rafmacalaba/datause-encoder-data huggingface dataset:
Table <u>9: Trade, Legal Origins and Control of corruption, 5-year averages</u> Dep. Var.: log of average trade flow

||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>IV|
|---|---|---|---|---|---|---|
|Years in Average|19|85-1989|19|90-1994|19|95-1999|
|Different Legal Origin|-0.01|-0.09|-0.08|-0.08|-0.11|-0.15|
||(-0.12)|(-1.13)|(-1.25)|(-1.29)|(-2.17)|(-2.83)|
|Control of Corruption|0.33|0.50|0.35|0.35|0.15|0.26|
||(19.47)|(13.97)|(19.59)|(11.93)|(11.25)|(7.43)|
|log distance|-1.25|-1.22|-1.13|-1.13|-1.12|-1.10|
||(-27.43)|(-25.78)|(-30.32)|(-29.47)|(-38.63)|(-36.39)|
|log product of GDP|1.21|1.14|1.20|1.20|1.13|1.11|
||(66.47)|(53.91)|(82.16)|(73.21)|(104.23)|(88.84)|
|log product of land area|-0.27|-0.25|-0.25|-0.25|-0.18|-0.16|
||(-20.37)|(-18.02)|(-21.07)|(-20.20)|(-18.97)|(-15.47)|
|no. of landlocked|-0.14|-0.22|-0.42|-0.42|-0.36|-0.38|
||(-1.79)|(-2.72)|(-7.20)|(-7.16)|(-8.24)|(-8.73)|
|no. of islands|0.22|0.17|-0.06|-0.06|-0.15|-0.22|
||(3.26)|(2.50)|(-0.99)|(-1.02)|(-2.94)|(-4.09)|
|common border|0.81|0.86|1.02|1.03|1.16|1.21|
||(4.29)|(4.45)|(6.01)|(5.99)|(7.41)|(7.56)|
|common language|0.63|0.65|0.65|0.65|0.61|0.62|
||(7.29)|(7.41)|(9.11)|(9.10)|(9.94)|(10.11)|
|currency union|2.42|2.39|2.47|2.47|1.73|1.83|
||(8.11)|(7.84)|(8.56)|(8.56)|(6.05)|(6.27)|
|Observations|5353|5353|5627|5627|7014|7014|
|R^2|0.62|0.61|0.71|0.71|0.74|0.73|
|First stage R^2||0.37||0.47||0.34|
|% increase in trade due to<br>1s.d. institution|101%|182%|89%|92%|29%|54%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant is not reported. Each column reports a regression performed on one cross-section of 5-year averages. The averages are taken over all _available_ years in the 5-year sub-sample, and the procedure takes into account missing year observations. IV estimators use the sum of absolute latitude as an instrument for the sum of control of corruption for both countries in a trading pair. The sum of control of corruption ranges from 0 to 12 with means 6.65, 7.19, 6.79, with standard deviations 2.09, 1.84, 1.66, in years 1985-89, 1990-94, 1995-99, respectively. The percent increase in trade due to 1s.d. institution (property rights) is calculated as follows: exp(coef*sd)-1.

44
Table 7 examines questions with the least degree of personal reference. These questions ask about behaviors of 'typical establishments in this line of business' or 'establishments in your sector of activity'. While the respondent’s company could obviously be included in the term 'in this line of business', he could just as well argue that he does not represent what 'typically' happens in his sector and might therefore have a way of excluding himself from the group he answers about. Thus he might not feel his answers are personally incriminating. Regression results in this category of questions do not lend any support to the hypothesis that reticent respondents tend to deny sensitive acts more strongly than their possibly candid counterparts. In fact in the cases in which we obtain a statistically significant coefficient, we find a (counterintuitive) positive effect, which would imply that reticent respondents give answers that indicate more involvement in sensitive acts than do possibly candid respondents.

Finally, in Table 8 we confirm that reticence effects are _not_ present when looking at responses to _nonsensitive_ questions. For this confirmatory exercise we focused on the set of 35 yes/no questions in the survey that we had not previously identified as sensitive in Tables 4-7. We then randomly selected a subset of five of them as placebo questions (by choosing every 7<sup>th</sup> question), and performed the same analysis as in Tables 4-7. With one exception we find no significant differences in the responses of reticent and possibly candid respondents to these questions. The one exception is the question on ISO certification where we find that reticent respondents are significantly more likely to claim their firms are ISO-certified.

The ISO-certification result could reflect reticence working in the opposite direction—respondents who are not fully candid might claim positive attributes that are in fact exaggerations. This is something that AM found in Romania, where reticent respondents claimed higher moral values when faced with a set of questions taken from the World Values Survey. For example, AM found that reticent respondents were much more likely to answer that 'Lying in your own interest' was unacceptable. We find a similar result for Nigeria. Respondents were asked to name the three most important skills for a successful entrepreneur and given twenty to choose between. One of these was 'demonstrate high moral standards', which reticent respondents named 33% more 13
3455 municipios reporting industrial employment and these municipios are therefore excluded from the analysis.

In the ensuing analysis, emissions for the Brazilian plants are imputed according to pollutants per employee, within a specific sector, for plants in a given size category, from the Mexican data base. To achieve this matching, both the Mexican and Brazilian data are first aggregated to the 3-digit level of International Standard Industrial Classification (ISIC).

# **4. Results: Pollution Intensity,** Scale and **Dirty** Sectors

The results from the Mexico data are presented first in this section, followed be the results of applying the Mexican emission intensity measures to the Brazilian plant data.

# **4.1 Scale and Pollution Intensity: Evidence from Mexico**

The first empirical exercise measures the relative air pollution intensity of small and large plants in the Mexican database. Intensities are computed as particulate emissions per unit of labor, because comparable output data are not available.<sup>3</sup> Using the sample of 6,000 plants, the logarithm of emissions per employee is first regressed on logarithm of actual plant level employment, with dummy variables to control for sectoral differences. The estimated elasticity of emission intensity with respect to employment is -.65, with an extremely small standard error (the t-ratio for a zero null hypothesis is 34.19). In other words this suggests that plant-level emissions per unit of labor decline by .65% for each 1% increase in employment.

Table 1 shows the average emission intensity for each sector and plant size category. While the general pattern is consistent with a negative relationship between pollution per employee and plant size, it also reveals substantial variation. Indeed in some sectors (such as basic foods, petroleum products and industrial chemicals), large plants are more pollution-intensive than small ones.

> 3 If small enterprises are more labor-intensive (per unit of output) than large facilities, then a negative relationship between plant size and the pollution/labor ratio implies that pollution/output declines more rapidly than labor/output.

8
# **References**

Addison, T., and A. Roe, eds. 2018. _Extractive Industries: The Management of Resources as a Driver of Sustainable Development_ . New York: Oxford University Press.

Arvis, J. F., G. Raballand and J. F. Marteau. 2010. _The Cost of Being Landlocked: Logistics Costs and Supply Chain Reliability_ . Washington, DC: World Bank.

Arvis, J. F., Y. Duval, B. Shepherd, and C. Utoktham. 2013. “Trade Costs in the Developing World: 1995–2010.” Policy Research Paper 6309, World Bank, Washington, DC.

Asiki, G., G. Reniers, R. Newton, K. Baisley, J. Nakiyingi-Miiro, E. Slaymaker, I. Kasamba et al. 2016. "Adult Life Expectancy Trends in the Era of Antiretroviral Treatment in Rural Uganda (1991–2012)." _Aids_ 30 (3): 487-493.

Aker, J. C., and I. M. Mbiti. 2010. "Mobile Phones and Economic Development in Africa." _Journal of Economic Perspectives_ 24 (3): 207-32.

Aker J. 2011. “Dial “A” for Agriculture: Using ICTs for Agricultural Extension in Developing Countries.” _Agricultural Economics_ 42 (6): 631-47.

Bhattacharyya, R., and A. Mandal. 2016. “India–ASEAN Free Trade Agreement: An Ex Post Evaluation.” _Journal of Policy Modeling_ 38 (2): 340-352.

Barofsky, J., T. D. Anekwe, and C. Chase. 2015. “Malaria Eradication and Economic Outcomes in Sub-Saharan Africa: Evidence from Uganda.” _Journal of Health Economics_ 44: 118-136.

Bayraktar, N., and H. Fofack. 2011. “Capital Accumulation in Sub-Saharan Africa: Income-Group and Sector Differences.” _Journal of African Economies_ 20 (4): 531-561.

Buigut, S. 2016. “Trade Effects of the East African Community Customs Union: Hype versus Reality.” _South African Journal of Economics_ 84 (3): 422-439.

Calderón, C., and L. Servén. 2010. “Infrastructure and Economic Development in Sub-Saharan Africa.” _Journal of African Economies_ 19, AERC Supplement 1: i13−i87.

Chaney, N. W., J. Sheffield, G. Villarini, and E.F. Wood. 2014. “Development of a HighResolution Gridded Daily Meteorological Dataset over Sub-Saharan Africa: Spatial Analysis of Trends in Climate Extremes.” _Journal of Climate_ 27, no. 15: 5815-5835.

Dauvin, M., and D. Guerreiro. 2017 “The Paradox of Plenty: A Meta-Analysis. ” _World Development_ 94: 212-231.

EBRD (European Bank for Reconstruction and Development). 2019. _Eight Things You Should Know About Middle-Income Transitions_ . April. London: European Bank for Reconstruction and Development.

European Commission. 2018. “Association Implementation Report on Moldova.” Joint Staff Working Document. European Commission: Brussels.

16
since 2005, at least for primary education, the school enrollment rates of girls and boys are mostly identical and close to 100%.

The regional differences in education are maily due to differences in development and urbanization. Investments in higher education of girls might still seem less beneficial to some parents in the Eastern regions, which are dominated by a large agricultural sector. In addition, the local offer in education is still sometimes limited to primary levels of education in some of the South-East regions. Increasing education of younger cohorts in these regions is likely to imply migration to urban areas and/ or Western parts of Turkey and, thus, measures of education levels in rural areas stay low and fertility rates high, while measured education levels in urban areas increase while fertility decreases.

**figure 5. CFR of women by regional background  and education (bold line South-Eastern women SE, dot line North-Western women NW)**

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1935 1940 1945 1950 1955 1960 1965<br>Less than primary completed SE Primary completed SE Secondary completed SE<br>University completed SE Less than primary completed NW Primary completed NW<br>Secondary completed NW University completed NW<br><!-- End of picture text --> Source: Census ( i-pums) 2000 Figure 5 shows that for cohort 1960, there is an important fertility difference of around 2 children between South-Eastern and North-Western women for the less educated women. For more educated women (at least secondary completed), this regional difference is reduced to 0,5 children only, suggesting that in Turkey, fertility differentials between North-Western and South-Eastern women are likely to dispappear once women of both groups get into secondary education. This education level is not the norm for cohort 1960, neither for South-Eastern nor for North-Western women, but more and more women born after 1960 have completed secondary education, especially in the North-West (figure C).

12
TABLE 4. Percentage Distribution of Observations by Allocation Method

|**Census**<br>**round**|**Pure**<br>**raw**|**Raw**<br>**scaled**|**Pure**<br>**remainder**|**Remainder**<br>**scaled**|**R&R**<br>**not**<br>**scaled**|<br>**R&R**<br>**scaled**|**Pure**<br>**interpolation**|**interpolation**<br>**and scaled**|**Missing**|
|---|---|---|---|---|---|---|---|---|---|
|**1960**|12.31|0.13|40.65|3.34|2.12|0.02|24.86|12.58|3.98|
|**1970**|12.07|0.02|34.88|2.03|2.54|0.17|33.64|10.68|3.98|
|**1980**|12.00|0.15|45.76|5.90|4.28|0.15|18.88|8.91|3.98|
|**1990**|13.82|0.27|47.13|5.88|6.50|0.06|15.23|7.12|3.98|
|**2000**|12.85|1.02|39.97|4.29|10.41|0.86|10.66|15.97|3.98|

_Source:_ Authors‘ calculations based on data described in text.

R&R: Raw and Remainder combined.

39
# Commercial Bank Provisioning Against Claims on Developing Countries

## by Graham Bird

## Table of Contents

|Summ|aryand Conclusions .................................|<br>1|
|---|---|---|
|Intr|oduction.|3|
|I.|Factual and Statistical Background.|6|
|II.|Factors Affecting Provisioning .....................|17|
||Secular Factors .18||
||Micro Factors .26||
||Assigning Importance: Case Studies .34||
|III.|Implications of Provisioning .37||
|IV.|Provisioning and Debt Strategies for the Future .....|<br>49|
|Tabl|es<br>....|57|

|Graham Bird is a Professor of Economics at the University of Surrey.|
|---|
||(0.294)|(0.278)|(0.287)|(0.274)|(3.189)|
|---|---|---|---|---|---|
|Female owner present Y:1|5.158***|5.100***|5.055***|4.599***|6.700|
|N:0||||||
||(0.496)|(0.491)|(0.487)|(0.484)|(4.766)|
|Top manager is female Y:1|13.277***|13.166***|13.163***|13.063***|13.089***|
|N:0||||||
||(0.795)|(0.790)|(0.788)|(0.760)|(0.760)|
|Age of firm (logs)||-0.052|-0.114|-0.187|-0.059|
|||(0.373)|(0.383)|(0.375)|(0.373)|
|Labor productivity (logs)||-0.255|-0.291|-0.282|-0.242|
|||(0.200)|(0.206)|(0.195)|(0.188)|
|Exports (% of sales)||0.031**|0.031**|0.025*|0.024*|
|||(0.013)|(0.013)|(0.013)|(0.013)|
|Foreign ownership (%)||0.019**|0.018**|0.018**|0.018**|
|||(0.009)|(0.009)|(0.009)|(0.008)|
|Firm offers formal training to<br>workers Y:1 N:0||2.103***|2.013***|1.727***|1.838***|
|||(0.568)|(0.561)|(0.550)|(0.524)|
|Inadequately educated<br>workers: Obstacle severity (0-||-0.158|-0.098|-0.041|-0.011|
|4 scale)||||||
|||(0.191)|(0.206)|(0.207)|(0.204)|
|Firm registered when started<br>operations Y:1 N:0|||-0.432|-0.639|-0.437|
||||(1.028)|(0.976)|(0.974)|
|Firm experienced losses due|||0.519|0.536|0.643|
|to crime Y:1 N:0||||||
||||(0.597)|(0.601)|(0.587)|
|Access to finance: Obstacle|||-0.557***|-0.483**|-0.548***|
|severity (0-4 scale)||||||
||||(0.212)|(0.201)|(0.201)|

61
WPS4021 <mark>B</mark> ACKGROUND <mark>P</mark> APER TO THE <mark>2007 W</mark> ORLD <mark>D</mark> EVELOPMENT <mark>R</mark> EPORT

# **A PROFILE OF THE WORLD’S YOUNG DEVELOPING COUNTRY MIGRANTS**

# David J. McKenzie

_Development Research Group, World Bank_

# **Abstract**

Individual level census and household survey data are used to present a rich profile of the young developing migrants around the world. Youth are found to comprise a large share of all migrants, particularly in migration to other developing countries, with the probability of migration peaking in the late teens or early twenties. The paper examines in detail the age and gender composition of migrants, whether or not young migrants move alone or with a parent or spouse, their participation in schooling and work in the destination country, the types of jobs they do, and the age of return migration. The results suggest a high degree of commonality in the youth migrant experience across a number of destination countries. In particular, developing country youth tend to work in similar occupations all around the world, and are more concentrated in these occupations than older migrants or native youth. Nevertheless, there is also considerable heterogeneity among youth migrants: 29 percent of 18 to 24 year olds are attending school in their destination country, but another 29 percent are not working or in school. This illustrates both the potential of migration for building human capital, and the fear that lack of integration prevents it from being used.

# World Bank Policy Research Working Paper 4021, October 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
stock of gross external debt stood at a very modest $4.3 billion (6.6 percent of GDP). Foreign reserves still stood at $10.6 billion<sup>2</sup> .

**Figure 2 Uses of Oil Windfall, Fiscal Balance, Inflation and External Position.**

<!-- Start of picture text --> Incremental Government Expenditure as % of Oil Expenditure and Oil Revenue (% of non-oil GDP)<br>Windfall compared to 1970 90<br>180 80<br>160 70<br>140 60<br>120 50<br>100 40<br>80 30<br>20<br>60<br>10<br>40<br>0<br>20<br>0<br>Recurrent Expenditure (% of non-oil GDP)<br>capital expenditure as % of oil windfall Capital Expenditure (% of non-oil GDP)<br>Oil Revenue (% of non-oil GDP)<br>current expenditure as % of oil windfall<br>Current Account and Net Foreign Assets Fiscal Balance and Inflation<br>80 30 100<br>60 20 80<br>10<br>40 60<br>0<br>20 40<br>-10<br>0 -20 20<br>-20 -30 0<br>-40 -20<br>-40<br>Fiscal balance (% of Non-Oil GDP)<br>Current Account Balance (% of Non-Oil<br>GDP) Inflation (%), right scale<br>Net Foreign Assets (% of Non-Oil GDP)<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text -->

# _II.2. The oil price collapse in 1980s - the origins of the debt problem_

Since the oil prices collapse in the early 1980s, Nigeria experienced rapid external debt built-up and dwindling foreign exchange reserves: public and publicly guaranteed external debt increased from $4.3 billion to $11.2 billion, while foreign exchange reserves were almost exhausted, from $10 billion to $1.23 billion, all between 1981 and 1983. Despite of its low-income status today, in early 1980s Nigeria

> 2 Data on domestic public debt for the 1980s are not available, so the public debt stock figures for that period consist of external public debt only. Since 1990, data on domestic public debt, although available, are likely to understate the true domestic debt burden, as information on state governments’ domestic debt is not available. In addition, these figures do not include the substantial stock of domestic contractual and pension arrears, which at least in 2003 were large.

5
We interpret these results as consistent with jobless growth in manufacturing. Even at the lag of 9 years an increase in the concentration of organized sector plants (in the same or in buying sectors) is not associated with an increase in employment in the unorganized sector.

# **Section IX. Discussion and Conclusion**

This paper is a detailed analysis of the vertical and horizontal linkages that exist between the organized and unorganized segments of the manufacturing sector. Our results support anecdotal evidence that the unorganized sector is an important supplier of inputs to the organized manufacturing sector. In particular organized sector employment and output is greater in states that have greater presence of unorganized suppliers of inputs. Conversely unorganized employment and output is greater in states that have greater presence of organized buyers of inputs. We identify two important asymmetries in the relationship between the organized and unorganized sectors. First the unorganized sector is much more dependent on/responsive to organized sector presence than vice versa. Second unorganized sector productivity is dependent on/responsive to organized sector productivity and presence but the reverse is not true.

Some of our results are markedly different from existing studies. For example, in Africa unorganized firms are important buyers of inputs from organized firms. On the other hand the organized sector does not buy much from the organized sector. This is contrary to our finding that the unorganized sector is an important supplier of inputs to the organized sector.

We find that there are strong linkages between the household (“traditional”) segment of the unorganized sector and the organized sector. Based on [Moreno-Monroy et al. (2012)] our expectations were to find little or no links. Of course consistent with [Moreno-Monroy et al. (2012)] we do find that vertical linkages are stronger between the “modern”/non-household segment of the unorganized sector.

Additionally [Moreno-Monroy et al. (2012)] find that horizontal linkages are insignificant. On the contrary we find large, positive and significant horizontal linkage between the organized and unorganized sector in all specifications.

25
Graph 1: GDP Growth Rates <!-- Start of picture text --> GDP Growth Rates (1987-1993)<br>6.00%<br>5.00% -<br>4.00% -<br>2.00% -/<br>1.00%-<br>0.00% I l l l I<br>1987 1988 1989 1990 1991 1992 1993<br><!-- End of picture text -->

* Derived from the IMF's International Financial Statistics

Table 3: ILF/OLF - all 24 cohorts (%)

||||<br>**women**|||**men**|,|
|---|---|---|---|---|---|---|---|
||all|wives|single<br>mothers|single no<br>children|all|' husbands|single|
|OLF|70.71 j|75.69|48.74|30.75|6.68|j<br>6.08|13.03|
|unemployed|0.84|0.69|1.47|2.0|1.59|<br>1.52|2.19|
|n|90239|77192|13787|1997|80239|'<br>77192|4021|

34
environment, it is not clear what type of size-age gap to expect in developing countries. A recent paper by Hsieh and Klenow (2013) suggests that in India and Mexico, in contrast to the case of the U.S., the life cycle of manufacturing plants is relatively flat - that in fact on average the surviving manufacturing plants in India shrink in size between ages 5 and 35 years. The dual economy view in development economics (originally associated with Harris and Todaro, 1970) predicts that informal firms should look very different from formal firms in terms of size, productivity, wages paid, and the industries/markets they operate in. Recent studies such as La Porta and Shleifer (2008) also show that while informal firms account for a large portion of economic activity in developing countries, growth and development come from the creation of highly productive formal firms. Strikingly, Hsieh and Klenow find that plants in both the formal and informal sectors share the same trajectories. Our results using data for the same year 1994/95 as in their paper stand in sharp contrast with their findings on the formal sector. Given the heterogeneity in firm formation we study the formal and informal sectors separately in this paper and do find differing life cycle patterns.

The remainder of the paper is organized as follows. In Section II we describe the data. In Section III we discuss in detail the relationship between establishment size and age in our crosscountry sample. In Section IV we examine plant life cycles in India. Section V concludes.

# **II.  Data**

# **_A. Cross-Country Sample_**

We use the World Bank Enterprise Surveys (ES), which are an ongoing initiative of the World Bank, to benchmark the investment climate in different countries across the world and to analyze firm behavior and performance. The Enterprise Surveys survey from the universe of 8