Data for the following variables is taken from the UBAEU (2003, 2004, 2006, 2009, 2010, 29
14 <!-- Start of picture text --> (14051.3) (7018185.8) (2415717.7)<br>Log Likelihood -37.225 -37.961 -47.609<br>funcion<br>The t statistic is given in parenthesis below the corresponding coefficient.<br><!-- End of picture text --> In addition, the analysis of the significance of the coefficients reported in Table 4 provides useful insights. The estimation of the frontier which assumes no technological change has only 3 non-significant coefficients while the specifications with technological change shows 4 non significant coefficients. Since we estimated the translog as an approximation to an unknown form, the first order coefficients are the elasticities of production at the expansion point and the other coefficients are less important. The negative sign on capital reflects the fact that for the period covered by the analysis, there is an excess capacity.This results from significant investments made by the government at the initial stages of the restructuring process. Addition to the capital factor is a burden in the short run rather than an asset and a source of efficiency gains for the system as a whole. To be as complete as possible we also need to compare the relative validity of the various specifications. This is achieved through likelihood ratios tests reported in Table 5.

||**Table 5. Tests of**|**the various sp**|**ecifications of**|**the frontier**||
|---|---|---|---|---|---|
|**Restrictions**|**Model**|**Log**<br>**Likelihood**<br>**function**|**Likelihood**<br>**Ratio Test**|**Critical value**<br>**x**<sup>**2 **</sup>**(5%)**|**Decision**|
||1.Translog<br>(non-neutral<br>technological change)|-37.22||||
|)°O|3.Translog (OLS)<br>(no technical<br>inefficiency)|-48.16|20.39|2.71 (*)|Rejeci|
|_A4=68=81 1=813=,6_<br>4=0|_1_2.Translog<br>(no non-neutral<br>technical change)|-37.96|1.47|11.07|Do not reject|
|_85=66= B7=A89l O=_<br>812=0|4. Cobb-Douglas<br>(no technological<br>change)|-47.61|19.29|12.59|Reject|

(*) Critical Value obtained from Table I in Kodde and Palm (1986).

Table 6 summarizes the various restrictions tested on each model, the test value calculated and the corresponding critical value of the x2 distribution at 5% of significance. The last column specifies the decision revealed by the test on each set of parameters restrictions. The first test checks for the presence or absence of technical inefficiency in the port industry. If there is no such inefficiency, Uit could be eliminated from the model and it could be estimated though OLS's. This corresponds to a test of _HO:_ r= 0 . The statistical value 20.39 is larger than the critical value and hence the assumption is rejected, meaning that technical inefficiency must be included.

15 When _Ho:rO_ is right, the production function is equivalen **t** o a regular average response function and OLS estimates will yield efficient estimates.
_2 0_ 1, The logistic form is expressed as <!-- Start of picture text --> exp (a~0 [ + k ix )<br>]<br>P - ~~~~k<br>1+ exp (a+k x<br>12~ ~ ~~~~~-<br><!-- End of picture text --> 12A cursory inspection of the data **suggests that the cyclical pattern** of spreads over time are related to some global and macroeconomic **events** such as the oil shocks and industrialized country **growth rates. These two** variables are also correlated with the number of banks that entered the Eurocurrency market. Hence a more sophisticated approach **would require a** model that employs such variables to explain the spread behaviour **over time.**

> 1 3 Loan data for the 1973-81 period are obtained from various **issues of** the World Bank's Borrowing in International CaDital Markets. The data for the prior period, however, have been obtained through an **exhaustive search** of the financial press as well as the central bank reports of the borrower countries. For more details on this data set see Ozler (1990).

> 14 For reviews of this literature see McDonald (1982) and Eaton **and** Taylor (1986). The debt-service ratio, imports to GNP ratio, imports **to** reserves ratio, GNP growth, and investment to GNP ratios are **among** the variables that are found to have significant impact on spreads. The total debt, and debt service variables are obtained from the World Bank's World Debt Tables. The remaining vari-ables are obtained from IMF's International Financial Statistics.

15Among the 27 countries that were sovereign, the countries that did not have standby agreements during the period are Egypt, Greece, newZealand, Portugal, Taiwan and Thailand.

16 Since Lindert and Morton (1987) use the spread data for the 19761980 we have also conducted our investigation by constaining our sample period to 1976-1980. All the findings continue to hold. 1 7These countries are Bolivia, Chile, Colombia and Peru. The cost of default for thiese countries respectively are: 92%, 69%, 37*, and 61%.

18 Beta coefficients are equal to the least squares estimates multiplied by the ratio of the sample standard deviations of the independent and dependent variables, In other words, beta coefficients are in units of sample standard deviations. See Madalla (1977) pp.<sup>1 19</sup> .
# **Introduction**

Since the introduction of the North American Free Trade Agreement (NAFTA), the dynamics of the Mexican economy have changed substantially. When NAFTA was introduced, manufactured products only accounted for 35 percent of Mexican exports. Since then, the manufacturing sector has grown to produce close to 90 percent of Mexican exports. However, the annual growth rate of labor productivity in Mexico is low compared to other developing countries (World Bank 1998a). Dar and others (2000) contend that one plausible explanation for the slow growth in labor productivity is that Mexican workers have a lower education level, resulting in the deficiency of human capital accumulation on-the-job as compared to elsewhere.' The empirical evidence on the links between human capital (schooling), on the one hand, and productivity and wage growth on the other, is strong. Numerous studies using worker-level data have also shown that more educated and/or trained individuals are also more productive in a rapidly changing environment in which cognitive abilities to process new information are most important, and thus earn higher incomes (Welch, 1970; Mincer, 1989; L6pez-Acevedo and Tan; 2002). Human capital (education) is viewed not only as an investment but also as a factor of production. Human capital is a stock of skills produced by education and training (Welch 1970 and Mincer 1989). Highly educated workers have a comparative advantage with respect to the adjustment and implementation of new technologies. For this reason, the productivity of highlyeducated relative to less-educated workers is greater (Bartel and Lichtenberg 1987).

There is limited, but growing, empirical literature on the link between human capital (training) and firms' perfornance (Koning 1994; Revenga 1995; Batra and Tan 1995; Barrett and O'Connell 1998; World Bank 1998a, 1998b, 1999, 2000, 2001a, 2001b; Dearden, Reed, and Van Reenen 2000). Using panel data, several studies have

> The increase in wages associated with an additional year of work experience for Mexican men is low compared to the increase for men with similar educational attainment in other countries (3.8 percent in Mexico compared with 8.1 percent in United States, 8.4 percent in Japan, and 9.1 percent in France). This rate is low even when compared with the rate in countries at a similar level of development and with comparable education indicators, such as Brazil (6.2 percent) and Colombia (5.8 percent).

2
Table A9: Uganda - Educational attainment of children and their parents by five-year birth cohort

||Average years<br>of schooling|No schooling|Primary|Secondary|Tertiary|
|---|---|---|---|---|---|
|Son||||||
|1937-1941|4.4|26.0|55.0|18.0|1.0|
|1942-1946|5.8|18.1|46.6|33.6|1.7|
|1947-1951|6.4|10.4|55.2|31.9|2.4|
|1952-1956|5.9|16.0|51.5|31.4|1.1|
|1957-1961|6.5|11.8|57.7|28.5|2.1|
|1962-1966|6.1|11.1|62.7|24.0|2.2|
|1967-1971|6.7|7.2|58.6|32.4|1.8|
|1972-1976|7.0|5.9|59.5|32.0|2.6|
|1977-1981|7.1|4.9|57.2|35.9|1.9|
|1982-1986|7.7|4.1|49.2|46.1|0.6|
|Daughter||||||
|1937-1941|1.4|74.1|21.8|3.7|0.5|
|1942-1946|1.9|60.6|33.3|5.7|0.4|
|1947-1951|2.7|47.4|42.1|10.3|0.3|
|1952-1956|3.4|38.5|49.9|11.4|0.3|
|1957-1961|3.8|38.6|45.3|15.5|0.6|
|1962-1966|3.9|32.0|54.7|13.0|0.3|
|1967-1971|4.3|28.0|55.3|15.9|0.8|
|1972-1976|4.9|21.8|57.9|19.8|0.5|
|1977-1981|5.4|17.3|56.4|25.7|0.6|
|1982-1986|6.6|9.6|53.6|36.3|0.6|
|Mother||||||
|1937-1941|0.4|91.8|7.9|0.3|0.0|
|1942-1946|0.6|86.3|13.2|0.5|0.0|
|1947-1951|0.7|82.4|16.6|0.8|0.2|
|1952-1956|0.8|81.8|17.6|0.6|0.0|
|1957-1961|1.1|76.2|22.8|1.0|0.0|
|1962-1966|1.2|72.7|25.9|1.4|0.0|
|1967-1971|1.6|66.4|31.6|2.0|0.0|
|1972-1976|2.2|57.3|38.4|4.2|0.1|
|1977-1981|2.8|48.0|45.2|6.5|0.3|
|1982-1986|3.5|39.7|49.0|11.0|0.3|
|Father||||||
|1937-1941|0.9|79.3|19.8|0.9|0.0|
|1942-1946|1.4|69.8|29.3|0.7|0.2|
|1947-1951|1.9|60.7|36.2|2.8|0.4|
|1952-1956|2.0|59.0|38.1|2.6|0.3|
|1957-1961|2.5|53.1|42.8|3.6|0.4|
|1962-1966|2.8|47.9|47.0|4.6|0.5|
|1967-1971|3.5|38.9|53.2|7.1|0.8|
|1972-1976|4.1|34.3|53.2|11.0|1.5|
|1977-1981|4.9|26.4|55.5|16.0|2.1|
|1982-1986|5.8|19.5|54.2|24.1|2.1|

41
- The adjusted gap between public and private schools in Urdu is _18 times_ the adjusted gap between children with literate and illiterate mothers.

That across the two countries and in all subjects, there is a clear decline in the relevance of socioeconomic characteristics once we control for the school that the child attends suggests that it is the school that matters. Clearly in the case of Pakistan, the learning-gap between private and public schools dwarves differences across children from different backgrounds. This is by no means a foregone conclusion: in most studies of high-income countries, the ability of differences across schools to explain differences in test-scores is _smaller_ than that of differences across households (OECD 2005).

A priori ̧ it also appears that there are differences _among_ government schools and _among_ private schools. Casual observation suggests that private schools range from elite institutions to temporary schools run by a local high school graduate seeking supplemental income. Likewise, government schools sometimes appear well-managed and staffed but just as often appear dilapidated and mismanaged. These observations suggest that gaps between the best and worst schools within each type may be just as important as the gaps between each type.

# **5.2. Gaps across Schools and Geographical Entities**

This section confirms the substantial differences across schools, with tremendous variation in learning achievement across the worst and best government and private schools. Continuing with the attempt to contextualize the results from Pakistan within a broader South-Asian context, we present similar results on differences across schools for India. The patterns, though, are not strictly comparable due to different sampling strategies (in India there are multiple districts, but one sampled school in every village; in Pakistan there are many sampled schools in every village, but only three districts). For the Pakistani data, we show that there are bad and good schools in _every_ village, so that most variation in learning is driven by differences _across schools_ in the same village. In India, there is currently no data that allows us to analyze differences across schools within the same village. We confirm though, that there are large differences across schools and that differences across districts are important. Villages in the same district report similar scores, but learning achievements are very different across sampled districts.

Figure 4 highlights these differences. For Pakistan we ranked villages by average mathematics

- 18 -
<!-- Start of picture text --> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Brahman/Chhetri 22 43 33<br>Terai Middle Caste 35 50 15<br>Dalits 51 42 7<br>Newar 8 35 51<br>Hill Janajati 34 45 20<br>Terai Janajati 35 52 12<br>Muslim 32 58 10<br>Other minorities 18 74 7<br>Poor Vulnerable Middle class Upper class<br><!-- End of picture text --> Source: World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11

# **4. Perceptions of Mobility**

In the preceding sections, we introduced, defined, described and characterized various notions of economic and social mobility in Nepal. A question that we attempt to answer next is the extent to which our findings square with perceptions about mobility. Do Nepalis feel and experience this mobility? Do they feel that they have necessarily done better than their parents? Do they expect their children to do better than their parents? Even within their lifetimes, do they expect to do better than their current economic and social position? We can answer some of these questions using data collected specifically for the purpose in Nepal and data from other global data sources such as the Gallup World Survey.

## **_4.1. Conceptual Background_**

How inequality is perceived can often be very different and disconnected from the actual level of inequality in society. Perceptions naturally have a strong correspondence with every individual’s actual experience with inequality, and are likely to be influenced and shaped not just by the extent of the magnitude of inequality but also by beliefs about the underlying processes that generate the observed inequality. For example, inequality generated by growth processes that are dynamic, broad based and rooted firmly in meritocratic principles of rewarding effort, talent and superior life choices (e.g., studying hard and getting higher education) are likely not to be too objectionable to most. Some may even say such inequalities are necessary to create the right incentives for enterprise, industry, innovation and risk taking. But inequality generated by special privileges (or the lack thereof) associated with circumstances determined by and large by the lottery of birth (one’s gender, ethnicity, race, parental wealth, etc.) is likely to be regarded very differently. For the same level of inequality, inequality perception may be entirely different in two societies where these two opposite “inequality generating processes” operate.

14
surveys, in autocratic regimes. Indeed, there is no necessary contradiction between infrequent bribery of public officials by firms (i.e., relatively good performance on BEEPS) on the one hand, and excessive state involvement in the economy, absence of protection for whistleblowers and journalists, etc. on the other (i.e., a poor rating on NIT; see Appendix B for criteria).<sup>38</sup> Omitting Belarus and Uzbekistan, correlations of NIT and CPIA with the BEEPS state capture measures (and with its “obstacle” measure) are somewhat stronger than those reported in Table 8. Belarus is not covered by EIU, and Uzbekistan is not covered by ICRG, and results for those sources change by less when those countries are deleted.

Neither Belarus nor Uzbekistan is included in the WEF sample, so none of the correlations reported in the lower part of Table 8 are affected by their deletion. The factor analysis using WEF data also indicated that the broadly-defined corruption indicators were mostly measuring administrative corruption rather than state capture. Even without these two countries, therefore, the evidence indicates that the broad measures reflect administrative corruption much more than they reflect state capture.

# **6. Summary and Conclusions**

The BEEPS and other sources of corruption data indicate that corruption in ECA overall is declining. The sources appear to differ somewhat on the magnitude of this decline, with the BEEPS indicating a more favorable trend. Discrepancies in magnitudes do not necessarily indicate inaccuracy in one or more sources, however, because of differences in the (explicit or implicit) definition of corruption. The BEEPS and WEF each contains multiple items on administrative corruption and state capture, and both

> 38 Regardless of the prevalence of bribery or other forms of corruption, it may be unrealistic to expect Nations in Transit – a product of Freedom House, funded in part by the U.S. government and conservative American philanthropists – to rate autocratic regimes highly on any indicator.

44
# _Regulation externalities_

Both the micro and macro-prudential approaches share a common concern about the destabilizing externalities of regulatory instruments. The micro approach is concerned with the externalities associated with crisis-contingent policies, such as the Lender of Last Resort (LOLR) and the provision of deposit insurance. In fact, it is feared that in both cases, the very availability of these tools – while defusing self-fulfilling runs – could feed instability through the familiar moral hazard channel. In the integrated micro-macro approach, the same set of considerations would extend to a much larger set of regulatory provisions and form of externalities, an example being the cyclical externalities of solvency rules. Externalities would become a standard feature of regulation. The notion that regulatory tools respond to market imperfections – for example, the LOLR as a response to moral hazard – would not make them immune to having parallel and possibly unintended effects. From this perspective, the traditional concern about the moral hazard implications of the financial safety net does not lose its validity; it simply becomes one element of a much larger puzzle (Rochet, 2005; Caballero, 2010).

# _Market concentration_

A survey of the regulatory implications of the integrated-prudential approach would be incomplete if it did not mention the specific effect of market concentration on endogenous risk. For the “public choice view,” the risk of excessive concentration of market power was linked to the risk of “regulatory capture” by concentrated special interests. For the “public interest view,” market concentration generates rent seeking strategies aimed at expropriating consumers of their surplus and small savers of their wealth. In both cases, the damage to social welfare comes from a monopolistic concentration of power. In the case studied here, instead, the damage comes from the fact that portfolios and institutions of growing size do not ensure the traditional diversification benefits expected of their smaller counterparties. Idiosyncratic risk growing in size ceases to be diversifiable and becomes an additional source of endogenous risk. The effects of market concentration are likely to be further magnified by the concurrent effect of complexity, which grows more than linearly with size, as exemplified by the Lehman’s case.

The goal of preserving market effectiveness by introducing size limits has not been addressed so far by existing regulations, which have limited outright prohibitions to the domain of allowed bank activities as in the case of the Volker clause. Careful monitoring of the market shares of 20
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