rafmacalaba/datause-encoder-data huggingface dataset:
example, research has found that effective enfranchisement of poor voters has direct consequences for the delivery of health services and improved health outcomes, while poor voters’ disenfranchisement, through vote buying, has the opposite effects (Fujiwara, 2015 and Khemani, 2015). But these ideas about the role of politics and exploration of its policy implications has not happened through RCTs for the simple reason that the suggested interventions are more complicated than what is feasible in most RCTs. The interventions involve not only changes in contract design (level of wages, whether high powered or not) which other RCTs have undertaken (Dal Bo et al, 2013; Ashraf et al, 2014; Ashraf et al, 2015), but crucially, these contract changes need to be combined with political and bureaucratic reforms if the insights of economic theory are to be fully tested and exploited. Collaboration with policy-makers who are willing to experiment with policy is hard enough for most RCTs, and compounded in this case by the complexity and potential sensitivity of targeting bureaucratic management culture and political contestation.

In the absence of opportunity to directly undertake a policy experiment suggested by economic theory, and evaluate its impact to the standards of econometric rigor of other RCTs, the research in Bihar we report on in this paper gathered descriptive data, through rich surveys using innovative modules to measure the variables that economic theory suggests are important (such as, intrinsic motivation, and peer norms). This survey provides evidence of the mis-match between actual policy practice and the insights from economic theory and offers ideas to policy makers to adopt a different approach to strengthening health systems using the current crisis as an opportunity.

# **3 Description of the survey in Bihar, India**

The survey was undertaken in the state of Bihar in India between November 2018 and March 2019.<sup>7</sup> Data were collected across different layers of government jurisdictions within the state–districts, blocks and village governments, known as Gram Panchayats (GPs)–and across different types of respondents–politicians, bureaucrats or public officials, frontline service 7Data were collected in two phases– one at the village level, and one above the village at blocks and districts 10
Haaland, V. F., Rege, M., & Votruba, M. (2013). _Nobody Home: The Effect of Maternal Labor Force Participation on Long-Term Child Outcomes_ (SSRN Scholarly Paper ID 2363912). Social Science Research Network. https://doi.org/10.2139/ssrn.2363912 Hubbard, M. N. (2009). _Effects of maternal employment and child care on the health of young children_ [Ph.D., The University of North Carolina at Chapel Hill]. <u>https://www.proquest.com/docview/304960202/abstract/4C8AF9FF2D534249PQ/1</u> James‐Burdumy, S. (2005). The Effect of Maternal Labor Force Participation on Child Development. _Journal of Labor Economics_ , _23_ (1), 177–211. https://doi.org/10.1086/425437 Mendolia, S. (2016). Maternal Working Hours and the Well-Being of Adolescent Children: Evidence from British Data. _Journal of Family and Economic Issues_ , _37_ (4), 566–580. https://doi.org/10.1007/s10834- <u>015-9480-1</u> Morrill, M. S. (2011). The effects of maternal employment on the health of school-age children. _Journal of Health Economics_ , _30_ (2), 240–257. https://doi.org/10.1016/j.jhealeco.2011.01.001 Pekkurnaz, D. (2014). _Dynamic analysis of maternal employment, child care, quality and early childhood development_ [Ph.D., The University of North Carolina at Chapel Hill]. <u>https://www.proquest.com/docview/1613182648/abstract/D6F7129E26342F5PQ/1</u> Pilkauskas, N. V., Brooks-Gunn, J., & Waldfogel, J. (2018). Maternal Employment Stability in Early Childhood: Links with Child Behavior and Cognitive Skills. _Developmental Psychology_ , _54_ (3), 410–427. <u>https://doi.org/10.1037/dev0000438</u> Rashad, A. S., & Sharaf, M. F. (2019). Does maternal employment affect child nutrition status? New evidence from Egypt. _Oxford Development Studies_ , _47_ (1), 48–62. <u>https://doi.org/10.1080/13600818.2018.1497589</u> Reynolds, S. A., Fernald, L. C. H., & Behrman, J. R. (2017). Mothers’ labor market choices and child development outcomes in Chile. _Social Science and Medicine - Population Health_ , _3_ , 756–766. <u>https://doi.org/10.1016/j.ssmph.2017.08.010</u> Shajan, J., & Sumalatha, B. S. (2020). Maternal employment and children’s health in India: An exploratory analysis. _Journal of Public Affairs_ , _n/a_ (n/a), e2580. https://doi.org/10.1002/pa.2580 von Hinke Kessler Scholder, S. (2008). Maternal employment and overweight children: Does timing matter? _Health Economics_ , _17_ (8), 889–906. https://doi.org/10.1002/hec.1357 Waldfogel, J., Han, W.-J., & Brooks-Gunn, J. (2002). The effects of early maternal employment on child cognitive development. _Demography (Pre-2011)_ , _39_ (2), 369–392.

Ziol-Guest, K. M., Dunifon, R. E., & Kalil, A. (2013). Parental employment and children’s body weight: Mothers, others, and mechanisms. _Social Science & Medicine_ , _95_ , 52–59. <u>https://doi.org/10.1016/j.socscimed.2012.09.004</u> 9
TABLE 20 Channels: Impulse Responses of TFP, Investment, Employment and Actual Growth Rates to Banking Crises and Epidemics

|**Dfiiti f ttil**||**B**|**anking cris**|**es**||**Epidemics**||
|---|---|---|---|---|---|---|---|
|**enon o poena**<br>**output**|**h**|**World**|**AEs**|**EMDEs**|**World**|**AEs**|**EMDEs**|
||0|-0.177***|-0.119***|-0.279**|-0.235***|-0.241***|-0.223***|
||1|-0.559***|-0.419***|-0.771***|-0.276***|-0.307***|-0.248***|
|**Total factor**|2|-0.627***|-0.566***|-0.748***|-0.296***|-0.306***|-0.278***|
|**productivity**|3|-0.562***|-0.619***|-0.531**|-0.394***|-0.389***|-0.388***|
||4|-0.54***|-0.655***|-0.446|-0.524***|-0.358***|-0.606***|
||5|-0.375**|-0.558***|-0.189|-0.315***|-0.093|-0.434***|
||0|-4.451*|-4.119|-4.576|-12.522***|-9.658***|-13.252***|
||1|-14.031***|-16.744***|-12.31***|-3.487**|-1.575|-4.275**|
|**Investment**|2|-1.649|-11.541***|4.509|-2.762*|2.696**|-4.803**|
||3|3.182|-2.718|6.846*|-3.202***|0.203|-4.575***|
||4|0.507|-6.409***|4.781*|-3.442**|-0.446|-4.772**|
||5|-2.145|-6.08***|0.303|-4.085***|1.671|-6.537***|
||0|-0.223|-0.677*|-0.03|-1.662***|-2.784***|-1.167***|
||1|-1.196***|-3.444***|-0.358|-0.951***|-1.419***|-0.764*|
|**Emloment**|2|-0.501|-2.528***|0.243|-0.866***|-0.584**|-1.009**|
|**py**|3|-0.166|-1.511***|0.339|-0.574*|-0.897***|-0.44|
||4|-0.198|-1.551***|0.316|-0.926***|-0.662*|-1.021**|
||5|0.12|-1.403***|0.692**|-0.828***|-0.377|-1.039***|
||0|0.382**|0.473**|0.355|0.869***|1.881***|0.465***|
||1|1.592***|2.81***|0.909***|1.063***|2.516***|0.497**|
||2|1.891***|3.574***|0.928***|1.089***|2.402***|0.599**|
|**Unemployment**|3|1.828***|3.822***|0.663**|1.151***|2.701***|0.592**|
||4|2.1***|4.494***|0.694**|1.316***|2.841***|0.742***|
||5|2.156***|4.684***|0.661**|1.033***|2.401***|0.51*|
||0|-0.629|-2.113**|0.026|-3.956***|-4.161***|-3.76***|
||1|-2.026|-5.123***|-0.64|-0.362|0.903|-0.871|
|**Actual rowth**|2|0.967|-0.462|1.609|-0.128|0.491|-0.403|
|**g**|3|1.809**|0.055|2.596**|-1.124***|-0.51|-1.379***|
||4|1.859**|-1.334|3.292***|-1.137***|-0.287|-1.491***|
||5|1.66*|-0.419|2.603**|-1.081***|0.183|-1.731***|

Source: Laeven and Valencia 2018; and World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Sample includes unbalanced panel of 32 advanced economies and 100 EMDEs for 1981-2020.

66
the second quarter of two consecutive years. With these observations, transition matrices can be produced. The following analysis makes use of these rotating panels for our periods of study.<sup>6</sup> The first transition matrix we analyze is an estimation of gross job flows before the recession—that is, between the second quarter of 2007 and the second quarter of 2008. In this case, we observe that around 9 million people became employed and 8.1 previously employed people became unemployed or inactive, thus a net job creation of around 0.9 million people was produced during the year before the recession. On the other hand, unemployment grew in only around 90 thousand people because around 1.5 million people previously employed or inactive became unemployed while nearly 1.4 million previously unemployed either found a job or left the labor market.

**Table 3.6a: Transition Matrices for the Recession Period**

||||Secondqu<br>|arter 2008<br>||
|---|---|---|---|---|---|
|||Inactive|Employed|Unemployed|Total|
||Inactive or out|23,687,896|8,199,919|684,809|**32,572,624**|
|Second quarter 2007<br>|Employed|7,385,503|34,766,246|754,907|**42,906,656**|
|<br>|Unemployed|451,074|900,531|153,591|**1,505,196**|
||Total|**31,524,473**|**43,866,696**|**1,593,307**|**76,984,476**|

**Source: Own estimates using rotating panels from ENOE.**

The second transition matrix refers to the recession—that is, between the second quarter of 2008 and the second quarter of 2009. According to this estimate, the fall in total employment was mostly due to the difference between to a gross job destruction (workers who were employed and became unemployed) of 1.2 million workers and a gross job creation (workers who were unemployed and became employed) of 0.833 million (see table 3.6b). The gross flows towards and from inactivity nearly cancel out (both around 8 million people) so most of the job destruction in 2009 can be attributed to firing existing workers in a proportion that more than compensated new hirings. Changes in unemployment can be attributed to the flows between employment and unemployment, already described, and to an additional flow from the inactive population. More than 951,000 initially inactive people became unemployed while only 544,000 formerly unemployed became inactive. This added 400,000 people (about half the total change) to the unemployed during the recession. In other words, the increase in unemployment is associated to the growth of labor supply.

> 6 According to INEGI, the panel follows the interviewers for five quarters and it is representative at the national level.

21
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16
were liberalized, and credit allocation quotas loosened. At the same time, while ending direct rationing of foreign exchange for the real sector, the government maintained a multiple exchange rate regime, thus opening a new area of arbitrage and rent seeking for financial institutions that had privileged access to foreign exchange auctions.<sup>5</sup> The consequence was the quick entry of many new players into the banking system, especially merchant banks that specialized in foreign exchange operations.<sup>6</sup> Very low entry requirements and the high market premiums that could be earned with arbitrage activities in the foreign exchange markets allowed for returns on equity of 300% or more (Lewis and Stein, 2002). In the following years, the number of banks tripled from 40 to nearly 120 (Table 1), employment in the financial sector doubled and the contribution of the financial system to GDP almost tripled (Lewis and Stein, 2002).

The financial sector boom, however, was accompanied by financial disintermediation. Deposits in financial institutions and credit to the private sector, both relative to GDP, decreased over the period 1986 to 1992 (Table 1). The increasing number of banks and human capital in the financial sector was thus channeled into arbitrage and rent-seeking activity rather than financial intermediation.<sup>7</sup> 5 The arbitrage potential arose from the spread between the official exchange rate and the interbank rate. After the trade liberalization, which was part of the SAP, there was an increasing demand for trade-related financing. Lewis and Stein (2002) describe the different arbitrage possibilities in more detail.

6 Another reason was the still existing guideline on credit growth, which made it more profitable to open a new bank than to expand an existing one.

7 For the empirical tests that follow, data were available from 1990 to 2001, and thus we cannot describe well the boom period in speculation which occurred largely in the last half of the 1980s.

7
This paper proposes a novel approach to estimating firm-level DVAR, based on an export revenue function, which relies solely on customs transaction data and does not require matching with industrial census data, significantly expanding its applicability. The comparison with previous estimates of Kee and Tang (2016) using both customs data and industrial census data confirms the validity of our new approach. Our estimations based on China’s customs transaction provide micro-level evidence of China’s rising DVAR during 2000 and 2016 and the increase are mainly driven by processing exporters.

HSR is the passenger-dedicated transportation infrastructure and mainly used for business purpose, which promotes the face-to-face communication between domestic buyers and sellers and reinforces domestic economic interactions and integration. To capture this IO linkages, this paper proposes a novel approach to measure firm-level IO linkages that only requires customs transaction data, by matching import product codes of exporting firms with export product codes of other domestic firms, implying that the latter can be viewed as the potential suppliers of the former. Empirical results based on this new measure of firm-level IO linkages, reveal that the rising DVAR can be attributed to HSR expansion, which enhances access to domestic materials by linking exporting firms with potential domestic suppliers. The effects of HSR connections are particularly significant for processing firms, foreign-invested firms, and firms in the electronics industry. Additionally, firms that import more homogeneous products and those connected to domestic suppliers over medium to long distances experience greater impacts from HSR.

In addition, this paper presents a two-sector theoretical model illustrating how HSR expansion affects firm DVAR by increasing the likelihood that exporting firms access domestic suppliers at lower communication costs. As the HSR network more domestic become accessible to grows, suppliers exporting firms, facilitating greater access to domestic materials. This improved access encourages the substitution of domestic materials for imported ones, thereby increasing firm-level DVAR. The findings of this paper indicate that, in addition to trade and FDI liberalization—previously recognized as key drivers of China’s rising DVAR—enhancing domestic transportation infrastructure can also play a critical role in enabling countries to capture more DVA and move up the GVC.

While it is beyond the scope of the current paper, our approach of estimating DVAR and capture firm-level IO linkages are general enough to applied to other countries, due to less data requirements. Regarding the last remark, we have started a new project on measuring the DVAR for a wide range of countries, based on the matched importer-exporter customs transaction data from the World Bank’s Exporter Dynamic Database and also applied it to Indonesia to analyze the impact of export ban on Nickel on downstream industry value-added creation.

35
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**Table 18 The structure of employment and unemployment by educational attainment, 2001**

|Education<br>Educatbon<br>|Sharein<br>Sharein<br>employment unemployment|"Excess<br>supply"|
|---|---|---|
|Lessthanprimary|5.6<br>3.3<br>|-2.3<br>|
|Primary|17.6<br>15.8|-1.8<br>|
|Vocationalschoola)|22.4<br>30.2<br>|7.8<br>|
|Secondarytechnicalb)|<br>32.9<br>35.5<br> <br>|2.6<br>|
|Secondarygeneral|3.5<br>4.9<br> <br>|1.4<br>|
|College|6.5<br>4<br>|-2.5<br>|
|University|11.5<br>6.3|-5.2|

a) 1 to 3 year courses b) 4 year courses Source: Labor Force Survey, CBS; Author's calculations.

As to the spatial mismatch, it is difficult to assess it degree due<sup>to the lackof</sup> relevant data.<sup>3</sup> 1 Other studies documented relatively strong regional<sup>variation of</sup> unemployment in Croatia, which may point to regional mismatches<sup>(Bisogno2000).</sup> Still, given relatively little variation in the job creation and job destruction<sup>ratesby</sup> region, these regional differences in labor market conditions are more<sup>likely to be of a</sup> historic nature rather than recent developments resulting from the differential<sup>rateof</sup> enterprise restructuring during the transition.

We can tentatively conclude that educational and spatial mismatches<sup>arising</sup> from industrial restructuring are not major factors behind high<sup>unemploymentin</sup> Croatia. Indirect evidence supporting this claim comes from slow pace of<sup>job</sup> reallocation in Croatia. As few jobs are being destroyed and created,<sup>thereis not</sup> much scope for mismatch between the old and new jobs. A more direct<sup>evidence</sup> comes from the comparison of the educational structure of employment<sup>and</sup> unemployment. Both are similar and there is no - common in other countries<sup>-</sup> disproportion between the fractions of low skilled jobs and low skilled<sup>unemployed.</sup> Nonetheless, there are reasons to believe that long term unemployment<sup>-whichis</sup> substantial in Croatia - is associated with inadequate skills.

3 1 The Central Bureau of Statistics does not publish data on unemployment by<sup>region.</sup> _-55-_
periods (persistent group) and (ii) the average consumption of those who moved out of (graduates) or into (entrants) the bottom 40 percent. As illustrated in figure 2.3, there is an average welfare improvement in the persistent group of the population staying in the bottom 40 percent during two periods. Moreover, entrants have a higher average consumption per capita than graduates (see Box 1 for definitions). Overall, this led to welfare gains across the whole distribution and positive consumption growth among the bottom 40 percent of the population.

**<u>Table 2.2:  Mobility matrix for the bottom 40 and top 60 percent of the population</u>** a) Upper bound b) Lower bound

|**2002**|**Top 60**|**2011**<br>**Bottom 40**|**Total**|**2002**|**Top 60**|**2011**<br>**Bottom 40**|**Total**|
|---|---|---|---|---|---|---|---|
|**Top 60**|43|17|60|**Top 60**|54|6|60|
|**Bottom 40**|17|23|40|**Bottom 40**|6|34|40|
|**Total**|60|40|100|**Total**|60|40|100|

Source: Authors’ calculations based on ECAPOV data.

Notes: Transition matrix is based on a synthetic panel for 2002-2011. Welfare aggregate is consumption per capita including health, durables and rent. Sample: heads of household 25-55 years of age. Detailed results of the regression predicting consumption per capita are presented in table A.2 in the annex. Methodological details are presented in Annex A2.

Box 1. Churning in the bottom 40 percent of the population The shared prosperity indicator, which measures consumption per capita growth of the bottom 40 percent of the population is an anonymous indicator. In other words, there is no guarantee to observe the same individuals in the bottom 40 percent of the population across years. In particular, several potential scenarios may occur. First, individuals can be the same in the bottom 40 percent in two periods _[(1) in Figure 2.2]_ . Second, individuals from the bottom 40 percent in the first period can move to the top 60 percent in the second period (graduates) _[(2) in Figure 2.2]_ . Third, individuals from the top 60 percent of the population in period one can move to the bottom 40 percent in the period two (entrants) _[(3) in Figure 2.2]_ . The forth scenario, in which the population in the top 60 percent is the same across periods, is not relevant to this analysis of the bottom 40 percent [ _(4) in Figure 2.2]_ . The overall impact of churning will depend on the magnitude of these movements.

There are several options to see the described scenarios. The first option is to track the mobility of people using panel data. This will allow tracking the same people over a particular period. The second option is to use a synthetic panel approach based on repeated cross-sectional data (see annex A2 for more details of this methodology). This provides an opportunity to predict the consumption of households in the first period using information about their consumption in the second period and permanent household characteristics. In this paper, the second approach is used.

12