rafmacalaba/datause-encoder-data huggingface dataset:
effects, as is done in column 2, reduces the estimated reduction in the markets share premium on being connected to Suharto associated with regime change to 1.2 percentage points. Column 3 replicates the specification presented in column 2 but now uses time-varying explanatory variables, which allows us to use the full sample. This hardly impacts the estimated reduction in the connectedness premium, which is now more precisely estimated to fall by 1.3 percentage points.

Columns 4-7 presents results from our event study specifications, in which both the PC family dummy and other firm and industry controls are interacted with a full set of year dummies. The regressions presented in columns 4, 5 and 6 are analogous to those presented in columns 1, 2 and 3, respectively. The OLS regression presented in column 4 confirms that connected firms enjoyed a significant market share premium before his fall. After his fall, this market share premium on Suharto connections was not only much smaller, but also no longer significant (except in 2003).<sup>13</sup> Replicating this specification but including firm fixed effects, as is done in column 5, which presents our preferred specification (1), yields similar results. The coefficients on the interactions between Suharto era year dummies (i.e. 1993, 1994 and 1995) and being politically connected are typically small and not significant, attesting to the absence of pretrends. By contrast, the coefficients on the interaction between being a Suharto family firm and the postSuharto era year dummies are consistently negative, hovering between -1.7 and -0.4 percentage points. They are significant at the 5% level in 2001 and at the 10% level in 2002. These results are also depicted in Figure C4.1 in the online appendix. When we estimate the same specification using time-varying measures, as is done in column 6, we find slightly larger reductions in the connectedness premium, which, are significant at the 5% level in 2001 and 2002, and at the 10% level in 2000 and 2005. The reduction in the premium on being connected is more precisely estimated (and hence more significant) when using time-varying measures partly because we are using a larger sample and not confining attention to firms already operating in 1996.

Finally, column 7 replicates the analysis presented in column 5 but confining the analysis to firms selected using propensity score methods based on firm age, foreign and state ownership, importing and exporting over 1993-1996 (see table C4.1 in the online appendix for balancing tests and Table 2 for descriptive statistics). This restricts the sample to firms that are most alike based on these observables, allowing for a more stringent test on whether connectedness itself matters or if these types of firms more

> 13 Tests for the joint significance of the PC family dummy and interactions between the PC dummy and year dummies are not presented to conserve space but available upon request.

14
for a family firm) can face several forms of penalties, ranging from fines and other administrative costs to criminal prosecution, depending on the type of work performed by the child. Children below the minimum employment age are not allowed to be selfemployed either. Note that the severity of the punishments for employers is greater for hiring a child worker than hiring an adult informal worker.<sup>14</sup> Parents can be penalized in two ways. First, they can lose guardianship of their children and, second, they can be prosecuted depending on the work performed by the child. It is important to emphasize that in neither case is the child subjected to any sort of penalty. The ultimate goal of the law is to protect the child (see Medeiros Neto, 2013).

One can question the enforceability of such a law in a country where informal activities are widespread. In the formal sector, the enforceability of the law is almost deterministic. The Ministry of Labor and Employment is the institution responsible for issuing the working permits. With the change in the law, it stopped issuing work permits to individuals who turned 14 after the law passed. Thus, the ban policy split similar children into two groups: those banned from the formal labor force (treatment group) and those unaffected by the ban (control group). Indeed, as shown below, while the law was perfectly enforced in the formal sector, it was less so in the informal sector.

If the law had no effect on the informal sector, its effect on child labor would have been very small as the formal labor participation was around 1 to 2 percentage points before the law change. If some children in the formal sector simply shifted to the informal sector after the ban, the law’s effect on child labor would have been negligible or even positive, if increase in the informal sector exceeded fall in the formal sector. In fact, Piza and Souza (2016) show that the decrease in the incidence of child labor was mostly driven by the fall in informal work. It seems some employers decided to no longer employ children under 16 to avoid legal consequences. It is reasonable to assume that the cost of verification of child-labor practices in firms is lower than the cost of verification of any other type of informal labor contracts.

The Federal Constitution of 1988, the Law No. 8069 from July 13, 1990 _(Lei do Estatuto e do Adolescente)_ , and Law No. 9394 from December 20, 1996 _(Lei das_

> 14 In Brazil, all formal labor contracts are recorded in the worker register card called _carteira de trabalho_ . This document is issued by the Ministry of Labor and Employment. A formal worker has access to social pension and other benefits (for example, vacation) other than market-rate wages. An informal worker is just paid earnings and thus has no work history formally recorded.

10
estimated to be Tk. 3576 crore (US$ 421 million – model 1a – Table 4) when matching by procuring entity, and Tk. 3163 crore (US$ 372 million - model 2a – Table 4) when matching by financial year.

Bangladesh has had an inflation rate of about 6% since 2012, when the e-procurement system was introduced. The inflation rates are given in the table below.

TABLE 38: BANGLADESH INFLATION RATES BY YEAR (CPI & GDP DEFLATOR)

|Year|Inflation, consumer<br>prices (annual %)<br>34|Inflation, GDP<br>deflator (annual<br>%)<br>35|
|---|---|---|
|2011|11.40%|7.86%|
|2012|6.22%|8.16%|
|2013|7.53%|7.17%|
|2014|6.99%|5.67%|
|2015|6.19%|5.87%|
|2016|5.51%|6.73%|
|2017|5.70%|6.28%|
|2018|5.54%|5.60%|

The savings are adjusted for inflation using the inflation deflator rates as it is indicative of the rate of change of prices of the economy. Adjusting for inflation the savings was estimated to be Tk. 4,004 crore (US$ 471 million – model 1a – Table 4) when matching by procuring entity, and Tk. 3,606 crore (US$ 424 million - model 2a – Table 4) when matching by financial year of tender at the current taka value for the 69,240 tenders.

2. Government Administrative Cost Savings

- a. Advertisement Cost Savings: Among the major savings that has resulted from the shift to the electronic procurement system was the decrease in the need for newspaper advertisements. All electronically administered tenders are published on the e-procurement portal and hence to need to advertise it decreases significantly. The advertisement cost savings is estimated by the difference of the product of the average manual advertisement cost and the predicted number of advertisements had the electronic tender been not done electronically and the product of the average e-procurement advertisement cost and the number of advertisements of that electronic tender. The number of advertisements of an

> 34 Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. (International Monetary Fund, International Financial Statistics and data files.)

> 35 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. (World Bank national accounts data, and OECD National Accounts data files.)

71
**Table 2: Dropout Rates by Income and Ability.**

|||Abi|lity qui|ntile|||
|---|---|---|---|---|---|---|
||1|2|3|4|5|Total|
|5+ MW|81.36|65.83|61.48|52.13|39.04|44.73|
|3-5 MW|74.23|69.44|62.21|57.86|43.77|51.33|
|2-3 MW|68.54|67.58|63.73|57.68|46.53|55.09|
|1-2 MW|71.59|66.64|62.21|57.66|50.56|57.82|
|_<_1 MW|69.04|67.95|61.34|55.94|50.30|58.71|
|Total|70.99|67.44|62.37|56.96|45.84|54.36|

_Source_ : Calculations based on SPADIES for students from the 2006 entry cohort (first semester). _Notes_ : Each cell reports percent of students from a given income bracket and ability quintile who drop out of their bachelor’s program. A student is classified as a dropout if she does not graduate within eight years. Income is reported in brackets; MW = monthly minimum wage. Ability is reported in quintiles of standardized Saber 11 scores. Quintile 1 is the lowest.

early as in year 1— is a powerful predictor of final outcomes. This point is further illustrated in panel b, which classifies students into tiers by the end of year 1. Consistent with panel a, it shows the strong predictive power of early performance—as explained by the next stylized fact.

**Figure 4: First-year Classes Completed and College Outcomes.**

<!-- Start of picture text --> 23 60<br>21<br>19 45<br>17<br>15 30<br>13<br>15<br>11<br>9<br>1 2 3 4 5 0<br>Ability quintile Tier 1 Tier 2 Tier 3 Tier 4<br>On−time graduate Late graduate On−time graduate Late graduate<br>Dropout after 1st year Dropout 1st year Dropout 1st year Dropout 2nd year<br>All students Dropout after 2nd year<br>a. First-year classes completed b. College outcomes by first-year<br>by college outcome tier of classes completed<br>completed Percent<br>Avg. number of classes<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES for students from the 2006 entry cohort (first semester). _Notes_ : In panel a, each color represents a college outcome. The green line, for instance, shows average number of classes completed by the end of year 1 by students of a given ability quintile who went on to graduate on time. The thick black line does the same for all students regardless of college outcome. In panel b, the graph shows the percent of students from a given performance tier by the end of year 1 that attain each college outcome. For the first year, tier 1 corresponds to 19+ classes completed; tier 2 to [17, 19); tier 3 to [13, 17); and tier 4 to [0, 13).

# _Fact 4: Cumulative performance is highly persistent over time._

To investigate whether a student’s performance changes over time, Figure 5 depicts a student’s probability of attaining each of four following outcomes—same-tier persistence, dropout, catch up, and fall behind—by the end of a year conditional on her previous year’s performance tier.<sup>7</sup>

> 7For example, a student who finished first year in tier 2 has second-year probabilities of persistence, dropout,

10
|Figure 4. 27. Central water and electricity 24 hours a day|44|
|---|---|
|Figure 4.28. House price to income ratios in capital cities|47|
|Figure 4.29. House price to income ratios in secondary cities|47|
|Figure 4.30. Residential mobility rates for households in capital cities, other urban settlements and|rural areas<br>48|
|Figure 4.31. Ratio of one square meter housing per capita for the bottom quintile to square meters h<br>per capita for the highest quintile in capital cities and other urban areas|ousing<br>50|
|Figure 4.32. Residential mobility by the poorest and richest quintiles in capital cities|50|
|Figure 4.33. Residential mobility by the poorest and richest quintiles in other urban settlements|51|
|Figure 4.34. Health outcomes|55|
|**_TABLES_**||
|Table 1.1. Urbanization rates and urban primacy rates by country of ECA region, 2001|3|
|Table 2.1. Data sources by country and year|6|
|Table 2.2. Availability of infrastructure poverty related indicators in ECA|10|
|Table 3.1. Urbanization rate for Russians and other non-titular Slavs|17|
|Table 3.2. World urban population growth and urbanization change|18|
|Table 4.1. Measures of relative poverty by settlement area|20|
|Table 4.2. Inequality by settlement area|24|
|Table 4.3. Household expenditures housing and communal services|33|
|Table 4.4. The percentage of population that changed residence in the preceding five years|49|

# **_BOXES_**

|Box 2.1: Millennium Development Goals (MDGs) ..........................................................................................7|
|---|
|Box 2.2. What happened to public transportation?..........................................................................................11|
|Box 3.1. Migration in the Kyrgyz Republic ....................................................................................................18|
|Box 4.2 Categorical Privileges (L’goti) in Russia............................................................................................41|
|Box 4.3. Living without services in Georgian apartment buildings................................................................44|
|Box 4.4. Armenia’s vicious circle ...................................................................................................................46|
|Box 4.5. Housing as a coping mechanism in Armenia and Moldova..............................................................52|
|Box 4.6. The Emergence of slums in the peri-urban areas of Bishkek............................................................53|
|Box 4.7. Urban Poverty in Tomsk city, Russia ...............................................................................................54|
|Box 4.8. Strategies of the extreme poor for reducing food consumption in urban areas in Armenia..............56|
the International Remittance Senders Household Survey of migrants from Senegal, Nigeria, and Congo in Belgium, the France 2MO survey, the Black/Minority Ethnic Survey (BME) in the United Kingdom, the Living Conditions of Immigrants (LKI) survey in Norway, and the CSR survey in the Netherlands).

A detailed data appendix provides a full description of the details of each survey, including its sampling methodology, the phrasing of the questions used to measure remittances, other variable definitions, and summary statistics per survey and sending country.

The database we have constructed is the most comprehensive microdata on African remitting patterns in the OECD currently available. Combining the different datasets offers the potential to at least provide an informed description of remitting patterns and how they vary with key covariates of interest. However, the obvious question which then arises is whether we can usefully pool and analyze data collected with different survey methodologies in different countries. We address this issue in two ways. First, all of our empirical analysis will include survey fixed effects, therefore controlling for any level differences arising from particular surveys. Second, we test the sensitivity of our main descriptive results to which surveys we pool together.

The use of destination country data is crucial for answering the questions of interest to this study. Surveys in African countries can tell us which households have a migrant, and which receive remittances, but cannot inform us about which migrants have moved abroad with their whole household and whether they send remittances or not. Asking household members in sending countries to report on the incomes, education and other characteristics of the migrant members abroad is also infrequently done, and subject to substantial problems of household members not being able to report such information.

For each dataset we construct comparable covariates to measure household income, remittance behavior, family composition, and demographic characteristics, measured at the individual level of the survey respondent. Remittances and income are typically measured at the household level, not the individual level. All financial values are reported in constant 2003 $US. In addition, we drop any observations where reported annual remittances are more than twice annual household income. For each survey we utilize survey weights if provided with the data. We also present pooled results using 5
Policy Research Working Paper 7773

# **Abstract**

A good understanding of the constraints on agricultural growth in Africa relies on the accurate measurement of smallholder labor. Yet, serious weaknesses in these statistics persist. The extent of bias in smallholder labor data is examined by conducting a randomized survey experiment among farming households in rural Tanzania. Agricultural labor estimates obtained through weekly surveys are compared with the results of reporting in a single end-ofseason recall survey. The findings show strong evidence of recall bias: people in traditional recall-style modules report working up to four times as many hours per person-plot relative to those reporting labor on a weekly basis. If hours are aggregated to the household level, however, this discrepancy disappears, a factor driven by the underreporting by recall households of people and plots active in agricultural work. The evidence suggests that these competing forms of recall bias are driven not only by failures in memory, but also by the mental burdens of reporting on highly variable agricultural work patterns to provide a typical estimate. All things equal, studies suffering from this bias would understate agricultural labor productivity.

This paper is a product of the Development Data Group, Development Economics. 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://econ.worldbank.org. The authors may be contacted at apalacioslopez@worldbank.org or kbeegle@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 of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Goreux, L. “Reforming the Cotton Sector in Sub-Saharan Africa (SSA).”, Africa Region Working Paper Series No. 62, World Bank, 2003.

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- Makdissi, P. and Q. Wodon. “The Impact on Farmers of Privatizing Parastatal Agricultural Monopsonies.” _Journal of Agriculture and Food Industrial Organization_ , 3(2005): Article 7.

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- Porter, G. and K. Phillips-Howard. “Comparing Contracts: An Evaluation of Contract Farming Schemes in Africa.” _World Development_ 25(1997): 227-38.

- Poulton C., P. Gibbon, B. Hanyani-Mlambo, J. Kydd, M. N. Larsen, W. Maro, A. Osario, D. Tschirley and B. Zulu. “Competition and Coordination in Liberalized African Cotton Market Systems.” mimeo, Imperial College at Wye, 2003.

- Raikes, P., M. F. Jensen and S. Ponte. “Global Commodity Chain Analysis and the French Filiere Approach: Comparison and Critique.” _Economy and Society_ 29(2000): 390-417.

- Siaens, C. and Q. Wodon. “Cotton Production, Poverty, and Inequality in Benin: 1992-1999.”, in W. G. Moseley and L. C. Gay (eds). _Hanging by a Thread: Cotton, Globalization and Poverty in Africa_ , Ohio University Press, Athens, 2008.

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# **Introduction**

This paper is a brief survey of the impact of international trade and trade reform on employment. It focuses mainly on empirical studies that have sought to establish the labor implications of greater trade and trade liberalization. As is revealed by the long bibliography attached to this paper—which represents only a selection from the literature—a huge amount of research has been undertaken on the subject of the relationship between trade, wages and employment. Within this there are numerous excellent literature surveys, many of which review the underlying theory, empirical strategies, methodology, and techniques in some depth.<sup>1</sup> Thus we make no attempt to be comprehensive, and those seeking a more rigorous and detailed discussion of specific papers should refer to these surveys and the papers themselves. Our emphasis is on the broad themes of the literature, with a view to deriving some stylized facts and a list of possible research questions. To keep the paper within reasonable bounds we do not discuss labor economicsoriented literature on labor market institutions, regulation and distortions, the design and effectiveness of possible instruments to facilitate the movement of workers across sectors or employers within sectors, or issues related to the relationship between trade openness and income distribution.<sup>2</sup> As noted by Goldberg and Pavcnik (2004), empirical research to date has offered mixed results regarding the direction and size of the effects of trade liberalization on employment and wages. There are a number of robust stylized facts in terms of outcomes, but debate continues on the impact of trade and trade policy. In part this is because it is hard to obtain a good measure of trade policy, even for OECD countries—the action is mostly on NTBs for which time series data are notoriously difficult to obtain. The weakness in the openness measures that confound the literature on trade and growth are equally problematic here. More fundamentally, trade policy is endogenous—among other things, labor market concerns are one determinant of trade policy, and the factors affecting the latter may affect the formation of wages. Moreover, it is

> 1 Surveys include Baldwin (1995), Cline (1997), Slaughter (1998), Johnson and Stafford (1999), Gaston and Nelson (2000), Greenaway and Nelson (2001), Acemoglu (2002), Feenstra and Hanson (2004), and Goldberg and Pavcnik (2004).

> 2 Income distributional effects extend of course beyond wages/employment to include the prices of produced outputs, the non-wage income, transfers, income from assets and consumption prices—see, for example, Winters, McCulloch and McKay (2004).

1