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Eaton J, Kortum S. 2002. “Technology, geography and trade”. Econometrica 70:1741–1779.

- Eaton J, Kortum S. 2012. “Putting Ricardo to Work”. Journal of Economic Perspectives 26:65– 90.

- Edmonds, Eric V., Nina Pavcnik, and Petia Topalova. 2010. “Trade Adjustment and Human Capital Investments: Evidence from Indian Tariff Reform.” American Economic Journal: Applied Economics, 2 (4): 42-75.

- Elliott, Kimberly Ann and Richard B. Freeman (2003) Can Labor Standards Improve under Globalization? Institute for International Economics, Washington, D.C.

- Erten, Bilge and Jessica Leight. 2017. “Exporting out of Agriculture: The Impact of WTO Accession on Structural Transformation in China. The Review of Economics and Statistics. 00:ja, 1-46.

- Erten, Bilge, Jessica Leight and Fiona Tregenna. 2019. “Trade liberalization and local labor market adjustment in South Africa.” Journal of International Economics, Elsevier, vol. 118(C), pages 448-467.

- Farole, Thomas; Yoonyoung Cho, Laurent Bossavie, and Reyes Aterido. 2017. Bangladesh Jobs Diagnostic. Jobs Series;No. 9. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/28498 License: CC BY 3.0 IGO.

- Frankel, Jeffrey, A., and David H. Romer. 1999. "Does Trade Cause Growth?" American Economic Review, 89 (3): 379-399.

- Goutam, Prodyumna, Italo A. Gutierrez, Krishna B. Kumar, and Shanthi Nataraj, Does Informal Employment Respond to Growth Opportunities? Trade-Based Evidence from Bangladesh. Santa Monica, CA: RAND Corporation, 2017.

- Hakobyan, Shushanik, and John McLaren. 2016. “Looking for Local Labor Market Effects of NAFTA.” Review of Economics and Statistics 98(4): 728–41.

- Hanson, Gordon H. 2012. "The Rise of Middle Kingdoms: Emerging Economies in Global Trade." Journal of Economic Perspectives, 26 (2): 41-64.

Harrison, Ann 2007. “Globalization and Poverty,” NBER Books, National Bureau of Economic Research.

33
13 Through voluntary retirement SUNASS<sup>reduced the number of workers from</sup> 3,769 in 1988 to 1,359 in 1996, at the same<sup>time the number of connections was</sup> expanding, so workers per 1,000 connections<sup>fell from over 6 to 2. This reduction in the</sup> labor force was accomplished partly by removing<sup>redundant workers and partly by</sup> outsourcing activities that were previously<sup>done in-house, especially bill collection and</sup> maintenance, installation and repair of connections.<sup>As a result, labor costs fell sharply.</sup> Even though outsourcing raised administrative<sup>and maintenance expenses and electricity</sup> costs increased, overall operating expenses<sup>per cubic meter of water sold stayed fairly</sup> constant (Figure 2).

_Figure 2. Operating Cost per Cubic Meter of Water_<sup>_Sold_</sup> <!-- Start of picture text --> 0.40<br>-<br>; 0.30 .<br>a 0 20 .10 l ;' ; s 0 II - e p<br><!-- End of picture text --> 1987 1988 1989 1990 1991 1992 1993 1994<sup>1995 1996</sup> Source: Author's Calculations based on data from<sup>SEDAPAL Annual Reports</sup> SEDAPAL also introduced a number of reforms designed<sup>to change management</sup> and personnel incentives in 1996. This included<sup>a performance-based pay system with</sup> specific targets for each employee and quarterly bonuses<sup>of up to10 percent of pay."'</sup> Partly as a result, the average annual salary of SEDAPAL's<sup>personnel began to increase</sup> in real terms, as can be seen in Figure 3.

> " To implement this change management won agreement<sup>of the union for the 55 percent of staff that is</sup> unionized.
vii. Calculate the benchmark share of foreign holdings for country c at time _T_ : , , + cc TT cc TT yyβ = γγβγγ , cc TT viii. λλUse portfolio maximization to obtain constrained optimal portfolio with benchmark λλ holdings <mark>∑</mark><sup>λλ</sup> <mark>i=1</mark> , : ii cc TT ωω ≤ yyβ wwβ<sup>∗</sup> TT ix. Compare gains of excess returns from current holding to constrained and unconstrained holdings.

# **5.3. Assets**

We based our analysis on four asset classes: domestic stocks, domestic fixed income assets, world stocks, and world bonds. This will be sufficient to consider the potential role of international assets in the investment portfolio, though in reality the pension fund may have a chance to invest more broadly in assets such as real estate, infrastructure projects, corporate bonds, private equity, inflation-protected bonds, hedge funds, options, derivatives, and more narrowly defined international investments involving particular sectors or regions.<sup>11</sup> Data is available through the beginning of 2020 for 49 countries, though the starting dates differ for countries, ranging from 2000 to 2010. For each country, we use the longest time period in which all the necessary data could be collected. The local bond and stock returns are calculated as the monthly percent change in local currency above the inflation rate for the major bond and equity indices respectively. Monthly excess returns are calculated relative to the domestic short-

> 11 For example, a potential benefit of international diversification is drawdown mitigation for the pension fund in periods when the domestic equity market is having a meltdown (which typically coincides with a real GDP crisis for the country, lower ability of the sponsors to meet pension contributions, currency depreciation, etc.). In those periods developed market assets and in particular USD denominated assets tend to outperform when translated in domestic currency terms. In such cases, there might be disproportionate benefits from having a specific type of international exposure as opposed to a generalized approach focused on global market indices. Whether such extreme scenarios would deliver more concentrated foreign holdings is left for future study.
As a simple way to understand the intuition behind the index and as a test of the validity of the index, we assess correlations of the country-level _AHI_ with macro-level data using national income and infant mortality rate (Figure 2), variables that are not part of the index creation. The _AHI_ is highly correlated with both concepts. The _AHI_ is low in low-income countries, while higher- and middle-income countries exhibit higher scores on the _AHI_ , consistent with the positive relationship established for the underlying dimensions of the AHI in the literature (cf. Section 3.2). Likewise, countries with a higher score on the _AHI_ exhibit lower infant mortality rates. This provides evidence for external validity of the _AHI_ as clear links have been established by literature on adequate housing conditions and health outcomes (e.g., Galiani et al. 2005; Jalan & Ravallion 2003; Nabassaga et al. 2019; Jamison et al. 1993). Ultimately, the _AHI_ as well as data on the adequacy dimensions from this paper are meant to allow further research on such relationships.

**Figure 2: Correlation of** **_Adequate Housing Index_ with GDP per capita and Infant Mortality**

<!-- Start of picture text --> UZBMARVNM TUNEGYUKRPERALBBRACOL DOMBLRMEXCRI ARG KAZTURRUSROU BLR RUSROUUKRCRIALBKAZARGTURMEXPERCOLBRATUNVNMEGYUZBMAR DOM<br>IRQIDNZAFLKA LKA IDNIRQ ZAF<br>GAB GAB<br>PAK NICGHA IND PHL NIC PHL IND GHA PAK<br>GMB SENNPLCMR BGDCIV AGO LAO BWA BGDNPL BWASEN LAOGMB CMRAGO CIV<br>BDI MWI TGOHTI UGARWA TZA BENCOGZMB SDNKEN NGAMMRDJI RWA KEN UGAMWICOGMMRTZA ZMBBDISDN TGOHTIDJI BEN NGA<br>BFAMLI GIN BFA MLI GIN<br>MOZ SLE ETH ETH MOZ SLE<br>COD GNB GNB COD<br>MDG MDG<br>TCD TCD<br>NER NER<br>7 8 9 10 11 0 20 40 60 80<br>LOG GDP per capita, PPP (current international $) WDI 2019 Mortality rate, infant (per 1,000 live births) WDI 2018<br>Source: World Bank Source: World Bank<br>1 1<br>.8 .8<br>.6 .6<br>Adequate Housing Index .4 Adequate Housing Index .4<br>.2 .2<br><!-- End of picture text -->

**Note** :  Relationship between the _Adequate Housing Index_ and GDP per capita (current international $) as well as child mortality per 1,000 live births. The correlation coefficient for _AHI_ and GDP is .90; the correlation coefficient for _AHI_ and infant mortality is -.83. (Source: Authors’ calculation based on countries’ latest available household consumption and expenditure survey, WDI 2018 / 2019 (GDP per capita, PPP) and WDI 2018 (Mortality rate, infant)).

In presenting national _AHI_ figures, this paper relies heavily on the national representation of HCESs. As informal settlements or slum dwellers – HHs more likely to live in inadequate housing – might be inadvertently omitted from or underrepresented in these surveys (e.g., Carr-Hill 2013; Lucci et al. 2018), we assess the correlation between slum population and _AHI_ (Figure 3). The correlation is strong and negative, providing suggestive evidence that at least at aggregate level, under-representation of slum population does not affect the cross-country interpretation of _AHI_ .
# TABLE 3

# BALANCE SHEET COMPOSITION OF A SUPPLY COOPERATIVE, SEPTEMBER 30, 1981 (in percent of total assets)

<!-- Start of picture text --> ASSETS LIABILITIES AND MEMBERS' EQUITY<br>Fixed assets 3.5 -S Equity 3.0<br>Long-term investments Long-term debt 19.5<br>and loans 13.7 22.5<br>17.2 Short-term loans 34.5<br>Receivables:<br>members 60.9 Suppliers' credit 21.8<br>regional enterprises 18.3<br>-<br>marketing agents 3.6 Notes payable 21.2<br>Total current<br>Total current assets 82.8 liabilities 77.5<br>Total 100.0 Total 100.0<br><!-- End of picture text --> Source: Kadishay (1985).

Note: The data are for the regional supply cooperative in the "Mountain Region." Up to the end of the 1970s, the supply cooperatives had relied mostly on short term credit. But then, as part of policy reforms, the government curtailed its supply of directed short term loans, and banks were also constrained in the expansion of credit. At the same time, the banks were permitted to raise capital on the stock exchange and on the world markets. They began offering medium and longer term loans linked to the exchange rate. This was the major source of the long term debt (19.5 percent in Table 3) which in 1981 was a relatively recent phenomenon. Unlike other sources of long term credit, mostly development assistance, these loans were not tied to specific users or projects and were therefore not transferred back to back to the individual moshavim. The shift to long term debt, which may have been then more expensive than short term, unlinked credit, served the 14
to taking up that work. The difference between the measures based on these two distributions then gives the impact on poverty. When instead the counterfactual is the basic income scheme, the poverty measure is based on the distribution **_y_ –** **_w_ +** **_f_** + _c_ where _c_ is the cost of the scheme per person (wage plus non-wage costs). (This can be scaled down to allow for leakage.) For the counterfactual based on the current assignment of ration cards, the relevant distribution is instead **_y_ –** **_w_ +** **_f_** + ( _c_ / _p_ ) **_r_** where **_r_** = ( _r_ 1,...., _r_ n) denotes the assignment of ration cards ( _ri_ =1 if _i_ has a BPL card and _ri_ =0 if not) and _p_ is the proportion of households with a ration card.

# **4. Wages**

There are a number of ways in which BREGS implementation differs from the formal guidelines, with bearing on the wages received and (hence) poverty impacts. During the survey period, the notified BREGS wage started at 89 rupees per day, rising to 102 in June 2009, and to 114 in May 2010. Productivity norms are stipulated such that an able-bodied worker can earn these notified wages. Scheme functionaries are required to ensure that these norms are being met by measuring the work done at the worksite. Yet, nearly half of the men and women workers interviewed reported seeing no one measuring work at their most recent worksite. When measurement was done, among those aware of the process, the majority (83% of the male and 75% of the female workers interviewed) reported equal payments to all workers at the site. When we asked Mukhiyas (elected village leaders) why workers reported not being paid the stipulated BREGS wage, they typically responded that workers had not performed to the productivity norms and hence were not owed the stipulated wage. They also told us that workers often did not understand this.

Since April 2008, in an effort to promote financial inclusion and transparency in wage payments, all wages are supposed to be paid through beneficiary bank or post office accounts rather than as direct cash payments. The practice is unclear, based on our surveys and field work. Officials report that the majority of wage payments are made through bank or post office accounts. But workers report that wage payments are more often made in cash at the worksite. In 2010, more than half of the workers interviewed—52% of women and 56% of men—reported receiving wages in cash from the Mukhiya, the contractor, the mate or another official at the most recent worksite at which they worked. In R1 the percentages were 78% and 64% for female and male workers, respectively. Thus, while there is evidence of a decline over time, the share of 11
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 20. Benin’s infrastructure spending needs are fairly average relative to GDP**

<!-- Start of picture text --> As percentage of GDP<br>SSA<br>LIC-Fragile<br>LIC-NoFragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Liberia<br>Congo, Dem. Rep.<br>Zimbabwe<br>Ethiopia<br>Sierra Leone<br>Kenya<br>Mali<br>Ghana<br>Benin<br>Uganda<br>Cote d'Ivoire<br>Nigeria<br>South Africa<br>Cape Verde<br>0 10 20 30 40 50 60 70 80<br>% of GDP<br>Capex O&M<br><!-- End of picture text --> Legend: LIC – low-income country, MIC – middle-income country, ECOWAS – Economic Community of West African States _Source_ : Foster and Briceño-Garmendia 2009.

In absolute and GDP terms, Benin’s infrastructure spending needs are close to the average for Sub-Saharan Africa. The burden of Benin’s spending needs, at 16.6 percent of 2005 GDP of $4.3 billion, is at the level of SubSaharan Africa and Benin’s West African peers, such as Côte d’Ivoire (figure 20). (Other countries—Liberia, the Democratic Republic of Congo, Zimbabwe, and Ethiopia—have considerably higher burdens.) Of the total, investment would absorb around 10 percent of GDP, about half of what China invested in its infrastructure during the mid-2000s.

Benin already spends a sizable amount ($452 million per year) to meet its infrastructure needs (table 18). Around 60 percent of the total is allocated to capital expenditure and the remaining 40 percent to operations and maintenance. Operating expenditure is entirely covered from the national budget, the resources of state-owned enterprises, and payments by infrastructure users. Around 35 percent of capital expenditure funding comes from public sources. Benin relies on official development assistance (from members of the Development Assistance Committee of the Organisation for Economic Co-operation and Development, OECD) for much of the funding of its basic infrastructure: ODA accounts for 43 percent of all capital spending. The private sector invests another 20 percent, and non-OECD financiers provide around 3 percent.

36
Table 6: Effect of Extended Provider Training on Patient Satisfaction by Accuracy of Pre-Consultation Malaria Beliefs

||(1)|(2)|(3)|
|---|---|---|---|
||Full Sample|Has Valid Ho|me-Based RDT|
||Overall<br>Satisfaction<br>Index|Overall<br>Satisfaction<br>Index|Overall<br>Satisfaction<br>Index|
|Extended Training|-0.154<sup>_∗∗_</sup>|-0.161<sup>_∗∗_</sup>|-0.129<sup>_∗_</sup>|
||(0.0695)|(0.0791)|(0.0688)|
|Extended Training _×_ Prior-Risk Match|0.128<sup>_∗_</sup><br>(0.0659)|0.162<sup>_∗∗_</sup><br>(0.0766)|0.0874<br>(0.0587)|
|Match: Malaria Prior and Malaria Risk|-0.114<sup>_∗∗_</sup><br>(0.0475)|-0.0935<br>(0.0578)|-0.0481<br>(0.0453)|
|P-value: ET + ET _×_ Prior Match = 0|0.532|0.985|0.326|
|Prior Match Measure|Expected<br>Prior Match|Expected<br>Prior Match|Actual Prior<br>Match<br>(Home RDT)|
|Mean (Basic Training, No Pat. Info.)|0.00|-0.02|-0.02|
|N|1429|1092|1092|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. Standard errors for regressions that include a derivative of predicted malaria risk in the set of independent variables are bootstrapped using 500 replications to account for the fact that predicted malaria risk is a generated regressor. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the potential covariate set and set of pairwise interactions. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

32
_Continued_ _<u>from</u> previous page_

|**Variable**|**Description**|**Survey**<br>**Source**|
|---|---|---|
|Variable Costs|Sum of spending on teacher salaries, non-teaching<br>staf salaries, rent and utilities for a given month.<br>Coded as 0 after school closure.|School|
|Sources of school<br>funding (Y/N)|Indicator variables for whether school items were<br>purchased through (i) self-fnancing- school fees or<br>owner’s own household income, or (ii) credit- loans<br>from a bank or MFI|School|
|Household<br>bor-<br>rowing (Y/N)|Indicator variables for borrowing behavior of the<br>school owner’s household: whether household ever<br>borrowed from any sources, formal sources (e.g.<br>bank, MFI) and informal (e.g. family, friend, pawn-<br>shop, moneylender) sources.|School<br>owner|
|Household<br>bor-<br>rowing:<br>Loan<br>value|Value of total borrowing in PKR by the owner<br>household from any source for any purpose.|School<br>owner|
|_Group C: Teacher _|_Level_||
|Teacher salaries|Monthly salary collected for each teacher present<br>during survey.|Teacher<br>roster|
|Teacher start date|YYYY-MM at which the teacher started her tenure<br>at the school. This allows us to tag a teacher as<br>a newly arrived or an existing teacher relative to<br>treatment date.|Teacher<br>roster|

28
# REFERENCES

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Bigsten, A., P. Collier, S. Dercon, M. Fafchamps, B. Gauthier, J.W. Gunning, A. Oduro, R. Oostendorp, C. Pattillo, M. Sö derbom, F. Teal, and A. Zeufack. 2000. Contract Flexibility and Dispute Resolution in African Manufacturing. _Journal of Development Studies_ 36 (4): 1–37.

Bloom, N., B. Eifert, A. Mahajan, D. McKenzie, and J. Roberts. 2012. "Does Management Matter? Evidence from India." Unpublished manuscript. Stanford University.

Bramoullé, Y., and R. Kranton. 2011. "Strategic Interaction and Networks." Mimeograph. Duke University.

Casella, A., and J. Rauch. 2002. "Anonymous Market and Group Ties in International Trade." _Journal of International Economics_ 58 (1): 19–47.

Clerides, S., S. Lach, and J. Tybout. 1998. "Is Learning-by-Exporting Important?

Micro-Dynamic Evidence from Colombia, Mexico, and Morocco." _Quarterly Journal of Economics_ 113 (3): 903–47.

Collier, P. 2007. _The Bottom Billion. Why the Poorest Countries are Failing and What Can Be Done About It._ New York: Oxford University Press.

de Mel, S., D. McKenzie, and C. Woodruff. 2009. "Are Women More Credit Constrained?

Experimental Evidence on Gender and Microenterprise Returns." _American Economic Journal: Applied Economics_ 1 (3): 1–32.

Fafchamps, M. 1994. "Industrial Structure and Microenterprises in Africa." _Journal of Developing Areas_ 29 (1): 1–30.

Fafchamps, M. 2004. _Market Institutions in Sub-Saharan Africa: Theory and Evidence_ . Cambridge, Mass: MIT Press.

31
Table 8 World Value Survey Regressions

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|Protect Environment|Always Vote|Right Wing<br>Ideology|
|Income|-0.000152|0.000768***|0.0135***|
||(0.000235)|(0.000213)|(0.00115)|
|Primary Education|0.0106|0.0305***|0.00879|
||(0.0124)|(0.0112)|(0.0608)|
|Lower Secondary|0.0225*|0.0226*|-0.0359|
||(0.0127)|(0.0115)|(0.0623)|
|Upper Secondary|0.0515***|0.0530***|-0.0985*|
||(0.0121)|(0.0110)|(0.0594)|
|Post-Secondary|0.0850***|0.104***|-0.192***|
||(0.0143)|(0.0130)|(0.0703)|
|Tertiary Education|0.0868***|0.104***|-0.167**|
||(0.0146)|(0.0132)|(0.0714)|
|Bachelors|0.133***|0.159***|-0.289***|
||(0.0131)|(0.0119)|(0.0642)|
|Masters|0.131***|0.138***|-0.266***|
||(0.0159)|(0.0144)|(0.0779)|
|Doctoral Degree|0.127***|0.190***|-0.662***|
||(0.0250)|(0.0228)|(0.123)|
|Right Wing Ideology|-0.0105***|0.00340***||
||(0.000986)|(0.000896)||
|Female|0.00680|-0.0198***|-0.0508**|
||(0.00470)|(0.00427)|(0.0231)|
|Urban|-0.0221***|-0.0199***|-0.199***|
||(0.00581)|(0.00528)|(0.0285)|
|Age 41 to 60|-0.0168***|0.153***|0.112***|
||(0.00545)|(0.00495)|(0.0267)|
|Age 60+|-0.0367***|0.241***|0.320***|
||(0.00726)|(0.00660)|(0.0356)|
|Constant|0.580***|0.482***|5.729***|
||(0.0142)|(0.0129)|(0.0638)|
|Nation Fixed Effect|Yes|Yes|Yes|
|Mean of Y|0.55|0.572|5.721|
|Observations|42,726|42,726|42,726|
|R-squared|0.054|0.146|0.078|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The Omitted Category are Males with early childhood education who are 40 years or younger.

7
**Table 1**

Cdte **d'Ivoire: Mean Household Size by Region (3-Month Residency)**

||C6te d'Ivoire|Abidjan|Other Urban|Rural|
|---|---|---|---|---|
|1985|8.31|7.11|8.88|8.54|
|1986|8.01|6.98|8.86|8.16|
|1987|7.01|6.18|7.31|7.22|
|1988|6.33|6.02|6.49|6.36|
|% Decline 1985-88|<br>24|15|27|26|

Source: Coulombe and Demery (1992)

**Table 2**

<!-- Start of picture text --> Cote d'Ivoire: Household Size by Residence Qualification*<br>Minimum<br>Months present: 9 6 3<br>1985 7.77 7.98 8.31<br>1986 7.50 7.70 8.01<br>1987 6.69 6.84 7.01<br>1988 ) 5 6.16 6.33<br>% Decline 1985-88 22 23 24<br><!-- End of picture text -->

* Minimum number of months present in the household during the past 12 months.

3