rafmacalaba/datause-encoder-data huggingface dataset:
The model’s coefficient estimates are not easy to interpret so we provide quantitative context via counterfactual analysis. In our thought experiment we move policy in all WTO member countries to the best-practice frontier. We conduct this exercise for all 12 policy bundles collectively, and then for each of the individual policy bundles we identify as significant. In our model, a move by all WTO members to best practice in all policies reduces the average number of expected days in import customs by 1.6. The same exercise implies an average reduction in days in export customs of 2.0. While the effects of policy are important in the case of import customs, reform does not eliminate cross-country variation in performance. In the case of export time in customs, though, policy explains almost everything. When we simulate countries’ adoption of best-practice customs procedures, the variation in the expected time in export customs almost entirely disappears. We speculate that the oversight of import customs is inherently more difficult than of export customs, so policy improvements alone are not as able to reduce cross-country variation in performance on the import side as on the export side.

In order to put our counterfactual estimates in perspective we apply an estimate of the tariffequivalent of a day in transit from Hummels and Schaur (2013) to our estimates of reduced time needed to clear customs. Using U.S. import data Hummels and Schaur estimate the tariff equivalent value of time in trade to be between 0.6 and 2.1 percentage per day _ad valorem_ . It is likely that time is less valuable in developing countries with poorly performing customs, so we use the lower end of these figures for our subsequent calculations. Combining a 0.6 percent tariff equivalent of a day with our estimate of average days reduced through TFA implementation provides a suggestive estimate: full implementation of the TFA would be roughly equivalent to a cross-country mean tariff cut of 0.9 percentage points on the import side and a 1.2 percentage point reduction in an export tax equivalent on the export side.

These average estimates obscure substantial cross-country variation in the size of implied trade cost reductions. Most developed countries are already quite near global best practice in trade facilitation policy, and we estimate only small improvements in developed countries. The TFA implies much larger policy changes in the developing world.<sup>6</sup> There is also substantial variation in policy within the developing world. We find that large traders tend to have better trade facilitation policies, a fact that limits the potential size of global gains from the TFA. A simple back-of-the-

> 6The TFA offers countries some flexibility on the timing and speed of reforms. Moreover some commitments are made on a ‘best endeavours’ basis. Our quantitative model assumes full implementation, and as such, our estimates reflect a best case scenario for implementation of the TFA.

3
To this end, we exploit the Ghana Urban Household Panel Survey (GUHPS), a data set unique in the African context and one of the very few longitudinal datasets in either the developing or advanced world containing several measures of subjective satisfaction with work, life and finances. By income, Ghana is broadly representative of sub-Saharan Africa and offers insight into low and low middle income countries more generally.<sup>4</sup> The GUHPS permits us to study job satisfaction across sectors, potentially controlling for unobserved heterogeneity, and thereby to better characterize developing country labor markets and assess how well the developed country literature generalizes to them. Using conventional ordered categorical data models, we first test for the existence of self-employment and small firm satisfaction premia. Since studies of job satisfaction in developing countries are scarce (see Clark and Senik, 2010a, for a review of the literature) it is unknown whether this finding translates to poor countries where such jobs often constitute the vast majority of employment, sometimes accounting for 70-80% of the labor force (see Kingdon et al., 2005).<sup>5</sup> The few studies of job satisfaction in developing countries that we are aware of tend to focus on a select subset of workers (Mulinge and Mueller, 1998), and rely exclusively on cross-sectional datasets (Pratap and Quintin, 2006; Perry et al., 2007; B ́oo et al., 2010; Pag ́es et al., 2008). In particular, we test whether the relative satisfaction premium for self-employment and informal salaried employment are more consistent with the competitive view emerging from the OECD literature, or a dualistic conceptualization of the informal sector: whether there is a positive satisfaction premium associated with formal jobs that would signal that markets are not competitive, as well as identifying which characteristics may be associated with it.

We then confront heterogeneity in conditional satisfaction head on by means of mixed (random parameter) ordered probit models. These models extend the mixed logit model to ordinal responses and can be seen as a generalization of the latent class techniques used by Clark (2004). The mixed model not only enables us to identify the mean premium associated

> 4Ghana’s 2010 GNI/capita of $US 1,230 is roughly the average for sub-Saharan Africa ($US 1,176). Though reliable representative labor market data are fairly sparse, our estimates of the share of selfemployment is broadly similar to that for sub-Saharan Africa.

> 5The ILO (2002) estimates that informal sector self-employment alone accounts for 53% of all nonagricultural employment in Sub-Saharan Africa and about a third of all non-agricultural employment in North Africa.

4
an assumption about the size of the economy. Thus, total value added is normalized at 100. This merely amounts to a choice of units and does not affect the cyclical properties n x of the model. Set λ = λ = 14.86 . Export demand elasticities are taken from Senhadji and Montenegro (1999) who report estimates for individual developing countries. Table 5 summarizes their estimates for LAC countries. The average long-run price and income elasticities are -1.25 and 1.06, respectively.

The proportion of restricted households (π) is taken from López et al. (2000). These authors provide panel data estimates for the proportion of non-Ricardian consumers in developing countries in the range of 60%-64%. Set π = 0.60 . To compute the steady state allocations of the two types of households, labor effort and consumption are treated symmetrically: N j = N oj = N rj , j ∈ {n, x}, C n = C on = C nr and C m = C om = C mr . Table 6 summarizes the results of the calibration exercise.

It remains to calibrate the stochastic structure of the economy. Shocks are jointly covariance-stationary stochastic processes. The vector of exogenous shocks, represented ∗ x n x n T by Z t = [ Yt , p t s, t , Z t , Z t , G t ] , has the following time series representation: The elements of<sup>ς are normally distributed and uncorrelated with</sup> t<sup>ς</sup> t − q<sup>,</sup> q > 0 , and the eigenvalues of Ξ all lie inside the unit circle. Z is the vector of unconditional means given by Z = [1, 1, 0.055, 1, 1, 14.5] T . The autocorrelation matrix Ξ and the variancecovariance matrix of innovations Ω are computed individually for nine LAC countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Mexico and Peru) and then averaged to get a representative estimates for the region. The entries in the autocorrelation matrix and in the main diagonal of matrix Ω are obtained from fitting univariate AR(1) processes. The remaining parameters - the off-diagonal elements of Ω - are computed by using the residual terms of these regressions. The exceptions are the covariances associated with the LEI spread<sup>s for which there is not sufficiently long,</sup> t annual time series information. Y is measured by the U.S. per capita GDP and taken t ∗ from the World Bank World Development Indicators (WDI) database;<sup>s is the average</sup> t LAC LEI spread taken from the JP Morgan bond database; individual country data for the terms of trade (p<sup>x</sup> t )<sup>and government spending</sup> (G<sup>n</sup> t )<sup>as well as the data required to</sup> compute sectoral Solow residuals (Z nt , Z xt ) are retrieved from the WDI database. Solow residuals are estimated as in Kose (2002). The set of yearly data generally spans the period from 1972 to 2000. The results of this parameterization strategy are summarized by the following matrices: 21
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# **Table 4.5: Average Footprints by Travel Class, Relative to Averages Across Classes, As Functions of Load Factors**

|||**Widebody**|||**Single Ais**|**le**||
|---|---|---|---|---|---|---|---|
||**Load**<br>**Factor**|**.3**|**.6**|**.9**|**3.**|**.6**|**.9**|
||Economy|1.92|0.96|0.64|2.06|1.03|0.69|
|**Class**<br>**Type**|Business|4.38|2.19|1.46|3.88|1.94|1.29|
||First|8.96|4.34|2.90|6.01|3.01|2.00|

Source: Authors’ calculations For business class, the average footprints in Table 4.5 are close to the multiple of 2 currently applied with the ICAO calculator for premium classes, given an average load factor of 0.6 for this class, which is reasonable from past experience. The ICAO calculator today makes no downward adjustment (relative to the “average footprint”) for economy class (represented here by these factors being less than unity). The relative factor ratios for business to economy class are thus somewhat greater here (3.04 and 2.51 respectively, instead of 2 as in the ICAO calculator).

**Table 4.6: Footprints by Travel Class, Relative to the Footprint of an Average Passenger, assuming Load Factors of 0.40 for First Class, 0.60 for Business Class, and 0.80 for Economy Class**

|**Class**|**Wide-**|**Single-**|
|---|---|---|
|**Type**|**Body**|**Aisle**|
|Economy|0.76|0.82|
|Business|2.30|2.07|
|First|6.89|4.79|

Source: Authors’ calculations The average footprints for business and first class travel, relative to economy class travel, can easily be greater than the numbers in Table 4.5. Table 4.6 provides what we view as a plausible example, using the overall load factor estimate of 0.71, and with load factors of 0.4 for first, 0.6 for business and 0.8 for economy class<sup>20</sup> . The ratios of numbers from table 4.6 for wide-body aircraft, for example, show the footprint from business class passengers being 3.04 times that of

> 20 These are still hypothetical. The example is however consistent since when an aircraft’s relative cabin sizes correspond to industry averages, the overall average load factor is 0.71 given the assumed class-specific factors.

15
is restored. Notable in this respect are the changes associated with the distance variables in Tanzania, the rented/other plot tenure category in Uganda, and the household size and the number of plots in the holding in both settings.

# 6. CONCLUSION

This paper attempts to address three interrelated questions concerning the use of the GPS technology for land area measurement in household surveys in developing countries: (i) Are the land area statistics informed by partial GPS-based land areas subject to selection bias?, (ii) Is it possible to fill the gaps in GPS-based land area measurements by using statistically valid techniques and other information available in the survey?, and (iii) Does having _complete_ GPS-based land areas matter for the analysis of policy relevant issues, such as the inverse scale-land productivity relationship? Our analysis confirms that the missingness in GPS-based plot area measures is a valid concern, not only because it is pervasive, but also because plots that do not get measured are not random picks among plots that households own or cultivate. This may be of particular concern in the context of large, nationally-representative household surveys, which suggests that the prevailing survey protocols for GPS-based plot area measurement and its supervision should be revisited to minimize the extent to which GPS-based plot area measurements are not taken.

However, it is important to recognize that no matter how effective the protocols and the field supervision could be, a non-negligible degree of missingness in GPS-based plot areas data is bound to remain, as completely eliminating it is unfeasible without incurring prohibitive costs. This paper has, therefore, sought not only to advance our understanding of the potential drawbacks of current GPS-based land area data collected as part of household surveys, but also to explore ways to remedy some of these shortcomings through sound statistical techniques. The good news is that the missingness is largely driven by observable variables. The better news is that the available, yet imprecise, farmerreported plot areas are powerful predictors of the observed GPS-based plot areas, and that these self-reported measures can be effectively used in generating reliable simulations of missing GPS-based plot areas using multiple imputation. Advances in computing power and the availability of multiple imputation routines in an increasing number of statistical software packages make the routine adoption and application of these techniques feasible even in developing country settings.

This is very timely as policies enabling open access to data are spreading quickly across the world, and GPS technology is becoming a standard feature in survey work. Having data producers, as opposed to data analysts, deal with handling missing GPS data has the advantage of (i) reducing the duplication of work, (ii) increasing the comparability between the final data used to perform analyses, and (iii) allowing more analysts (who do not 18
CPBA; this is nearly equivalent to the proportion of children attending a CPBA in treatment communities, as very few children in control communities attended a CPBA or playgroup. Approximately 21 percent of 3- and 4-year-olds in treatment communities attended CPBAs. Finally, the school readiness intervention had a positive effect on preschool participation for boys of mothers without high-school education but had a negative effect on those of mothers with high-school education.

# _Cost effectiveness_

The annual recurrent cost of the intervention was US$ 67,802.75. This included visits by the intervention team to support community leaders in setting up and running CPBAs as well as training and mentoring of volunteer facilitators. This cost excludes the opportunity cost of the facilitators’ time as well as any donations, either in-kind or monetary, provided by community members. Effect sizes of the school readiness intervention are per child, regardless of whether they attended a CPBA. All children in treatment communities aged 0 to 5 are potential beneficiaries. The number of children aged 0 to 5 in treatment communities was estimated to be 5,373 based on the 2017 TEHCI. The subsequent annual recurrent cost per child was US$ 12.62.

A common measure of cost effectiveness in the impact evaluation literature is standard deviations per US$100 (e.g.: Abdul Latif Poverty Action Lab 2014). Table 5 presents the effect sizes of the intervention in terms of a standard deviation per US$100 per child; this is equivalent to the effects presented in Table 4 divided by the cost per child in 100s of US$. 3- and 4-yearolds sampled in the 2017 TEHCI would have been exposed to the school readiness intervention for approximately two years. The cost per child of two years’ exposure was US$ 25.24. Positive effects were found on overall school readiness among girls of mothers with no high-school education and on the average of the literacy domains for both girls and boys of mothers with no high-school education. The effect size per 100 US$ for these sub-populations was 0.79, 0.75 and 0.67 standard deviations, respectively.

19
# **References:**

Ahmad, Ehtisham and Nicholas Stern. 1986. "Taxation for Developing Countries." The Development Research Programme, London School of Economics.

Bahl, Roy. 1971. "A Regression Approach to Tax Effort and Tax Ratio Analysis." _International Monetary Fund Staff Paper_ :18, pp. 570-612.

Bird, Richard M. 1978. "Assessing Tax Performance in Developing Countries: a Critical Review of the Literature," in _Taxation and Economic Development: Twelve Critical Studies_ . J.F.J. Toye ed. London, UK: Totowa, N.J., pp. 33-61.

Bird, Richard M. 2007. "Tax Challenges Facing Developing Countries: A Perspective from Outside the Policy Arena." Bird, Richard M. and Pierre-Pascal Gendron. 2007. _The VAT in Developing and Transitional Countries_ : Cambridge University Press.

Bird, Richard M., Jorge Martinez-Vazquez, and Benno Torgler. 2004. "Societal Institutions and Tax Efforts in Developing Countries." _Working Paper 04-06_ . Andrew young School of Policy Studies, Georgia State University: Georgia, Atlanta.

Chelliah, Raja J., Hessel J. Baas, and Margaret R. Kelly. 1975. "Tax Ratios and Tax Effort in Developing Countries." _IMF Staff Papers_ .

Chen, Duanjie, John Matovu, and Ritva Reinikka. 2001. "A Quest for Revenue and Tax Incidence in Uganda." _IMF Working Paper 01/04_ . International Monetary Fund: Washington, DC.

Fox, William F. and Tami Gurley. 2005. "An Exploration of Tax Patterns around the World." _Tax Notes International: Special Reports_ , 37:9, pp. 793-807.

Gauthier, Bernard and Ritva Reinikka. 2006. "Shifting Tax Burdens through Exemptions and Evasion: an Empirical Investigation of Uganda." _Journal of African Economies_ .

ICRG. 2006. "International Country Risk Guide Index." The PRS Group: New York.

Lora, Eduardo and Mauricio Olivera. 2006. "The Political Determinants of Taxation (preliminary version)." LACEA-PEC.

Lotz, Jorgen R. and Elliott R. Morss. 1967. "Measuring "Tax Effort" in Developing Countries (Evaluation de l'effort fiscal dans les pays en voie de developpement) (Medicion del "esfuerzo tributario" de los paises en desarrollo)." _Staff Papers - International Monetary Fund_ , 14:3, pp. 478-99.

- 19 -
# Corruption as a Push and Pull Factor of Migration Flows:

# Evidence from European Countries

Andrea Bernini<sup>∗</sup> Laurent Bossavie<sup>†</sup> Daniel Garrote-S ́anchez<sup>‡</sup> Mattia Makovec<sup>§</sup> _JEL classification:_ D73, F22, R23 _Keywords:_ Corruption, International Migration, Regional Migration, Gravity Model

> ∗University of Oxford; andrea.bernini@economics.ox.ac.uk

> †The World Bank; lbossavie@worldbank.org

> ‡The World Bank; dgarrotesanchez@worldbank.org

> §The World Bank; mmakovec@worldbank.org
experiencing heightened entry of industries that are less land and building intensive after the GQ upgrades—and no consistent patterns are observed for districts 10-50 km from the GQ network.

These patterns suggest that the GQ upgrades may have helped with the efficient sorting of industries across locations. Ghani et al. (2012) find that infrastructure aids efficient sorting of industries and plants within districts, and these patterns show a greater efficiency across districts. Many studies have warned about the misallocation in the Indian economy (e.g., Hsieh and Klenow 2009), and these results suggest better connectivity across districts may be able to reduce some of these distortions. More speculatively, these results also suggest that infrastructure may improve upon land market distortions caused by the “rent Raj” and similar.<sup>10</sup>

# **Highways and Spatial De-Concentration**

The development and growth of the Indian economy in the last two decades has been accompanied by widening spatial disparities. Cities like Gurgaon in Haryana and Bangalore in Karnataka have experienced high growth in economic activity and real estate developments, while many other places remain mired in poverty and stagnation. These differentials are common to many developing economies (e.g., World Development Report 2009), as well as advanced economies. For instance, China’s growth is attributable mainly to its coastal provinces. However, unlike China, growth in India’s moderate-sized cities is relatively lower.<sup>11</sup> Desmet et al. (2012) argue that manufacturing in India is slowly moving away from high-density districts to districts that are less congested, allowing industrial activity to spread more equally across space.

In this section, we examine whether investment in infrastructure such as highways can play a role in facilitating the shift of manufacturing activity to intermediate-sized districts. We group districts into three bins based on their population density: low-density districts are below the median density for India (up to 353 persons per square km); moderate-density districts are those in the middle two quartiles (353-693 10 We also find evidence of heightened entry following GQ upgrades within industries in the top quartile of the skill distribution in nodal districts and in districts 0-10 km from the GQ network. Unlike land-intensity, this entry is uniform and does not display a sorting pattern.

11 McKinsey Global Institute (2012) highlights the increasing prominence of intermediate cities in many economies, predicting they will account for half of future GDP growth. McKinsey Global Institute (2010) argues there are between 70 and 100 medium-sized specialist cities in India whose economies focus on an anchor sector like manufacturing, resources, transportation, or tourism.

19
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