rafmacalaba/datause-encoder-data huggingface dataset:
26 different countries. It would seem plausible that income distribution changes slowly over time, which may indicate that this is the most appropriate way to deal with the sparseness of data. The income distribution data used to construct COEFFVAR, GINI and THEIL are from the eighteen year period between 1958 (Jamaica) and 1976 (Botswana, Dominican republic, Italy, Nepal and El Salvador) with the majority of these observations coming from between 1960 and 1970. Since endogeneity is a potential concern, data before 1970 is preferred to data from between 1970 and 1976. The final measure RTP40 includes additional observations from the early eighties for a few countries. Since these data are in quintiles, and not deciles as used to construct the other measures, COEFFVAR, GINI and THEIL do not include these points.
- 43 -

Another important policy interpretation of the measured large elasticities is this: Suppose that public utility’s energy prices increases. Then, firms might be likely to switch their energy sources from public utilities to private backup generators in these countries. This may fundamentally stem from the firms’ distrust in the public infrastructure services. In Albania, nearly 15 percent of total sales are estimated to be lost due to power outages (Enterprise Surveys, 2007). About 80 percent of firms in the country have their own backup generators, which are estimated to have generated some 30 percent of firm electricity consumption in 2006. With poor quality public services, firms are more motivated to rely on self-protective measures, which are normally costly for the economy as a whole.

Unlike Albania and Macedonia, the expected demand response is predicted to be very limited in the rest of the countries considered: Croatia, Romania, Bosnia and Herzegovina, Bulgaria and Serbia. In the first two countries, in addition, the electricity tariffs are already significantly high. A risk is evident when these countries focus on price adjustments to balance demand and supply: Electricity prices may be skyrocketing without visible response from consumers. In this case, pricing cannot be the only solution.<sup>41</sup> Notably, this does not mean that pricing would be useless. It is still an essential element in the sector reform to achieve full cost-recovery and phase out cross-subsidies, as stipulated by IEA (2008) and others.<sup>42</sup> One useful, complementary policy measure may be to facilitate reduction in technical inefficiency. Recall that Romania’s predicted technical inefficiency at the firm level is twice as high as the regional best performer in this regard, Croatia. Hence, the country can

> 41 For instance, Romania has a much low price elasticity of –0.21; thus, one cannot expect that any negative supply shock would induce firms to reduce energy consumption. They would keep consume energy as they are doing. Accordingly, the domestic energy market would come under enormous upward price pressure. But Romania’s nonresidential electricity price, for example, reached 12.5 U.S. cents per kWh. In this regard, the past history of price adjustments reflected in Figure 6 seems to have been consistent with the paper’s estimation result.

> 42 For instance, IEA (2008) calls for further end-use price adjustments to finalize full cost-recovery in Bosnia and Herzegovina, and Serbia. Our analysis implies that pricing may not be the only solution but should be combined with other incentive measures. The difference in policy emphasis is attributable to the difference in methodology and scope of analysis. This paper focuses on micro behavior and analyzes the nonresidential energy market, while most of the earlier documents discussed a wider range of issues at the country level. Although the claims are not necessarily contradictory, it is noteworthy that direct policy implications could appear different.
**Table 5B: Manufacturing value added as a share of GDP and household and nonfinancial corporation credit as a share of GDP**

|Dep. Var.:<br>manuf._share|2SLS<br>(1)|2SLS<br>(2)|2SLS<br>(3)|2SLS<br>(4)|2SLS<br>(5)|2SLS-FE<br>(6)|2SLS-FE<br>(7)|
|---|---|---|---|---|---|---|---|
|ln_hhcredit|-1.7051<br>(0.131)|-1.8920*<br>(0.093)|-1.5068<br>(0.191)|-1.0565<br>(0.308)|1.3588<br>(0.329)|2.1457*<br>(0.079)|-0.2249<br>(0.834)|
|ln_nfccredit|7.4963***<br>(0.001)|6.8041***<br>(0.004)|6.2357**<br>(0.011)|4.0814*<br>(0.067)|6.2325**<br>(0.019)|5.4867**<br>(0.019)|1.8937<br>(0.427)|
|fdi|-0.1562***|-0.1490***|-0.1386**|-0.1222**|-0.1530***|-0.1359**|-0.1086**|
||(0.004)|(0.006)|(0.011)|(0.012)|(0.005)|(0.019)|(0.022)|
|enrolment|-0.0349|-0.0314|-0.0366|-0.0056|-0.0576|-0.0512|0.0141|
||(0.131)|(0.161)|(0.112)|(0.838)|(0.105)|(0.107)|(0.673)|
|ln_initialgdppc|-3.4189***<br>(0.000)|-3.0163***<br>(0.001)|-3.2188***<br>(0.000)|-3.5582***<br>(0.000)|-5.7100***<br>(0.000)|-6.2978***<br>(0.000)|-3.4586***<br>(0.000)|
|tradeopenness|-0.0251**|-0.0224*|-0.0253*|-0.0206|-0.0003|0.0138|-0.0142|
||(0.036)|(0.076)|(0.073)|(0.138)|(0.983)|(0.336)|(0.341)|
|financialcrisis||-1.5486<br>(0.242)|-1.4077<br>(0.282)|||||
|ln_population|||-0.2769<br>(0.464)|||||
|tot||||-0.0790***<br>(0.000)|||-0.0841***<br>(0.000)|
|exchangerate|||||0.0317<br>(0.470)|0.0357<br>(0.277)||
|Constant|9.0677<br>(0.151)|11.2095*<br>(0.080)|16.3257*<br>(0.059)|26.0424***<br>(0.000)|<br>7.9310<br>(0.284)|4.7304<br>(0.485)|29.0034***<br>(0.000)|
|Observations|169|167|167|126|137|137|126|
|R-squared|0.211|0.228|0.250|0.361|0.137|0.302|0.408|
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

Regressions (1) – (5) are 2SLS regressions with legal origin and religious composition as instruments. Regressions (6) and (7) are 2SLS regressions with the same instruments adding with year fixed effects. Credit variables, initial GDP per capita and population are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

19
**Table A5:** Mean squared error by model and number of question for various coverage rates (approx. no. of questions in brackets)

|||PMT|Truncated|Earl|y stop|TrE|St|
|---|---|---|---|---|---|---|---|
|_10% _|_coverage_|||||||
|37.0||–|0.16 (37.0)|–||–||
|30.0||–|0.16 (30.0)|–||–||
|22.8||0.17 (22.8)|0.16 (23.0)|–||–||
|20.0||–|0.16 (20.0)|0.19|(20.5)|–||
|15.0||–|0.16 (15.0)|–||0.23|(12.1)|
|10.0||–|0.17 (10.0)|0.24|(10.5)|0.23|(10.3)|
|5.0||–|0.18 (5.0)|0.27|(5.1)|0.25|(4.9)|
|0.0||–|0.30 (0.0)|0.30|(0.8)|0.30|(0.1)|
|_20% _|_coverage_|||||||
|37.0||–|0.16 (37.0)|–||–||
|30.0||–|0.16 (30.0)|–||–||
|22.8||0.17 (22.8)|0.16 (23.0)|0.19|(23.0)|–||
|20.0||–|0.16 (20.0)|0.20|(19.8)|0.19|(19.1)|
|15.0||–|0.16 (15.0)|0.22|(15.3)|0.21|(14.8)|
|10.0||–|0.17 (10.0)|0.25|(9.8)|0.21|(10.0)|
|5.0||–|0.18 (5.0)|0.27|(5.2)|0.24|(5.0)|
|0.0||–|0.30 (0.0)|–||0.30|(0.1)|
|_30% _|_coverage_|||||||
|37.0||–|0.16 (37.0)|–||–||
|30.0||–|0.16 (30.0)|0.17|(29.7)|–||
|22.8||0.17 (22.8)|0.16 (23.0)|0.19|(23.2)|–||
|20.0||–|0.16 (20.0)|0.20|(20.2)|–||
|15.0||–|0.16 (15.0)|0.22|(14.9)|0.19|(15.0)|
|10.0||–|0.17 (10.0)|0.24|(10.2)|0.20|(9.8)|
|5.0||–|0.18 (5.0)|0.26|(4.7)|0.22|(5.0)|
|0.0||–|0.30 (0.0)|–||0.30|(0.1)|
|_40% _|_coverage_|||||||
|37.0||–|0.16 (37.0)|–||–||
|30.0||–|0.16 (30.0)|0.17|(29.5)|0.17|(27.2)|
|22.8||0.17 (22.8)|0.16 (23.0)|0.19|(23.2)|0.17|(22.6)|
|20.0||–|0.16 (20.0)|0.20|(20.3)|0.18|(20.1)|
|15.0||–|0.16 (15.0)|0.21|(14.7)|0.19|(15.0)|
|10.0||–|0.17 (10.0)|0.23|(10.3)|0.19|(10.1)|
|5.0||–|0.18 (5.0)|0.24|(5.6)|0.21|(5.0)|
|0.0||–|0.30 (0.0)|–||0.27|(0.5)|
|_50% _|_coverage_|||||||
|37.0||–|0.16 (37.0)|–||–||
|30.0||–|0.16 (30.0)|0.17|(30.5)|0.17|(30.2)|
|22.8||0.17 (22.8)|0.16 (23.0)<br>|0.18|(22.5)|0.17|(22.7)|
|20.0||–|0.16 (20.0)<br>42|0.19|(20.3)|0.18|(19.8)|
|15.0||–|0.16 (15.0)|0.20|(15.4)|0.17|(15.0)|
|10.0||–|0.17 (10.0)|0.22|(9.8)|0.19|(9.9)|
|5.0||–|0.18 (5.0)|0.24|(5.2)|0.21|(5.0)|
|0.0||–|0.30 (0.0)|–||0.27|(0.6)|
15 this “increase in violence” is illusory in the sense that underlying prevalence has not changed, but the increase in reported cases may have real consequences for police and other service providers. Finally, few evaluations have measured program outcomes over a long period of time; evaluations which measure only short-term effects cannot determine whether attitudinal or behavior changes are sustained in the long run.

# **2.1.1 The structure of this section**

The following sections review what is known about more and less effective ways to prevent and respond to GBV. The review is divided into four different sections organized by sector, namely: justice, health, education, and multi-sectoral approaches (i.e., social services and economic development). A table at the beginning of each section succinctly summarizes the objectives of interventions in that sector and provides concrete examples of promising approaches.

In keeping with the ecological model, each sectoral section is subdivided into four subsections, depending on the level at which programs have operated, namely:

- Law and policies

- Institutional reforms

- Community level interventions

- Individual behavior change strategies

This review emphasizes efforts to prevent violence against women rather than initiatives aimed solely at assisting survivors with recovery. Unfortunately, even less is known about how to prevent violence than about how to care for survivors once it has occurred. While this review will explore some initiatives that address the needs of survivors, a reader looking for a comprehensive discussion of survivor services should look elsewhere (e.g. Warshaw and Ganley, 1998, for the health sector response).

Finally, as mentioned earlier, this review will focus primarily on the two most common types of GBV: intimate partner violence (physical and sexual) and sexual violence by any perpetrator. As a result, this review will not provide a comprehensive discussion of interventions that address other types of GBV, such as trafficking of women, female genital mutilation, dowry deaths, or rape used as a weapon of war.

# **2.1.2 Methods used to compile the review**

This review draws from many published and unpublished sources, using databases such as Popline, Medline and Current Contents. Many program evaluations from middle and lowincome countries appear only the grey literature, so this review relies heavily on unpublished sources. Many national and international organizations have produced reviews of GBV initiatives in recent years. These reviews often focus on a single sector, such as health, education or communication, and they sometimes place a greater emphasis on describing program strategies than on synthesizing what is known about effective prevention. This paper benefited greatly from their work, notably the reviews of unpublished evaluations by the
# **8. TABLES AND CHARTS**

**Table 1. Characteristics of the hypothetical individuals:**

||(1)|(2)|
|---|---|---|
|Year of arrival|1975|1985|
|Age at arrival|25|25|
|Level of education|At least college degree|At least college degree|
|Age as of Census 1980|30|-|
|Age as of Census 1990|40|30|
|Age as of Census 2000|50|40|

**Table 2. Summary Statistics of Datasets Extracted from the US Census Samples**

||**DE**|**CADE 1970-19**|**79**|**DECADE**|**1980-1989**|
|---|---|---|---|---|---|
||**Census 1980 **|**Census 1990 **|**Census 2000 **|**Census 1990 **|**Census 2000**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|**Individuals in the sample**||||||
|**(unweighted)**|41,234|39,445|35,880|75,851|78,745|
|**Total number of people**<br>**(after weighting)**|824,680|847,573|750,289|1,668,342|1,704,575|
|**Age interval**|16-40|26-50|36-60|16-50|26-60|
|**Median age**|30|40|49|32|41|
|**Years since arrival**||1970-1974||1980|-1984|
|six to ten|41%|40%|38%|47%|43%|
|||1975-1979||1985|-1989|
|one tofive|59%|60%|62%|53%|57%|
|**By education:**||||||
|None or preschool|4%|7%|8%|8%|6%|
|Grade 1, 2, 3, or 4|9%|9%|7%|6%|4%|
|Grade 5, 6, 7, or 8|25%|20%|21%|17%|19%|
|Grade 9|5%|5%|4%|6%|6%|
|Grade 10|4%|3%|2%|3%|3%|
|Grade 11|4%|2%|2%|3%|2%|
|Grade 12|19%|20%|21%|23%|24%|
|1 to 3 years of college|9%|13%|13%|13%|14%|
|4+ years of college|21%|21%|22%|21%|21%|

34
Given the prevalence of small firms in low-income countries, the analysis has important implications for policies aimed at encouraging innovation and entrepreneurship in the low-income country context.

# **2. Data**

We use data from the baseline of the Sri Lanka Longitudinal Survey of Enterprises (SLLSE), a survey designed by the authors and collected between January and May 2008.<sup>1</sup> The survey was designed to obtain a representative sample of micro, small and medium enterprises in urban Sri Lanka, irrespective of their registration status. Firms were restricted to be privately owned with a Sri Lankan owner, since much of our analysis is interested in the characteristics of the enterprise owner. There are 20 districts in Sri Lanka, excluding the Northern province (which is inaccessible due to civil conflict). Among those 20 districts, the 31 largest cities and towns were chosen, and in each city or town, 5 GN divisions (the smallest administrative unit, of approximately 300 households) were randomly selected. A listing exercise was then carried out on approximately 70-100 households per GN division, to list households with a male or female self-employed worker, and also households with male and female wage workers (the wage worker sample is not used in this paper). Altogether the listing exercise covered 12,736 households, which was used to select a sample of approximately 1500 male self-employed and 800 female self-employed.

This door-to-door survey placed no limits on the size of the enterprise owned. However, 55 percent of the 2,255 enterprises surveyed had no employees other than the owner, and 94.6 percent had 4 or fewer employees, leaving only 121 firms with 5 to 50 workers. We had anticipated that a representative survey of firm owners will not yield many owners of large firms, and so also designed a booster sample of 610 small and medium enterprises with 5 to 250 employees. The sample frame for this dataset came from two sources. 400 enterprises were selected from a recent census of firms carried out by AC Nielsen, Lanka. The Nielsen census covered only part of the geographic area of our survey. Therefore we supplemented this sample by asking wage workers in the

> 1 The survey was undertaken by the Nielsen Company Lanka (Pvts) Ltd.

- 4 -
Deininger, K., A. Goyal, and H. Nagarajan. 2010. _Can Change in Inheritance Legislation Improve Female Asset Ownership? Evidence from India's Hindu Succession Act._ World Bank Working Paper Dekker, M., and H. Hoogeveen. 2002. “Bridewealth and Household Security in Rural Zimbabwe.” _Journal of African Economics_ 11 (1): 114–45 Dharmalinganm, A. 1996. “The social context of family size preferences and fertility behavior in a south Indian village.” _Genus_ 52 (1-2): 83-103 Dodoo, F.N. 1993. “A couple analysis of micro level supply/demand factors in fertility regulation.” _Population Research and Policy Review_ 12: 93-101 Doepke, M., and M. Tertilt. 2008. “Women’s Liberation: What’s in it for Men?” _Quarterly Journal of Economics_ , 124 (4): 1541-91.

Duflo, E. 2004. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa.” _World Bank Economic Review_ 17 (1): 1-25.

Duflo, E. 2005. _Gender Equality in Development._ Bureau for Research in Economic Analysis and Development, Policy Paper No. 011.

Dugan, L., D. Nagin, and R. Rosenfeld. 1999. “Explaining the Decline in Intimate Partner Homicide: The Effect of Changing Domesticity, Women's Status and Domestic Violence Resources _._ ” _Homicide Studies_ 3 (3): 187-214.

Edlund, E. 1999. “Son Preference, Sex Ratios, and Marriage Patterns.” _The Journal of Political Economy_ 107 (6): 1275-304.

Ekberg, J., R. Eriksson, and G. Friebel. 2005. _Parental Leave - A Policy Evaluation of the Swedish "Daddy-Month" Reform_ . IZA Discussion Papers 1617, Institute for the Study of Labor (IZA).

Estudillo, J., A. Quisumbing, and K. Otsuka. 2001. _Gender Differences in Land Inheritance and Schooling Investments in the Rural Philippines._ Land Economics. 77(1): 130-143.

Eswaran, M., and N. Malhotra. 2009. _Domestic Violence in Developing Countries: Theory and Evidence._ Mimeo University of British Columbia.

Evertsson, M., and M. Nermon. 2007. _Changing Resources and the Division of Housework: A Longitudinal Study of Swedish Couples_ . _European Sociological Review_ 23 **: 455-70.** Ezeh, A.C., M. Seroussi, and H. Raggers. 1996. _Men's Fertility, Contraceptive Use, and Reproductive Preferences._ Calverton, MD: Macro International Inc.

Ezeh, A.C. 1993. “The influence of spouses over each other’s contraceptive attitudes in Ghana.” _Studies in Family Planning._ May-June 24 (3): 163-174.

32
**Table 4: Factors related to provincial poverty**

||||||Depend<br>|ent variable is the<br>|poverty indexes o<br>|f provinces<br>||
|---|---|---|---|---|---|---|---|---|---|
|Explanatory variables||Poverty headcoun|t||Poverty gap inde|x||Poverty severity i|ndex|
|`|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Log of per capita expenditure|-0.420***|-0.328***|-0.261***|-0.036***|-0.104***|-0.099***|-0.013***|-0.047***|-0.047***|
||(0.013)|(0.061)|(0.054)|(0.003)|(0.015)|(0.015)|(0.001)|(0.009)|(0.009)|
|Gini index|-0.507**|-0.350*|-0.125|0.180**|0.085|0.136**|0.086**|0.040|0.066*|
||(0.203)|(0.201)|(0.166)|(0.073)|(0.055)|(0.055)|(0.041)|(0.036)|(0.037)|
|Log of state spending|||0.002*|||0.001*|||0.000*|
||||(0.001)|||(0.000)|||(0.000)|
|Log of investment spending|||0.001|||0.002|||0.002|
||||(0.005)|||(0.002)|||(0.001)|
|Lo of oulation densit|||0.293***|||-0.007|||-0.013|
|g pp y|||(0.099)|||(0.021)|||(0.011)|
|Share of urban population|||0.003**|||0.001**|||0.000**|
||||(0.001)|||(0.000)|||(0.000)|
|Share of population with high-|||0.060|||-0.091*|||-0.047|
|school diploma|||(0.297)|||(0.053)|||(0.031)|
|Share of ethnic minority|||0.001***|||0.000***|||0.000***|
|population|||(0.000)|||(0.000)|||(0.000)|
|Share of wage income|||-0.310***|||-0.026|||-0.005|
||||(0.106)|||(0.031)|||(0.016)|
|Share of non-farm income|||-0.273***|||-0.044|||-0.019|
||||(0.102)|||(0.027)|||(0.015)|
|Constant|4.078***|3.355***|3.160***|0.311***|0.898***|0.795***|0.112***|0.400***|0.343***|
||(0.100)|(0.479)|(0.419)|(0.025)|(0.119)|(0.106)|(0.013)|(0.073)|(0.063)|
|Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|630|630|630|630|630|630|630|630|630|
|Number of provinces<br>|63|63|63|63|63|63|63|63|63|
|R<sup>2</sup><br><br>|0.873|0.892|0.158|0.550|0.716|0.843|0.507|0.647|0.746|
|σu<br><br>|0.0584|0.0609|0.316|0.0430|0.0335|0.0218|0.0217|0.0173|0.0129|
|σe<br>|0.0724|0.0609|0.0545|0.0247|0.0175|0.0167|0.0129|0.0101|0.00968|
|ρ<br>Notes: i) Authors’ estimation from|0.394<br>VHLSSs using pr|0.501<br>ovince FE models,|0.971<br>ii) Robust standa|0.752<br>rd errors in parenth|0.786<br>eses clustered at t|0.632<br>he province level,|0.739<br>iii)*** p<0.01, **|0.746<br>p<0.05, * p<0.1.|0.640<br>|

29
biases due to sample truncation.

We check a plausible explanation for the evidence that IPW corrected estimates are not substantially dierent. If coresidency rates across districts are not signicantly correlated with Inpres treatment intensity, then we expect IPW corrected estimates of the causal eects to be similar to the unweighted estimates in Table 2 and Table 3. For this exercise, we estimate the eects of Inpres schools on coresidency rates across districts using the same DiD design adopted in our main empirical analysis. The estimates are reported in Table B.8 below. The coecient of Inpres treatment intensity is not signicant at the 10 percent level for both sons and daughters. This suggests that sample truncation from coresidency in the census data do not bias the estimated causal eects in any signicant manner. This is consistent with the evidence of Akresh et al. (2018) who nd that correction of coresidency bias does not have any substantial eect on their estimates of intergenerational eects of Inpres schools (eects on the children of mothers exposed to Inpres while school aged).

# References

- Akresh, R., Halim, D., and Kleemans, M. (2018). Long-term and Intergenerational Eects of Education: Evidence from School Construction in Indonesia. NBER Working Papers 25265, National Bureau of Economic Research, Inc.

- Azam, M. and Bhatt, V. (2015). Like Father, Like Son? Intergenerational Educational Mobility in India. Demography, 52(6):19291959.

- Emran, M. S., Greene, W., and Shilpi, F. (2018). When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries. Journal of Human Resources, 53(3):589607.

- Emran, M. S. and Shilpi, F. (2011). Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam. Journal of Human Resources, 46(2):427458.

- Nicoletti, C. and Francesconi, M. (2006). Intergenerational mobility and sample selection in short panels. Journal of Applied Econometrics, 21(8):12651293.

- Pesaresia, M., Ehrlicha, D., Ferria, S., Florczyka, A., Freirea, S., M., F. H., Halkiaa, A., Juleaa, M., Kempera, T., and Soillea, P. (2015). Gloabl Human Settlement Snalysis for

62
**Costing Household Surveys for Monitoring Progress Toward Ending Extreme Poverty and Boosting Shared Prosperity**

Talip Kilic<sup>†</sup> , Umar Serajuddin<sup>‡</sup> , Hiroki Uematsu<sup>*</sup> and Nobuo Yoshida<sup>**1</sup>

# **JEL Codes:** C80, I32, O10.

**Keywords:** Household Surveys, Household Survey Costing, Poverty Measurement and Analysis, Inequality and Shared Prosperity Measurement and Analysis, International Development Association.

1 †Corresponding author: tkilic@worldbank.org. Senior Economist, Development Data Group (DECDG), The World Bank, Via Labicana 110, Rome, Italy.<sup>‡</sup> Senior Economist, Development Data Group (DECDG), The World Bank, Washington, DC (userajuddin@worldbank.org).<sup>*</sup> Economist, Poverty and Equity Global Practice, The World Bank, Washington, DC <u>(huematsu@worldbank.org).</u><sup>**</sup> Lead Economist, Poverty and Equity Global Practice, The World Bank, Washington, DC (nyoshida@worldbank.org). This study was commissioned by the World Bank Household Survey Working Group (HSWG) in March 2015, and served as an input into the “World Bank Shared Strategy for Household Surveys: Tracking the Twin Goals and Informing Investment and Policy Decisions” and the “World Bank Implementation Strategy for Household Surveys 2016-2030.” The authors would like to thank (in alphabetical order) Amparo Ballivian, Raka Banerjee, Calogero Carletto, Andrew Dabalen, Gabriel Demombynes, Olivier Dupriez, Geoffrey Greenwell, Dean Jolliffe, and Joao Pedro Wagner De Azevedo for their comments on the earlier versions of this paper, and (in alphabetical order) Alejandro Medina Giopp, Elizaveta Perova, Espen Beer Prydz, and Renos Vakis for their help in the compilation of the cost information.