rafmacalaba/datause-encoder-data huggingface dataset:
The focus of this note has been on trade in merchandise. Policies affecting trade in services and the movement of factors of production (labor, capital) are important for regional competitiveness and welfare as well. Indeed, for some countries in the region services trade is as or more important than trade in goods. PAFTA members have identified services as an area for deeper cooperation. Much less is known about policies affecting trade and investment in services than about policies affecting merchandise trade. The type of data collection and monitoring exercise reported here for merchandise could also be undertaken for services. Templates that can be used for this purpose have been developed by the World Bank and have been implemented on a pilot basis in a number of Arab League member countries. The same is true for diagnostic tools to assess trade and transport facilitation performance of specific ports/border crossings and transport routes/corridors. Combining these tools into a regular process of monitoring and assessment of trade incentives and performance would help governments benchmark performance and identify priority areas for action, at both the national and the (sub-)regional level.

# **References**

Abedini, J. and N. Péridy. 2008. “The Greater Arab Free Trade Area (GAFTA): An Estimation of the Trade Effects,” mimeo.

- Arvis, J.F. and others. 2007. _Connecting to Compete: Trade Logistics in the Global Economy_

- _The Logistics Performance Index and Its Indicators_ (Washington DC: World Bank).

- Dennis, A. (2006), “The Impact of Regional Trade Agreements and Trade Facilitation in the MENA Region,” World Bank Policy Research Working Paper No. 3837, Washington DC: The World Bank.

- Galal, A. and B. Hoekman (eds.). 2003. _Arab Economic Integration between Hope and Reality_ . Cairo: Egyptian Center of Economic Studies and Brookings Institution.

- Harb, G. 2007 “Trade facilitation and intra-Arab trade: an empirical assessment,” Sciences Po, mimeo.

- Hoekman, B. and P. Messerlin. 2001. _Harnessing Trade for Development in the Middle East and North Africa._ New York: Council on Foreign Relations.

- Kee, H.L., A. Nicita and M. Olarreaga. 2009. “Estimating trade restrictiveness indices,” _The Economic Journal_ 119(534): 172-199.

- Peridy, N. 2007. “Toward a Pan Arab Free Trade Agreement,” _The Developing Economies_ 43(3): 329-45.

- World Bank. 2008. _Economic Developments and Prospects: Regional Integration for Global Competitiveness._ MENA Region report, World Bank.

- Zarrouk, J. 2001.  “A Survey of Barriers to Trade and Investment in Arab Countries,” in Hoekman and Messerlin. Reprinted in Galal and Hoekman.

17
with positive changes in the average unit value of imports. The average unit value‘s rise is a composition effect, as buyers concentrate on high-quality suppliers. Thus, a straightforward test would consist of regressing, on a panel of products or (importer × product) pairs (recall that we are looking at concentration across source countries), first differences in Theil indices on first differences in average unit values across sources, expecting a positive correlation. However, we can sharpen this test using a key corollary.

Table 6 Regression results, <u>change in Theil on change in unit values</u>

|Dep. Var|Δ Theilik(t-(t-1))|Δ Theilik(t-(t-1))|Δ Theilik(t-(t-1))|Δ Theilik(t-(t-1))|
|---|---|---|---|---|
|Regressors|(1)|(2)|(3)|(4)|
|Δ UVik(t-(t-1))/ (Δ Theilik(t-(t-1))>0)|1.05E-06 **<br>_(5.50E-07)_|9.78E-07 *<br>_(5.70E-07)_|-|-|
|Δ UVik(t-(t-1))/ (Δ Theilik(t-(t-1))<0)|-3.47E-07<br>_(1.95E-06)_|-2.04E-07<br>_(2.30E-07)_|-|-|
|Δ UVik(t-(t-1))/ (Δ Theilik(t-(t-1))>0.1)|-|-|1.45E-06 **<br>_(7.54E-07)_|1.43E-06 **<br>_(7.42E-07)_|
|Δ UVik(t-(t-1))/ (Δ Theilik(t-(t-1))<0.1)|-|-|-2.02E-07<br>_(1.48E-07)_|-1.56E-07<br>_(1.63E-07)_|
|Observations_(ikt)_|1,059,984|1,059,984|1,059,984|1,059,984|
|Nber of products_(k)_|1,299|1,299|1,299|1,299|
|Nber of importers_(i)_|29|29|29|29|
|Years_(t)_|1963-2006|1963-2006|1963-2006|1963-2006|
|Observations with Δ Theil>_x_|640,038|640,038|377,294|377,294|
|Importer×Product Fixed Effects|Yes|-|Yes|-|
|Importer Fixed Effects|-|Yes|-|Yes|
|Product fixed effects|-|Yes|-|Yes|
|Y ear fixed effects|Yes|Yes|Yes|Yes|

Notes: estimation with OLS; standard errors in parentheses: heteroskedasticity-consistent and adjusted for product-level clustering; * : p=0.1, **: p=0.05, ***: p=0.01.

The corollary is that the effect is asymmetric: whereas the model predicts that unit values should rise during _concentration_ phases, it is silent on the evolution of unit values during _diversification_ phases _._ At the beginning of a search (i.e. diversification) phase, all suppliers are tested independently of their initial quality draw. This corollary can be used to sharpen our test of the model‘s base prediction. To do this, we replace first differences in unit values as a regressor by two interaction terms defining two distinct regimes. In regime 1 (concentration), first differences in unit values are interacted with a dummy equal to one if concentration is rising (Δ _Tkt_ > 0).

24
<!-- Start of picture text --> Figure 4: Earnings by age group and gender: 2000-2015. Restricted to workers with<br>college.<br><!-- End of picture text --> <!-- Start of picture text --> A. Males B. Females<br>Year - Quarter Year - Quarter<br>23-30 31-40 23-30 31-40<br>41-50 51+ 41-50 51+<br>20000 20000<br>16000 16000<br>Monthly Labor Earnings 12000 Monthly Labor Earnings 12000<br>8000 8000<br>2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2 2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2015. Earnings are in constant Mexican Pesos 2014:1. Earnings refer to ful-ltime workers (at least 30 weekly hours of work) with a valid wage. Includes workers with college education: salaried, self-employment and owners. Earnings below and above the 1 and 99 percentile (within gender and education group) are dropped. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the previous and post observation.

As discussed, if a degraded tertiary effect is taking place, we would expect that inequality among younger workers would increase more (or decrease less) than for workers of other age groups. However, as shown in Figure 5, wage inequality among the younger age group declined. There is no clear difference in the pattern of inequality change among workers of different ages (whether males or females). Figure 5 shows a decline in inequality for all workers, including college-educated older workers. Skill-obsolescence (or lower relative demand for older workers in recent cohorts) might imply that high-wage college-educated older workers are earning a lower wage and, hence, inequality for this group would decline. Thus, Figure 5 is more consistent with the skills-obsolescence effect than with the degraded tertiary education hypothesis.

7
<!-- Start of picture text --> Share of sales —_ City population<br>~ ©4 11%<10%- 20% } <55 0 ,000,000- 500,000<br>0 21% - 30% 00.000 SOUTH SUDAN<br>m 31%<br>- 40% O ’<br>m > 40%<br>°<br>50 - 4 5<br>i] wooo O<br>Ora °<br>. ()<br>mo.Oo —o |<br>DEMOCRATIC 5 ap VQ<br>REPUBLIC oe<br>OF CONGO | -_ no a,<br>P BL] © =<br>a | a as Z<br>= os<br>° a a ea| se 0 Os\<br>Pe fer 4 e ag a () 5 ve<br>) @i",> oO<br>Van ed ' ae: ;<br>aap.pl -_ CJ : ay<br>a<br>. . é :<br>L RWANDA :r _F . £ TANZANIA<br><!-- End of picture text -->
|||Table|2. Concept2|international|population-|weightedine|quality and|global inequ|ality|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||Using Maddi|son’s GDP pe|r capita and 1|990 PPPs|||Using WDI<br>GDPs and<br>"old"<br>benchmark<br>PPPs|Using<br>household<br>survey means<br>and 2005<br>benchmark<br>PPPs|Using WDI<br>GDPs and<br>2005<br>benchmark<br>PPPs|
||1820|1850|1870|1913*|1929|1950|1960|1980|2002|2002|2002|
|(1) Concept 2Gini|15.2|25.9|31.9|44.3|48.0|55.0|54.0|56.8|52.6|63.0|59.9|
|(2) Concept 2 Theil<br>(0)|4.55|12.0|17.1|32.6|39.3|54.4|53.5|62.7|50.6|77.2|69.2|
|(3) Global average<br>income (in$PPP)|689|916|911|1,599|1,941|2,111|2,778|4,520|7,099|3,665|8,123|
|(4) Total population<br>included (in m)|941|623|1,178|1,666|1,775|2,518|3,030|4,423|5,775|5,802|6,004|
|(5) Estimated world<br>population|1,057|1,201|1,266|1,719|2.042|2,518|3,030|4,423|6,172|6,172|6,172|
|(6) Population<br>coverage (4)/(5)<br>(in%)|89|52|93|97|87|100|100|100|94|94|99|
|(7) Number of<br>countries|53|24|63|66|53|141|141|141|114|120|153|
|(8) Global inequality<br>(Ginipoints)|43|53.2<br>(50)|56.0<br>(53)|61.0<br>(58)|61.6<br>(62)|64.0<br>(65)|63.5<br>(64)|65.7<br>(65)|65.4|70.7|70.7|
|(9) Global inequality<br>(Theil0)|58|48.5|54.4|66.8|69.0|77.5|76.6|85.0|82.5|104.8|104.8|
|(10) Share of<br>between inequality in<br>globalGini(1)/(8)|35|49|57|73|78|86|85|86|80|89|85|
|(11) Share of<br>between inequality in<br>global Theil(2)/(9)|8|25|31|49|57|70|70|74|61|74|66|

Sources: All Concept 2 statistics calculated from Maddison’s 2004 data, except for 2002, calculated from World Development Indicators (WDI) and World Income Distribution (WYD) database. Estimated global population from Bourguignon and Morrisson (2002, Table 1).Global inequality estimates (line 8) for the period 1850-1980 are from Bourguignon and Morrisson (on top) and from Baten, Foldvari, van Leeuwen and van Zanden (between brackets). Global inequality for the year 1820 as calculated here; for 2002, from Milanovic (2009).* Bourguignon and Morrisson population and global inequality data are for the year 1910. [2002 values calculated from my global_new.dta, except 2002 with 1990 PPPs calculated from world2002.dta.] 12
Table 5. Estimation Results Dependent Variable: log�ufpdt�

||1|2|3|4|5|
|---|---|---|---|---|---|
|(ADduty)pdt|0.236|0.232|0.228|0.196||
||(0.07)***|(0.07)***|(0.06)***|(0.08)***||
|(ADduty)pdtx Chemical/Steel|||||0.278<br>(0.05)***|
|(ADduty)pdtx Non Chemical/Steel|||||0.178<br>(0.08)**|
|(ADduty)pdt−1||||0.199<br>(0.09)**||
|(ADduty)pdt−2||||0.206<br>(0.10)**||
|(Threat)kdt−1<br>(Threat)kdt−1x Chemical/Steel|0.102<br>(0.04)**|0.102<br>(0.04)**|0.098<br>(0.05)*|0.088<br>(0.02)**|0.145<br>(0.03)***|
|(Threat)kdt−1x Non Chemical/Steel|||||0.079<br>(0.04)*|
|(Threat)kdt−2||||0.061<br>(0.02)**||
|(Threat)kdt−3||||0.016<br>(0.00)*||
|log (mpdt)||0.045<br>(0.02)**|0.056<br>(0.02)**|0.054<br>(0.02)*|0.054<br>(0.02)*|
|log (revenue)_fdt_||0.028<br>(0.00)***|0.021<br>(0.00)***|0.020<br>(0.00)***|0.020<br>(0.00)***|
|log (revenue)_ft_||0.108<br>(0.00)***|0.096<br>(0.00)***|0.114<br>(0.05)***|0.114<br>(0.05)***|
|log (employment)_ft_|||0.035<br>(0.01)***|0.039<br>(0.01)***|0.039<br>(0.00)**|
|_Country-year dummies_|Yes|Yes|Yes|Yes|Yes|
|_Firm-product-destination fixed effects_|Yes|Yes|Yes|Yes|Yes|
|_R_<sup>_2_</sup>|0.17|0.23|0.29|0.29|0.29|
|_Observations_|443253|443253|343252|343252|343252|

_Source:_ Authors’ analysis based on data described in the text.

_Notes_ : _p_ denotes six-digit HS product, _k_ denotes three-digit ISIC industry, _f_ denotes firm, _d_ denotes destination, and _t_ denotes time in years. Standard errors are in parentheses (clustered by firm-product combinations).<sup>***</sup> ,<sup>**</sup> , and<sup>*</sup> indicate the level of significance at 1%, 5% and 10%, respectively. All specifications include a constant term that is suppressed.

22
income between zero and one. This would be concave, but the elasticity would neither rise nor fall with increases in income. Concavity is compatible with a constant elasticity, a decreasing elasticity, _and_ an increasing elasticity. It is therefore compatible with health inequalities rising, staying the same, or falling with rising average incomes.

**Figure 7. The health-income relationship in the demand-for-health framework**

<!-- Start of picture text --> Health (H)<br>Medcal care (M) Corwsumption (C)<br>Corimw tion (C)<br><!-- End of picture text --> There is, in any case, an additional complication. In looking at the effect of rising average incomes, we have assumed everything else stays the same. This is unlikely. One obvious bit of the model that is likely to change with rising incomes is health technologythe health production function in the northwest quadrant is likely to move upward as average incomes rise. It is not just that as countries get richer, they have more dollars per person to spend on health and other things. In addition, each dollar spent on health care produces more health. With upward shifts in the production function, come outward movements in the frontier in the northeast quadrant, and upward shifts in the healthincome relationship in the same quadrant. These shifts in the health-income relationship over time were shown graphically in the 1999 World Heath Report (World Health Organization 1999). What also seems likely is that countries with different income levels also have different levels of health technology. It is not just the low incomes of SubSaharan African countries that account for their low levels of health. A part of the explanation lies in the fact that the opportunities for transforming money into health are very limited in these countries. In principle, these countries could have in place the same health technologies as the richer countries. But in reality they don't. Elites often manage to get access to high quality primary and hospital care, but for the bulk of the population of the poorer countries-especially the rural population-the scope for generating health from spending on medical care is fairly limited.

_15_
tween lightning intensity and the two measures of outages. The plot confirms the (strong) positive association between outages and lightning activities.

# **4.2.2 Main IV Results**

Table 2, reports the OLS, second-stage IV and reduced formed estimates. I estimate two variant specifications by alternating between survey year and round<sup>31</sup> fixed effects. Starting with OLS estimates column 1, the results show a negative association between exposure to electricity outages and employment outcomes. For instance, in column 2, living in a community with outages is associated with an increase in the probability of being unemployed by 2 pp. Likewise, the effect is negative and statistically significant in relation to non-agric employment and unskilled jobs. The relationship between outages and employment of skilled and agric-sector workers are statistically insignificant. While these estimates are non-causal they provide suggestive evidence that outages are negatively associated with lower employment outcomes.

Turning to the IV estimates, the results also show that outages indeed have negative effects on employment, albeit the IV estimates are relatively large compared to the OLS estimates.<sup>32</sup> In column 2 for instance, I find that living in a community that experiences frequent electricity outages reduces the probability of employment by 13.5 pp. Interestingly, it appears that these effects are driven entirely by the effect on employment in non-agric sectors (column 3-4) as the effect on the probability of employment in agric related jobs is almost zero (5-6) and statistically insignificant. There are at least two candidate reasons behind the null effect of outages on agric-related employment: first, the agric sector in many African countries have low technology intensity, as a result, the potential effect of electricity outages on production in the sector is minimal. Secondly, given the definition of employment as measured in the Afrobarometer as being having a “cash paid” job,

> 31The differences arise because survey rounds often overlap calender years across countries

> 32This suggest the possibility of a downward bias in the OLS estimates plausibly due measurement errors in the measure of outages Allcott et al. (2016).

21
Roubini, N., and X. Sala-i Martin. 1992. “Financial Repression and Economic Growth.” _Journal of Development Economics_ 39 (1): 5–30.

- Rousseau, P. L., and P. Wachtel. 2000. “Equity Markets and Growth: Cross-Country Evidence on Timing and Outcomes, 1980–1995.” _Journal of Banking and Finance_ 24 (12): 1933–57.

- ———. 2002. “Inflation Thresholds and the Finance-Growth Nexus _.” Journal of International Money and Finance_ 21 (6): 777–93.

- ———. 2011. “What Is Happening to the Impact of Financial Deepening on Economic Growth?” _Economic Inquiry_ 49 (1): 276–88.

- Rousseau, P. L., and H. Yilmazkuday. 2009. “Inflation, Financial Development, and Growth: A Trilateral Analysis.” _Economic Systems_ 33 (4): 310–24.

- Schaeck, K., M. Cihak, and S. Wolfe. 2009. “Are More Competitive Banking Systems More Stable?” _Journal of Money, Credit, and Banking_ 41 (4): 717–34.

- Shen, C.-H., and C.-C. Lee. 2006. “Same Financial Development Yet Different Economic Growth: Why?” _Journal of Money, Credit, and Banking_ 38 (7): 1907– 44.

- Sundararajan, V., D. Marston, and R. Basu. 2001. “Financial System Standards and Financial Stability: The Case of Basel Core Principles.” _IMF Working Papers_ 01/62, International Monetary Fund, Washington, DC.

- Tornell, A., F. Westermann, and L. Martinez. 2004. “The Positive Link Between Financial Liberalization, Growth and Crises.” NBER Working Papers 10293, National Bureau of Economic Research, Cambridge, MA.

- Wachtel, P. 2003. “How Much Do We Really Know about Growth and Finance?” _Economic Review_ (Q1): 33–47.

- Walker, E., and F. Lefort. 2002. “Pension Reform and Capital Markets: Are There Any Hard Links?” _Abante_ 5 (2): 77–149.

- Webb, I., M. Grace, and H. Skipper. 2002. “The Effect of Banking and Insurance on the Growth of Capital and Output.” Working Paper Series 02, Center for Risk Management and Insurance, Atlanta, Georgia.

- World Bank. 2009. Impact of the Financial Crisis on Employment. News and Broadcast, The World Bank Group, Washington, D.C.

- Yartey, C. A. 2008. “The Determinants of Stock Market Development in Emerging Economies: Is South Africa Different?” _IMF Working Papers_ 08/32, International Monetary Fund, Washington, DC.

- Yeyati, E. L., and A. Micco. 2007. “Concentration and Foreign Penetration in Latin American Banking Sectors: Impact on Competition and Risk.” _Journal of Banking and Finance_ 31 (6): 1633–47.

- Yilmazkuday, H. 2011. “Thresholds in the Finance-Growth Nexus: A Cross-Country Analysis.” _World Bank Economic Review_ 25 (2): 278–95.

- Zandberg, E., and L. Spierdijk. 2013. “Funding of Pensions and Economic Growth: Are They Really Related?” _Journal of Pensions Economics and Finance_ 12 (2): 151– 67.

- Zhang, J., L. Wang, and S. Wang. 2012. “Financial Development and Economic Growth: Recent Evidence from China.” _Journal of Comparative Economics_ 40 (3): 393– 412.

37
Table 6. Average Lead Time

|Indicator|Number of Months in Advance of<br>the Crisis When First Signal Occurs|
|---|---|
|Real exchange rate|17|
|Real interest rate|17|
|Imports|1 6|
|M2 multiplier|16|
|Output|16|
|Bank deposits|15|
|"Excess" Ml balances|15|
|Exports|15|
|Terns of Trade|15|
|International reserves|15|
|Stock prices|14|
|Real interest differential|14|
|M2/international reserves|13|
|Lending rate/deposit rate|13|
|Domestic credit/GDP|12|
# Input Subsidies and the Depletion of Natural Capital: Chinese Distant Water Fishing<sup>∗</sup>

Gabriel Englander<sup>†</sup> Jihua Zhang<sup>‡</sup> Juan Carlos Villase ̃nor-Derbez<sup>§</sup> Qutu Jiang<sup>¶</sup> Mingzhao Hu<sup>‖</sup> Olivier Deschenes<sup>∗∗</sup> Christopher Costello<sup>††</sup>

**JEL:** Q28, H23, Q22, O13. **Keywords:** Fuel Subsidies, Overfishing, Chinese Fishing

> ∗We thank Patrick Behrer, Aaron Berman, Youssef Benzarti, Eyal Frank, Megan Lang, Andr ́es de Loera, Alisa Tazhitdinova, and seminar participants at the ASSA 2023 Annual Meeting, Resources for the Future, and World Bank for helpful comments. GE gratefully acknowledges support from PROBLUE. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †Development Research Group, World Bank. Correspondence: aenglander@worldbank.org

> ‡School of Economics and Institute of Marine Development, Ocean University of China. zhangjihua6811@gmail.com

> §Hopkins Marine Station, Stanford University. juancvd@stanford.edu

> ¶Department of Geography, University of Hong Kong. jiangqt@hku.hk

> ‖Department of Statistics and Applied Probability, University of California, Santa Barbara. mingzhaohu@ucsb.edu

> ∗∗Department of Economics, University of California, Santa Barbara. olivier@econ.ucsb.edu

> ††Environmental Markets Lab, Bren School of Environmental Science and Management, and Marine Science Institute; University of California, Santa Barbara. costello@bren.ucsb.edu
Table 2: National accounts and Survey means: Cˆote d’Ivoire, Ghana and Mali

||Cˆote d’Ivoire|Ghana|Dif.<sup>_e_</sup>|
|---|---|---|---|
|1986–8||||
|Ofcial: Consumption<sup>_a_ </sup>per cap.<br>|651|308|+344|
|Survey: Consumption<sup>_b_ </sup>per cap.|665|290|+375***|
|Idem in border districts<sup>_d_</sup>|593|311|+282***|
|Ofcial: Cocoa + Cofee<sup>_c_ </sup>prod. income p.c.|123|10|+113|
|Survey: Cocoa + Cofee income per cap.|95|8|+87***|
|Idem in border districts<sup>_d_</sup>|192|18|+174***|
|1993||||
|Ofcial: Consumption<sup>_a_ </sup>per cap.|505|341|+164|
|Survey: Consumption<sup>_b_ </sup>per cap.|417|323|+94***|
|Idem in border districts<sup>_d_</sup>|350|274|+76***|
|Ofcial: Cocoa + Cofee<sup>_c_ </sup>prod. income p.c.|42|7|+35|
|Survey: Cocoa + Cofee income p.c.|29|5|+24***|
|Idem in border districts<sup>_d_</sup>|30|13|+17***|
||Cˆote d’Ivoire|Mali|Dif.<sup>_e_</sup>|
|1993||||
|Ofcial: Consumption<sup>_a_ </sup>per cap.|575|237|+338|
|Survey: Cash expenditures<sup>_b_ </sup>per cap.|395|174|+221***|
|Idem in border districts<sup>_d_</sup>|188|79|+110***|
|Ofcial: Cotton<sup>_c_ </sup>income per cap.|5|8|+2|
|Survey: Cotton income per cap.|7|7|0|
|Idem in border districts<sup>_d_</sup>|57|14|+43***|

_Source_ : Authors’ analysis based on data described in the text. _Notes_ : Figures in US dollars. Top panel (Ghana): 1988 prices and exchange rates. Bottom panel (Mali): 1993 prices and exch. rates. 1986–8: Averages of 1986–8. a: National accounts household final consumption expenditure per capita.Source: World Bank 2012. b: Top panel (Ghana): Household consumption per capita, including consumed own food production. Bottom panel (Mali): Household cash expenditures per capita. c: Cocoa beans, green coffee, or seed cotton output multiplied by the corresponding producer price and divided by total population. Source: FAOSTAT 2012. d: Administrative districts lying along the border. Ghana border: Abengourou, Aboisso, Agnibil ́ekrou, Bondoukou, Tanda (Cˆote d’Ivoire side); Western and Brong-Ahafo (Ghana side). Mali border: Boundiali, Ferkess ́edougou, Korhogo, Odienn ́e, and Tingr ́ela; Bougouni, Kadiolo, Kolondi ́eba and Yanfolila. See also map S2.1 in the supplemental online appendix. e: Difference between the first and second columns. For survey means comparisons, errors are clustered by PSUs: ***: _p < ._ 01; **: _p < ._ 05 ; *: _p < ._ 10 22