rafmacalaba/datause-encoder-data huggingface dataset:
Table 4.21. Health utilization by initial total expenditure quintile

||||Pre-crisis|||Post-cnsis||
|---|---|---|---|---|---|---|---|
|||Visited|Climc|Hospital|Visited|Clinic|Hospital|
|_Allsample:_||||||||
||Mean|0.335|0.319|0.052|0.268*|0.256*|0.043|
||Std.err.|0.011|0.011|0.005|0.011|0.010|0.005|
||Obs.|1766||||||
|_Quintile1:_||||||||
||Mean|0.253|0.232|0.051|0.197|0.194|0.024|
||Std.err.|0.023|0.023|0.012|0.021|0.021|0.008|
||Obs.|353||||||
|_Quintile2:_||||||||
||Mean|0.348|0.330|0.044|0.280|0.269|0.035|
||Std.err.|0.025|0.025|0.011|0.024|0.024|0 010|
||Obs.|353||||||
|_Quintile3:_||||||||
||Mean|0372|0.355|0.043|0.315|0.296|0.050|
||Std.err.|0.026|0.026|0.011|0.025|0.024|0.012|
||Obs.|353||||||
|_Quintile4:_||||||||
||Mean|0.292|0.275|0.047|0.266|0.244|0.056|
||Std.err.|0024|0.024|0.011|0.024|0.023|0.012|
||Obs.|353||||||
|_Quintile5:_||||||||
||Mean|0.411|0.400|0.074|0.284*|0.274*|0.050|
||Std.err.|0.026|0.026|0.014|0.024|0.024|0.012|
||Obs.|354||||||

(*) Statistically different (lower) than the corresponding pre-crisis value.

In table 4.22, we divide the sample by country region. This table shows that the largest drops in health care utilization are outside large cities. The utilization drop in other towns is of about 8.5 percentage points (19.7%) and in rural areas of 7.1 percentage points (25.5%), while utilization in large cities drops by only 2 percentage points (6%). Due to the size of the standard errors, pre and post-crisis differences can be established statistically only in rural areas.

Table 4.23 shows that health care utilization increases with household size. This suggests that more people in the household increases the propensity to someone in the household having to visit a health clinic. Comparing pre and post-crisis values, for households of all sizes, there is a decrease in utilization. The largest utilization decrease is for households with 4 members (9 percentage point, 25%). However, because pre and post values are largely not statistically different, table 15 does not provide a clear picture about household size and health care utilization changes after the crisis.

36
opportunities.<sup>27</sup> But other inequalities have been less benign in that they generated inequality of opportunity. In this respect, the emerging inequalities in health and schooling in China have created concerns for future growth and distributional change. The large geographic disparities in living standards found in China are symptomatic of deeper biases in public resource availability, which also contributes to unequal opportunities, depending on where one lives.

The pattern of growth has also influenced the evolution of inequality in China, reflecting both good inequalities (as resource flows respond to new opportunities) and bad ones (as some poorly endowed areas get caught in geographic poverty traps). Rural and (in particular) agricultural growth tended to bring inequality down in China, and lack of growth in these sectors has done the opposite. Rural economic growth reduced inequality within both urban and rural areas, as well as between them (Ravallion and Chen, 2007).

Two dimensions of the pattern of growth have been of concern from a distributional perspective. The first is the urban-rural dimension. Mean income is not only lower in rural areas, but long-run growth rates have been lower, yielding divergence between the economic fortunes of China’s cities and their vast rural hinterland. The divergence has been particularly strong since the mid-1990s (once one allows for the higher rate of inflation in urban areas; see Ravallion and Chen, 2007). Similarly, while there was rapid agricultural growth in some periods, including the early 1980s, the sector’s growth has not been particularly impressive since then. The primary sector had an average growth rate of 7.5% over 1980-85, but the average has been under 4% since then, as compared to an average growth rate for the industrial sector of about 12%. So China’s growth rate in agriculture has not been exceptional; a 4% growth rate for agriculture is the average growth rate of the “agriculture-based” developing countries over the same period, the bulk of which are in SSA (World Bank, 2007).

The inequality-increasing sectoral imbalance of China’s growth process has attenuated the poverty impact of high overall growth rate. To assess the contribution of this imbalance in the growth process, imagine if the same aggregate growth rate had been balanced across sectors. Then it would have taken 10 years to bring the poverty rate down to 10%, rather than 20 years (Ravallion and Chen, 2007). This is not to say that a balanced growth process at the same overall growth rate was feasible for China, which would seem unlikely. This calculation is merely an

> 27 On the distinction between “good” and “bad” inequalities in China’s economic development see Chaudhuri and Ravallion (2007).

10
# **Not All That It Seems: Narrowing of Gender Gaps in Employment during the Onset of COVID-19 in Indonesia**<sup>*</sup>

Daniel Halim Sean Hambali Ririn Salwa Purnamasari World Bank World Bank World Bank

**Keywords:** female employment, COVID-19 pandemic, Indonesia, informal sector. **JEL codes:** J16, J21, O17

> * We thank Kenneth Couch, Charles Gottlieb, Corrine Dubois, Hillary Johnson, Miriam Muller, Jacobus De Hoop, Josefina Posadas, Budi Resosudarmo, Lia Amelia, Joseph E. Zveglich Jr., and Albert Park well as seminar participants at the International Centre for Economic Analysis (ICEA) Gender Inequality Conference, EAPCE Half Baked Seminar, Asian Economic Development Conference, and the 17<sup>th</sup> IRSA Conference, for helpful discussions and suggestions. All errors remain our own.

Contact information: World Bank Group, 1818 H Street, NW, Washington, DC 20433. Email: <u>dhalim@worldbank.org; shambali@worldbank.org; rpurnamasari@worldbank.org.</u>
22

## Table 4

# Tariff Rates (Indirect Tax Rates in Parentheses)

|||<br>Actual|Optimal Revenue-C<br>|onstrainedRates|
|---|---|---|---|---|
|||TariffRates|Ind Taxes>0|Ind Tax • 0|
|Food Crops|(0.02)a/|0.22|-0.06|0.09|
|<br>Cash Crops|(0.19)-|0.23|-0.28<br>|0.13|
|<br>Fore•try|(0.06)|0.28|9.31_E.!_|0.12|
|<br>FoodProcessing|(0.04)|0.35|0.27|0.11|
|Con•waer Good•|(0.10)|0.38|0.28|0.11|
|Intera.Goods|(0.03)|0.18|0.22|0.15|
|Services|(0.00)|o.oo|0.04|0.19|
|UNIFORM|RATE||0.16|0.15|

### ROTIS

Source: Dahl, Devarajan, van Wijnbergen (1986, table 2) The •i.ulations assume zero export taxes.

a/ Indirect production tax rates in parenthesis. _]t_ The 931 percent tariff on for~stry is due to the fact that imports in this •ector are ainu•cule.

I •• '.
Deuchert, E., and C. Felfe. 2015. “The Tempest: Short- and Long-Term Consequences of a Natural Disaster for Children׳s Development.” _European Economic Review_ 80 (November): 280–94.

Erman, A., E. Motte, R. Goyal, A. Asare, S. Takamatsu, X. Chen, S. Malgioglio, A. Skinner, N. Yoshida, and S. Hallegatte. 2020. “The Road to Recovery the Role of Poverty in the Exposure, Vulnerability and Resilience to Floods in Accra.” _Economics of Disasters and Climate Change_ 4 (1): 171–93.

Felbermayr, G., J. Gröschl, M. Sanders, V. Schippers, and T. Steinwachs. 2022. “The Economic Impact of Weather Anomalies.” _World Development_ 151 (March): 105745.

Giannelli, G. C., and E. Canessa. 2022. “After the Flood: Migration and Remittances as Coping Strategies of Rural Bangladeshi Households.” _Economic Development and Cultural Change_ 70 (3): 1159–95.

Gunby, N., and T. Coupé. 2023. “Weather-Related Home Damage and Subjective Well-Being.” _Environmental and Resource Economics_ 84 (2): 409–38.

Hallegatte, S., M. R. Bangalore, A. Vogt-Schilb, M. Fay, M. Bangalore, L. Bonzanigo, D. Treguer, U. Narloch, J. Rozenberg, and T. Kane. 2015. “Shock Waves: Managing the Impacts of Climate Change on Poverty.” Washington, DC: World Bank.

Hallegatte, S., A. Vogt-Schilb, M. Bangalore, and J. Rozenberg. 2016. “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters.” Washington, DC: World Bank.

Hallegatte, S., A. Vogt-Schilb, J. Rozenberg, M. Bangalore, and C. Beaudet. 2020. “From Poverty to Disaster and Back: A Review of the Literature.” _Economics of Disasters and Climate Change_ 4 (1): 223–47.

Hallegatte, Stephane, J. Rentschler, and J. Rozenberg. 2019. _Lifelines: The Resilient Infrastructure Opportunity_ . Washington, DC: World Bank.

Hoddinott, J., and B. Kinsey. 2001. “Child Growth in the Time of Drought.” _Oxford Bulletin of Economics and Statistics_ 63 (4): 409–36.

Hsiang, S., R. E. Kopp, A. Jina, J. Rising, M. S. Delgado, M. Delgado, S. Mohan, et al. 2017. “Estimating Economic Damage from Climate Change in the United States.” _Science_ .

Hsiang, S., P. Oliva, and R. Walker. 2019. “The Distribution of Environmental Damages.” _Review of Environmental Economics and Policy_ 13 (1): 83–103.

Kahn, M. E. 2005. “The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions.” _The Review of Economics and Statistics_ 87 (2): 271–84.

Keerthiratne, S., and R. S. J. Tol. 2018. “Impact of Natural Disasters on Income Inequality in Sri Lanka.” _World Development_ 105 (May): 217–30.

Kim, N. 2012. “How Much More Exposed Are the Poor to Natural Disasters? Global and Regional Measurement.” _Disasters_ 36 (2): 195–211.

Klomp, J., and K. Valckx. 2014. “Natural Disasters and Economic Growth: A Meta-Analysis.” _Global Environmental Change_ 26 (May): 183–95.

21
(Table 17). Compared with transition economies, Lithuania has relatively less new hires than the Czech Republic but somewhat more than Poland.

What all these figures tell us about the labor market in Lithuania? Consistent with earlier findings, they point to "one-way" labor market dynamics. There is substantial "transition dynamics", which has manifested itself in a substantial number of job separations, and has shortened employer tenure of a large number of workers.. However, there is still little "development dynamics" which would manifest itself in intense hiring and job creation. Lithuanian workers have lost job security, but have not yet gained the hope for finding a new job.

The overall picture presented above conceals significant variation in job stability across firms and sectors. There are distinct differences in the distribution of job tenure between the public and the private sector as well as depending on the firm size (Table 17). Not surprisingly, jobs in the public sector are much more stable than in the private sector. A mirror image of this is a much greater dynamics in the private sector. A median worker in the private sector has tenure of 3.2 years, while his public sector counterpart's tenure is 8 years. At the same time, 22 percent of workers in the private sector work for less than year, compared with only 6 percent in the public sector.

Expectedly, small finms offer less secure jobs than large firms. The smaller the firm, the shorter job tenure and the higher the proportion of new hires. To illustrate, median tenure in firms employing up to 10 workers is 3 years and the proportion of workers with less than one year tenure is 30 percent. In comparison, in firms employing 50 or more workers median job tenure is 7 years-more than twice as long as in small firms-and the proportion of new hires is 10 percent, that is one-third of that in small firms. Thus, the worker looking for ajob has the biggest chance to find it in a small firm, however his/her chances to keep it for a longer period will be limited.

> Employer tenure data prove that small private firms constitute the most flexible and dynamic segment of the labor market, despite facing the same regulatory environment as larger or public firms. One likely reason that large, state owned firms are

> characterized by less labor turnover is that they are strongholds of trade unions, which are

54
- 22 -

**ANNEX 2**

# **International Trade in 1991**

|_Country_|_Exports_<br>_(billion US$)_|_Total % of_<br>_Exports_|_Imports_<br>_(billion US$)_|_Total % of_<br>_Imports_|
|---|---|---|---|---|
|U.E.|24.0|74.7|18.4|65.0|
|Industrialized|||||
|Countries of Asia|1.2|3.7|1.4|_5.0_|
|A.L.E.N.A.|1.7|5.3|2.5|8.8|
|P.E.D.|4.2|13.1|4.9|17.3|
|Maghreb|1.0|3.2|1.1|3.9|
|Total|32.1|100.0|28.3|100.0|

_Source: CIDC, 1993_

**Trade of the Maghreb Countries in 1991**

|_Country_|_Exports (in t_|_housands of_|_US$)_|_Imports (i_|_n thousands_|_ofUS$)_|
|---|---|---|---|---|---|---|
||To||Part|From||Part|
||Maghreb|Total|(1)/(2)|Maghreb|Total|(3)/(4)|
||(1)|(2)|%|(3)|(4)|%|
|Algeria|152|12,314|1.2|151|9,104|1.6|
|Libya|190|10,775|1.7|482|6,001|8.0|
|Morocco|357|4,528|7.9|244|7,254|3.36|
|Mauritania|9|515|7.9|22.2|472|4.7|
|Tunisia|340|3,895|8.7|210|5,459|3.8|
|Total|1,048|32,027|3.2|1,109|28,289|3.9|

_Source: CIDC, 1993_
clude an aggregate housing quality index, total household consumption, respondent mental health, and child socio-emotional well-being, measured using the standardized Strengths and Difficulties Questionnaire (SDQ). In fact, the analysis unexpectedly shows that the socio-emotional well-being of children in treated households decreases substantially, by 0.34 standard deviations on average; we return to interpretation of these patterns below. Due to the non-compliance noted above, estimated instrumental variable (IV) treatment on the treated (TOT) effects are relatively imprecise. But the primary analysis, which pools across multiple rounds of follow-up survey data, does allow us to reject the existence of some moderate positive treatment effects.

A second main finding of the study is that the refugee-targeted housing subsidy program led to a meaningful and statistically significant deterioration in community perceptions towards Syrian refugees among the Jordanian neighbors of treatment households. We estimate a 0.33 standard deviation unit decrease in an index of their social attitudes and perceptions of refugees. The analysis also shows that neighbors become better informed about how much assistance Syrian refugees receive. There is no evidence of effects on neighbors’ own economic outcomes. We speculate that the substantial transfers received by treatment refugee households were observed by Jordanian neighbors and led to tensions in some cases, perhaps especially given that these neighbors also live in the same relatively poor neighborhoods as the refugees and are typically not well-off themselves. The easy visibility of assistance in this case – including construction activity in the treated housing units – may have raised the salience of the intervention relative to some other forms of aid (i.e., mobile money transfers or health care subsidies) that are easier to keep private.

The estimated null and even negative results for most pre-specified primary outcomes are in contrast to the predictions of experts. Building on an increasingly common practice in economics and other social sciences (DellaVigna and Pope 2017; DellaVigna et al. 2020), the research team gathered forecasts from both researchers and policy experts regarding the primary impacts of the program. Forecasters generally predicted positive but modest program impacts on refugees, and predicted zero effects on average for Jordanian neighbors. They were thus were somewhat more optimistic about likely program impacts than the estimated effects, though in most cases we are 4
# **Table 1: Regional incidence of civil strife in Uganda, 1992 and 1999**

||||**1992**|||
|---|---|---|---|---|---|
||All Uganda|Central|Eastem|Northem|Westem|
|Householdsaffectedbycivilstrife|5.9%|2.5%|8.4%|9.6%|5.5%|
|...theft|8.5%|6.9%|9.1%|13.6%|7.1%|
|...personalattacks|2.6%|1.5%|2.6%|4.0%|2.9%|
|Communitiesaffected bycivilstrife|35.8%|20.5%|45.3%|47.6%|38.2%|
|Total value_of_assets|1828.08|2138.67|1369.41|552.03|2628.21|
|Shareofcommunitiesgrowingcoffee|13.7%|26.3%|10.9%|0.9%|14.1%|
|Shareofcommunitiesgrowingcotton|5.3%|0.2%|11.1%|8.1%|2.1%|
||||**1999**|||
||AllUganda|Central|Eastem|Northeem|Westem|
|Households affectedbycivilstrife|9.7%|4.3%|10.7%|13.0%|12.4%|
|...theft|14.7%|13.5%|17.1%|17.5%|11.8%|
|...personal attacks|4.2%|2.8%|5.3%|6.3%|3.2%|
|Communitiesaffectedbycivilstrife|51.2%|32.3%|53.8%|54.1%|69.7%|
|Totalvalueofassets(US$)|2490.55|3181.98|1827.47|1144.91|3261.03|
|Shareofcommunitiesgrowingcoffee|32.6%|62.5%|26.8%|1.6%|27.3%|
|Shareofcommunities growingcotton|5.4%|0.6%|11.0%|12.3%|0.3%|

Source: Own computation from the 1992 and 1999 Uganda National Household Surveys 31
- 26 -

# **2. Rapid Growth of Credit**

**_The Credit Boom._** The liberalization of the capital account coupled with the liberalization of interest rates (after 1992), led to a lending boom (see Figure 12) in the first half of the 1990s. Total credit outstanding grew on average 22 percent p.a. in real terms over 1988 to 1995. The loan portfolio of finance companies grew at an even faster pace-on average 30 percent in real terms p.a. during these years compared to 20 percent for commercial banks (see Figure 6 below). Overall, loan growth on average outpaced the growth of GDP 1.8 times (or 2.3 times in the case of finance companies).<sup>36</sup> Finance companies' credit grew fastest in 1992, with loans growing three times faster than GDP, but the rate of growth has been declining since. Compared to commercial banks, finance companies had a stronger incentive to lend because of a signal of the Bank of Thailand suggesting that the size of their credit portfolio would be an important determining factor for the award of a much coveted banking license, i.e., the bigger they are (in terms of asset size) the higher the c:hances to receive a banking license.

**Figure 6. Credit and GDP Growth in Thailand**

<!-- Start of picture text --> 45 0<br>40.07<br>15.0<br>10.0<br>20.0 f<br>5.0<br>1988 1989 1990 1991 1992 1993 1994 1995 1996<br>-- Commercial Banks -}-Finance Cos. -GD P<br><!-- End of picture text --> _Source:_ Bank of Thailand Bulletin.

36 World Bank, Shadow Financial Sector Review (1997).
2.3 percent a year but this was equivalent to 5,000 more people working in commercial farms in 2010 than 25,000 who did in 2001.

Among presumed comparative advantage industries within manufacturing, employment grew the fastest in the garments, leather and leather products and basic metal industries at respective annual rates of 25 percent, 36 percent and 18 percent. While the high growth rates have partly to do with the low bases against which measurement is taken, it should be noted that all three industries were significant employers by 2001. The high rates thus correspond to the net addition of thousands of jobs within each over the decade, which had made the garments industry one of the largest employers in the country and seem to have put the other two on course to being likewise. Employment also expanded in food and beverages albeit at the lower pace of 2 percent a year and added as many jobs in absolute terms as did the other presumed comparative advantage industries combined. By contrast the textile industry shed jobs at an average rate of 5 percent a year‐casting doubt on the validity of it perception as a comparative advantage industry.

==Table 4 here==== ===Table 5 here=== What was the implication of the shift in employment from traded activities to non‐traded services to aggregate productivity in the2000s? Was it productivity enhancing or was it associated with declining aggregate productivity. This is a question for which the answer is best sought through the analyses of returns to the 2002 and 2009 waves of the UBI. However, we should first draw attention to two features of the sectorial shift in employment read from UBR2001 and UBR2010 that should have strong bearing on that answer. One of the features is that the reallocation of labor from the traded sector to non‐ traded services entailed increasing informalization of employment in as far as it involved a sharp rise in the employment share of microenterprises in retail trade and a corresponding decline in SMEs and larger businesses in the tradable sector. A measure of the scale of this second shift (or “informalization”), shown in Table 4, is that it led to a rise in the employment share of microenterprises by 12 percentage points at the expense of large enterprises the share of which declined by that amount while leaving the share of the SME sector largely unchanged.

The second feature was that the shift raised the employment shares of established businesses at the expense of startups and younger (or up and coming) enterprises. Thus Table 5 shows that the employment share of established businesses, defined as those that had been operating for more than 10 years rose by 15 percentage points over the decade. Moreover the rise would have been even higher if it were not for the shift in employment from the traded sector to non‐traded services. This is because the employment share of startups happens to be way higher in the non‐traded sector in general and in retail trade in particular which was the part of the non‐traded sector where employment shares increased the most. As a result the shift in employment from the traded sector to non‐traded services 17
<!-- Start of picture text --> 100 95.1 94.7<br>89.4 91.2<br>86.4 80.9<br>= 80<br>oo<br>F<br>60 55.7<br>Fa647.3647.347.3<br>5 40 43.5<br>2oo31.6 31.6<br>a2oo31.6<br>20<br>ie}<br>Men Women<br>HE Both parents [EM Father involvement EME During breastfeeding<br>M8 First months NN, «Who earn less<br><!-- End of picture text --> <!-- Start of picture text --> 100 95.7 945<br>90.0 88.7 90.8<br>84.3<br>= 80<br>o<br>F 58.8<br>5 60<br>6 47.0<br>Fa55 40 36.8<br>2<br>a2<br>21.1<br>20<br>0<br>Men Women<br>HM Both parents [EE Father involvement [MEM During breastfeeding<br>M8 First months [NN Who earn less<br><!-- End of picture text --> <!-- Start of picture text --> 100 95.1 94.7 100 95.7 945<br>89.4 91.2 90.0 88.7 90.8<br>86.4 80.9 84.3<br>= 80 = 80<br>==<br>oo<br>oF F 58.8<br>2oo60 55.7 5 60<br>Fa647.3647.347.3 6 47.0<br>5 40 43.5 Fa55 40 36.8<br>a2oo31.6 a2<br>21.1<br>20 20<br>ie} 0<br>Men Women Men Women<br>HE Both parents [EM Father involvement EME During breastfeeding HM Both parents [EE Father involvement [MEM During breastfeeding<br>M8 First months NN, «Who earn less M8 First months [NN Who earn less<br>100 100<br>2xxg80 2gg 80<br>22oo>oO oO><br>Bcc260 B 60<br>o a o2 a<br>33<br>33<br>84040 8 40<br>2<<2<br>@OO o @OO o<br>aa<br>20 20<br>0 0<br>Men Women Men Women<br>HE Onlyme = Onlymypartner = Both HE Onlyme =.sOOnly my partner.sOOnly my partner my partner partner<br>M85 Neither Not yet decided ME Both M8 Neither<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>g8080<br>>oOoO<br>26060<br>o a<br>33<br>84040<br>@OO o<br>aa<br>20<br>0<br>Men Women<br>HE Onlyme = Onlymypartner = Both<br>M85 Neither Not yet decided<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>2gg 80<br>>oO><br>B 60<br>2o2 a<br>3<br>8 40<br>2<br>@OO o<br>a<br>20<br>0<br>Men Women<br>HE Onlyme =.sOOnly my partner.sOOnly my partner my partner partner<br>ME Both M8 Neither<br><!-- End of picture text -->