rafmacalaba/datause-encoder-data huggingface dataset:
However, most of these positive impacts have not applied to importers and brokers classified by customs as high-risk, especially those labeled “informal” by customs agents because of the practice of using other registered company names to declare imports at customs. Informal importers often operate on a small-scale basis.

Customs administrations in developing countries face a common dilemma: they must facilitate trade and yet they need to fight fraud and smuggling to increase tax and public revenues. One of the problems facing them is the increased risk of fraud and smuggling (both of which affect the terms of taxation and some administrative procedures) when too much pressure is placed on importers (a classic moral hazard problem).

Consequently, customs administrations have developed institutionalized bargaining procedures with the would-be smugglers in order to shift them to the “legal track.” Among these procedures are sector-level agreements on the minimum values declared for various goods. Negotiations on the acceptable minimum taxation level are common and seem to some extent efficient given the actual means and capacities of the customs administration. Contracts with well-identified importers were also inspired by a culture of negotiation that is not common in African countries. For example, exchanging promises of compliance by importers with promises of facilitation by customs is common to all customs administrations. An example is the Authorized Economic Operator (AEO) agreements in developed countries.

However, devising contracts and new rules of the game with “informal” importers is difficult because of information asymmetries for which little knowledge is available on practices and constraints. Moreover, identification in the customs IT system is difficult because of bulking (consolidation of loads in containers), the lack of formal registration, and the tendency to work with informal brokers.

For analytical purposes, the definition of _informality_ requires clarification. It is a controversial subject because of the variety of existing, and sometimes contradictory, definitions. Here _informality_ refers to any trading activity associated with importers’ business practices (no bank account or no formal accounting) using the customs approach, which is in line with the definition of _informal sector_ by the National Institute of Statistics in Cameroon (even though they may be registered and pay _some_ taxes) .

3
the UK and Ireland) feature very high dependent informality rates, whilst it is not the case for Finland and the Netherlands.

In the literature, survey-based prevalence of informal employment has been presented as a percentage of (i) working-age population; (ii) labor force; (iii) total (or non-agricultural) employment; (iv) salaried workers. The choice of base depends on the definition of informality, on the information available in the survey, and on the purpose of the study. The first approach is used in studies focusing on transitions between sectors and labor market states (e.g. Bosch et al. (2007), Bosch and Maloney (2010), Nikolova et al. (2010)), as well in cases when data come from surveys where questions on shadow activities refer to a much longer period (e.g. 12 months) than the ones used in ILO definitions of employment (e.g. Riedmann and Fischer, 2008; Williams and Renooy, 2008). Loayza et al. (2009: Figure 1) apply (ii) and (iii), whilst Perry et al. (2007: Figure 2) use all four approaches. The third approach is used also by ILO (2002) and Feld and Schneider (2010: Table 14<sup>11</sup> ). Given that unemployment and ̳discouragement‘ are alternatives to formal or informal employment that are shaped by the same policies and economic circumstances, we argue that the labor force extended to include discouraged workers is a more reasonable base for measuring the size of informal employment, especially for the purposes of international comparisons.

To allow comparability with other studies, in Table 2 we present various measures of prevalence of informal employment in Southern, Eastern, Western, and Northern Europe. As we are mostly interested in comparisons between countries and in the effects of institutions, most of our results are either based on within-countries calculations or derived assuming that a respondent from any country is equally likely to be surveyed (i.e. countries are not weighted by population size); in Table 2, however, we present both equally-weighted and populationweighted estimates for the four above mentioned geographical areas, as well as for Europe as a whole. Equally-weighted estimates (means shown in Table 2 and medians found e.g. in Figure 2) refer to prevalence of informal employment in ̳an average country‘ in a country group; in this case the size of country‘s population does not affect the estimate – Belgium has the same weight as Germany. Population-weighted estimates refer to the share of informal employment in the adult population (or labor force, or total employment) of European regions. Such estimates are of interest on their own, but being dominated by large countries they are less useful for policy analysis.

> 11 Feld and  Schneider (2010) express the estimated  full-time equivalent shadow labor force as percentage of  ̳official labor force‘, but the figures suggest that by labor force they mean employed population.

10
compared to the ratio of 60+ year olds to 20-59 year olds between 1960 and 1992. The demographic ratio is taken from Mitchell (1983) and interpolated for intermediate years. The maturation process is illustrated as a convergence of the two ratios over time as pension rights are attained by a growing number of retired persons. The actual story is more complicated of course, with labor force coverage and pension eligibility rules changing over time.

Figure 4.11 is somewhat more complicated for several reasons in addition to those already mentioned. While all demographic dependency points refer to 1990, system dependency ratios are based on different years which roughly correspond to those found in the coverage table. It should also be noted that the number of contributors in some of the countries which are outliers on the graph have experienced rapid and dramatic changes in their respective rates of evasion. In Argentina for example, the rate of evasion rose to more than 50% during the late 1980s. In Hungary, the transition from socialism has raised the number of working age disability pensioners while the contributor base declined, the victim of a growing informal labor market. The surprisingly low Swedish figure can be partly attributed to an expensive partial pension program which allows for persons above age 60 to continue to contribute while receiving partial benefits. Italy meanwhile can blame its record high rate on the fact that the average Italian enjoys one of the longest retirements in the industrialized world.

# **F. Design Features**

Definitions and Concepts Appendix Table A.7 "Main Publicly-Mandated Pension Scheme Design Features, 1991" is based on the U.S. Social Security Administration's, "Social Security Programs Throughout the World". The data refer to the parameters of the main pension scheme which covers private employees. In the case of Japan, the retirement age refers to the National Pension. The employment-related pension scheme which is currently maturing has a retirement age of 60 for men and 57 for women. "Normal retirement age" refers to the age at which retirement would normally occur without actuarial reductions for workers in occupations which are not covered by special early retirement rules for hazardous work conditions or other factors. This should not be confused with "effective retirement age", which is the average age at which new pensioners retire.

38
Opportunity? The Geography of Intergenerational Mobility in the United States,” The Quarterly Journal of Economics, Oxford University Press, vol. 129(4), pages 1553-1623.

Cohen, A (1991), Truncated and Censored Samples: Theory and Applications, CRC Press.

Corak, M (2013). “Income Inequality, Equality of Opportunity, and Intergenerational Mobility,” Journal of Economic Perspectives, American Economic Association, vol. 27(3), pages 79-102, Summer.

Dahl, Molly W., and Thomas DeLeire, (2008), The Association between Children’s Earnings and Father’s Lifetime Earnings: Estimates Using Administrative Data, Institute for Research on Poverty, University of Wisconsin Madison, 2008.

Deaton, A (1997), The analysis of household surveys: A microeconometric approach to development policy. Oxford University Press.

Emran, M Shahe, William Greene and F. Shilpi (2018), When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries, Forthcoming in Journal of Human Resources, Summer 2018.

Emran, M. Shahe and F. Shilpi (2011). “Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam”, Journal of Human Resources, issue 2, 2011.

Emran, M. Shahe and F. Shilpi (2015). Gender, Geography and Generations : Intergenerational Educational Mobility in Post-reform India”, World Development, Vol. 72, 362-380.

Fan, Y, J. Yi and J. Zhang (2015), The Great Gatsby Curve in China: Cross-Sectional Inequality and Intergenerational Mobility, Working Paper, CUHK, Hongkong.

Fox, L, Florencia Torche, and Jane Waldfogel (2016), Intergenerational Mobility, Oxford Handbook of the Social Science of Poverty.

Glewwe, P (2000), Household Roster, Chapter 6 in Grosh, M and P. Glewwe ed. Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 years of the Living Standards Measurement Study, vol 1.

22
# **Table 4. Estimations for the likelihood that the household has a loan from a financial institution.**

This table shows marginal effects from the estimations of a probit model. The dependent variable is 1 if the household received a loan from a formal financial institution and zero otherwise. All independent variables are defined in Table 1. In columns (1)-(4), the variable _Remittances_ refers to a dummy for whether the household receives remittances from overseas, whereas in columns (5)-(8) _Remittances_ refers to per capita amounts received in remittances from overseas. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

||i|Depe<br>d f|ndent variable:<br>h hhl|Dummyfor whe<br>i|ther household r<br>i|eceived a lo<br>h|an<br>f i||
|---|---|---|---|---|---|---|---|---|
|Variables|Remttances<br>remittances<br>|: ummy or w<br>from overseas<br>|eter ouseo<br>|receves<br>|Remttance<br>capita recei<br>|s: te amoun<br>ved from ov<br>|t o remttan<br>erseas<br>|es per<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Remittances|-0.014|-0.006|-0.031|-0.086|-0.008|-0.005|-0.014|-0.114|
||[-1.13]|[-0.54]|[-1.59]|[-0.82]|[-1.43]|[-1.13]|[-1.65]*|[-1.85]*|
|Real per capita income|0.001|0.001|0.002|-0.020|0.001|0.001|0.002|-0.017|
||[0.58]|[0.71]|[0.67]|[-1.66]*|[0.59]|[0.75]|[0.70]|[-1.29]|
|House and land ownership|0.020|0.021|0.031|0.019|0.020|0.022|0.032|0.015|
||[1.85]*|[2.22]**|[1.88]*|[0.20]|[1.88]*|[2.30]**|[1.93]*|[0.15]|
|Adults average education|0.006|0.006|0.012|0.006|0.006|0.006|0.013|0.004|
||[3.30]***|[3.16]***|[3.71]***|[0.27]|[3.38]***|[3.23]***|[3.92]***|[0.20]|
|Agriculture|0.009|0.011|-0.012|-0.127|0.009|0.011|-0.011|-0.147|
||[0.62]|[0.91]|[-0.50]|[-1.12]|[0.63]|[0.89]|[-0.45]|[-1.24]|
|Electricity|0.017|0.019|0.034|-0.062|0.018|0.020|0.035|-0.057|
||[1.56]|[1.95]*|[1.87]*|[-0.60]|[1.60]|[2.00]**|[1.87]*|[-0.55]|
|Returned migrant|0.013|0.026|0.028|0.704|0.017|0.031|0.036|0.747|
||[0.54]|[1.07]|[0.67]|[4.64]***|[0.68]|[1.24]|[0.81]|[9.84]***|
|Observations|2347|2347|1365|372|2321|2321|1354|362|
|Pseudo R<sup>2</sup>|0.03|0.06|0.11|0.09|0.03|0.06|0.11|0.10|
|Log likelihood|-545.8|-528.5|-421.9|-225.4|-543.4|-526.5|-419.3|-217.2|
|Department fixed effects|No|Yes|No|No|No|Yes|No|No|
|Municipalities fixed effects|No|No|Yes|No|No|No|Yes|No|
|Household fixed effects|No|No|No|Yes|No|No|No|Yes|

23
in Ellison and Glaeser (1997), Maurel and Sédillot (1999), and Duranton and Overman (2005) respectively. Fourth, the average level of concentration increases as one moves from the 4 to the 6-digit industry classification (concentration is relatively more intense among more specific industry segments). This result is consistent with Rosenthal and Strange (2003) for the u.s. case and with Behrens and Bougna (2015) in the case of Canada.

Table 3: Mean and median eg indices, naics 4- and 6-digit industries (2004 – 2014)

||2004||2009|2014||
|---|---|---|---|---|---|
||Manufacturing|Overall|Manufacturing<br>Overall|Manufacturing|Overall|
||||naics 6-digit Industries|||
|Mean|0.381|0.256|0.388<br>0.26|0.43|0.282|
|Median|0.284|0.146|0.297<br>0.156|0.319|0.154|
|Minimum|-0.436|-0.739|0.045<br>-0.168|0.031|0.005|
|Maximum|1.001|1.006|1<br>1.006|1.353|1.013|
|Share_<_0|1.23|0.95|–<br>0.27|–|–|
|Share_∈_(0, 0.05]|1.63|20.14|2.1<br>20|1.61|21.85|
|Share >0.05|97.14|78.91|97.9<br>79.73|98.39|78.15|
||||naics4-digit Industries|||
|Mean|0.344|0.222|0.348<br>0.216|0.359|0.236|
|Median|0.264|0.116|0.273<br>0.12|0.285|0.118|
|Minimum|-0.085|-0.225|0.033<br>0.004|0.02|0.005|
|Maximum|1|1|0.992<br>0.997|0.999|1|
|Share_<_0|1.16|0.38|–<br>–|–|–|
|Share_∈_(0, 0.05]|2.33|25.95|3.49<br>24.23|3.49|25.84|
|Share_>_0.05|96.51|73.66|96.51<br>75.77|96.51|74.16|

_Notes:_ Mean and median values for 248 (resp. 245 in 2004, and 238 in 2009) 4-digit and 6- digit naics classification of industries. Share _<_ 0 means ‘not clustered’. Share _∈_ (0, 0.05] means ‘weakly clustered’. Share _>_ 0.05 means ‘strongly clustered’. See Ellison and Glaeser (1997) for details.

Figure 4 plots the distributions of the eg indices for the 6-digit industries in 2004, 2009, and 2014, for all industries (left panel) and for manufacturing only (right panel). As can be seen, these distributions are skewed towards 0.05, which shows that many industries are highly agglomerated, whereas only few of them are weakly agglomerated (the eg index is positive but smaller than 0.05).

# **3.5.2 Trends in specialization : 2004 – 2014**

We now examine how specialization evolved between 2004 and 2014. For each localilty and for the years 2009 and 2014, we calculate the Krugman specialization index (henceforth denoted 13
# **1. Introduction**

Panel datasets that follow individuals and households over time are considered to be an excellent source of information for measuring intra-generational economic mobility. However, panels are often difficult to administer, costly, and not representative of the entire population of a country; they usually cover short periods of time; and suffer from non-random attrition that may bias mobility estimates. In addition, panels are only available in few countries, which restricts their use in regional analysis. Given all these limitations, panel datasets can impose serious constraints to the empirical analysis of the dynamics of poverty.

There are many alternatives to overcome the main limitations of panel datasets. “Synthetic Panels”, developed by Dang et al. (2014) and improved by Dang and Lanjouw (2013), is the most recent. Dang et al. (2014) implement a non-parametric approach to estimate an upper and a lower bound for mobility in and out of poverty at the household level as if we had actual panel datasets. The method estimates an income<sup>1</sup> model in the second round of cross-section data using a specification that includes only time-invariant covariates and retrospective regressors when available. Parameter estimates from this income model are also obtained in the first round of crosssectional data and then applied to the same covariates in the second round. By doing this, we can predict the unobserved income in the first round of data for all households surveyed in the second round. Depending on how error terms are treated, the method generates an upper and a lower bound estimate of economic mobility. Cruces et al. (2015) perform a validation in three countries in Latin America and the Caribbean (LAC) where actual panel data are available: Chile, Nicaragua, and Peru. The authors show that the approach performs well in all three settings. Ferreira et al. (2012) apply the lower bound technique to predict upward economic mobility out of poverty in 18 countries in LAC between circa 1990 and 2010.

Dang and Lanjouw (2013) improve the methodology to obtain a point estimate—as opposed to lower and upper bounds—of poverty dynamics. The authors assume normality of the error terms and use the age-cohort correlation of residuals obtained from cross-sectional data in the first and second rounds to produce a point estimate of inter-generational poverty mobility. The authors validate the methodology using actual panel data from Bosnia-Herzegovina, Lao People’s

> 1 For simplicity, I will refer to income, but consumption can also be used as the welfare measure.

2
- Blattman, C., N. Fiala, and S. Martinez. (2011). “Can Employment Programs Reduce Poverty and Social Instability? Experimental Evidence from a Ugandan aid program.” Social Protection and Labor Discussion Paper No. 1120. Washington, D.C., World Bank

- Bruhn, M. and D. McKenzie (2009). "In Pursuit of Balance: Randomization in Practice in Development Field Experiments." _American Economic Journal: Applied Economics_ 1(4).

- Bruhn, M. and B. Zia. (2011). “Business and Financial Literacy for Young Entrepreneurs: Evidence from Bosnia-Herzegovina.” Mimeo. Washington D.C., World Bank.

- Card, D., P. Ibarraran, F. Regalia, D. Roasas-Shady, and Y. Soares. (2011). “The Labor Market Impacts of Youth Training in the Dominican Republic.” _Journal of Labor Economics,_ 29 (2).

-

- Carneiro, P., E. Galasso, and R. Ginja. (2010). “The Impact of Providing Psycho Social Support to Indigent Families and Increasing their Access to Social Services: Evaluating Chile Solidario.” Evaluation report, UCL, London.

- Cobb-Clark, D. A. and M. Tan (2010). “Noncognitive skills, occupational attainment, and relative wages.” _Labour Economics_ 18(1).

- De Mel, S., D. McKenzie and C. Woodruff. (2010). “Who are the Microenterprise Owners? Evidence from Sri Lanka in Tokman v. de Soto.” In: J. Lerner and A. Schoar (eds.) _International Differences in Entrepreneurship_ , NBER, 2010.

- De Mel, S., D. McKenzie, and C. Woodruff. (2012). “Business Training and Female Enterprise Start-up, Growth and Dynamics in Sri Lanka.” Policy Research Working Paper No. 6145, World Bank, Washington D.C.

- Djankov, S., Y. Qian and G. Roland & Ekaterina Zhuravskaya. (2006). “Entrepreneurship in Brazil, China, and Russia.” CEPR Working Papers No. 0066, London.

- Drexler, A., G. Fischer and A. Schoar. (2010) “Keeping it simple: Financial Literacy and Rule of Thumbs.” CEPR Working Paper No. 7994, London.

- Elston, J.A., G.W. Harrison and E.E. Rutström. (2005). “Characterizing the Entrepreneur Using Field Experiments”. Max Planck Institute of Economics Working Paper.

- Fairlie, R., D. Karlan and J. Zinman (2012). “Behind the GATE Experiment: Evidence on Effects of and Rationales for Subsidized Entrepreneurship Training”. CEPR Discussion Paper No. 17804. London.

Gollin, D. (2002). “Getting Income Shares Right, _Journal of Political Economy_ 110 (2).

- Gosling, S. (2003). "A very brief measure of the Big-Five personality domains." _Journal of Research in Personality_ 37(6).

28
> <sup>15</sup>

those who answer Republican or Democrat would also answer “yes” to the Afrobarometer question, “Do you feel close to any particular political party?” The fraction of respondents who do not answer Republican or Democrat ranges from eight to 18 percent (in 1974, after the Nixon administration and the Watergate scandal), and is most typically eleven or twelve percent, well below even the lowest rates of partisan indifference in these 16 countries.<sup>6</sup>

# **Measuring ethnic clustering**

Ethnic clustering is an important indicator in nearly all discussions of ethnicity in politics. Easterly and Levine (1997) motivate their cross-country findings that ethnic fragmentation reduces growth with evidence from Kenya. For example, the Luo candidate for president won 75 percent of the vote in the Luo region and the Kalenjin candidate 71 percent of the vote in the Kalenjin region. The literature is silent on how precisely to measure ethnic clustering, however, generally suggesting only that ethnic groups disproportionately support a party or parties. This “lax” clustering standard allows a single ethnic group to cluster on multiple parties or multiple ethnic groups to cluster on the same party. However, the lax standard is likely to show high rates of clustering in countries in all multi-ethnic countries, regardless of whether parties use ethnicity to mobilize support. The polarization literature, in contrast, emphasizing homogeneous groups with heterogeneous 6 American National Election Studies, _http://www.electionstudies.org//nesguide/toptable/tab2a_2.htm_ . The ANES asks follow-up questions to probe the strength of partisan affiliation more deeply, identifying “leaning independents”, “weak partisans” and “partisans”. The sum of true independents and leaning independents ranges from 25 to more than 40 percent of the electorate. A comparable procedure in the Afrobarometer survey would likely have a similar effect on measures of partisan indifference, by identifying, among those who say they are “close to a party”, a large group that is “less close”.
to generate a 27 percent rate of unsatisfactory projects, in line with historical experience over the 19952012 period, in which the model was estimated.

**Table 4:  Basic Prediction Model Estimated Using All Projects 1995-2012**

|`Probit regression Number of obs = 2729`|
|---|
|`LR chi2(6) = 197.77`|
|`Prob > chi2 = 0.0000`|
|`Log likelihood = -1492.9973 Pseudo R2 = 0.0621`|

|`iegsat`<br>|`Coef. Std. Err. z`|`P>|z| [95% Conf. Interval]`|
|---|---|---|
|`lntotalcom`<br>|`.0436975 .0234646 1.86`|`0.063 -.0022923 .0896874`|
|`preptime`<br>|`-.0036075 .0016079 -2.24`|`0.025 -.006759 -.0004561`|
|`effdelay`<br>|`-.0040676 .0056336 -0.72`|`0.470 -.0151093 .0069741`|
|`planlength`<br>|`-.0031519 .0016926 -1.86`|`0.063 -.0064693 .0001655`|
|`ttlqual`<br>|`.9578754 .0963074 9.95`|`0.000 .7691164 1.146634`|
|`cpiaav`<br>|`.3770521 .0575975 6.55`|`0.000 .2641632 .4899411`|
|`_cons`<br>|`-1.175633 .226064 -5.20`|`0.000 -1.61871 -.7325552`|

The distribution of predicted unsatisfactory projects across regions and different stages of project life is summarized in Tables 5 and 6. The first column of Table 5 contrasts the prediction model with the ISR-DO ratings, pooling all 1591 projects. By assumption, the prediction model generates a 27 percent rate of unsatisfactory projects, in line with the historical data. This, however, is much higher than what is reflected in the ISR-DO ratings, which rate only 13 percent of projects as making unsatisfactory progress towards their development objectives. The predicted rate of unsatisfactory projects varies substantially across regions, ranging from 15 percent in ECA to 41 percent in AFR and 43 percent in MNA. In all regions other than ECA, the prediction model suggests a higher rate of unsatisfactory projects than the ISR-DO ratings, and sometimes by a substantial margin. The largest disconnect between the two is for projects in AFR -- while the ISR-DO ratings predict that only 10 percent of the Bank’s 483 active projects in the AFR region will lead to unsatisfactory outcomes, the corresponding prediction provided by the model is 41 percent.

It is also interesting to compare the predicted rate of unsatisfactory projects with the historical averages by region. If the prediction model (which is estimated pooling data for projects in all regions) is stable across regions, and if the distribution of explanatory variables in the prediction model is reasonably stable over time, we should expect to see regionally-disaggregated predictions that are similar to historical average rates of project success. This is reasonably true for most regions. For example, in AFR the historical rate of unsatisfactory projects is 38 percent while the prediction model 15