rafmacalaba/datause-encoder-data huggingface dataset:
ence of heterogeneous<sup>29</sup> and dynamic<sup>30</sup> treatment effects (Sun and Abraham 2021; Borusyak et al. 2021; Goodman-Bacon 2021; De Chaisemartin and d’Haultfoeuille 2020; Callaway and Sant’Anna 2021).

We examine the validity of the pre-trends assumption and the properties of our TWFE regressions as the impact of refugee inflow is likely to vary across subnational regions and over time in Figure A1. In line with the result presented in Figure 3, none of the results reported in all panels of Figure A1 provide evidence of pre-trends.

# **_Refugee waves and attitudes toward immigrants_**

Table 3 replicates the specification from columns 3 and 5 in Table 2 for different definitions of events. Columns 1 and 2 lower the cutoff to 5,000 refugees in one year, while columns 3 and 4 raises the cutoff to 50,000 refugees per year. Columns 5 and 6 define the event as an increase of at least 100%, representing an absolute change of at least 5,000 refugees in the year, and set the population to 10 in region-years before the event with zero refugees in order for the percentage to be defined. The results are mostly consistent with the results in Table 2, with regiononly samples showing positive, insignificant coefficients at multiple cutoffs and the all-region samples showing effects close to 0, except for column 4. Columns 3 and 4 together suggest large waves (more than 50,000 refugees) led to more positive attitudes in the receiving region (column 3), but grew by 4.5 percentage points less on average than the other regions that did not receive the largest wave within the country. This is likely due to the binary nature of the event definition, as other _β_ ˆ _T W F E_ 29In theis a caseweightedof heterogeneousaverage of grouptreatmenttime-leveleffects,averagethetreatmentproblem ariseseffects,becausewhere thetheweightsestimatedare unequal over groups and time, and may be negative. In a general design, weights are more likely to be negative for periods in which many groups are treated and to groups treated for many periods (De Chaisemartin and d’Haultfoeuille 2020). In a staggered adoption design (a setting where units can move into, but not out, of a binary treatment with heterogeneous timing between groups), this implies that weights on later time periods are more probable to be negative (Borusyak et al. 2021).

30When considering a setting with two time periods and one treatment (treatment status changes by one unit) and one control group (treatment status is unchanged), the possibility of dynamic effects requires one to account for the prior path of treatment and control group. Intuitively, a TWFE difference in differences regression does not control for past treatment history, and is thus not robust to dynamic effects. Similarly, Sun and Abraham (2021) show that the pre- and postevent effect estimates in the canonical event study setting may mix, leading to incorrect estimates of pre-event trends, as well as the instantaneous and dynamic effect of treatment.

23
4 _One issue that often preoccupies economists is the issue of whether the supply would be there. Could the Industrial countries (once ve get rid of the U.S. deficits and so on) actually grow at 4 percent? Is the labor there? Is the capital there? Is the technology there? Are the savings there? My biased conclusion is that most of these problems have been overstated._ _On the labor front, I would have thought that there was a fair amount of avallable labor In most of our economies. I mean, the United States today may be an exceptlon in that It Is close to full employment; but then the growth problem is more an European and, perhaps, Japanese problem than It is an American problem, at least ac present. In Western Europe in particular, to my mind, there are ample supplies of labor. Take the British example, as a case for an extremely rigid labor market. In Britain, the natural unemployment had risen par _ Passu with the actual unemployment, to about 3 million. Over the last year, British growth has been very rapid by international standards. Unemployment has fallen rapidly and sharply, and there have been no upward pressures on wages. Wages have grown rapidly but they were growing already beforehand while the British economy was not growing rapidly. There has been no upward Inflationary pressure and labor has been reabsorbed into the economy._ _On the capital side, there may be need for a bit more caution, if only because ten or fifteen years of slow growth have led to a shortfall of investment and hence to capacity problems. Again, Britain doesn't seem to be evidencing these capacity problems, -- nor Japan, whose growth has also accelerated significantly apparently thanks to a fiscal boost. But the rates of capacity utilization at present, for instance, do not suggest that the margins of spare capacity are as large as one might have expected after ten or fifteen years of slow growth._ _Nonetheless, my prudence ought to be colored by experience of the 1950s and 1960s, for instance, when we were running very close to full capacity utilization. Capacity was fully utilized in all the OECD economies for a long-run of years. Yet, we could go on growing at 4 to 5 percent. Demand just created its investment needs and the capacity that went with it. With the large margins of spare capacity we have now, one would think that the scope for expansion was there. And, as expansion took place, because of the accelerator mechanisms, the investment would then follow._ _Raw materials..._ _On the raw materials' side, I would doubt that there can be very many problems for p;imary products other than oil. And even for oil, I would have thought the chances of OPEC sharply raising prices again are remote unless we really were to accelerate to an inconceivably rapid growth rate, to 4 or 5 percent for ten years. Then, no doubt, we might face problems in the oil market again. But this is clearly not on the cards. And perhaps influenced by the present oil market situation, I predict that oil will not be a major constraint on future growch._ WPIPROC/SF:Jsq
# **Table 2A: Growth and Uphill Flows of Sophisticated Exports** **<u>(Panel Regressions; Scale-free measure of uphill flows)</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Variable|OLS|OLS|OLS|OLS|Fixed<br>effects|IV|IV|
|Per capita GDP (log)|-0.006**<br>(0.003)|-0.006**<br>(0.003)|-0.005**<br>(0.002)|-0.004*<br>(0.002)|-0.033***<br>(0.007)|-0.015*<br>(0.009)|-0.015*<br>(0.009)|
|Uphill flows (median sophistication)|0.011***<br>(0.003)|0.011***<br>(0.003)|||0.001<br>(0.005)|0.024***<br>(0.009)||
|Years of primary schooling|0.009**<br>(0.004)|0.009**<br>(0.004)|0.009**<br>(0.005)|0.010**<br>(0.005)|0.040**<br>(0.017)|0.013<br>(0.020)|0.012<br>(0.020)|
|Uphill flows (75th percentile<br>sophistication)|||0.010***<br>(0.003)||||0.025***<br>(0.009)|
|Uphill flows of sophisticated relative to<br>unsophisticatedproducts||||0.009***<br>(0.003)||||
|Observations|267|267|266|267|267|256|255|
|Adjusted R-squared|0.076|0.106|0.090|0.080|0.117|0.005|-0.045|
|F-test|7.07|4.44|4.74|3.79|7.96|3.25|2.79|
|Number of countries|||||65|||

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The instruments for uphill flows in columns 6 and 7 are population and remoteness (in logs). All columns, except column 1, include time effects. Fixed effects included only in column 5.

29
the professional way.” A human resources manager of a medium-size industry in Madani said, “No, it needs more, it’s advanced but slow, it needs more concentration, more training for the employee and more experience.” In this case, vocational training could fill some of the skills gaps that might improve employment possibilities. At present, only 36 percent of the attendees are women (compared to 64 percent men) and 27 percent are youth (compared to 73 percent adults). The low attendance of women and youth is in part due to their limited access to training centers, which eventually results in a mismatch between the areas of employment in which women and youth are interested and the skilled labor demanded by the labor market. Case in point, a 2018 International Labour Organization (ILO) study conducted in White Nile State found that women and youth were highly interested in taking part in vocational and/or skills training opportunities that might improve their chances of getting a job. However, the lack of training centers in their communities forced them to travel more than two hours to find one, which was costly, timewise and monetary-wise, and many could not afford (ILO 2018, 18).

# **_Gender wage gap_**

Globally, the more education women have, the higher the economic return on education **.** While women with some primary education earn 14 to 19 percent more than those with no education at all, women with secondary education make almost twice as much and women with tertiary education earn almost three times as much as women with no education (Wodon et al. 2018). Women who have secondary and tertiary education tend to have higher labor force participation and more often full-time work. Studies point to several factors contributing to women’s lower earnings than men, including the fact that fewer women are economically active than men and often work fewer hours (for example, Wodon et al. 2020). However, there is also a tendency for women to be paid less than men when working. In addition, social norms affect the occupational segregation across sectors and types of employment as well as between the paid and unpaid work as household and care responsibilities are considered women’s work, while men are considered providers. (This is discussed in greater detail in the section on informal institutions).

In Sudan, earnings have gone down, and the decrease in real wages has been more pronounced among women and the better-off. More people in urban areas earned less than US$1.90 a day in 2014, compared to 2009, a pattern followed by a decline in the median (from 6.1 in 2009 to 3.8 in 2014) and average daily wages (from 8.4 in 2009 to 6.1 in 2014). While men’s average wages declined by 11 percent, women’s wages declined by more than one-third. This is expected as the increase in labor force participation and employment among women has been concentrated in low-productivity activities such as agriculture (Figure 14).

20
Furthermore, if the firm experiences financial distress, the fact that the banker controls the firm's stock can reduce potential conflicts of interest between stockhiolders and creditors in developing a reorganization plan.

Much of the research on the benefits of allowing banks to combine equity and debt finance has emphasized these advantages. Similar benefits from allowing banks to own or control shares appear in studies of contemporary Germany and Japan and the pre-Glass-Steagall United States. That research has showin that close multidimensional relationshiips between banks and firms can reduce the costs of obtaining funds for firms, improve firm performance, make investment decisions less dependent on retained earnings, and make it easier for firms to resolve financial distress. In their study of banking relationshiips before and after Glass-Steagall, DeLong and Ramirez (1995) found that the value of the banking relationship for the firm was substantially reduced when the relationship narrowed to lending alonie.I Information and control advantages may also occur in a dynamic context. During their financial life cycles corporations often progress from reliance on bank loans to the public issuance of common stock. Under universal banking of the German type, the same intermediary can hold the debt of the firm in the early stage of the life cycle, later underwrite shares of the firm, and then control voting proxies for the purchasers of those shares. Empirical evidence suggests that there are information cost advantages to havinig the same intermediary guide the firm through its life cycle in this way (Slovin, Sushka, and Polonchek 1992; Petersen and Rajan 1994). If the firm's financial service needs change over time, it is economical to give intermediaries the flexibility to provide different services and hold various types of claims on the firm.

. Additional studies in the same spirit include Hoshi, Kashyap, and Scharfstein (1990, 1991); Ramirez (1995); DeLong (1991); and Gorton and Schm idt (1995).

9
- Ruebush, T. K., M. K. Kern, C. C. Campbell, and A. J. Oloo. 1995. "Self-treatment of Malaria in a Rural area of Western Kenya." _Bulletin of the World Health Organization_ 73(2).

- Sahn, David E., and D.C. Stifel. 2000. "Poverty Comparisons Over Time and Across Countries in Africa." _World Development_ 28(12).

- Sindelar, Jody, and Duncan Thomas. 1991. "Measurement of Child Health: Maternal Response Bias." Discussion Paper No. 633. Yale University Economic Growth Center, Connecticut.

- Slutsker, L., L. Chitsulo, A. Macheso, R.W. Steketee. 1994. "Treatment of Malaria Fever Episodes among Children in Malawi: Results from a KAP Survey." _Tropical Medicine and Parasitology 45._

- Stecklov, Guy, Antoine Bormmier, and Ties Boerma, 1999. "Trends in Equity in Child Survival in Developing Countries: A Illustrative Example using Ugandan Data." MEASURE _Evaluation_ Project/Carolina Population Center, INED. Processed.

- Strauss, John, and Duncan Thomas. 1996. "Measurement and Mismeasurement of Social Indicators." _American Economic Review Papers and Proceedings_ 86(2).

- Verhoef, Hans, Elsa Hodgins, Clive E. West, Jane Y. Carter, and Frans J. Kok. 1998. "Correspondence: Diagnosis of Fever in Africa." _The Lancet_ 351(9099): 372.

- Wagstaff, Adam, and N. Watanbe, 1999. "Inequalities in child malnutrition in the developing world." World Bank, Development Research Group, Washington, DC. Processed.

- World Bank. 1999. _World Development Indicators 1999._ Washington, DC: The World Bank.

- World Bank. 2000. _World Development Report 2000/2001: Attacking Poverty._ New York: Oxford University Press.

- World Health Organization. 2002. _World Health Organization Report on Infectious Diseases._ Geneva: WHO. Available at http://www.who.int/infectious-diseasereport/2002/ (February 4 2002).

_34_
# **Time allocation in rural households:**

**The indirect effects of conditional cash transfer programs**

Amer Hasan<sup></sup> Poverty and Gender Group Poverty Reduction and Economic Management Unit Latin America and the Caribbean Region (LCSPP) 1850 I Street, NW Washington, DC 20433 <u>ahasan1@worldbank.org</u>

>  I would like to thank Dan Black, Jishnu Das, Asim Khwaja, Robert LaLonde, Robert Michael and Marcos Rangel for helpful discussions on the topic. Haeil Jung, Daniel Kreisman, Patrick Wightman provided valuable feedback on earlier versions of this paper. All remaining errors are my own. Mohsin Chandna and Syed Sohail Raza were instrumental in gaining access to the administrative data used in parts of this paper.
# **The Polish chemical industry**

The chemical industry in Poland dates back to salt mining operations south of Cracow in the fifteenth century. The beginning of the modern chemical industry can be traced back to the early 1920s, after the formation of the ncw Polish state. Under the leadership of the chemical engineer-statesman Ignacy Moscicki, the huge complex at Tarnow in southeastern Poland was completed in 1927, placing Poland among the modern industrial nations. The industry expanded rapidly during World War II, when Polish plants were used for wartime production under German occupation. The plants were heavily bombed during the war. Some of the equipment that survived was taken to Germany by retreating forces, and the rest was later removed by the Soviet Union as war reparations. As a result, the Polish chemical indu ,-r.Y had to be almost entirely rebuilt after the war.

The new plants were designed, constructed, and equipped by local engineers and workers using mostly local technology. Production was geared toward domestically available raw materials. Until the 1960s, efforts concentrated on getting the new units operating. Very little expansion occurred. Then in 1964, the first major complex was initiated at Plock. The Plock complex was followed by another at Wloclawek in the late 19608, one in Pulawy in 1970, and the modern plant at Police in the mid-1970s. (For more detail, see World Bank 1987).

After a period of rapid growth from 1970 to 1980, production stagnated, despite a chronic inability to meet domestic demand, because of shortages of materials and a slowdown in investment. The industry suffered from a high degree of product imbalance.' Too much emphasis went to inorganic chemicals and those derived from coal and not enough to organic chemicals, especially petrochemicals.2 Other problems included lack of specialization, poor financial management, weak marketing, overemployment, and high levels of pollution and worker health hazards.

-2-
**Table 10:  Instrumental variable regression results of project impact on duration of enrolment at playgroup and kindergarten services: difference between Batch 1 and Batch 3. Dependent variable is months of enrolment in specified service.**

||Project pl|aygroup|Non-projec|t playgroup|Kindergart|en, MoEC|Kindergart|en, MoRA|
|---|---|---|---|---|---|---|---|---|
||Unadjusted|Adjusted|Unadjusted|Adjusted|Unadjusted|Adjusted|Unadjusted|Adjusted|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Child’s village is Batch 1|||||||||
|(1=Yes)|1.016|0.967|-0.0368|-0.122|-0.555|-0.407|-0.264|-0.250|
||(0.788)|(0.804)|(0.423)|(0.460)|(0.444)|(0.438)|(0.318)|(0.308)|
|Constant|8.181***|2.232|0.752|3.053|1.596***|-5.303**|0.414|-1.811|
||(1.120)|(3.502)|(0.498)|(2.047)|(0.422)|(1.729)|(0.257)|(1.271)|
|Observations|1989|1717|1989|1717|1989|1717|1989|1717|
|R-squared|0.142|0.141|0.086|0.088|0.160|0.203|0.027|0.046|
|**Controls Included**|||||||||
|Child Characteristics|N|Y|N|Y|N|Y|N|Y|
|Household Characteristics|N|Y|N|Y|N|Y|N|Y|
|Height and Weight|N|Y|N|Y|N|Y|N|Y|
|Parenting practices at|||||||||
|baseline|N|Y|N|Y|N|Y|N|Y|
|District dummies|Y|Y|Y|Y|Y|Y|Y|Y|
|First stage F-Statistic||97.630||97.630||97.630||97.630|
|First stageR-squared||0.775||0.775||0.775||0.775|

_Note:_ Robust standard errors clustered at the village level in parentheses. Child characteristics include: child’s age and gender. Household characteristics include household size, household wealth and mother’s education. Height and weight include stunting and wasting status at baseline. Parenting practices are self-reported by caregiver at baseline. District dummies are controlled for in both adjusted and unadjusted models. *** p<0.01, ** p<0.05, * p<0.1 44
**Table 3: The** Determinants **of** Time **Allocated to House ork by Gender in Peru, 1994 and 1997 a**

||Gi<br> <br>|rls<br>**(1)**<br>||Boys<br>(2)<br>|
|---|---|---|---|---|
||coeff|t-ratio|coeff|t-ratio|
|Age<br> <br>|0.335"<br>|<br>5.39<br>|0.408<br>|<br>6.23<br> <br>|
|Square<br>ofage<br> <br>|-0.008-|-3.37|-0.015|<br>-5.49|
|Female-headed<br>household<br> <br>|0.067<br>|0.46<br>|0.123<br>|<br>0.90<br> <br>|
|Rural xfemale-headed<br>household<br>|0.091|0.27|0.153|<br>0.51|
|Native|0.463|1.86|0.324|<br>1.28|
|Rural x native<br> <br> <br>|-0,581"<br>|-2.07<br>|-0.287<br>|<br>-1.03<br> <br>|
|Wealth<br>per capita<br>(log)|-0.091"|-2.66|-0,018|<br>-0.50|
|Rural xwealth per capita<br> <br> <br>|0.127"<br>|2.32<br>|-0.031<br>|<br>-0.57<br> <br>|
|# of children<br>under<br>4years<br> <br> <br>|0.023<br>|0.34<br>|0.005<br>|<br>0.08<br> <br>|
|# of adult females<br>between<br>15 & 60<br> <br> <br>|-0.124"<br>|-2.58<br>|-0.139<br>|<br>-2.67<br> <br>|
|# of elderly<br>females,<br>over 60<br> <br>|-0.101|-0.79|-0.220|<br>-1.67|
|Household<br>size<br> <br> <br>|-0.143"<br>|-4.26<br>|-0.116<br>|<br>-3.62<br> <br>|
|Adult wage (cluster<br>median;<br>log)<br> <br> <br>|-0.076<br>|-0.89<br>|0,008<br>|<br>0.10<br> <br>|
|Female<br>unemployment<br>rate<br> <br> <br>|-1.607<br>|-4.39<br> <br>|-0.969<br>|<br>-2.54<br> <br>|
|Rural xfemale<br>unemployment<br>rate|1.938"|<br>2.57|0.399|<br>0.47|
|Household<br>energy:<br>fuelwood/coal<br> <br> <br>|-0.160<br>|-1.10<br>|0.269|<br>1.66|
|Rural<br>xhousehold<br>energy:<br>fuelwood/coal|0.275|1.35|0.085|<br>0.39|
|Water<br>source:<br>outside.<br> <br> <br>|0.208<br>|1.00<br>|-0.197<br>|<br>-0.99<br> <br>|
|Rural<br>xwater source<br>outside|-0.488"|-1.96|0,259|<br>1.07|
|School<br>expenses<br>(cluster<br>average)<br>)x10-3<br> <br> <br>|-0.129|-1.01|0,068|<br>0.49|
|Time to school<br>(cluster<br>average)<br> <br>|0.002<br>|0.67<br>|-0.008<br>|<br>-1.17<br> <br>|
|Rural<br>xtime to school|0.002|0.34|0,003|<br>0.33|
|Educational<br>of oldest prime<br>age female<br> <br> <br> <br>|-0.005<br>|-0.40|-0,011|<br>-0.89|
|Rural<br>xhighest<br>education<br>in household|-0.025|-1.04|0.017|<br>0.75|
|Birth order<br>(=1 for youngest)|0.128"|2.58|0.090|<br>1.89|
|Rural x birth order|-0.131"|-2.14|-0.030|<br>-0.56|
|Rural|-2.164"|-2.49|-0.410|<br>-0.43|
|Child sickb|0.420"|2.42|0.304|<br>1.62|
|Rural x child sick|0.133|0,57|0.127|<br>0.53|
|Adult sickb<br>|-0.021|-0.10|0.125|<br>0.57|
|Rural**x**adult sick|-0.201|-1.59|-0.219|<br>-1.55|
|_N_<br>_Wald_statistic**_(X2_**_with_24d.f.)<br>p_(=a la+ a1)_<br>_R_<sup>_2 _</sup>_(within)_<br><sup>2</sup>|||||
|R _(between)_<br>R<sup>2 </sup>(overall)|||||

a Random-effects regression. The sample consists ofchildren aged 6-18 in panel households only. The regressions also included an intercept term, dummies for regions (Lima, Costa, Selva) and a time dummy.

b Both child and adult sickness are consider **e** d ndogenous. See the appendix for details. Statistically significanat he 5% level. Statistically **t** significant at the 10% level.

35
One could argue that universal coverage should imply equal access. In other words, universal coverage will ensure equality in access. In theory, maybe, but in practice it is seldom true. In poor and developing countries, where coverage is low, governments can easily increase “coverage” but exacerbate inequality. For example, consider a hypothetical society of 100, with 10 educated men, 40 uneducated men, 5 educated women, and 45 uneducated women. A government can increase literacy rates from 15 percent to 40 percent by educating 25 of the 40 uneducated men. In this hypothetical scenario coverage of public education would have increased from 15 percent to 40 percent while exacerbating the literacy gap between men and women from 5 percent to 35 percent.

In developed countries, where literacy often reaches saturation levels (near 100 percent of the population), unequal access may be less relevant but inequality in quality remains salient. For example, within the United States — where literacy rates are almost 100 percent — one could argue that access to public education is nearly 100%. But given the large variation in outcomes across regions and across racial or ethnic groups, unequal access to quality public schools among groups remains salient and notable (Hero, 1998).

This paper uses data from a new module of questions— _Measuring User Experience with Service Delivery_<sup>7</sup> —added to the “Gallup 2016 India State Survey”. The survey measures: access to government provided goods and services; inequality in access to goods and services; and quality of service delivery. The survey—conducted by Gallup, Inc. on behalf of the World Bank— provides the first detailed portrait of service delivery at the local level in India. The indicators are based on survey responses for a sample of 13,000 adults in 13 Indian states: Andhra Pradesh (including Telangana), Bihar, Chhattisgarh, Himachal Pradesh, Jharkhand, Kerala, Madhya

> 7 The questionnaire is shown in Appendix 1.

7