rafmacalaba/datause-encoder-data huggingface dataset:
definition proposed by Rauch (1999).<sup>35</sup> We estimate our main specification allowing the effect of the services FDI linkage to vary across differentiated and non-differentiated products’ industries. The results in Columns 1-2 of Table 5 show that services FDI seems to have a stronger effect on the productivity growth of plants in differentiated product industries. However, unreported F-tests show that the difference in the effect of services FDI across the two types of industries is not statistically significant.

Next, we ask whether the impact of services FDI on productivity growth differs by plant size. On the one hand, larger plants may be able to internalize some services and have less to gain from FDI-related services improvements, relative to smaller plants which outsource most services due to scale indivisibilities. On the other hand, larger plants may be technologically more advanced and thus require stronger usage of highly specific and complex services. It is an empirical question whether smaller or larger plants benefit more from services FDI. We estimate our main specification allowing the effect of the services FDI linkage to differ across small and large plants and show the results in Columns 3-4 of Table 5. To define plant size, we compute the median employment for each plant across its sample years and then divide plants according to this measure: plants with less than 25 employees are considered to be small. In Columns 5-6 we separate plants into small and large based on employment in the first plant year. The evidence is mixed depending on whether the first or second criterion is used. Moreover, unreported F-tests show that the difference in the effects of services FDI across smaller and larger

> 35 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized exchanges (e.g., steel) nor (ii) reference-priced products which have listed prices in trade publications (e.g., some chemical products) and require a more important degree of buyer-seller interaction. To apply Rauch’s definition, we establish a correspondence between his classification of products based on 4- digit SITC rev. 2 codes and our 4-digit ISIC rev. 2 codes. For the printing industry (ISIC 342), we are unable to establish an unambiguous correspondence and thus drop it from the regressions in Table 5.

29
Figure 1: Italy 2002–2006–2010: Growth Incidence Curve <!-- Start of picture text --> 0 20 40 60 80 100<br>% population<br>GIC 2002−2006<br>GIC 2006−2010<br>5<br>0<br>yearly % growth<br>−5<br><!-- End of picture text --> _Source: Authors’ calculation from SHIW (Bank of Italy)_ Table 1: Italy 2002-2006-2010: descriptive statistics and partition in types

|_Area_|_Education_|_Occupation_|_rank_<sup>02</sup>|_sample_<sup>02</sup>|_q_<sup>02</sup><br>_i_|_μ_<sup>02</sup><br>_i_|_rank_<sup>06</sup>|_sample_<sup>06</sup>|_q_<sup>06</sup><br>_i_|_μ_<sup>06</sup><br>_i_|_rank_<sup>10</sup>|_sample_<sup>10</sup>|_q_<sup>10</sup><br>_i_|_μ_<sup>10</sup><br>_i_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|South|No-edu/Elementary|Blue c./not in l.f.|1|1241|0.2174|14065.82|2|1273|0.2291|15279.71|3|1512|0.2385|14974.97|
|South|Lower secondary|Blue c./not in l.f.|2|110|0.0214|14386.26|4|124|0.0214|17783.99|1|198|0.0408|13593.33|
|South|Higher|Blue c./not in l.f.|3|137|0.0233|15673.90|1|104|0.0150|14800.64|2|126|0.0214|14749.59|
|South|No-edu/Elementary|White c.|4|682|0.1130|16949.30|3|604|0.1098|17149.07|4|594|0.0990|17021.24|
|South|Lower secondary|White c.|5|213|0.0324|17917.02|6|230|0.0421|20127.67|5|228|0.0372|17903.09|
|Centre|No-edu/Elementary|Blue c./not in l.f.|6|657|0.0822|19477.92|7|604|0.0755|21970.48|9|622|0.0729|23528.86|
|Centre|Lower secondary/Higher|Blue c./not in l.f.|7|51|0.0068|20106.76|12|49|0.0082|26077.04|13|60|0.0111|26010.30|
|North|Lower secondary|Blue c./not in l.f.|8|135|0.0237|20910.44|10|182|0.0301|24799.79|10|162|0.0294|23548.54|
|North|No-edu/Elementary|Blue c./not in l.f.|9|1137|0.1623|22095.60|8|1121|0.1591|23292.56|8|1022|0.1465|23063.41|
|Centre|No-edu/Elementary|White c.|10|316|0.0384|22579.76|9|287|0.0401|23873.59|14|260|0.0268|26348.91|
|South|Higher|White c.|11|270|0.0406|22828.57|13|239|0.0356|26290.72|11|295|0.0375|24052.45|
|North|No-edu/Elementary|White c.|12|594|0.0996|23922.43|11|543|0.0839|25240.80|12|474|0.0709|25209.78|
|Centre|Lower secondary|White c.|13|107|0.0187|24702.06|16|93|0.0128|30371.49|16|119|0.0202|28257.28|
|North|Higher|Blue c./not in l.f.|14|71|0.0094|25625.36|14|94|0.0140|27060.96|7|100|0.0160|22652.13|
|Centre|Higher|Blue c./not in l.f.|15|32|0.0039|25664.17|5|45|0.0059|20096.12|6|30|0.0034|21798.12|
|North|Lower secondary|White c.|16|253|0.0421|26890.26|15|250|0.0387|27748.28|15|247|0.0471|27114.15|
|North|Higher|White c.|17|296|0.0452|29955.46|17|363|0.0519|32143.62|18|343|0.0543|32106.09|
|Centre|Higher|White c.|18|126|0.0197|30786.71|18|149|0.0268|33395.35|17|187|0.0268|30670.72|

_Source: Authors’ calculations on SHIW (Banca d’Italia)._ Types are ranked in ascending order according to the average income at the beginning of each growth period.

Two features stand out. First, the GICs for the two periods lie in two different domains: positive for the first period and negative for the second period, with the exception of the last percentile. This feature is further captured by the mean income growth rate relative to each period, which is 20
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

## Roads

This section looks at the national segments of the regional road network. To this end, the regional road network is defined as the network needed to interconnect all national capitals with one another and with the major deep seaports. Overall, 43 percent of this network is already paved (table 2.3).

ECCAS members vary substantially in their track record on the maintenance of the regional road network. Overall, 48 percent of the regional network is in good or fair condition (table 2.3). As a general rule, most member countries are not doing enough to maintain their share of the regional network in good or fair condition. But there are three exceptions to this pattern. In Burundi and the Central African Republic, over 90 percent of the roads are in good or fair condition. A distant third is Gabon, where threequarters of the roads are at least in fair condition. This may be symptomatic of wider deficiencies in the funding and implementation of road maintenance works in these countries, and/or denote that regional routes are not being prioritized in their national road plans.

**Table 2.3. Condition of ECCAS’s regional road network, by member country (%)**

|||Con<br>|dition<br>|||Type<br>||
|---|---|---|---|---|---|---|---|
||Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|
|Angola|71.4|5.5|23.2|0|71.4|28.6|0.0|
|Burundi|42.4|27.9|24.2|6|94.4|5.6|0.0|
|Cameroon|35.1|27.6|35.8|2|54.9|45.1|0.0|
|Central African Republic|62.6|17.0|17.6|3|97.2|2.8|0.0|
|Chad|9.0|44.7|14.9|31|12.5|87.5|0.0|
|Congo, Dem. Rep. of|14.2|12.6|61.5|12|17.7|82.1|0.2|
|Gabon|0.0|0.0|0.0|100|74.4|21.6|4.0|
|Republic of Congo|0.0|19.3|72.3|8|25.3|66.3|8.4|
|**ECCAS**|**29.1**|**18.4**|**39.3**|**13.2**|**42.6**|**56.7**|**0.7**|
|**East Africa**|**29.8**|**26.5**|**11.7**|**32**|**57.2**|**25.4**|**17.4**|
|**ECOWAS**|**45.1**|**28.4**|**22.5**|**4**|**91.9**|**8**|**0.1**|
|**SADC**|**46.7**|**24.1**|**22.8**|**6.4**|**74**|**25.8**|**0.2**|

_Source:_ AICD various sources. _Note:_ ECOWAS = Economic Community of West African States; SADC = Southern African Development Community.

# Railways

Central Africa has a handful of disconnected railway systems that are far from forming a regional network (figure 2.7). This is in contrast to southern Africa, where interconnected national railway systems form a regional railway network that spans half a dozen countries and extends from the southern Democratic Republic of Congo all the way to Durban in South Africa. The railway network in Central Africa operates along 6,000 route-km—the smallest distance in Africa—and carries 4,000 net tonnes-km, the smallest load in Africa.

18
those that could not have been exposed to pre-primary education. We create a placebo dummy that equals one for the 1990 and 1991 cohorts in the beneficiary communities, and zero otherwise. The placebo indicator would measure any differences in trajectories for the variable of interest prior to the construction of pre-primaries. The results for this specification are presented in columns (1) and (2) of Table 8. The point estimates for the placebo indicator are close to zero and not statistically different to zero at conventional levels.

The second test takes advantage of the entire sample and, in addition to our preprimary access dummy, we add a placebo indicator that takes the value of 1 for cohorts born one or two years prior to the first exposed cohort in beneficiary communities. For example, if the 1995 cohort was the first to be exposed to a pre-primary in a certain community, then the 2-year prior placebo indicator would take the value of 0 for cohorts 1990 to 1992, 1 for the 1993 and 1994 cohorts and 0 for cohorts 1995 and 1996. Columns (3) through (6) present the results for this test. The point estimates on the placebo indicators are similar to those obtained in the first two columns, albeit more precisely estimated given that we are including the entire sample. Note that the estimates for pre-primary access remain unchanged.

Finally, we use the 1994 and 2002 Population censuses to test whether the change in community characteristics between these two points in time is different between beneficiary and comparison communities, which could potentially bias our results. We present the estimates for this test in Table 9. We find that the change in several socio-economic characteristics is, in almost all cases, not significantly different between beneficiary and comparison communities, again suggesting that our main identification assumption is valid.

# **7 Concluding remarks**

Many developing countries have recently expanded (or are considering to expand) preprimary coverage as an instrument to stimulate human capital accumulation. Evidence from the United States, Uruguay and Argentina suggests that these investments yield large returns (Cascio, 2009; Berlinski et al., 2008; Berlinski et al., 2009). But this evidence cannot be easily extrapolated to poorer countries, where the potential for policy interventions in this domain is much greater.

We have contributed to filling this gap in the literature by exploiting a large-scale expansion in pre-primary coverage in rural Guatemala from 1998 to 2005. Combining administrative and population census data in a difference-in-differences framework, we have estimated the medium-run impacts of opening a pre-primary on enrollment and adequate progression in primary school by age 12. We find positive effects on the proportion of stu16
Policy Research Working Paper 10100

# **Abstract**

This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that experienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful. The estimated impacts on measures of reconciliation, post-conflict justice, trust and participation in community groups are mostly statistically insignificant. The paper also explores how these effects differ across different sub-samples based on ethnic composition, land scarcity and attitudes towards return. The results highlight the possible role of new migration-related societal divisions (i.e. returnees versus stayees) in affecting post-return social cohesion.

This paper is a product of the Social Sustainability and Inclusion 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 authors may be contacted at carlos.vargas-silva@compas.ox.ac.uk.

_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
# **Table 17: Framework conditions for containing undeclared work**

||Tax<br>level|Labor<br>tax<br>wedge|Other tax<br>policies|Ease of tax<br>compliance|Overall<br>assessment<br>of tax factors|Ease of<br>doing<br>business|Efficiency of<br>institutions<br>(corruption)|Educational<br>attainment|Overall<br>assessment of<br>policies to<br>reduce<br>informality<br>(ranking)|Size of un-<br>declared<br>work|
|---|---|---|---|---|---|---|---|---|---|---|
|Estonia|+|+/-|+|+ +|+ + + +|+|+|+ +|(1)|(1)|
|Lithuania|+|-|+|+|+ +|+|-|+|(2)|(5)|
|Latvia|+|+/-|+|-|+|+|+/-|+|(2)|(8)|
|Slovak Republik|+|+|+|-|+ +|+/-|+/-|+|(2)|(3)|
|Slovenia|-|-||+|-|+/-|+|+ +|(5)|(6)|
|Hungary|-|-|+|-|- -|+/-|+/-|+ +|(6)|(7)|
|Croatia|+/-|.||+|+|-|-|+|(6)|.|
|Poland|-|-|+|-|- -|-|+/-|+ +|(8)|(4)|
|Czech Republic|-|+/-|+|- -|- -|-|+/-|+ +|(8)|(2)|
|Romania|+|-|+|+|+ +|+/-|- -|- -|(10)|(10)|
|Bulgaria|-|-|+|- -|- - -|+/-|- -|- -|(11)|(11)|

Note: Overall tax level: measured by tax revenue as a percent of GDP. Labor tax wedge: measured various indicators such as .... Other tax policies: family taxation, in-work benefits, simplified tax regimes etc..Tax administration: measured by the time spent by a typical firm for tax compliance. General efficiency of institutions: measured by the Control of Corruption index of the World Bank. Size of informal work: measured by European Employment Observatory EU Commission (+ +) means that the size of the informal sector is significantly below, and (+) means just below the average of the country group, (0) means that it is around average. (-) means that it is above and (- -) that it is much average the country average.

50
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel B: Regulatory Framework and Practices (0 100)</u>

||V|<br>ersion 1(Ful sampl|<br>Depvar: Portfolio at<br>es)|Risk(>30days)<br>Version 2|<br>(DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory Framework and Practices (0-100)|-0.0002|-0.0006***|-0.0006*|-0.0002|-0.0005***|-0.0006**|
||[0.610]|[0.002]|[0.051]|[0.651]|[0.004]|[0.028]|
|NBFI dummy * Regulatory Framework and Practices (0-100)|0.0001|0.0002|0.0004|0.0002|0.0002|0.0003|
||[0.779]|[0.377]|[0.332]|[0.661]|[0.430]|[0.433]|
|Combined group dummy * Regulatory Framework and Practices (0-100)|-0.0009|0.0003|0.0003|0.0005|0.0005|0.0007*|
||[0.162]|[0.280]|[0.375]|[0.520]|[0.119]|[0.096]|
|NBFI dummy|0.0024|-0.0125|-0.02|-0.0032|-0.0117|-0.0195|
||[0.935]|[0.412]|[0.391]|[0.918]|[0.410]|[0.415]|
|Combined group dummy|0.0723*|-0.0081|-0.0068|-0.0102|-0.0282|-0.0359|
||[0.074]|[0.608]|[0.762]|[0.837]|[0.123]|[0.136]|
|Observations|627|627|598|572|572|546|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.076|0.205|0.162|0.069|0.24|0.171|
|Adjusted R-squared|0.0468|0.18|0.135|0.0371|0.214|0.141|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.0000685|-0.000337|-0.000146|0.0000519|-0.0003|-0.000231|
||[0.874]|[0.147]|[0.716]|[0.905]|[0.166]|[0.566]|
|Combined group dummy|-0.0011*|-0.00028|-0.000211|0.000324|0.0000151|0.000174|
||[0.063]|[0.248]|[0.613]|[0.634]|[0.960]|[0.657]|

50
**Table** **<u>3: Effects of Parent Eligibility on Utilization by Child Eligibility (ITT)</u>**

||Any<br>Provider|Pharmacy|EMP|Public<br>Facilities|Private<br>Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Visit|(1)|(2)|(3)|(4)|(5)|
|6 Month Subsidy|0.002|0.004|0.070***|0.018|-0.002|
||(0.020)|(0.025)|(0.015)|(0.024)|(0.023)|
|6 Month Subsidy*Ineligible|-0.055|-0.042|-0.064***|-0.040|-0.078**|
||(0.039)|(0.040)|(0.016)|(0.037)|(0.032)|
|Ineligible|0.072|0.008|0.019|0.104**|0.049|
||(0.047)|(0.048)|(0.017)|(0.043)|(0.038)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.098|0.073|0.052|0.041|0.09|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.139|0.296|0.473|0.516|0.002|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|6 Month Subsidy|0.392**|0.104|0.174***|0.065|0.035|
||(0.192)|(0.090)|(0.048)|(0.088)|(0.081)|
|6 Month Subsidy*Ineligible|-0.847***|-0.226|-0.162***|-0.103|-0.292***|
||(0.280)|(0.142)|(0.047)|(0.124)|(0.106)|
|Ineligible|0.680**|0.132|0.035|0.332**|0.188|
||(0.335)|(0.168)|(0.051)|(0.134)|(0.132)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.105|0.07|0.046|0.033|0.057|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.041|0.309|0.408|0.715|0.000|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from ITT estimates where "6 Month Subsidy" is the parent's random assignment status and 6 Month Subsidy*Ineligible is the interaction of these two variables. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

27
we use household-specific inflation rates to account for the impact of differential price trends and household consumption baskets.

# **2.3 Key Assumptions and Limitations**

The simulation approach depends on certain key assumptions on the underlying income generation process of household, labor markets, and their propensity to consume.

The first assumption is that individual labor incomes grow at the nominal rate of the employment sector, implying that there is a full passthrough to workers via wages. The effective passthrough, , rate is period-dependent as it is the result of the composition of the economy and the capacity of workers to adjust their wages, with evidence suggesting countries with large informal sectors θθ and high inequality present lower passthrough rates as economic growth is more likely to be captured by wealthier, and non-poor, deciles (Tateno et.al., 2021).<sup>13</sup> We assume a passthrough rate of one since labor income is modeled independently and not directly transformed into consumption. Changes in this assumption will attenuate the impact of sectoral growth on (θθ= 1) poverty, but only with a small net impact.

The second modeling assumption is also related to labor markets and implies that individual workers remain in their respective employment sectors, with only wages adjusting in the direction of sectoral growth. Furthermore, we assume no shifts in the level of unemployment in response to crises and lower growth/constant sector-level labor share. While both these assumptions stem from data limitations, as the lack of a labor panel implies we cannot test for sectoral transitions, we believe that adjusting for these dynamics would not be associated with a substantial shift in our projections. As highlighted in Appendix Figure B1, the labor force participation across demographic and spatial groups (Panels E-F), as well as the distribution across sectors and by employment type (Panels A-D), has been stable over the years. This is likely linked to a highly degree of informality which absorbs shocks to the labor market and allows for relatively fast adjustment of wages in the short to medium run (Loayza & Rigolini, 2006). Consequently, while important, these dynamics are unlikely to strongly impact short-term projections in a context like Pakistan.

Third, we assume there are no changes in the composition of households. That is, we keep the HIES 2018/19 sampling weights constant across the period, not allowing for population growth, the labor force, or changes in the territorial distribution of households.<sup>14</sup> This is a strong assumption, yet the results appear to be robust.

> 13 <mark>In Pakistan, a passthrough often used 0.86 as the consumption share is around 86 percent in total GDP.</mark>

> 14 We implemented several tests to update the 2018/19 sampling weights to reflect the information on population size and distribution from latest population census. All of our results were robust to these changes.

10