rafmacalaba/data-use-mentions-extended huggingface dataset:
discrimination in Pakistan and thus no affirmative action programs to mitigate its impacts. Caste mobility through land transactions is also extremely limited.

We construct a caste-status identifier that categorizes dozens of distinctly named caste/clan (zaat/biradari) groups into "high" and "low" caste. High-caste includes all such groups that self-identify on the basis of traditional access to land (zamindars). The low-caste group comprises zaats that were historically considered either out-castes (similar to the dalits in India) or were in clientalist relationships with zamindars as providers of services in the village economy; i.e. barbers, metalworkers, clothes washers, etc. Based on this definition, around 25% of the population from which we draw our sample consists of low-caste households, with the highest proportion (35%) found in Sindh province.<sup>6</sup> Village census data, summarized in Table 2, provide insight into residential patterns by caste in rural Pakistan. Villages typically contain many more distinctly named zaat/biradari groups than do their constituent settlements. Moreover, while more than half of all settlements are comprised of just one caste-type (mostly high), this is true of just 3% of villages. In short, settlements are far more segregated along caste/clan lines than villages.

We define a caste-type as dominant if it owns the majority of land in the settlement, based on land shares calculated from village census data. Anderson (2011), citing Dumont’s (1970) emphasis on economic power rather than numerical preponderance, uses precisely this definition of caste dominance for her study of Indian villages. Of the 496 settlements from which our sample children are drawn, only 76 (15%) are low-caste dominant, reflecting both the smaller low-caste population and their lower landownership.<sup>7</sup> All but five of these settlements are located in Southern Punjab or Sindh. Among the low-caste children in our sample, 48% reside in high-caste dominant settlements. By contrast, only
# **4. The Broken Relationship between Imports and Labor Markets: A Bartik Analysis**

# • **Overview of the Bartik Approach**

Canonical trade models predict that increased trade increases factor income and alters the wage bill. However, recent empirical evidence suggests that labor markets are not as flexible as most models assume them to be, implying that adjustment to changes in trade volumes can be very slow (Autor, Dorn & Hanson, 2013; Dix-Carneiro & Kovak, 2014). Furthermore, since the effects of trade are typically heterogeneous across sectors, individuals and regions are differentially exposed to sector-specific shocks, giving rise to distributional concerns.

In the previous section we analyzed how trade agreements impact trade flows from Egypt to the rest of the world. Liberalization policies do seem to have a positive effect on exports but negligible on imports. Yet, labor market outcomes have worsened. To estimate the relationship between labor markets and trade, we follow a common methodological framework to specific local conditions and employ a geography-based Bartik (1991) approach to quantify exogenous export demand shocks from the US and EU countries for Egyptian imports.

Bartik models exploit the spatial variation in sectoral-composition assuming that changes in sector-specific foreign demand is different across regions. In other words, regions more exposed to trade for a particular sector and with major employment for the same sector will be more affected from a trade shock particular to the sector. Differences in trade exposure of regions to this shock serves as an identification tool. For the trade exposure to be regionally identifiable, some barriers or rigidities in the labor market should exist. Sluggish or segmented regional labor markets are a fundamental principal of the approach (Raymond et al, 2020).

Figures 8 and 9 provide evidence of the concentrated trade exposure and segmented labor markets. Using the Enterprise Census (EC) from 2017 and the Labor Force Survey (LFS) from the same year, we show in Figure 8 that regions with a higher number of exporting firms (Panel A) are also the regions
Page 40

**Table 11. Minimum Unfined Offer**

|**Minimum Un-fined Offer**|**Percent**|**Cum.**|
|---|---|---|
|0|16.44|16.44|
|0.1|14.22|30.67|
|0.2|10.67|41.33|
|0.3|13.00|54.33|
|0.4|22.33|76.67|
|0.5|21.44|98.11|
|0.6|0.78|98.89|
|0.7|0.78|99.67|
|0.8|0.33|100|

Source: Serra, Barr and Packard (2011) 120. To investigate the correlations between parents‟ behavior in game and their behavior and attributes in the school context two series of multivariate analyses are conducted. The first of these is a series of Probit analyses focusing on a dependent variable that equals one if a parent in the third-party role participates in fining and zero if the parent never fines (columns 1 to 3 in Table 12). The second is a series of linear regressions taking the minimum un-fined offer chosen by each parent in the third-party role as the dependent variable (columns 4 to 6 in Table 12). The sample upon which this analysis is based is restricted to the parents who fine at least once. Positive (negative) coefficients in the Probits identify variables that are associated with parents deciding to participate in (abstain from) fining. Positive (negative) coefficients in the linear regressions identify variables that are associated with a lower (higher) degree of tolerance of teachers‟ selfish behavior.

121. We start our empirical analysis by testing whether parent class representatives are more likely to participate in holding teachers to account in the game, while controlling for parents‟
# **Results**

Including all the asset variables of the 1999 Census dataset that are related to household economics (presented in Table 1 above) results in an asset index with 15 components. Thus when we perform the PCA the dimension of the variable will be 15, and the proportion of variance explained from the first PC will be compared with the total variance with 15 variables. If a smaller number of variables are used, the proportion of the variance explained from the first PC will be higher, but this PC will be based on a small number of variables with supposedly smaller variance, but also with less information about the household. In this paper we apply the PCA to the full correlation matrix of all 15 variables. Table 2 presents the result of the variance of the principal components analysis.

**Table 2: Total variance explained by each component.**

|_Component_|_Eigenvalues_|_Differences_|_Proportion _|_Cumulative_|
|---|---|---|---|---|
|1|4.767|2.881|0.3178|0.3178|
|2|1.885|0.653|0.1257|0.4436|
|3|1.231|0.105|0.0821|0.5257|
|4|1.125|0.143|0.0751|0.6008|
|5|0.982|0.180|0.0655|0.6662|
|6|0.801|0.040|0.0534|0.7197|
|7|0.760|0.115|0.0507|0.7704|
|8|0.645|0.088|0.0430|0.8134|
|9|0.556|0.064|0.0371|0.8505|
|10|0.491|0.036|0
product-destination dataset. The authors find a positive relationship between export unit values and both distance and GDP per capita (GDPpc) but find a weak negative relationship between unit values and market size – as previously found in Baldwin and Harrigan (2011). Görg et al. explain that this might be evidence for quality differentiation across markets and they find that tariffs do not affect Hungarian firms’ fob unit values significantly.

The work of Kugler and Verhoogen (2009) focuses on product-level heterogeneity and investigates the hypothesis that input quality and plant productivity are complementary in generating output quality. They use product-level data for Colombian manufacturing plants to test the cross-sectional predictions of the model. Their findings support the quality-complementarity hypothesis that output prices are positively correlated with plant size within industries, with a stronger effect in industries with more scope for quality differentiation.

In conclusion, this short review of the literature shows that there is weak consensus on how firms adjust their prices according to the characteristics of their trade partners. However, there is evidence that some adjustment happens and that more investigation is needed to explain the different patterns observed in different countries. This work contributes to the literature on pricing behavior in an effort to analyze how exporters in small Least Developed Countries (LDCs) – normally considered price takers - behave with respect to pricing decisions and how firms adjust their prices according to their trade partner’s characteristics. This paper’s analysis benefits from a novel, firm-level dataset from Tanzanian exporters<sup>7</sup> . By accessing this disaggregated information, this paper is able to examine diverse aspects of pricing behavior across both firms and destinations.

# **3. Tanzania Trade Performance**

Tanzania is considered one of the best performers in Sub-Saharan Africa (SSA) for its economic performance over the last decade<sup>8</sup> . Over the 2003-2009 period the economy grew at an average rate of 6.7 percent, nevertheless GDP per capita performed less brilliantly (on average 3.7 percent over the 20032007
savings at the 5 percent level, both during and after the commitment period. The point estimates for both periods are just short of GHS 600, very close to total program savings of the median SSP participant (GHS 570).

In contrast, heavy overdrafters saved significantly less in other formal sector accounts, more than offsetting the additional savings stored with NVRB – as a result, point estimates on total savings are negative and not statistically significant. We reject equality of treatment effects on total savings for heavy and light overdrafters at the 1 percent level both during and after the commitment period.

Columns 6-8 of Table 4 show that this heterogeneity persists when accounting for debt. Only heavy overdrafters took on significantly more debt during the commitment period. As a result, treatment effects on savings net debt are large and negative for heavy overdrafters, while treatment effects on savings net debt are similar to treatment effects on total savings for light overdrafters. Here, we reject equality of effects by overdrafter status at the 5 percent level during and after the commitment period.

Given that SSP helped light overdrafters save more without taking on new debt, where did the additional money come from? Appendix Table A8 presents impacts on income and expenditure. Given the modest monthly changes needed to generate our point estimates, we cannot definitively identify consumption or income as the source of the new savings, although We do find some significant survey-based evidence that light overdrafters in the treatment group earn more. We view this result with caution, as we only collected income data in one follow-up survey. Table A8 also helps rule out the hypothesis that heavy overdrafters leverage SSP payouts and new debt to make lumpy investments in the post-commitment period: SSP had no significant impact on investment for either group.

# **3.5 Mechanisms and Discussion**

Overall, our results for heavy overdrafters are consistent with a model of partially-naïve hyperbolic discounting, e.g. where workers sign up for SSP expecting that it will help with their commitment problems, but fail to account for the
Policy Research Working Paper 10006

# **Abstract**

Informal public transport has been growing rapidly in many developing countries. Because urban infrastructure development tends to lag rapid population growth, informal public transport often meets the growing gap between demand and supply in urban mobility. Despite the rich literature primarily focused on formal transport modes, the informal transport sector is relatively unknown. This paper analyzes the demand behavior in the “informal” minibus sector in Antananarivo, Madagascar, taking advantage of a recent user survey of thousands of people. It finds that the demand for informal public transport is generally inelastic. Essentially, people have no other choice. While the time elasticity is estimated at −0.02 to −0.05, the price elasticity is −0.05 to −0.06 for short-distance travelers, who may have alternative choices, such as motorcycle taxi or walking. Unlike formal public transportation, the demand also increases with income. Regardless of income level, everyone uses minibuses. The estimated demand functions indicate that people prefer safety and more flexibility in transit. The paper shows that combining these improvements and fare adjustments, the informal transport sector can contribute to increasing people’s mobility and reducing traffic congestion in the city.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@worldbank.org.

_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
# **Bibliography**

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- Ahir, H., Bloom, N. & Furceri, D. (2022), The world uncertainty index, Working Paper 29763, National Bureau of Economic Research. **URL:** _http://www.nber.org/papers/w29763_

- Akcigit, U., Chen, M. W., Diez, M. F. J., Duval, M. R. A., Engler, P., Fan, J., Maggi, C., Tavares, M. M. M., Schwarz, M. D. A., Shibata, M. I. et al. (2021), _Rising corporate market power: emerging policy issues_ , International Monetary Fund.

- Autor, D., Dorn, D., Katz, L. F., Patterson, C. & Van Reenen, J. (2020), ‘The Fall of the Labor Share and the Rise of Superstar Firms*’, _The Quarterly Journal of Economics_ **135** (2), 645–709.

**URL:** _https://doi.org/10.1093/qje/qjaa004_

- Bessen, J. (2022), _The New Goliaths: How Corporations Use Software to Dominate Industries, Kill Innovation, and Undermine Regulation_ , Yale University Press. **URL:** _http://www.jstor.org/stable/j.ctv2jn91q7_

- Casella, B., Borga, M. & Wacker, M. K. (2023), Measuring Multinational Production with Foreign Direct Investment Statistics: Recent Trends, Challenges, and Developments, IMF Working Papers 2023/113, International Monetary Fund. **URL:** _https:
in education (5.4 and 5.5 respectively), as opposed to 2012-2016 where spending represented 4.7 and 4.2% of GDP, respectively. Importantly, further analysis will be needed to understand to what extent this decline can be explained by differences over time in data availability. When looking at health and education spending, LAC stands out as being the region with steep increases in both sectors (Table 5).

The average spending for Social Protection programs, as defined in section II, is equal to 1.6% of GDP in the period 2012-2016, with Europe and Central Asia displaying the highest share (2%) and South Asia the lowest (1%). Conclusions about changes over time cannot be drawn given that each country only appears once in the ASPIRE data set.

12
br>(ODB)|Global/ Annual|116|2013-2017|Nested weight|46|15%|30%|37%|0%|15%|Expert Survey|
|3|Global Data Barometer<br>(GDB)|Global/ Biennial|109|2019-2021|Nested weight|55|9%|4%|20%|16%|45%|Expert Survey|
|4|Statistical Performance<br>Indicators & Index (SPI)|Global/ Annual|186|2016-2022|Nested weight|51|10%|16%|31%|20%|24%|Public International<br>Databases (86%), NSO<br>Websites (14%)|
|5|Statistical Capacity<br>Indicators & Index (SCI)|Global/ Annual|145|2004-2020|Simple<br>arithmetic<br>average|25|0%|0%|40%|20%|40%|Public International<br>Databases (80%), NSO<br>Websites (20%)|
|6|Ibrahim Index of African<br>Governance Statistical<br>Capacity (IIAG)|Regional/ Annual|54|2010-2022|Simple weight|3<br>statistical<br>capacity<br>indicators|0%|4%|37%|19%|41%|Expert Survey (33%),<br>ODIN (33%), SCI<br>databases (33%)|
|7|EU Snapshot tool|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|131|6%|12%|10%|8%|64%|Expert self-<br>assessment|
|8|UN NQAF self-checklist|Self-assessment/<br>N.A.|N.A.|N.A
dollars ( _GDP per capita_ ). The variable is lagged by two years to allow for lagged effects. The data source is WDI, World Bank.

The availability of human capital is an important driver of growth and overall economic development. One possibility is that greater availability of human capital can push individuals to seek job in the formal sector rather than work in the informal sector. Higher levels of human capital are also likely to contribute positively to firm growth. Thus, failure to control for human capital availability can cause the observed informal competition and employment growth relationship to be more negative than what it is truly. To guard against this problem, we control for a measure of human capital that equals the gross enrollment rate in the country in primary education ( _Primary Enrollment_ ). To avoid too many missing data, average values over three years prior to the year covered by the ES are used (for more details, see Table 1). Data source for the variable is WDI, World Bank.

Our last macro-level control is the level of inflation in the country. While low and moderate level of inflation may reflect fast growing countries, high levels may signal macro-economic instability with adverse effects on employment growth among private firms. Thus, a systematic correlation between employment growth and inflation cannot be ruled out. If informal competition also varies systematically with inflation, our main results could suffer from spurious correlation. Thus, we control for inflation as measured by the annual change in the consumer price index ( _Inflation_ ). Data source for the variable is WDI, World Bank.

The controls mentioned above are used for our main or baseline estimation (baseline controls). There are additional factors that may impact employment growth and can also be potentially correlated with informal competition. In the robustness section, we check if our main result survives controls for these factors. We consider factors associated with differences in 15
especially for agricultural commodities, are collected systematically in these countries. While this provides a snapshot of costs for widely produced commodities, it is restrictive in quantifying the potential implications of transport cost reductions on trade flows and welfare. One way to bridge this gap and generate simulated trade flows is to start by surveying firms from various sectors of interest in order to estimate their freight demand, as Herrera Dappe et al. (2019) do for Bangladesh. In the absence of a comprehensive nationwide commodity flow survey, the authors sample four thousand economic establishments across the six largest freight-intensive sectors and across the country in order to understand where freight is likely to be originated. As a secondary data source informative about flows, they use truck traffic measurement from key arteries. Fitting observed traffic to the Freight Origin-Destination Synthesis (FODS) model of Holguin-Veras and Patil (2008), they estimate origin-destination flows. They complement this with links between ports and exporting or importing firms obtained from customs data, as discussed in Section 5.1. The fitted model facilitates the construction of a domestic trade flow matrix for Bangladesh.

A common limitation of all the approaches discussed so far is their inability to separate transport costs incurred in the first- and last-mile from those incurred in the main haul. The sizable share of first- and last-mile costs are well-known in transportation economics. However, unless explicitly asked, most surveys do not differentiate them from total shipment costs, thereby missing a potential source of nonlinearity in the transport cost function. A recent example overcoming this shortcoming by synthesizing multiple data sources and methodologies is Kebede (2019). Studying of the impact of the Universal Rural Road Access Program in Ethiopia, he uses surveybased information from the Ethiopian Socioeconomic Survey on costs incurred by farmers while transporting recorded agricultural commodities from the farm-gate to the nearest market town. For an average distance of 12 km traveled, farmers report ad-valorem transport costs of 11.4% on average, with the median at 6.5%. Kebede then uses responses at the village
check for our empirical findings. In order to check the consistency of our empirical results, we use an alternative variable of institutional quality from a source other than ICRG. We employ the _government effectiveness_ index from the Worldwide Governance Indicators, 2015 Update database of the World Bank.<sup>5</sup> The Government effectiveness reflects “perceptions of the quality of public service, the quality of civil service and the degree of independence of political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to these services.” It was initially comprised between -2.5 and 2.5 with high values indicating more efficient governments. However, we normalize it between 0 and 5.

Table 4 displays the estimation results when we perform the same regressions as in the previous

> 5The whole data set comprises data on the quality of governance provided by “a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, international organisations, and private sector firms.”

20
<!-- Start of picture text --> Figure 6. Policy effect 2010-17, by types of Figure 7. Policy effect 2010-17, by market income<br>households, age groups and locations, change in deciles, change in consumable income, %<br>consumable income, % 10<br>10<br>5<br>5<br>0 0<br>-5<br>-5<br>-10<br>-10<br>1 2 3 4 5 6 7 8 9 10 Total<br>direct taxes and SIC pensions<br>other transfers indirect taxes<br>direct taxes and SIC pensions total<br>other transfers indirect taxes<br>total Source: Authors’ calculations using RUSMOD-2010 and<br>2017 and 2010-2016 RLMS-HSE-data.<br>Source: Authors’ calculations using RUSMOD-2010 and<br>2017 and 2010-2016 RLMS-HSE-data.<br><!-- End of picture text --> While the total increase in average consumable income in Russia over the period 2010-2017 was around 60 percent, the policy effect was negative on average. The average consumable income contraction driven by tax-benefit policy related factors in line with the above explained methodology contracted by 2 percent. Most of the population groups lost because of policy changes (Figure 6 and Figure 7). However, it turns out that the only big group that benefited from the changes in tax-benefit policies in Russia during 2010-2017 were pensioners.

As the largest increase in disposable income due to policy changes in Russia over his period can be attributed to a growth in pensions (by 4 percent in real terms), the only net beneficiaries of the policy changes over 2010-17 were people of the retirement age and households with pensioners (Figure 6 and Figure 7). As most of pensioners do not receive any market income, the beneficiaries of policy effect are mostly concentrated in the lower part of the distribution if deciles are defined based on the market income.