rafmacalaba/datause-encoder-data huggingface dataset:
percent in the general Bulgarian population. Remarkably, 23.7 percent of the Roma population are simultaneously at risk of poverty and materially and socially deprived, while this figure is much lower at 7.5 percent among Bulgarians. The most severe situation is observed among individuals simultaneously deprived in all three dimensions, with 17.9 percent of the Roma population experiencing poverty, low work intensity, and material and social deprivation, compared to 2.9 percent in the general Bulgarian population. Overall, these findings underscore the disproportionate levels of multidimensional poverty and social exclusion faced by the Roma population compared to the general population of Bulgaria as well as the intersecting challenges.

**Table 10.** Percentage of individuals deprived in each AROPE dimension, National average and Roma population (2021)

|**AROPE**|**Bulgaria**|**Roma**|
|---|---|---|
|**Deprived in only 1 dimension**|||
|low work intensity|0.9|0.3|
|severe material and social deprivation|7.7|15.8|
|at risk ofpoverty|10.4|12.9|
|**Deprived in only 2 dimensions**|||
|several material and social deprivation + low work intensity|1.0|3.8|
|at risk of poverty + low work intensity|1.3|4.4|
|at risk ofpoverty+ several material and social deprivation|7.5|23.7|
|**Deprived in 3 dimensions**|||
|at risk of poverty + low work intensity + several material and social<br>deprivation|2.9|17.9|
|national AROPE rate|31.7|78.7|

Note: Percentage of individuals (using survey weights). See Table 1 and Table 2 for a detailed description of each AROPE dimension.

Source: Own estimates based on 2021 Bulgaria SILC. We report the survey year, and not the income year. AROPE definition based on EU2030 target.

**Even after accounting for relevant individual and family characteristics, Roma individuals still exhibit a significantly higher likelihood of experiencing income poverty, living in households with low work intensity, and enduring severe material and social deprivation compared to the Bulgarian population (Figure 4 and Table A4.9).** Additionally, when examining conditional probabilities of being deprived in a particular dimension of multidimensional poverty, the AROPE rates for Roma households surpass those of all other vulnerable household types. Specifically, the probability of experiencing severe material and social deprivation among Roma households is notably higher by 12.8 percentage points, surpassing the estimated probability for other vulnerable households. Consequently, the overall likelihood of being classified as AROPE is markedly greater for Roma individuals, with a notable increase of 15.9 percentage points. This underscores the critical importance of properly identifying Roma as a vulnerable group using official household surveys and measures, targeting social policies to protect this group, and prioritizing fiscal support for the Roma community in national poverty reduction strategies aimed at alleviating poverty and social exclusion.

29
markets improved with the adoption of IT. Historically, evaluations of transmission in India have been mixed. Mishra, Montiel, and Sengupta (2016) examined the strength of transmission using a structural VAR methodology. They found that a tightening of policy is associated with a significant increase in bank lending rates. Although passthrough to lending rates is only partial, they conclude that their result for India compares favorably with those for other developing countries. Consistent with these findings Acharya (2017) and Dua (2020) argue that transmission to money market and long-term interest rates is rapid and relatively complete, but that bank deposit and lending rates adjust more slowly and less completely.

We collated monthly data on government bonds yields of 1, 2, 5 and 10 year maturities, treasury bill rates, and average lending rates on new and outstanding loans. We used the repo rate as the relevant policy rate. Table 13 confirms that transmission is greater for treasury bill and short-tenure bonds. Transmission to government bonds yields and bill rates improved somewhat following the adoption of inflation targeting. Transmission to bank lending rates is relatively weak, as other authors have shown, and has not improved with the adoption of IT.

**Table 13: Transmission of policy rate**

||1 year govt bond<br>yield|91 day T bill rate|Bank lending<br>rate on<br>outstandingloans|Bank lending<br>rate on new loans|
|---|---|---|---|---|
|Repo Rate|0.94<sup>***</sup>|1.15<sup>***</sup>|0.61<sup>***</sup>|0.64<sup>***</sup>|
||(20.25)|(20.23)|(12.56)|(17.77)|
|IT|-1.35<sup>***</sup>|-0.41|0.87<sup>**</sup>|-0.12|
||(2.99)|(0.89)|(1.99)|(0.34)|
|Repo Rate x IT|0.33<sup>***</sup>|0.18<sup>**</sup>|-0.23<sup>***</sup>|-0.06|
||(4.59)|(2.59)|(3.38)|(1.03)|
|Constant|0.12|-1.52<sup>***</sup>|7.30<sup>***</sup>|6.31<sup>***</sup>|
||(0.38)|(3.93)|(19.49)|(23.00)|
|Observations|232|233|102|71|
|Adjusted_R_<sup>2</sup>|0.59|0.59|0.91|0.91|

Note: Robust t statistics in parentheses; *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

# **6. Are expectations better anchored?**

Kose et al. (2019) find that long-term inflation expectations have declined in the past two decades in both advanced economies and emerging markets and developing economies (EMDEs). Although inflation expectations are less well anchored in EMDEs, their sensitivity to domestic and global shocks has declined. They suggest that an IT regime and greater central bank transparency are associated with better anchoring.<sup>37</sup>

> 37 Lower public debt and greater trade openness are also associated with better anchoring of expectations. Using monthly survey data from Consensus Economics for a sample of 22 EMDEs and 14 advanced economies in a structural vector autoregressive model, Davis (2014) similarly finds that the introduction of inflation targeting is associated with

21
Central and Eastern European emerging economies and four other emerging economies was considered by Oskooee, Kutan and Xi (2013). Results confirmed that the uncertainty’s impact is transitory in most countries and money demand was correctly specified and stable in most countries.

Bahmani and Kutan (2010) tested the stability of money demand for Armenia, Bulgaria, the Czech Republic, Hungary, Poland, the Russian Federation, and the Slovak Republic. They used the bounds testing approach to co-integration that shows stable money demand in these countries.

To address issues of data aggregation, Hsiao, Shen and Fujiki (2005) used Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. Aggregate data appear to support the contention that there was no stable money demand function, while disaggregate data showed that there was a stable money demand function. The diametrically opposite results are attributed to the neglected heterogeneity among micro units.

During the past five years, new modeling techniques have evolved for country level research related to money demand. Focus was directed to investigating the presence of a non-linear relationship between monetary aggregates and interest rate, as well as the possibility of considering time-varying behavior of coefficients.

Lee and Chang (2013) employed second-generation Random Coefficient (RC) modeling to investigate the time-varying behavior and the predictability of the money demand function in Taiwan, China, over the period 1982Q1 to 2006Q4. Empirical results indicated that the values of elasticity in the RC estimation are significantly different from those in other studies, because of the use of coefficient drivers that can make an association with real events occurring in Taiwan, China, such as the financial liberalization after 1989 and the Asian financial crisis of 1997–1998.

Bae and De Jong (2007) combined the logarithmic specification, which models liquidity better than a linear model, with the assumption that the interest rate itself is an integrated process. Results for the US produced larger coefficient estimates than previous research in addition to superior outof-sample prediction.

Recent research also challenged the conventional wisdom of instability of demand for narrow money in many advanced economies across the globe (mainly the US, Australia, and Italy). These 10
papers also find little evidence that common determinants of growth, as well as a number of other variables, are robustly correlated with patterns of distributional change that matter for poverty reduction.

The rest of this paper proceeds as follows. Section 2 reviews standard poverty decomposition techniques and uses them to illustrate the channels through which growth and distributional change matter for changes in a number of poverty measures. Section 3 describes the dataset of changes in poverty in a large sample of developing countries on which the empirical analysis is based. Section 4 provides evidence on the relative importance of the sources of pro-poor growth, as well as evidence on some of the correlates of these sources. Section 5 concludes.

4
<!-- Start of picture text --> ROIC vs Markups<br>= oe<br>2<br>+ weet eee<br>=<br>o<br>a<br>ae ~<br>= ”<br>~ ral<br>af<br>‘~,<br>=<br>S<br>0 3 1 1.5 2 2.5<br>Markups<br>—'-'— - All other firms ——- ROIC Stars<br>——-— ROIC Stars for >=5 years ~ereeees= ROIC Stars for >=3 years<br><!-- End of picture text -->
case for the study of emigration in a source country. For methods 1 and 3, one needs information on the relative prevalence of emigration by PSU, and for methods 2 and 3 one needs information about emigration within PSUs.

One further decision to make, assuming that migrants can be properly identified in the frame, is whether to select based on the proportion of households with migrants out of the total number of households or to select based on the proportion of migrants over the population in the reference area. Given that a significant amount of analysis on migration is performed at the household level, the first option may be preferable (Bilsborrow et al., 1997).<sup>8</sup> For the specific purpose of using surveys to learn about emigrants in sending countries, the lack of a suitable sampling frame would still be an obstacle to implementing a disproportionate sampling design. One possible modification, but a departure from a full probability sample, would be to use alternative data sources to identify high-emigration areas in a country. These sources may include, for example, expert opinions, qualitative surveys or surveys in destination countries where, in addition to the immigrant’s country of origin, the specific location of departure is asked. However, the last method is not recommended unless most emigration from the source country has a specific destination and all of these main destinations are covered.

Lacking a proper sampling frame, a less than perfect alternative would be to select all area sampling units (or clusters) at different stages with probability proportional to the estimated size (PPES) of the overall population (or the number of occupied dwelling units) and carry out a full listing operation only in the area sampling unit last selected.

> 8. For a worked example of a three-stage disproportionate sample of immigrants using a suitable sampling frame, see Bilsborrow et al. (1997, pp. 280–83).

23
# **Gender-Specific Transportation Costs and Female Time Use: Evidence from India’s** **_Pink Slip_ Program**<sup>*</sup>

Yutong Chen<sup>†</sup> Kerem Cos ̧ar<sup>‡</sup> Devaki Ghose<sup>§</sup> Shirish Mahendru<sup>¶</sup> Sheetal Sekhri<sup>||</sup>

Originally published in the Policy Research Working Paper Series on _February 2024._ This version is updated on _May 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords** : Transport, Gender, Time-Use, Female Labor Force Participation **JEL codes** : J16, J22, R41

> *We thank the University of Virginia Department of Economics for financial support. For helpful comments and feedback, we thank S Anukriti and the World Bank Office of the Chief Economist, South Asia Region. The authors gratefully acknowledge support by the Umbrella Facility for Trade, which is funded by the governments of The Netherlands, Norway, Switzerland, Sweden and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are solely those of the authors and do not necessarily represent the views of the World Bank, its affiliated organizations, or the Executive Directors or the countries they represent. All errors are our responsibility.

> † `yc3jk@virginia.edu` (University of Virginia)

> ‡ `kerem.cosar@virginia.edu` (University of Virginia, CEPR, CESifo, NBER)

> § `dghose@worldbank.org` (Development Economics Research Group, The World Bank)

> ¶ `shirish.mahendru@giz.de` (Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India)

> || `ssekhri@virginia.edu` (University of Virginia)
# **Graph (7a): Scaled Residuals of Unrestricted Closed Economy VECM:**

<!-- Start of picture text --> 0.50<br>r Drm2 0.2 r Drgdp<br>0.25<br>0.00 0.0<br>-0.25<br>-0.2<br>1980 1990 2000 2010 1980 1990 2000 2010<br>20 5.0<br>r DINFL r DInterestrt<br>10 2.5<br>0 0.0<br>-10 -2.5<br>1980 1990 2000 2010 1980 1990 2000 2010<br>r DDISC<br>2.5<br>0.0<br>-2.5<br>1980 1990 2000 2010<br><!-- End of picture text -->

# **Graph (7b): Long Run Stability Test of Unrestricted Closed Economy VECM:**

<!-- Start of picture text -->
1.00 Roots of companion matrix<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>-0.75<br>-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0<br><!-- End of picture text -->

60
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3).<sup>27</sup> First, while a higher percentage of occupations that can be performed remotely in a sector still mitigates the negative trade effect of a production shock in exporting countries, the coefficient of 0.77 in column (4) is 0.35 percentage point smaller compared to the original country sample (Table 3) and also shows a lower statistical significance. Second, third-country effects in column (3) have the correct sign and are statistically significant, suggesting that China’s exports grew in destination markets as other exporters were affected by Covid-19. This coefficient remains with the right sign but loses significance in column (4). Finally, regarding the GVC channel, the positive role of an exporting country’s share of imported inputs in mitigating shocks is 0.2 percentage point larger in the extended country sample including China, whereas the negative role of GVC participation in a partner country’s sector is 0.15 percentage point smaller. The upstream shock, however, matters more strongly for bilateral trade growth, consistently with the centrality of China in modern global value chains.

# **_Including developing countries_**

In a final extension we use mirror trade data, i.e. import data to our 28 countries, to obtain a larger sample of exporting countries. Since the weights used for the computation of the upstream shock variable are based on 64 countries in the TiVA data set, we need to limit the number of exporters to the TiVA sample too. This country sample includes several upper- and lower-middle-income countries and thus allows us to examine whether the previous findings also hold when more middle-income countries are included. The caveat is that the trade data only consider bilateral exports to 26 EU member states, Japan and the United States.

The results reported in Table 7 show that the general findings still hold in the larger country sample. The only exception is the role of remote work in a country and sector which is no longer significant and shows a much smaller coefficient. It appears that in middle-income countries, the possibility of remote work in a sector matters much less in hampering the negative trade effect from shocks, possibly because the type of occupations in a sector cannot be performed remotely. This finding is consistent with the lower role of remote work when we included China to the country sample. Another interesting difference is the size of the upstream shock which becomes smaller when including more middle-income countries. A possible explanation could be the composition of export goods in the larger sample which are likely to be more upstream in the supply chain, implying that a negative shock in an exporter’s source countries has a

> 27 Appendix 4 presents the comparison. We cannot rule out that the aggregation of January and February data is driving some of these differences.

25
The data covers 27 countries in Eastern Europe and Central Asia as part of the Business Environment and Enterprise Performance Surveys conducted jointly by the World Bank and the EBRD. The initial survey was fielded to 6667 firms in 2002, with a follow-up round in 2005 that covered 4813 of the same firms. Table 1 provides basic summary statistics. Firms were asked a number of questions on the business environment in which they operate, including both objective and subjective questions. Objective questions include the monetary costs or time needed to comply with various regulations or procedures. The range of topics covered includes: property rights, crime, access to finance and infrastructure services, corruption, cronyism, competition and regulation (see Table 2 for description and Table 3 for correlation matrix). In addition, there is information on firm performance, including the size of the firm, its sales and employment growth in the previous 3 years and many characteristics of the firm.

# _Measuring ‘exit’_

The measure of ‘exit’ is defined in two ways. The stricter definition is ‘observed exits’. It is based on confirmation that the business interviewed in 2002 had indeed closed or gone out of business by 2005. However, this was not possible to ascertain with certainty in many cases as the entrepreneurs could not always be located. So, a second definition is also used. This includes all the ‘observed exits’, plus those cases where enumerators were unable to locate the enterprise – it was no longer in operation at the same location or with the same phone number and no new contact information was available with the same firm name. Firms that were acquired and remained in the same location are classified as survivors. Only if they moved and changed their name and contact information would the firm be classified as an exit. It is possible that in this second measure, some firms did in fact remain in 16
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26