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McKenzie, David and Hillel Rapoport (2007) “Network Effects and the dynamics of Migration and Inequality: Theory and Evidence from Mexico”, _Journal of Development Economics_ , 84(1): 1-24 McKenzie, David, Caroline Theoharides, and Dean Yang (2014) “Distortions in the International Migrant Labor Market: Evidence from Filipino Migration and Wage Responses to Destination Country Economic Shocks”, _American Economic Journal: Applied Economics_ , 6(2): 49-75.

McKenzie, David and Dean Yang (2015) “Evidence on policies to increase the development impacts of international migration”, _World Bank Research Observer_ , 30(2): 155-92.

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Quoidbach, Jordi, Daniel Gilbert and Timothy Wilson (2013) “The end of history illusion”, _Science_ 339: 96-98 Rodríguez, Marisol and Sandra Rozo (2021) “How does it feel to be part of the minority? Impacts of perspective-taking on prosocial behaviors”, IZA Discussion Paper no. 14303.

Sarvimäki, Matti, Roope Uusitalo, and Markus Jäntti (2020) “Habit Formation and the Misallocation of Labor: Evidence from Forced Migrations”, Mimeo.

25
<!-- Start of picture text --> Census Farm Households All Census Households<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Head mean land - Tail mean land Head mean land - Tail mean land<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Head mean land - Tail mean land Head mean land - Tail mean land<br>.15<br>.2<br>.1<br>.1<br>.05<br>0<br>0<br>Marginal effect of FO tenure Marginal effect of FO tenure<br>-.05 -.1<br>.08 .1<br>.08<br>.06<br>.06<br>.04<br>Fraction Fraction<br>.04<br>.02<br>.02<br>0 0<br><!-- End of picture text --> Figure 5: Marginal effect of 1st FO tenure by log _θ_ _Notes:_ Left and right upper panels show marginal effects of first FO tenure at different values of relative influence (log _θ_ ) based on, respectively, census farm households (panel (a), col. 2, Table 4) and all census households (panel (b), col. 2, Table 4). Lower panels show histograms of log _θ_ for the corresponding specifications. Bars in upper panels denote 95% confidence intervals. Short-dashed vertical lines denote one standard deviation above and below the mean of log _θ_ (long-dashed vertical line).

We summarize our key finding in Figure 5, which plots the marginal effect of FO tenure based on the restricted estimates (column 2 of Table 4). In channels with relatively greater average landownership at the head than at the tail, the post-reform allocation of canal water worsened (disfavored the tail) to a significantly _greater_ extent. In other words, decentralization not only appears to have aggravated rent-seeking, but to have aggravated it by more in channels along which political power asymmetry reinforces locational asymmetry (Proposition 2). Tail-end irrigators on channels along which large landowners most predominate at the _head_ (specifically, on which log _θi_ = _L_<sup> ̄</sup> _iH − L_<sup> ̄</sup> _iT_ is two standard deviations above the mean) saw roughly a doubling of their relative shortage after reform, whereas tail-end 33
**APPENDIX 2- Variables Definitions and Data Sources.**

**(s/y)t** = private saving rate = (private savings/ GNDI)t

_private savings =_ gross national savings - public sector savings _gross national savings,_ Source: WB "World Savings Database" Rev. 3.00.

_public sector savings(1)_ for CHL, KOR, MEX, MYS, TUR - savings of the nonfinancial public sector (= consolidated central government + state and local governments + non financial public enterprises) computed as revenues minus consumption - Source: WB "World Savings Database" Rev. 3.00. (2) for IDN savings of the consolidated central government computed as revenues rninus consumption - Source: WB "World Savings Database" Rev. 3.00. (3) for Ghana - savings of the consolidated central government computed as buget surplus plus public investment - Source: Ghana-Quarterly Digest of Statistics (4) for Zimbabwe - savings of the consolidated central government computed as buget surplus plus public investment - Source: World Bank National Accounts + Easterly database.

_GNDI: GNP_ + External Transfers - Source: WB "World Savings Database" Rev. 3.00. yt = log of real per-capita income = ln(GNDI/ population*defl), _population:_ Source WB BESD database.

-, = inflation rate =Aln(deflt+j).

- _defl:_ implicit consumption price deflator year average- Source: WB "World Savings Database" Rev. 3.00.

rt = real interest rate = (1) for IDN, KOR, MYS rt = In (1+ it') - Aln(defl,+1<sup>) (2) for CHL,</sup> GHA, MEX, TUR, ZWE r, = 0.5(ln (1+ itd) + 0.5( ln (1+ it_ld)) - Aln(deflt+i)). _ia_ = nominal interest rate = short term deposit rate, year average - Source : Central Banks Bulletins.

**_jd_** = nominal interest rate = short term deposit rate, December value - Source : Central Banks Bulletins.

flit = index of financial liberalisation - Source: our calculations govst = public sector saving rate (relative to GNDI) - Source: WB "World Savings Database" Rev. 3.00.

38
TABLE 1. OLS ESTIMATES.

|Dependent variable:**_LIBE_**|**_RALIZATI_**<br>(1)|**_ON_**<br>(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|_CONSTANT_|0.14***<br>(2.89)|0.09**<br>(2.10)|0.09**<br>(2.70)|0.08***<br>(3.34)|0.08**<br>(2.11)|0.08**<br>(2.57)|0.1`1**<br>(4.23)|
|**_LIBERALIZATION_**<br>**_(-1)_**|0.71***|0.72***|0.72***|0.72***|0.75***|0.74***|0.80***|
||(15.89)|(18.0)|(20.5)|(17.2)|(21.0)|(23.2)|(27.5)|
|**_FREEDOM_**|0.006*<br>(1.68)|0.01**<br>(2.39)|0.01***<br>(3.03)|0.01***<br>(2.62)|0.01***<br>(2.92)|0.01***<br>(2.89)||
|**_GOVCHANGE_**|0.005<br>(0.13)|0.01<br>(0.20)|0.01<br>(0.15)|-||||
|**_FREESTAB_**|0.004<br>(0.55)|0.004<br>(0.52)|0.003<br>(0.48)|0.002<br>(0.84)|-|-|-|
|**_COHESIDEOL_**|0.02*<br>(1.77)|0.02*<br>(1.85)|0.02**<br>(2.07)|0.02**<br>(2.10)|0.02**<br>(2.11)|0.02**<br>(2.20)|0.02***<br>(2.63)|
|**_COHESION_**|-0.03*<br>(1.90)|||||||
|**_AID_**|0.003**<br>(2.04)|0.004***<br>(3.49)|0.004***<br>(3.69)|0.004***<br>(2.73)|0.004***<br>(3.63)|0.004***<br>(3.67)|0.004***<br>(2.87)|
|**_WAR_**|-0.04**<br>(2.06)|-0.04**<br>(2.09)|-0.04**<br>(2.22)|-0.04**<br>(2.06)|-0.04**<br>(2.26)|-0.04**<br>(2.40)|-0.06***<br>(3.49)|
|**_INITMACRO_**|0.00<br>(0.63)|0.00<br>(0.05)|-|-|-|-|-|
|**_INITDEVELOP_**|-0.00<br>(0.55)|0.00<br>(0.37)|-|-|-|||
|**_INITREGIME_**|0.02<br>(1.00)|-|-|||||
|**_INITFREEDOM_**|0.01***<br>(3.25)|0.01*<br>(1.93)|0.01**<br>(2.21)|**0.01****<br>(2.84)|0.01**<br>(1.99)|0.01**<br>(2.33)|0.013***<br>(3.49)|
|**_FSU_**|_-_|-|-|-|0.01<br>(0.50)|-|**-**|
|**Adjusted R-squared**|0.91|0.91|0.91|0.91|0.91|0.91|0.91|
|F-**statistic**|107***|125***|158***|183***|182***|213***|239***|
|**White test for**<br>**heteroscedasticity**|116.0**|58.5|50.8|46.9*|37.3|34.4|30.8**|
|**Number of observation**|**s**<br>125|125|125|125|125|125|125|

Absolute value of t-statistic in parenthesis. *, **, and *** mean that the coefficient is significant respectively at 10%, 5% and 1 % level of confidence. The t-statistics in equation (1), (4) and (7) are obtained using the White heteroscedasticity-consistent standard errors.

13
in an automation-prone occupation; in the other half, no statistically significant association is found.

Table 1. Logit model (eq. 8) estimates PIAAC countries

|country|||||||
|---|---|---|---|---|---|---|
||Belgium|Chile|Cyprus|Czech Rep.|Denmark|Spain|
|literacy achievement|‐0.09|‐0.1|‐0.06|‐0.11|‐0.16***|‐0.16**|
||(0.06)|(0.1)|(0.06)|(0.09)|(0.05)|(0.07)|
|educational attainment (relative|<br>to general<br>|<br>post‐second<br>|<br>ary)||<br>|<br>|
|none|3***|4.34***|n.a.|n.a.|2.37|2.02***|
||(0.89)|(0.71)|||(1.66)|(0.36)|
|primary|2.26***|3.74***|1.88***|n.a.|2.97***|2.03***|
||(0.3)|(0.46)|(0.2)||(0.73)|(0.17)|
|lower secondary|2.23***|3.43***|1.49***|2.58***|2***|1.86***|
||(0.2)|(0.38)|(0.2)|(0.36)|(0.15)|(0.13)|
|vocational upper secondary|2.03***<br>(0.18)|n.a.|n.a.|2.2***<br>(0.2)|1.45***<br>(0.12)|1.58***<br>(0.29)|
|general upper secondary|1.73***|3.04***|1.49***|1.34***|1.3***|1.52***|
||(0.14)|(0.35)|(0.12)|(0.17)|(0.12)|(0.13)|
|technical post‐secondary|0.64***|1.89***|0.86***|0.55**|0.34***|1.43***|
||(0.12)|(0.33)|(0.14)|(0.27)|(0.12)|(0.12)|
|age|‐0.01**|‐0.02***|‐0.01**|‐0.01|‐0.02***|‐0.01**|
||(0)|(0.01)|(0)|(0.01)|(0)|(0)|
|female|0.28***|‐0.05|0.55***|0.43***|‐0.17**|0.27***|
||(0.1)|(0.15)|(0.1)|(0.11)|(0.07)|(0.1)|
|constant|‐0.97***|‐0.93*|‐0.78***|‐0.79***|‐0.18|‐0.52**|
||(0.19)|(0.52)|(0.21)|(0.26)|(0.13)|(0.2)|
|observations|3083|3428|2496|3419|4547|3157|
|gen ‐ voc upper secondary|‐0.301**|n.a.|n.a.|‐0.865***|‐0.153|‐0.064|
||(0.139)|||(0.16)|(0.111)|(0.269)|

Authors' calculations using OECD PIAAC data. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, **, *, respectively.

26
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA

**Table 2.3 Travel time, population, and crop production in Sub-Saharan Africa**

|Travel time decile|Average travel<br>time (hrs)<sup>1</sup>|Total population<br>(mns)|Total crop production<br>(mn $)|Share of high-input /<br>rain-fed production|Total crop production<br>/ potential|
|---|---|---|---|---|---|
|1|1.7|213.9|12,469.3|0.174|0.411|
|2|3.0|69.3|10,167.9|0.184|0.456|
|3|4.1|52.6|7,822.9|0.188|0.466|
|4|5.1|46.5|6,958.5|0.188|0.332|
|5|6.3|38.3|4,593.6|0.186|0.202|
|6|7.6|30.8|3,478.9|0.180|0.163|
|7|9.3|23.8|2,580.3|0.180|0.082|
|8|11.7|18.3|2,030.6|0.179|0.059|
|9|15.4|14.2|1,315.8|0.177|0.047|
|10|24.8|8.4|1,404.5|0.166|0.029|
|Total/average|4.6|516.1|52,822.3|0.182|0.191|

_Source:_ Own calculations.

_Note:_ Travel time is the estimated time to the nearest city with 100,000 people or more; 10 percent of [land] area in Sub-Saharan Africa is within 1.7 hours of a city with 100,000 people or more; 40 percent of [land] area is more than 7.6 hours from a city with 100,000 people or more. Crop production is estimated using median prices (in US dollars) for each of the 20 crops.

**Table 2.4 Travel time, population, and crop production in Sub-Saharan Africa**

||Popula|tion|Crop pro|duction|Production per capita|
|---|---|---|---|---|---|
|Travel time|(mns)|(%)|(bn $)|(%)|($ per person)|
|<2.5 hrs|213.9|41.4|12.5|23.6|58.3|
|2.5–8.4 hrs|237.5|46.0|33|62.5|139.0|
|> 8.4 hrs|64.7|12.5|7.3|13.9|113.3|
|Average|516.1|100.0|52.8|100.0|102.3|

_Source:_ Own calculations. _Note:_ Travel time (in hours) is calculated to the nearest city with 100,000 people or more.

Moreover, actual crop production as a share of potential, which averages 0.191 overall, declines monotonically from the third to the tenth travel time deciles, from 0.466 to only 0.029 (table 2.3). Note that in no case is the ratio of actual production to potential production greater than 0.466, suggesting that agronomic potential production is perhaps an unrealistically high standard. Even so, the ratio of actual to potential production in the last four travel-time deciles falls far below the maximum ratio actually achieved, suggesting that there is enormous untapped potential for expansion of agricultural production in the more remote areas of Sub-Saharan Africa. Inadequate supply of labor and insufficient demand (either for self-consumption or for sale) are probably the major factors for lower output in these remote areas.

There are, however, some differences in the impacts of road connectivity across the three cropproduction systems. Farmers who adopt the high-input/rain-fed production system are likely to be more sensitive to transport costs for intermediate inputs, such as fertilizer and pesticide, than those using lowinput/rain-fed technology. Therefore, we expect high-input/rain-fed production will be more concentrated in the areas of well-connected road networks. Interestingly, the pattern we obtained from the sample shows an inverted-U-shape relationship between the relative adoption of the high-input production system 7
Figure 2: Contemporaneous vs medium-term Effects <!-- Start of picture text --> Lags Lags<br>1−period 1−period<br>3−period 3−period<br>5−period 5−period<br>Advertisement/output<br>Machinery/output<br>Vehicles/output<br>Gross addition/output<br>−0.2 −0.1 0.0 0.1 0.2 0.3 −0.05 0.00 0.05 0.10<br>Investment<br><!-- End of picture text --> <!-- Start of picture text --> (a) Investment effects electricity prices (b) Investment effects fossil fuel prices<br><!-- End of picture text --> <!-- Start of picture text --> Lags Lags<br>1−period 1−period<br>3−period 3−period<br>5−period 5−period<br>Output/worker<br>Value−added/worker<br>TFP (ACF CD)<br>−0.02 0.00 0.02 0.04 0.06 0.08 0.10 −0.015 −0.005 0.005 0.010 0.015<br>(c) Productivity effects of electricity prices (d) Productivity effects of fossil fuel prices<br>Productivity<br><!-- End of picture text --> _Note:_ Estimates based on Equation 11. Scatters and error bars: FE point estimates and 90% confidence intervals.

28
<u>Education Equity Index (EEI)</u> seeks to measure the socio-economic status (SES) of students with access to tertiary education. The indicator is the ratio of the percentage of university students whose fathers’ have a tertiary education degree, which measures the SES of the student population, and the percentage of men aged 45-64 who have a tertiary education degree, which measures the SES of the general population. The horizontal axis indicates the EEI score. A high EEI indicates more equal access to tertiary education. A lower EEI refers to less equity (Figure 12).

**Figure 12. Education Equity Index**

<!-- Start of picture text --> Netherlands<br>United Kingdom<br>Canada<br>Ireland<br>Finland<br>Australia<br>United States<br>France<br>Sweden<br>High Income Average<br>Italy<br>Average<br>Germany<br>Austria<br>Belgium<br>Peru<br>Colombia<br>LAC Average<br>Mexico<br>Brazil<br>0 10 20 30 40 50 60 70 80<br>Education Equity Index<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Overall, the average EEI score of the LAC countries is the lowest among the countries in this study. This is expected given the high level of overall inequality in Latin America. Anglo-Saxon and Northern European countries offer more equal access to tertiary education, while the majority of Continental European countries fall below the average.<sup>22</sup> Within Latin America, there are differences. For example, Peru seems to have less inequality than Brazil.

The differences in equity in access are likely to stem from a combination of at least seven factors: (i) a country’s overall inequality, (ii) inequality in graduation of secondary education, (iii) affordability of tertiary education, (iv) government and institutional policies favoring access to low-income students, (v) efficiency of a tertiary education system, (vi) the composition of tertiary education supply and learning paths available, and (vii) participation rate. For example, a larger supply of short term tertiary education

> 22 As explained in the methodology section, this indicator reflects composition only, and not access. For example, Peru’s high EEI (38) is equal to that of Belgium, although the participation rate in Belgium is twice the level of Peru’s.

24
Policy Research Working Paper 5536

# **Abstract**

Consumer protection and financial literacy can contribute to improved efficiency, transparency, competition, and access in retail financial markets by reducing information asymmetries and power imbalances between providers and users of financial services. Financial consumer protection has gained significance in policy debates, especially since the onset of the financial crisis in 2008. This paper presents the results of a survey on consumer protection regulations in 142 countries. The findings indicate that although consumer protection legislation is in place in the majority of countries, these do not necessarily address the issues specific to financial services. There is some evidence that enforcement powers and monitoring capacity are limited in many countries, obstructing the effective implementation of the existing regulations. Furthermore, independent third party dispute resolution mechanisms are not widespread. The paper also compiles comprehensive information on laws and regulations relevant for consumer protection and discusses a number of challenges related to empirical analyses of financial consumer protection to enable crosscountry comparison.

This paper is a product of the Financial Access Team in Consultative Group to Assist the Poor, Financial and Private Sector Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at nmylenko@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the intensity of treatment across clusters. RS designs therefore generate a tradeoff compared with the standard blocked, clustered, and partial-population designs: while they allow the researcher to identify novel effects on both the treated and the non-treated, this comes at the cost of reduced power to detect average effects. With input on the magnitude of ICCs and the relative importance of the various estimands, we use the power calculations to provide insight on design choices such as the optimal degree of variation in saturations and the size of the pure control.<sup>4</sup> We conclude the theoretical presentation with three additional uses of the RS design. First, we show that one can recover an estimate of the treatment on the compliers effect (TOC) by assuming that the observed spillover effects on those not offered treatment are a reasonable proxy for the spillovers experienced by non-compliers. This technique is critical because interference between units within clusters violates the exclusion restriction in the standard technique of instrumenting for treatment with randomized assignment to identify the TOC. Second, we consider experiments that use within-cluster controls to form the counterfactual. Imposing a functional form assumption on the saturation distribution allows the researcher to project the desired counterfactual outcome: untreated clusters with a saturation of zero. This value can be used to correct the naive estimate of the ITT, even in studies without a pure control. Finally, we show that an RS design implemented on a non-overlapping network also produces exogenous variation in the treatment saturation of overlapping networks (for example, social groups), variation that is generally superior to what would be obtained from _blocked_ or _clustered_ designs.

We close with an empirical application of these techniques using a cash transfer experiment in Malawi, wherein the fraction of eligible school-aged girls offered treatment was randomized across clusters. The study seeks to understand whether cash transfers could help adolescent girls improve schooling outcomes as well as delay marriage and pregnancy. In previous work, we have shown that, compared with a pure control group, conditional cash transfers (CCTs) significantly improved schooling outcomes while unconditional cash transfers (UCTs) caused substantial reductions in marriage and fertility rates among program beneficiaries (Sarah Baird, Craig McIntosh and Berk Ozler<sup> ̈</sup> 2011). In this paper, we exploit the sample of within-cluster controls and the RS design to investigate spillover effects on both program beneficiaries and eligible non-beneficiaries. Spillovers are a central concern for two distinct reasons. First, a large literature indicates that schooling cash transfer programs can alter the welfare of non-beneficiaries due to congestion effects in the classroom (Jere R Behrman, Piyali Sengupta and Petra Todd 2005), shifts in local norms around education

> 4In the Supplemental Appendix, we provide a Matlab program that allows a researcher to calculate the power of different potential RS designs.

3
35

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