rafmacalaba/datause-encoder-data huggingface dataset:
# **6. References**

Ahmad, O. and others (2001). “Age Standardization of Rates: A New WHO Standard,” World Health Organization.

Aron, J. and J. Muellbauer (2020). “Measuring Excess Mortality: The Case of England during the Covid-19 Pandemic,” INET Oxford COVID-19 Research, Economics Department, Oxford University. https://www.inet.ox.ac.uk/news/inet-oxford-covid-19-blog/ Baud, D. and others (2020). “Real Estimates of Mortality Following COVID-19 Infection,” The Lancet. March 12.

Begley S. (2020). “What Explains Covid-19 Lethality for the Elderly? Scientists Look to “Twilight” of the Immune System,” STAT. March 30. https://www.statnews.com/2020/03/30/whatexplains-coronavirus-lethality-for-elderly/ Belligoni, S. (2020). “5 Reasons the Coronavirus Hit Italy So Hard,” The Conversation. March 26. https://theconversation.com/5-reasons-the-coronavirus-hit-italy-so-hard-134636 Bloomberg (2020). “Why the World’s Highest Virus Death Rate Is in Europe’s Capital,” April 25. https://www.bloomberg.com/news/articles/2020-04-25/why-the-world-s-highest-virus-deathrate-is-in-europe-s-capital Burn-Murdoch, J., V. Romei and C. Giles (2020). “Global Coronavirus Death Toll Could Be 60% Higher Than Reported,” Financial Times. April 26. https://on.ft.com/2VF4Bfi Cash, R. and V. Patel (2020). “The Art of Medicine. Has COVID-19 Subverted Global Health,” The Lancet. May 5. https://www.thelancet.com/action/showPdf?pii=S0140-6736%2820%29310898 Ciminelli, S. and S. Garcia-Mandicó (2020). “Mitigation Policies and Emergency Care Management in Europe’s Ground Zero for COVID-19,” mimeo. May. Accessed online May 19: https://drive.google.com/file/d/1P1ob4Ug6WlZ3OnaJBebtVQsirWrb8IL-/view Courage, K. (2020). “The Stark Differences in Countries’ Coronavirus Death Rates, Explained,” VOX, April 22. https://www.vox.com/2020/4/1/21203198/coronavirus-deaths-us-italy-chinasouth-korea Dowd, J. and others (2020). “Demographic Science Aids in Understanding the Spread and Fatality Rates of COVID-19,” Proceedings of the National Academy of Sciences. April. https://www.pnas.org/content/early/2020/04/15/2004911117 Dylan, S. (2020). “The COVID-19 Risks for Different Age Groups, Explained,” Vox. March 23. https://www.vox.com/2020/3/23/21190033/coronavirus-covid-19-deaths-by-age The Economist (2020a). “Why a Study Showing That Covid-19 Is Everywhere Is Good News,” April 11.

The Economist (2020b). “Deaths from Cardiac Arrests Have Surged in New York City,” April 13.

The Economist (2020c). “Tracking Covid-19 Excess Deaths across Countries,” April 16.

25
WPS 3235

# **Household Composition and the Response of Child Labor Supply to Product Market Integration: Evidence from Vietnam**<sup>*****</sup>

# Eric V. Edmonds Department of Economics Dartmouth College and NBER

**Abstract:** Market integration raises the relative price of a community’s export product.  This study examines how the response of child labor supply to an increase in the relative price of a primary export product varies with a child’s household composition.  The specific context for this study is the liberalization of rice markets in Vietnam in the 1990s.  Between 1993 and 1998, Vietnam lifted export restrictions on rice, allowing the domestic price to rise toward international levels, and eliminated internal restrictions on the flow of rice between regions of Vietnam.  Thus, the relative price of rice increased overall in Vietnam, but the degree of price change varied across communities with the lifting of restrictions on internal flows.  This study finds that the response of child labor supply to rice price increases is increasing the amount of time children work.  Thus, household composition attributes that are associated with higher levels of child labor are also associated with larger declines in child labor with rice price increases.  The results are consistent with girls particularly benefiting from product market integration, because they work more than boys do.  These results suggest that economic factors associated with economic reform may attenuate differences in the activities of siblings that are typically associated with cultural traditions and norms.

JEL Codes: F15, J22, O15 Keywords: Trade Liberalization, Sibling Sex Composition, Birth Order, Child Labor, Gender

> *I am grateful to Nayantara Mukerji, Harry Patrinos, Nina Pavcnik, Susan Razzaz, and Alexandra van Selm for their comments and to Savina Rizova for outstanding research assistance.  This research was funded in part by the World Bank-Netherlands Partnership Program's Economic Policy and Gender Initiative.  Contact Information: 6106 Rockefeller Hall, Department of Economics, Dartmouth College, Hanover NH 03755 USA, eedmonds@dartmouth.edu.
**Table 15: Average rate of return by sector and level of constrain in running enterprise**

|||**(N=3,023)**|||
|---|---|---|---|---|
|**Activities**|**Enterprises are**<br>**not constrained**|**Enterprises are**<br>**constrained**|**Among the enter**<br>**constrained**|**prises that are**<br>**(N=1,907)**|
||||**Enterprises are**<br>**not credit-**<br>**constrained**|**Enterprises**<br>**are credit-**<br>**constrained**|
|**Manufacturing**|0.534|0.586|0.544|0.665|
|**and processing**|||||
|**Transport**|0.691|0.731|0.748|0.641|
|**Trade**|0.507|0.597|0.636|0.554|
|**Service**|0.525|0.586|0.573|0.604|
|**Miscellaneous**|0.529|0.562|0.568|0.550|
|**small activity**|||||
|**All activities**|0.550|0.594|0.587|0.606|

Source: HIES 2000, 2005 and 2010

**Table 16: FE estimates of the impacts of credit and non-credit constraints on microenterprise productivity (N=2,849)**

|**Explanatory variables**|**Mod**|**el 1**|**Mod**|**el 2**|
|---|---|---|---|---|
||**Log profit**|**Profit**|**Log profit**|**Profit**|
||**(Tk./year)**|**margin**<sup>**†**</sup>|**(Tk./year)**|**margin**<sup>**†**</sup>|
|Enterprise is credit constrained|0.053|-0.065|-0.024|-0.136|
||(0.45)|(-5.28)|(-0.06)|(-3.51)|
|Enterprise is non-credit constrained|-0.003|-0.037|-0.185|-0.085|
||(-0.02)|(-3.00)|(-0.53)|(-2.37)|
|Enterprise is credit constrained**X**|-|-|0.579|0.107|
|Activity is manufacturing and processing|||(1.16)|(2.10)|
|Enterprise is credit constrained**X**|-|-|0.058|-0.033|
|Activity is transport|||(0.09)|(-0.48)|

47
Arampatzi, E., M. J. Burger, and R. Veenhoven. 2015. “Financial Distress and Happiness of Employees in Times of Economic Crisis.” _Applied Economics Letters_ 22 (3): 173–79.

Arezki, R., and M. Brückner. 2011. “Food Prices and Political Instability.” IMF Working Paper, International Monetary Fund, Washington, DC.

Barro, R. J., and J. W. Lee. 2010. “A New Data Set of Educational Attainment in the World, 1950-2010.” NBER Working Paper No. 15902, National Bureau of Economic Research, Cambridge, MA.

Bellin, E. 2004. “The Robustness of Authoritarianism in the Middle East: Exceptionalism in Comparative Perspective.” _Comparative Politics_ 36: 139–57.

- ———. 2012. “Reconsidering the Robustness of Authoritarianism in the Middle East: Lessons from the Arab Spring.” _Comparative Politics_ 44: 127–49.

Bromley, R. 2014. “The ‘Arab Spring’ Stress Test: Diagnosing Reasons for the Revolt.” Working Paper, University of Wisconsin-Madison.

Bruni, L. 2004. “The ‘Technology of Happiness’' and the Tradition of Economic Science.” _Journal of the History of Economic Thought_ 26(1): 19–43.

- Cammett, M. C., and I. Diwan. 2013. _The Political Economy of the Arab Uprisings_ . Perseus Books Group.

Campante, F. R., and D. Chor. 2012. “Why Was the Arab World Poised for Revolution? Schooling, Economic Opportunities, and the Arab Spring.” _Journal of Economic Perspectives_ 26: 167–88.

Cantril, H. 1965. _Pattern of Human Concerns_ . New Brunswick: Rutgers University.

Chamlou, N. 2013. “The Economics of Happiness and Anger in North Africa.” Working Paper UNU-Merit.

- <mark>Chekir, H., and I. Diwan. 2012. “Center for International Development. C</mark> ingranelli-Richards Human Rights Dataset, 2015.”

Clark, A., A. Knabe, and S. Rätzel. 2010. “Boon or Bane? Others’ Unemployment, Well-Being and Job Insecurity.” _Labour Economics_ 17: 52–61.

- Clark, A. E., and A. J. Oswald. 1994. “Unhappiness and Unemployment.” _The Economic Journal_ 104: 648–59.

25
In the Western U.S., a water right must be continually used in order to be valid. Non-use results in "abandonment" or "forfeiture". 'l.,is principle has a two pronged effect on security and therefore water allocation. Some economists argue that in order to protect the security of a water right, right holders use their maximum entitlement, regardless of need. This 3ncentive results in overuse of water resources. On the other hand, these loss doctrines are necessary in order to prevent "sleeper rights", which introduce uncertainty into the overall system.

Enforcement e- these doctrines requires both technology and institutions. The number of senior rights expected to be fulfilled in a given year is determined by snowpack measurements and sat,dllite monitoring of streamflows. Water commissioners are "river cops" who also monitor water withdrawals to ensure actual use and compliance with priority. Violations of priority and absence of damage rules are institutionalli enforced either administratively, or through a system of water courts. For example, in Colorado,any right holder can bring litigation against anotkPer user who violates these principles.

# Licenses and Security

Some aspects of the Australian system of water allocation results in water rights that are significantly less secure than those discussed above. All water supplies are held by the state, and rights to use are acquired by obtaining a license, which are not prioritized. Rather than relying on strict legal principles, state officials have wide discretion in determining the conditions that apply to licenses (Delforce, Pigram, Musgrave and Anderson, 19°00).

The security of a licenses is lessened by several factors. First, the license can be modified at any time, at the discretion of governmental agencies. Second, licenses ate not granted in perpetuity; they expire periodically. Finally, there are a significant number of "sleeper licenses" on the books, meaning licenses that have been granted, but are not currently in use. These sleeper licenses are not lost through nonuse, and can be activated at any time, which introduces another element of uncertainty into the water allocation system.

The security of water rights in the Western U. S. is compromised by sleeper "reserved" water rights. Reserved water rights are those accruing to Indian or other Federal reservations of land. These lands carry an implicit right to the water necessary to achieve the designated purpose of the land and carry a priority date equal to the reservation date. Unlike other water rights, reserved rights are not lost from non use. To date, 0
**Preferential Market Access Design: Evidence and Lessons from African Apparel Exports to the US and the EU** *

# Jaime de Melo<sup>♣</sup>

University of Geneva and FERDI and Alberto Portugal-Perez<sup>♦</sup> World Bank JEL classification number: F12, F13, F15.

Keywords: Rules of Origin, AGOA, Everything-But-Arms, African Caribbean Pacific, African Least Developed Countries.

## ♣ E-mail: jaime.demelo@unige.ch

## ♦ E-mail:  aportugalperez@worldbank.org.  Sector Board: Economic Policy (EPOL)

* Without implicating them, we would like to thank Olivier Cadot, Céline Carrère, Patrick Conway, Caroline Freund, Marco Fugazza, Bo Honoré, Hiau Looi Kee, Jaya Krishnakumar, Leonardo Iacovone, Daniel Lederman, Aaditya Mattoo, Caglar Ozden, Marcelo Olarreaga, Nicolas Schmitt, James Tybout, and participants at seminars at the University of Geneva, the World Trade Organization, and the World Bank for helpful comments as well as the editor and two referees on earlier versions of this paper. Any opinions expressed in this paper are the authors’ and not those of their respective institutions. We take responsibility for remaining errors.
<!-- Start of picture text --> Polly, Recwoh, and Extel Afbfs<br><!-- End of picture text --> <!-- Start of picture text --> Macroonomlc Adjustment<br>and Growth<br><!-- End of picture text --> WPS 608

- This paper a product of Fe Macroecoomic Adjusunent and Gowth Clivision, Country Economics Deparnment-is pan of a largereffort inPgEto underand empirically the links between policy and longrun growth. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact CECMG staff, room Ni 1-024, extension 39175 (54 pages).

Levine and Renelt review the conceptual, methodological, and statistcal problems associated with drawing inferences from cross-country regressions. They elaborate on the particular problems associated with empirica ttempts to link particular policies with long-run growth.

They hope to stimulate improvements in the design, implementation, and interpretability of cross-country investigations and to caution readers about the confidence they place in existing findings.

(See also WPS 609.) The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal AffairsComplex. Anobjective oftheseries is to get these findings outquickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in thcsc papers do not necessarily represent official Bank policy,

> P'roduced by the l'RE Dissemination Center
2

# **1 Introduction**

The precipitous decline in coffee prices around the turn of the 20th century attracted a lot of attention among policy makers in Sub-Saharan Africa. Indeed, studies and policy debates of the effects of shocks on household welfare are very often instigated by a politically salient event—a drought or an economic crisis—and focus on a particular risk factor<sup>2</sup> . Yet, not only are the actual welfare effects for coffee growers in SubSaharan Africa of the coffee price decline poorly understood, the focus on coffee prices as a major risk factor also distracted attention away from the wide array of risks coffeegrowing households faced, including health and drought shocks.

This paper takes a more holistic approach and jointly examines the immediate effects of different shocks on household welfare in the context of two coffee-growing regions in Tanzania, Kilimanjaro and Ruvuma. In a directly administered shock module as part of a comprehensive household survey, rural households identified health shocks, droughts, and commodity price declines as their major risk factors both in terms of the frequency of their occurrence as well as the severity of their effects. This underscores the need for a more comprehensive perspective on household vulnerability even when looking at the livelihoods of commodity producing households.

The paper addresses three broad questions. First, it explores the (immediate) welfare effects of idiosyncratic health, covariant drought and more systemic commodity price shocks. Second, it examines the occurrence and effectiveness of self- and mutual

> 2 Dercon and Krishnan (2000), Dercon, Hoddinott and Woldehanna (2005), and Kenjiro (2005) are welcome exceptions.
# **Table 6: Role model effects: Main results on income, decision-making power and expenditures**

## **Panel A: Effects on income and decision-making power**

||(1)<br>Personal<br>income<br>(Taka)|(2)<br>Household<br>income<br>(Taka)|(3)<br>Decision-<br>making<br>(Index)|||
|---|---|---|---|---|---|
|exposed|235_._54 **|_−_45_._71|0_._36***|||
||(117_._85)|(631_._78)|(0_._09)|||
||[0_._10]|[0_._56]|[0_._00]|||
|N|488|488|488|||
|Non-exposed mean|9387_._21|21166_._26|_−_0_._26|||
|Non-exposed SD|2863_._31|7325_._44|1_._08|||
|**Panel B: E**|**ffects on assig**<br>Women|**nable goods ex**<br>Girls|**penditures and**<br>Men|**remittan**<br>Boys|**ces**<br>Remittances|
|||(All outcomes as|% in househol|d income)||
|exposed|_−_0_._89|_−_0_._19|_−_1_._06 **|_−_0_._06|_−_1_._39|
||(0_._74)|(0_._37)|(0_._44)|(0_._37)|(1_._10)|
||[0_._29]|[0_._56]|[0_._06]|[0_._56]|[0_._29]|
|N|488|255|488|262|483|
|Non-exposed mean|6_._48|2_._41|4_._69|2_._35|8_._49|
|Non-exposed SD|6_._63|2_._50|5_._25|2_._41|11_._81|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The nonexposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Sharpened q-values in squared brackets. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory, enumerator and month dummies. The estimation in Panel A, Column 2 excludes the household income variable from the set of possible controls. The sample in Panel B, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

**Table 7: Role model analysis: Effects on attitudes and beliefs**

||(1)<br>Overall self-beliefs<br>(Index)|(2)<br>Confdence<br>(Index)|(3)<br>Negotiating<br>(Index)|(4)<br>Gender attitudes<br>(Index)|(5)<br>Aspirations<br>(Dummy)|
|---|---|---|---|---|---|
|exposed|_−_0_._03|0_._00|0_._17|_−_0_._05|0_._03|
||(0_._13)|(0_._11)|(0_._11)|(0_._09)|(0_._05)|
|N|488|488|488|488|488|
|Non-exposed mean|0_._04|0_._09|0_._02|0_._10|0_._50|
|Non-exposed SD|1_._23|0_._84|1_._04|0_._93|0_._50|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

43
Denmark), the ratio of _TAC_ to potential gains is actually greater than 1 (because area _B_ is greater than area _C_ ). In all the remaining countries, _TAC_ are bounded by PG. The share of _TAC_ in _PG_ varies a lot, from as high as 91.79 in Turkey and 93.87 in Chile to as low as 3.46 in South Africa and 3.5 in Japan. The average for developing countries is 33.

The ratio of _TAC_ to the actual gains _G_ can vary widely. In South Africa and Japan, the ratios are 3.58 and 3.62, respectively. These economies, among others, can quickly enjoy the gains from trade because the costs of adjusting labor, _C_ , are relatively low. In other cases, _TAC_ are huge. In Turkey, _TAC_ can be more than 11 times higher than the gains from trade, and in Chile more than 5 times as high. The average for developing countries is 101.7, so _TAC_ and _G_ are similar, but the median is 27. The size of _TAC_ , in these cases, is dictated by the various parameters of the model, not only by _C_ . For instance, the ratios are roughly the same in Bangladesh, a country with one of the highest _C_ , as in Egypt, a country with the average _C_ for developing countries. The ratios depend on both _TAC_ and _G_ . A large mobility costs makes _TAC_ large and _G_ small, so the ratio tends to be high in these cases. But a large share of food prices in the CPI will create a large _G_ , even for a given _TAC_ , and thus a lower relative importance of trade adjustment costs.

To complete the illustration of some of the uses of _C_ , we end with an analysis of the gains from trade and the trade adjustment costs following a 30 percent decline in the price of textiles.<sup>16</sup> In the UNIDO data, the textile sector tends to be larger than the food sector (in terms of employment). In turn, the weight of textiles and clothing in the consumer price index is much smaller than the weight of food. As a result, there will be larger losses from wage responses in the textile sector, and lower gains from CPI changes. In fact, we find welfare losses across most countries (Table 4). In column 2, the potential gains from trade are negative for most developing countries (with the exception of Chile, Ecuador, Russia, South Africa, and Latvia); in column 3, actual losses from trade are found all over the developing world, except in South Africa and Latvia.

_TAC_ s, as a share of initial welfare, are in column 4. As in the case of Food and Beverages, we find worldwide positive trade adjustment costs in textiles, even when the simulations show welfare costs. This is an interesting result of our model. It is often argued that, when there are losses from trade as in the case of textiles, factor adjustment costs can actually protect workers in the

> 16A full set of simulation results as in Figures 8 to 15 can be found in the online appendix to this paper.

18
<!-- Start of picture text --> Percent<br>100 = Advanced economies = EMDEs<br>75<br>50<br>0 =<br>2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> <!-- Start of picture text --> Percent of country-year pairs<br>80 m Stable or falling trajectory<br>= Rising trajectory<br>60<br>40<br>0<br>World Advanced economies EMDEs<br><!-- End of picture text -->
Table 4: _Date of_ de facto _liberalization of wholesale interest rates_

|_Approach:_|<br>"Control off"|Marked v<br>increase|olatility<br>1980-97||Expert dat|ings||
|---|---|---|---|---|---|---|---|
||||Volatility|Galbis|D&D|W&|M|
||Date A|Date B|ratio|||Start<br>L|argely|
|_60C Treasury Bill_||||||||
|Fiji|82:08|85:06|4.7|||||
|Ghana|84:08-84:11;<br>87:10|91:10|2.1|||||
|Guyana|87:03-88:07;<br>89:10|89:10|_5.5_||91|||
|Jamaica|80:08|91:03|4.3|85:10|91|||
|Kenya|pre-80|93:03|4.2||91|||
|Sri Lanka|85:09|88:03|1L.6||80|78|-|
|Mexico|pre-80|83:11|2.6|85:03|89|74; 89|92|
|Malawi|92:06|92:06|5.3|||||
|Nepal|89:08|89:10|32.5|86:05||89|-|
|Philippines|pre-80|84:06|3.8|82:12|81|81|94|
|<br>Papua New Guinea|<br>80:07|86:01|1.1||pre-80|||
|Swaziland|82:12|None||||||
|Trinidad and Tobago|80:02|84:07;<br>94:11|6.7|||||
|Sierra Leone|91:12|87:08|4.9|||||
|Uganda|92:03|81:10|135.0|88:07||||
|<br>South Africa|pre-80|None||||80|84|
|Zimbabwe|pre-80|92:04|2.4|||||
|_60B Money market_||||||||
|India|pre-80|90:03|4.6||91|92|-|
|Korea|<br>pre-80|None||Not lib|84-88; 91|83|--|
|Malaysia|pre-80|None||78:10|pre-80|78|92|
|<br>Pakistan|<br>pre-80-86:12;<br>89:12|92:03|3.7|||||
|Singapore|pre-80|None||||78|73|
|Thailand|pre-80|90:03|2.1|90:03|89|mid-80s|<br>92|
|Cote d'Ivoire|82:07-90:05|None||89:10||||
|Mauritius|85:02|None||81:11||||
|_Industrial countries_||||||||
|Australia|pre-80|82:02|2.3||81|||
|New Zealand|<br>85:02|83:08|7.9||pre-80; 84|||
|Spain|84:01|83:01|21.3|87:03||||
|<br>Portugal|83:01|82:06|90.5||94|||

Notes: Industrial countries included are those for which regime changes (b) were identified post 1980.

Volatility ratio is the ratio of the standard error of estimate of dynamic regression model in the postliberalization period to that in the pre-liberalization period.

Dates from other studies: Galbis (1993); D&D=Demirguc-Kunt and Detragiache (1998); W&M: Williamson and Mahar (1998) - showing start of liberalization and 'largely liberalized' dates.

16