rafmacalaba/data-use-mentions-extended huggingface dataset:
the author focuses on financial frictions, we take interest in how limited contract enforcement affects different industries based on their contract-intensity. Second, we explore the trade effect by utilizing unique harmonized firm-level data that are ideally suited to studying these operating forces and mechanisms. Thus, this paper improves the understanding of the relationship between domestic institutions and the trade pattern at the firm level.

In another related contribution, Kim et al. (2017) have disentangled the effect of institutions on trade into the intensive and extensive margins. Kim et al. (2017, p. 4) assert that “The decision to trade at all is political ... [whereas the] decision of how much to trade, if one trades at all, is economic.” Their estimates also show that the influence of (political) institutions on trade is stronger and more robust along the extensive margin than the intensive margin. However, while they focus on the quality of institutions in the partner countries, our focus is on the effects of exporters’ domestic institutions.

Our paper is also related to the nascent literature that explores the effects of institutions on trade patterns and dynamics using firm-level data. Ma et al. (2010), using data from 28 countries taken from the World Bank’s Enterprise Surveys, show that firms located in areas with better institutional quality export goods that are more contract intensive. Araujo et al. (2016) develop a model of trade with imperfect contract enforcement to study how the dynamics of exporting firms depends on the strength of the contracting institutions of the destination countries. Using panel data of Belgian firms, they find that producers selling to countries with good contracting institutions start their activities there with higher volumes. They also document that the quality of contracting institutions in the destination markets enhances exporter survival rate. A related paper by Aeberhardt et al. (2014), using firmlevel data from France, documents that better institutional quality improves the persistence of trade 4
disability (i.e., age of father and the district proportion of disabled people). The probit regressions are reported in Table A.4 in the Appendix.

Table 4 presents the estimate of ATT for different child groups. We are not able to reject the equality of the impact estimates between different groups. However, Table 4 shows some interesting findings. The point estimate shows a stronger (negative) effect of parental disability on boys than on girls, given that culturally females are more likely to care for people in need, this may suggest that the main driver for lower enrollment may be resulting more from the need to undertake livelihood production to account for the extra costs associated with disability. This is also consistent with the finding that the biggest impact on enrollment is for children aged 15 to 17.

These results are also consistent with the descriptive data presented in Table 1, in which there is a large gap in the schooling rate between girls and boys in households with parental disability. The adverse effect of parental disability on child education is found larger in urban areas and Kinh children.

Table 4: The impact of parental disability on child schooling rate for different groups

|Model specification|Groups|Y1|Y0|ATT|
|---|---|---|---|---|
||All|0.731***|0.816***|-0.086***|
|Mdl ildi||(0.024)|(0.011)|(0.023)|
|oe ncung<br>interaction between|Boy|0.684***|0.810***|-0.126***|
|child gender and<br>disabilit of arents||(0.032)|(0.014)|(0.030)|
|y p|Girl|0.776***|0.
measures that relate to non-market access and non-discriminatory measures that are of particular interest for our TFiS categorization, namely measures related to **(a) regulatory transparency** and to some extent **(b) barriers to competition** .<sup>12</sup> In the category of **Regulatory transparency** , the OECD provides several measures of which many of them are in turn country-wide measures, although re-scheduled under each services sector for which it has relevance. Some of the measures are nonetheless sector-specific, for instance in the case of audio-visuals (whether IPR are effectively enforced) or construction permits (whether they are costly or not). In fact, most policy restrictions under Regulatory transparency are taken form the World Bank’s Doing Business database (see above) but do not necessarily overlap with the three areas as previously discussed, except for one or two indicators measured under the area of “Resolving insolvency”; other measures under Regulatory transparency relate to the areas of “Starting a business” and “Dealing with construction permit”.

In addition, for many sections the Regulatory transparency category also includes two other measures that are close to the ones as measured in the World Bank’s Regulatory Governance data set, which are: (a) whether regulations are published or otherwise communicated to the public prior to entry into force, and (b) whether there is a public comment procedure open to interested persons, including foreign suppliers. Besides, several measures under Regulatory transparency also deal with visas, namely the cost, number of days, and number of procedures it takes to obtain a visa. As such, these latter policy measures coincide with the TFiS concepts that are related to Mode 4 in our conceptual discussion above.

Regarding the category of **Barriers to competition** , the OECD STRI provides a whole list of measures under each services sector but not all of them are of interest for the TFiS framework. Although many of them are non-discriminatory in nature, some of
Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1107|1107|1107|1096|1107|1107|1107|1107|1107|
|R-squared|0.831|0.724|0.949|0.372|0.775|0.715|0.579|0.830|0.968|

_Note:_ Dependent variables at the 3-digit industry level are, for each column, entry rate, exit rate, share of employment in firms up to 5 years of age, standard deviation of employment growth rate, gross job creation, gross job destruction, net employment change, job reallocation, and the Herfindahl–Hirschman index (HHI). The share of employment in SOEs is the fraction of employment in all SOEs relative to total employment. All models include year and sector-fixed effects. Data includes private sectors from RAIS, 2016-2019. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.
U.S. interest rates, expressed as the percentage change of dirty price if all U.S. interest rates change by 100 basis points. _Average Life_ is the weighted average period until principal repayment, and _CDS_ denotes the five-year credit default swap spread of USD-denominated sovereign bonds. Sources: Bloomberg, Datastream, J.P. Morgan Markets, Morningstar Direct, and authors’ calculations.

of funds benchmarked against the EMBIGD and EMBI Global Core for 2016–2017.<sup>14,15</sup> For each fund, we compute their _Passive Share_ = 100 _− Active Share_ , where _Active Share_ is the measure developed by Cremers and Petajisto (2009). We first estimate this variable at the country level, which is the level of the FIR measure.<sup>16</sup> This allows us to separate, even for active funds, the fraction of a fund’s portfolio that might be passive or active, consistent with the model in Section 2. We then compute the average _Passive Share_ weighted by each fund’s AUM. With this strategy, we obtain an estimated passive fund share of 50%.<sup>17</sup> We calculate _At_ by adjusting the AUM tracking the EMBIGD index, using a rescaling factor of 50%, thus obtaining the estimated passive funds tracking the index we use to compute the FIR.<sup>18</sup> We gather data on individual bond prices from Datastream and obtain several bond characteristics (maturity and duration, among others) directly from J.P. Morgan Markets. To clean our dataset, we drop extreme values of daily returns, stripped spreads, and _Zc,t_ .<sup>19</sup> We drop stripped spreads below 0 or above 5 _,_ 000 basis points as well as observations below

> 14The EMBI Global Core uses the same diversification methodology as the EMBIGD to calculate the bond weights, as described in Appendix . The criteria for including bonds in
in 2022 and 0.8 billion in 2030. In this low-case trajectory of poverty reduction, it would not be until 2060 that one billion people would likely be lifted out of poverty.

The guiding principle for the optimistic benchmark is that the recent success against extreme poverty in the developing world as a whole will be maintained. Two main ways are used here to quantify such a benchmark. The first is based on time-series projections of past experience while the second method (in this section and the next) uses simulations.

> 10 The required poverty rate falls from 3.3 percent in 2012 to 2.8 percent in 2030. The Bank’s current population projections in millions for the developing world, as defined in 2012, are 5,884 in 2012, 6,230 in 2017, 6,561 in 2022 and 7,048 by 2030. Using the 1990 classification of developing countries (to be consistent with _<u>PovcalNet</u>_ ), the numbers are 6,009, 6,355, 6,687 and 7,173, respectively. This difference does not change the poverty counts reported in this paper at the stated number of statistically significant figures.

> 11 The projected poverty rate for the developing world outside China in 2012 (based on the trajectory since 1999) is 21.5 percent (standard error = 0.4 percent), and the rate of poverty reduction based on the pre-1999 trajectory is

> −0.38 percentage points per year.

11
0.000390||-0.000388|-0.000297|||||
||(0.000361)||(0.000360)|(0.000366)|(0.000169)**||(0.000169)**|(0.000169)*|||||
|Share of hhlds with computer||0.0901|0.0894|0.102||0.0418|0.0413|0.0523|||||
|||(0.0658)|(0.0662)|(0.0658)||(0.0609)|(0.0613)|(0.0606)|||||
|IDN exports per worker *||||-0.00257||||-0.00216|||||
|Share of hhlds with computer||||(0.00123)**||||(0.00107)**|||||
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|||||
|R-squared|0.970|0.970|0.970|0.970|0.817|0.818|0.817|0.817|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population
2.8|0.7|0.3|2272|74.2|10.6|2.5|
|small towns|2.9|0.8|0.4|1659|53.8|15.9|14.7|
|rural|3.1|0.9|0.4|1324|32.5|18.3|41.7|
||||geograph|ic zones||||
|north|2.9|0.8|0.4|1377|35.8|16.9|37.0|
|center|3.1|0.9|0.3|1398|40.0|17.9|31.6|
|south|3.1|0.9|0.4|1377|36.4|20.5|35.5|
|Chisinau|2.8|0.7|0.3|2361|75.0|9.0|3.9|

Source: authors’ calculations using HBS-2017 data.

# **4. Results and discussion**

# **4.1. Main results – incidence of the tax benefit system**

Most households contribute and benefit from the system at the same time, but the net effect is not the same for all. On average, households are net receivers from the tax-benefit system in Moldova - they receive 5 percent of their final income as net benefits from the system (Table 9, Figure 7).<sup>7</sup> The most important components of the benefit system are pensions (21 percent). Other direct transfers, in-kind education and health benefits are relatively less important (5, 6 and 5 percent of final income respectively). The average burden of social contributions and

> 7 This section relies on the scenario when pensions are treated as transfers. The redistribution effect
10 While payment of severance and long-service leave after a job separation is mandated by legislation in Indonesia, information from survey data suggests that there is a high rate of non-compliance with the regulations. Approximately one-third of legally eligible wage-and-salary employees receive a severance payment after a job separation occurs; depending on the survey, the average ratio of the severance payment received to the legally entitled amount is either slightly over one-third (IFLS, 2007) or just under one-third (Sakernas, 2008).

There are two aspects of non-compliance. The first aspect is the proportion of eligible formal sector employees who receive a payment. The second aspect is the proportion of the entitlement amount that is actually received. The receipt of severance pay depends on two factors: the proportion of job separators who are eligible (as described in the previous section) and the proportion of those eligible who actually receive a payment after a job separation occurs. Both factors determine the ratio of actual severance payment to the legally entitled amount. The present discussion emphasizes this ratio.

In both surveys, the proportion of eligible employees who received severance pay increases with age but is essentially the same for individuals with differing levels of formal education. In the IFLS (2007) survey, there is no difference between women and men in terms of severance pay receipt. In the Sakernas (2008) survey, however, eligible men are about 39 percent more likely to receive severance than eligible women.

There is variation in the share of eligible persons who received severance pay by industry in both surveys, but there is no apparent pattern. There is a strong systematic relationship between the share of eligible employees who received severance pay and tenure: the share increases with the years in continuous employment with the same firm. Similarly, there is a strong systematic association between the share of eligible employees who received severance pay and the size of the firm: the proportion steadily increases with firm size. About one-quarter of eligible wage-and-salary workers receive severance pay if they are employed by a government agency. Eligible wage-and-salary
.85 *|
|Distance to Public University|continuous|14276|7286|15276|6882|1000|329*|
|<br>Distance to PrivateUniversity|continuous|18238|6263|16700|6053|-1538|. <br>-5.82 *|
|Note: A t-test for difference in means was calc<br>*|ualted for all va<br>|riables.<br>||||||
|Refers to a difference that is statistically sign<br>Source: World Bank Staff Calculations using of|ficantly differ<br>ficial 2002 and|ent than 0<br>2010 Hou|at less than 5<br>sehold Income|.<br>and Expendit|ure Surveys|.||

33
Policy Research Working Paper 9622

# **Abstract**

While city migrants see their welfare increase much more than those moving to towns, many more rural-urban migrants end up in towns. This phenomenon, documented in detail in Kagera, Tanzania, begs the question why migrants move to seemingly suboptimal destinations. Using an 18-year panel of individuals from this region and information on the possible destinations from the census, this study documents, through dyadic regressions and controlling for individual heterogeneity, how the deterrence of further distance to cities (compared to towns) largely trumps the attraction from their promise of greater wealth, making towns more appealing destinations. Education mitigates these effects (lesser deterrence from distance, greater attraction from wealth), while poverty reduces the attraction of wealth, consistent with the notion of urban sorting. With about two-thirds of the rural population in low-income countries living within two hours from a town, these findings underscore the importance of vibrant towns for inclusive development.

This paper is a product of the Social Protection and Jobs 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 lchristiaensen@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
709|64|
|Post-secondary education|710<br>451|64|
|Higher education|803<br>510|64|
|**Total education**|**8,324**<br>**5289**|**64**|

Source: Ministry of Finance, National Office for Social Insurance, authors’ calculations using HBS-2017 data.

Main summary statistics on household and income structure are presented in Table 8. As can be seen, the families with lowest incomes before any interventions are families with children and pensioners, that do not have much of market incomes. These categories as well as rural residents also rely on remittances and incomes from agriculture more than other groups of population.

17
Figure 1: Average Job Quality Ratio by Country <!-- Start of picture text --> Arg Bol Bra Chl Col CriDom EcuGtm Hnd Mex Nic Pan Per Pry Slv Ury<br>.6<br>.4<br>.2<br>Average Job Quality Ratio<br>0<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data. Sample size and years covered differs by country as described in Table 1.

for the particular country-year combination. Job quality sum will be referred to as _JQ_<sup>_S_</sup> while job quality ratio will be referred to as _JQ_<sup>_R_</sup> . Job quality sum is found by adding all of the job quality successes for an observation, where each success is counted as 1 and each failure is 0. Therefore, it is a number between 0 and 7 for each observation where a higher number denotes a higher level of job quality. The job quality ratio normalizes job quality to take into account that some countries do not report some quality dimensions. The ratio is found by dividing _JQ_<sup>_S_</sup> by the total number of quality dimensions available for each country and year. Thus, job quality ratio is a number between 0 and 1 and is our preferred method of measuring job quality.

The average job quality ratio in each country is shown in Figure 1. The standout 15
>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: World Development Indicators (World Bank 2016b) The dramatic fertility transition in the last two decades and the reduction in average household sizes accompanied by the declining dependency ratio had a significant effect on the observed 19
# the impact of RTAs.<sup>8</sup>

We are also interested in quantifying the impact of CAFTA-DR on bilateral crossborder production linkages. For this purpose, we need data that distinguishes between final and intermediate goods on the one hand, and between domestic and foreign-valueadded parts of trade flows. Eora itself provides bilateral trade data in final and intermediate goods. Our value-added trade data comes from Borin et al. (2021), which is based on Eora input-output tables, and can be accessed through the World Integrated Trade System (WITS). This data provides different variables that disaggregate total bilateral trade into traditional trade and GVC-related trade. Traditional trade corresponds to the domestic value added embodied either in final or intermediate goods or services that are directly consumed by the importing economy (this value only crosses one border). Pure forward GVC-related trade is the value-added in goods and services entirely generated within the domestic chains (without any border crossing) exported to partners, which in turn reexport it to other markets. In this case, the exporting sector is engaged in GVC activities at the origin of the chain. Pure backward GVC-related trade is the value of goods and services produced with imported inputs and exported to the final destination market. In this case, the exporting sector engages in GVC activities at the end of the chain. Finally, two-sided or mixed GVC-related trade is the value of goods and services produced with imported inputs, exported by the sector to partners, which in turn re-export it to other markets. The exporting sector is located in a position of the chain that is more central. Our main variable of interest is the domestic value added to exports.<sup>9</sup> It determines the contribution of a country’s own economy to the production of exports, rather than simply counting the total gross export value. It is obtained by summing the pure forward GVC trade and the traditional trade flows.<sup>10</sup> Data on trade agreements come from the
outstanding data at a sufficient level of detail was impossible given the uneven conditions of data generation and sharing in Nepal.

2 The HRVS sample frame was all households in non‐metropolitan areas per the 2011 Census definition, excluding households in the Kathmandu valley (Kathmandu, Lalitpur and Bhaktapur districts). The country was segmented into 11 analytical strata, defined to correspond to those used in the Nepal Living Standards Survey (NLSS‐III: excluding the three urban strata used there). To increase the concentration of sampled households, 50 of the 75 districts in Nepal were selected with probability proportional to size (the measure of size being the number of households). PSUs were selected with probability proportional to size from the entire list of wards in the 50 selected districts, one stratum at a time. 5,835 households out of 6,000 in the survey 2016 were re‐ surveyed in 2017 and an additional 170 households were surveyed only in 2017. Similarly, an additional 197 households were surveyed in 2018 only.
) Gender-shares<br>of HH earnings|(T4) Mixed typology|
|---|---|---|---|---|
|Types|Femalemajority<br>or male majority<br>(or equal numbers)<br>of household<br>members|Earners are a<br>femalemajority,<br>Earners are a male<br>majority,equal<br>number of male<br>and female earners|Female earnings >60%<br>of total earnings,<br>male earnings >60%<br>of total earnings,<br>No earners|Earners are female<br>majority + dependent<br>children + dependent<br>elderly, Earners are male<br>majority + dependent<br>children + dependent<br>elderly|
|Usefulness/<br>relevance<br>for gender<br>analysis|Exploits variation in<br>consumptionpatterns<br>and gender-specific<br>demands or needs|Introduces<br>women’seconomic<br>opportunities and<br>householdroles|Exploits variation in<br>bargainingpower|Captures care needs and<br>domestic responsibilities,<br>and interaction with<br>incomes and labor<br>decisions|

_Source:_ Based on Fuchs and Gonzalez (2022).

> 24. In some countries, like Jordan, the cell sizes for a combined economic/demographic classification may be too small for a fiscal incidence analysis. In this case, the analyst will need to make choices about which classifications make most sense in any particular country context (see Rodriguez, Wai-Poi, and Woodham 2022).

> 25. A household type defined by household headship is included in Fuchs and Gonzalez (2022). However, Grown and Komatsu (2010) discuss the lack of relevant information in standard household survey techniques that ask household members to identify the head of household.