rafmacalaba/datause-encoder-data huggingface dataset:
(or may not) induce firms to reorganize production, adding layers and increasing overall productivity. Empirical evidence related to this theory can be found in Tag (2013), Bernini, Guillou and Treibich (2016), Caliendo, Monte and Rossi-Hansberg (2015), Caliendo, Monte and Rossi-Hansberg (2017), Caliendo, Mion, Opromolla and Rossi-Hansberg (2016), Cruz, Bussolo and Iacovone (2016), Friedrich (2016) and Spanos (2016).

Our results for Chile can be rationalized by this theory. The lower layer of production would include the non-specialized blue-collar workers. Engineers supervise blue-collar workers and, plausibly, can work in production as well. Exposed to increases in export opportunities, firms reorganize their structure by expanding the layer of engineers needed to supervise blue-collar workers (and, again, possibly to substitute some of them in production as well). This would explain why exporters demand more skilled tasks and, in turn, why the utilization of engineers increases more than the utilization of directors or administrative skilled workers. These impacts may be particularly large at the extensive margin. In this context, our paper contributes to this literature by providing evidence of these mechanisms using less detailed data for lower developed or advanced middle-income countries rather than the employer-employee datasets used in the studies referenced above. This can shed light on the role of exporting and employment in relatively more advanced developing countries.

# **4 Conclusions**

In this paper, we have explored the link between exports and the demand for skilled tasks using a panel of Chilean firms. We have found that exporting raises the demand for specific sets of skills, favoring technical skills such as engineering skills over other profession skills such as “desk” skills. In addition, engineers seem to replace blue-collar unskilled labor in certain tasks associated with exporting.

Our findings have implications for trade theory and should illustrate the importance of recent assignment models of factors to tasks in international trade. The results have implications for empirical research and policy design as well. The notion that trade, and exports in particular, affects the wage premium and thus wage-inequality needs to be carefully assessed. Our results should contribute to our understanding of the skilled tasks needed to exports, the role of potential education policies consistent with a successful long-run export performance (e.g., fostering technical careers), and the design of social policies to reduce wage inequality and help the losers from trade. Finally, we have provided evidence on the link between trade and the employment structure of exporting firm in Chile, which is useful to illustrate the interplay between exports, firm reorganization, and efficiency at the firm level in middle-income/developing countries.

# **References**

Acemoglu, D., and D. Autor (2011) “Skills, Tasks and Technologies: Implications for Employment Earnings,” in Ashenfelter, O., Card, D. (Eds.), The Handbook of Labor Economics, vol. 4b. Elsevier, Amsterdam, pp. 1043–1171 11
Figure 2: Italy 2002–2006–2010: Individual Opportunity Growth Incidence Curve <!-- Start of picture text --> 0 20 40 60 80 100<br>% population<br>Individual OGIC 2002−2006<br>Individual OGIC 2006−2010<br>4<br>2<br>0<br>yearly % growth<br>−2<br>−4<br><!-- End of picture text --> _Source: Authors’ calculation from SHIW (Bank of Italy)_

1.96% for 2002–06 and -0.66% for 2006–10. Second, the two growth processes show very different and symmetric patterns. The income dynamic is progressive between 2002 and 2006, but it becomes quite regressive between 2006 and 2010. Their symmetrical shape suggests that the two processes might have an equally opposed redistributional impact. The sign of the variation over time of their respective aggregate indexes of inequality confirms this supposition: income inequality decreases during the first period and increases during the second period<sup>24</sup> (see Table 2).

We proceed in our analysis with the assessment of the distributional effects of growth in the space of “opportunities”. The individual OGIC for the periods considered are reported in Figure 2.

The individual OGIC of 2002–06 shows that growth acts by increasing the value of the opportunities for all quantiles of the smoothed distributions.<sup>25</sup> However, the growth rate is not stable across quantiles. In particular, the slightly increasing pattern of the individual OGIC over the

> 24The results for the second period are consistent with other empirical evidence on the effect of the last financial and economic crisis. See, for example, Jenkins et al. (2013).

> 25To make the individual OGIC and the type OGIC graphically comparable, we partitioned the smoothed distributions into 18 quantiles.

21
62

# **TABLE 16 USE OF COLLECTIVE SUPPORT MECHANISMS BY RATTAN FIRMS, BY START-UP SIZE AND ETHNIC GROUP OR ENTREPRENEUR**

||.<br>Pri<br>|bumi<br>|Non-|pribumi||All|
|---|---|---|---|---|---|---|
|Number of Forms<br>Utiling Support From:|5 o<br>**50**or<br>Fewer|5 o<br>**51**or<br>More|**50**or<br>Fewer|**51**or<br>More|**50**or<br>Fewer|**51**or<br>More|
|Industry Association/<br>NGQ|6|2|1|6|7|8|
|Foster Father|1|0|0|0|1|**0**|
|Public Technology<br>Support Agency|2|1|0|0|2|0|
|Private Consultant|3|0<br>|1<br>J|0|5|0|
|Courses|6|1|0|6|7|E8|
|At Least One of the<br>Above/Total Number of<br>**Firms**|10/11|2/3|2/6|8/10|13/18|11/15|
|At Least One of the<br>Above with Score of At<br>Least**3a/Total**Number<br>of Firms|4/11|2/3|f1/6|5/10|7/18|7/15|

Notes:

* Refers only to the first four categories since firrns were not asked to rate the value of the courses they had participated in.
Table A.6: Overall performance (percent correct) for moderators

||All c|ases|||By case|||
|---|---|---|---|---|---|---|---|
||(1)<br>Across<br>cases|(2)<br>Across<br>responses|(3)<br>Simple 1|(4)<br>Simple 2|(5)<br>Complex<br>1|(6)<br>Complex<br>2|(7)<br>Complex<br>3|
|_A. Interaction with status in larger_|_trial_|||||||
|Reward|9.82***|2.73**|15.50*|28.15***|3.05|-0.89|3.29*|
||(2.71)|(1.18)|(8.87)|(8.78)|(1.86)|(1.39)|(1.93)|
|Penalty|4.34*|1.40|4.67|13.97*|2.43|-0.26|0.88|
||(2.54)|(1.13)|(8.86)|(8.29)|(1.80)|(1.33)|(1.77)|
|DFF|5.04**|3.62***|15.34**|-0.42|7.82***|-1.85|4.33***|
||(2.05)|(0.99)|(7.28)|(6.51)|(1.63)|(1.15)|(1.59)|
|PFP|6.07***|5.04***|13.07*|2.58|8.94***|0.47|5.31***|
||(2.10)<br>|(1.00)|(7.27)|(6.52)<br>|(1.61)|(1.13)<br>|(1.65)|
|Reward _×_ DFF|-5.96*<br>|-0.55<br>|-12.50<br>|-17.71*<br>|-2.72<br>|3.20**<br>|-0.06<br>|
||(3.06)|(1.48)|(9.95)|(9.91)|(2.31)|(1.61)|(2.33)|
|Reward _×_ PFP|-5.40*|0.09|-9.11|-20.25**|0.01|2.47|-0.10|
||(3.15)|(1.52)|(9.95)|(9.95)|(2.29)|(1.63)|(2.44)|
|Penalty _×_ DFF|-1.23|0.66|-4.88|-3.94|-1.01|1.90|1.81|
||(2.91)|(1.44)|(10.00)|(9.51)|(2.29)|(1.58)|(2.20)|
|Penalty _×_ PFP|0.00|1.31|0.82|-5.24|0.36|1.70|2.37|
||(2.99)|(1.48)|(9.95)|(9.53)|(2.28)|(1.58)|(2.30)|
|Constant (Information)|48.06***|52.43***|55.93***|25.42***|54.60***|52.42***|51.94***|
||(1.80)|(0.77)|(6.48)|(5.69)|(1.27)|(0.98)|(1.29)|
|Facility fxed efects|No|No|No|No|No|No|No|
|P-values from tests of coefcients||||||||
|Control: Penalty v Reward|0.043|0.277|0.206|0.116|0.740|0.634|0.197|
|DFF: Penalty v Reward|0.124|0.425|0.430|0.177|0.456|0.407|0.411|
|PFP: Penalty v Reward|0.089|0.440|0.302|0.143|0.879|0.635|0.308|
|N respondents|1,363|1,363|1,363|1,363|1,363|1,363|1,363|
|_B. Interaction with knowledge_||||||||
|Reward|4.09***|3.08***|0.91|10.68**|3.42***|1.77**|3.66***|
||(1.48)|(0.66)|(4.89)|(4.35)|(1.14)|(0.69)|(1.16)|
|Penalty|3.04*|2.78***|-0.90|7.85|4.23***|1.18|2.81**|
||(1.55)|(0.67)|(5.35)|(5.12)|(1.14)|(0.76)|(1.16)|
|Knowledge _≥_median|1.92|3.10***|1.22|-1.34|6.16***|1.02|2.57|
||(2.05)|(0.87)|(6.48)|(6.40)|(1.52)|(0.98)|(1.58)|
|Reward _×_ Knowledge _≥_median|0.49|-1.94**|8.57|0.67|-4.00**|-0.98|-1.79|
||(2.06)|(0.97)|(6.41)|(6.74)|(1.64)|(1.07)|(1.87)|
|Penalty _×_ Knowledge _≥_median|0.61|-1.73*|6.61|2.53|-5.57***|0.00|-0.53|
||(2.08)|(0.94)|(7.18)|(7.03)|(1.74)|(1.19)|(1.66)|
|Constant (Information)|52.20***|54.84***|67.97***|27.52***|59.09***|51.44***|54.99***|
||(1.26)|(0.52)|(4.07)|(3.73)|(0.91)|(0.54)|(0.90)|
|Facility fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|P-value Penalty v Reward|0.447|0.666|0.705|0.545|0.457|0.451|0.483|
|N respondents|1,363|1,363|1,363|1,363|1,363|1,363|1,363|

* p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Unadjusted OLS models; s.e. clustered at facility level.

52
18 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# **Table 6.** Estimates of the relationship between metropolitan fragmentation/coordination and city

productivity premium (OLS).

Y = City productivity premium (λ������)

|Variable|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|ln(pop 2010)|0.087***<br>(0.0274)|||||
|ln(density)||0.070**|0.223***|0.230***|0.199***|
|||(0.0289)|(0.0591)|(0.0595)|(0.0521)|
|ln(area_km2)||0.112***|0.285***|0.281***|0.270***|
|||(0.0310)|(0.0644)|(0.0647)|(0.0577)|
|ln(no_ adminunits)|||-0.202***<br>(0.0666)|-0.198***<br>(0.0671)|-0.180***<br>(0.0589)|
|no_au_100th_2010|||0.197**|0.196**|0.168**|
||||(0.0776)|(0.0777)|(0.0701)|
|cc_pop2010_ue|||-0.369***|-0.329**|-0.214*|
||||(0.1305)|(0.1362)|(0.1222)|
|gov_body||||-0.043|-0.050|
|||||(0.0502)|(0.0451)|
|its_cov||||0.000<br>(0.0006)|0.001<br>(0.0005)|
|spd_sum||||0.036<br>(0.0272)|0.038<br>(0.0242)|
|pop_radio300km|||||0.007***<br>(0.0017)|
|coast_2010|||||0.062<br>(0.0681)|
|Port|||||-0.082<br>(0.0603)|
|Constant|0.049|-0.073|-1.974**|-2.067**|<br>-1.909***|
||(0.3812)|(0.3829)|(0.7885)|(0.7918)|(0.6988)|
|Country dummies|Y|Y|Y|Y|Y|
|Observations|73|73|73|73|73|
|Adjusted R-squared|0.630|0.641|0.692|0.692|0.768|

Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
sample was still not large enough for our study, we implemented a third approach to screening people for an interest in a financial literacy course, as described below.

# **4.3 Approach Three: Street and Branch Surveys**

We conducted screener surveys on the streets of Mexico City and outside branches of our partner institution. Surveyors were placed in busy locations within the city during a period of eight weeks (from April 25 to June 25, 2011) where they tried to interview people passing by. We also placed surveyors outside branches of our partner institution between July 6 and 19, 2011, where they approached exiting customers and people waiting in line.

For this approach, interviewers asked people if they would be interested in participating in a financial literacy course, providing the same information as stated in letters sent during the mailing campaign. As in the letters, the name of the course was not disclosed. If the respondents expressed interest in the course<sup>12</sup> , interviewers asked them to fill out the screener survey, so that we could contact them later with further information about it. The questionnaire included the same set of question as the mail and online surveys. We did not offer a monetary incentive for completing the surveys, but people who answered the survey were offered cookies and a pen as a small thank you token. We obtained a total of 6,945 completed questionnaires from the street survey and 2,294 from the branch survey.

# **4.4 Treatment Randomization and Balance**

For all individuals who had expressed interest in a financial literacy course either through the mail, online, street or branch screener survey, we conducted phone audits to verify the contact information they had provided. This eliminated about half of the respondents. We further dropped respondents who lived outside the Mexico City metropolitan area or who had participated in a financial literacy program in the past. We also dropped observations that had missing answers to the questions we stratified on in the randomization, as described below. Our final sample includes eight respondents from the mail survey, five from the online survey, 2,490 from the street survey and 1,000 from the branch survey, giving a total sample of 3,503 people. We divided this sample into a control group of 1,752 individuals, and a treatment group of 1,751 individuals, using stratified randomization. The randomization was conducted by the authors by computer.

> 12 Subject recruiters reported that, roughly, only 2 out of every 5 people approached expressed an interest in the course and agreed to fill out the survey.

13
position decline. These patterns of upward and downward mobility across population groups have shown some attenuation over time but have not disappeared. Mobility patterns are more heterogenous across the states, but are qualitatively similar to the national trends within the states.

We offer a review of the literature in the next section. In Section III we provide a brief overview of the methods employed for the construction of synthetic panels, and for specifying the vulnerability line. We subsequently gauge in Section IV the validity of the synthetic panels methodology by implementing it in the IHDS survey. Section V reports our main findings on mobility trends at the national and state levels and for different population and consumption groups. We offer concluding remarks in Section VI.

# **II. Literature Review**

The last quarter-century has seen gross domestic product (GDP) per capita rise significantly in India. As seen in Figure 1, the study period can be readily divided into two periods: in the first, extending up to around 2002, GDP per-capita growth rates were generally below 6 percent per annum (with two exceptions); thereafter, growth accelerated and generally ranged between 6 and 8 percent per annum. As documented by Datt and Ravallion (2011) and Ravallion (2011), there was considerable attention to poverty trends in India following economic reforms introduced in the early 1990s. However, the evidence suggests that the pace of poverty decline started to increase dramatically only during the 2000s, after the 2004/05 round of the NSS, and in particular between 2009/10 and 2011/12.<sup>4</sup> Interestingly, the dramatic falls in poverty between 2009/10 and 2011/12 occurred when per-capita growth rates were in fact lower than in the 2004/05–2009/10 interval (Figure 1).

> 4 Dang and Lanjouw (2018) explore the possibility that the marked acceleration in measured poverty decline between the last two survey years might have been driven by changes in survey methodology, but they conclude that this is unlikely to have been the case.

6
~,iicy Re&earch Working Paper Siries

|**Title**|Author|Date|Contact<br>**for**paper|
|---|---|---|---|
|WPS1911 The Internationalization of Financial<br>Services in Asia|Stijn Claessens<br>Tom Glaessner|April 1998|R. Vo<br>33722|
|WPS1 912 Pay and Grade Differentials at the<br>World Bank|Deon Filmer<br>Margaret Grosh<br>Elizabeth King<br>Dominique van de Walle|April 1998|C. Bernardo<br>31148|
|WPS1913 The 1994 Currency Crisis in Turkey|Qya Celasun|April 1998|K. Labrie<br>31001|
|WPS1914 Distinguishing between Types of<br>Data and Methods of Collecting Them|Jesko Hentschel<br>|April 1998|PREM Advisory<br>87736|
|WPS1915 Distortionary Effects of State Trading<br>in Agriculture: Issues for the Next<br>Round of Multilateral Trade<br>Negotiations|Merlinda Ingco<br>Francis Ng|April 1998|M. Fernandez<br>33766|
|WPS1916 The Size, Origins, and Character<br>of Mongolia's Informal Sector<br>during the Transition|James H. Anderson|May 1998|P. Sintim-Aboagye<br>37656|
|WPS1917 Financial Liberalization and Financial<br>Fragility|Asli Demirguc-Kunt<br>Enrica Detragiache|May 1998|P. Sintim-Aboagye<br>37656|
|WPS1918 How Does Foreign Entry Affect<br>the Domestic Banking Market?|Stijn Claessens<br>Ashi Demirgo9-Kunt<br>Harry Huizinga|May 1998|R. Vo<br>33722|
NGOs and CBOs remain little understood (Buckley and Kalarickal, 2006), and a great deal more empirical work will be require before the gap between service providers and the urban poor can be consistently overcome.” (Kharas et al., 2010, p. 21) 3. <u>The research agenda</u> Against this backdrop, Kharas et al. (2010) propose six specific items for an urban research agenda: three items for enhancing the effectiveness of urban aid, and three for a better understanding of urban development‐oriented partnerships.

For effective urban aid:

- <u>Results measurement: Develop indicators, measure results and provide</u> metrics for urban development that can be readily collected at a city level and aggregated to the country level.

- <u>Political economy: Understand the political economy of urban interventions,</u> develop tools to assess and deal with risks of corruption and elite capture, and explore how to scale up local initiatives into national urban development programs.

- <u>Urban management: Explore how to build capacity and professionalism of</u> urban and city management at the national and local levels, including the development of think tanks, research centers and urban management graduate programs.

For a better understanding of urban partnerships:

- <u>Hybrid partnership models: Investigate viable cost structures for hybrid</u> partnership models, which allow an effective, scalable and socially acceptable sharing of costs among partners and customers.

- <u>Municipal bond markets: Research where and how the development of</u> municipal bond markets is feasible and could serve as an effective financing instrument for urban investments.

- <u>Inter‐city competitions: Explore new models for inter‐city competition,</u> which would be “designed to give incentives to municipal authorities, working in partnership with other urban stakeholders, to improve the standard of living in cities and towns.” (Kharas et al. 2010, p. 29)

# **The political economy of urban development**<sup>**21**</sup>

One common theme running through the exploration of research issues in the four papers reviewed so far is the importance of understanding the political economy of urban development. The fifth and last paper by Desai (2010) scopes out some of the research issues regarding the political economy of urban poverty, by way of example of how political economy issues might be explored more generally.

Economists and urban planners tend to assume that because they can demonstrate that some intervention is for the public and social good, their arguments will carry

> 21 This section draws on Desai (2010).

22
political base. The other is the changing position of large median groups within the income distribution. Early 20th century Europe and America, argues Lindert, saw these large enfranchised groups nearer the poor end of the income distribution and much more likely to fall down thap climb up. Hence, their support for welfare policies generous to the poor and hard on the rich.

There is some truth to Lindert's story. Certainly the first two thirds of this century has seen an increase in state support of the poor. but this is only half the story. If we accept the myth of a simple two stage path from "traditional" to "modern" safety nets, we will miss a key lesson of history.

Thu first big error embedded in the myth is its romanticized image of traditional society. The considerable efforts of demographic historians over the past three decades has made it clear that extended family systems were never the norm in Northwestern Europe (Hajnal, 1982), where, after all, the industrial revolution began. Far from undermining the extended family, industrialization may actually have strengthened it. Michael Anderson's (1972) studies of 19th century Lancashire found more old people living with their married children in industrial Preston than in nearby rural areas. The same has been found in industrializing Massachusetts in the 19th century (Harevin and Chudacoff, l9xx). Other studies of household structure may not provide a clear verdict on whether the extended family increased or decreased during the 19th century (for example Wall, 1984), but they do make it clear that the family was not the typical safety nor. in pre-industrial Britain. Such a finding is hardly surprising. Most parents didn't live long enough to be a burden on their children anyway. As mortality rates fell during the industrial revolution, parents lived longer, giving them a greater opportunity to be supported by their children in old age. And t' ir children, enjoying higher incomes, were better equipped to support them. On the other hand, children could bette- escape those re3ponsibilities and default on their parents'

-83-
ii

# List of tables.

|Text|tables,|Page|
|---|---|---|
|1.|Unit costs of education-type interventions.|25|
|2.|Unit costs of targeted food subsidies.|26|
|3.|Unit costs of feeding programs (school feeding and MCH):|27|
||summary.<br>||
|4.|Unit costs of micronutrient interventions.|28|
|_5._<br>|Non-food costs of selected NGO feeding programs.<br>|29|
|6.|Food cost as percent of total cost, feeding progtams:|30|
||summary.||
|7.|Costs of some of Brazil's nutrition programs, 1978-80.|31|
|8.|Summary of costs by different types of food distribution<br>programs.|32|
|9.|Cost-effectiveness of nutrition interventions: program data.|33|
|10.|Cost per death averted of nutrition, health and combined<br>interventions, Narangwal, India.|34|
|11.|ExpendItures on nutrition programs, selected countries.|35|
|12.|Expenditures on nutrition interventions, UN agencies and|36|
||<br>USAID.||
|ApD|endix tables.||
|1.|Unit costs of feeding progams (school feeding and MCH).|37-39|
|2.|Food cost as percent of total costs of feeding programs.|4042|
|3.|Percent of program cost financed externally.|43|
|Al.|Chile: annual expenditures on nutrition intervention|47|
||programs.||
|A2.|Chile: sources of central government expenditures on PNAC.|<br>48|
|A3.|Chile: financing of school feeding program (PAE).|49|
|A4.|Chile: cost per beneficiary and per calorie of major<br>interventions.|49|
|Bl.|Philippines: summary of main nutrition interventions.|52-55|
|B2.|Philippines: population, health and nlutrition share of|56|
||government spending (%).||
|B3.|Philippines: local government expenditures on population,<br>health and nutrition.|57|
|B4.|Philippines: government health funds by source.|57|
|B5.|<br>Philippines: average cost to supply 1000 calories of food<br>value.|58|
|B6.|Philippines: targeting of feeding programs.|58|
|B7.|Philippines: implicit average cost to supply 1000 calorie of<br>|58|
||food to targeted population.||
|C1.|Malawi: UNICEF and WFP expenditures on nutrition.|61|
**How Well Did Real-Time Indicators Track Household Welfare Changes in Developing Countries during the COVID-19 Crisis?**<sup>**1**</sup>

**David Newhouse, Rachel Swindle, Shun Wang, Joshua D. Merfeld, Utz Pape, Kibrom Tafere, Michael Weber**

**JEL:** D31, D74, I31, O15

**Keywords** : COVID-19; Well-being; Work; Income; Google mobility; NO2 ; Google search

> 1 The team would like to thank the Korea Trust Fund for Economic and Peace-Building Transitions (KTF) for providing funding. The KTF, supported by the Ministry of Economy and Finance, Republic of Korea, is a global fund administered by the World Bank to finance critical development operations and analysis in situations of fragility, conflict, and violence.