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37
1 Rotman S. (2011), “Branchless Banking in Brazil” CGAP technology blog, Feb 5 2 The data reflect information available at January 12-14, 2011 3 The way people are using M-PESA is analyzed in Jack and Suri, 2010.

4 New draft regulations on electronic retail transfers issued by the Kenyan Central Bank in February 2011 use the term “cash merchant”.

5 SIM cards are the Subscriber Identification Modules of GSM phones.

6 Generic regulatory issues for mobile payment schemes are discussed in Lyman, Pickens and Porteous, 2008, Porteous, 2009, Tarazi and Brefloff 2010, Dias and McKee, 2010 and Alexandre, Mas and Radcliffe, 2011. The Basel Committee on Banking Supervision issued a broader report on microfinance activities in August 2010.

7 In the case of M-PESA the company itself may have a business interest to establish identity so as to ascertain that an individual who wants to send money to a recipient pays this into her own account rather than the account of the recipient, which would avoid the transfer fee.

8 Makin, 2009 explains how M-PESA adopted good practices of credit card schemes to ensure adequate encryption 9 In a similar vein the Basel Committee on Banking Supervision states in its report on “Microfinance activities and the Core Principles for Effective Banking Supervision” (August, 2010): "As long as ...the [cash] collateral is not intermediated, there is no risk to the “depositor” and this activity should not trigger prudential oversight.” 10 For an account of the genesis of M-PESA viewed from the perspective of the Central Bank of Kenya see Kimenyi and Ndung’u, 2009.

11 Oral communication by Michael Joseph, former CEO of Safaricom, January 12, 2011.

12 Kenya’s new regulation of e-money requires non-bank account providers not to pay interest, but gives them the option of deploying the interest earned on deposits in banks, for example, to reduce fees.

31
appear to be driving student learning, while others _within the same public education system_ are not. This suggests a role for government to encourage schools with weak operations management to follow best practice. As we observe from our mapping exercise, ‘strong’ operations management practices do two things, they: actively promote quality of delivery in the classroom (e.g. via personalization of learning, and encouragement to follow best educational practice); and put processes in place to review school performance and drive change (e.g. dialogue and meetings focused on continuous improvement, collection and use of student assessment data). Such practices could be adopted more widely in the public sector and, our analysis suggests, should improve both teacher selection and household incentives.

To be sure, our goal has not been to produce a global theory; given the diversity of real-world education systems, we have deliberately focused on one region, Latin America, and developed a theoretical framework for that context. One could adapt this framework to study different settings, for instance South Asia and parts of East Africa where there is a preponderance of ‘low-cost private schools’. Such a model would need to allow for the possibility of ‘queues’ for jobs in public schools and, as a result, to explicitly model _demand_ -side selection. To the extent that strong management practices enable public school principals to offer higher levels of compensation and then choose motivated and able teachers from the resulting queue, then qualitatively similar predictions would likely still apply. We hope that our new index will enable fertile ground for further research.

# **4 Conclusion**

Policy makers have begun to set ambitious, universal learning goals. To achieve these targets it will be necessary to understand why, within and across current education systems, some students are learning more in some schools than others. Although there are likely many factors at work, it has been suggested that part of this variation in learning might stem from differences in school management. To explore this issue and develop policy, academics and practitioners need to be able to measure school management accurately and cost-effectively at scale across schools and countries, and be in a position to postulate mechanisms behind any observed relationship between school management and student learning outcomes.

26
experiments that investigate both the direct and indirect effects of finance frequently find that while both effects are operational, the indirect effects are more important in reducing inequality. On the other hand, randomized experiments of specific interventions and other household studies often just focus on identifying the direct impact of using financial services on households and do not examine potential indirect effects of improvements in the financial system. Furthermore, some studies use proxy measures of access to finance, such as durable assets or land, which can be used as collateral, or distance from a financial institution, rather than the actual use of financial services. This shortcoming makes it difficult for some studies to draw sharp inferences about the role of financial frictions since it is difficult to distinguish between wealth and access to finance.

# **_3.2 Impact on Firms and Industries_**

Although an enormous body of research examines the impact of financial services on firm performance, we seek to focus only on the subset of this research that provides information on linkages between finance and the intergenerational persistence of relative income differences. Toward this end, we first review firm-level and industry-level studies that examine the impact of financial development on economic growth. As discussed earlier, economic growth that increases wage rates may indirectly broaden access to financial services by reducing the comparative cost of using those services. Thus, we briefly note empirical research making this aggregate point.

We then turn to research on whether financial development disproportionately helps small firms. This does not provide direct, unambiguous information on intergenerational persistence of relative incomes. Very rich dynasties can own many small firms. Furthermore, financial development that spurs the growth of small firms could primarily increase demand for highly-skilled labor, intensifying income inequality. Nonetheless, it is valuable to review research on whether financial development exerts a particularly pronounced effect on the growth opportunities of small, or even start-up, firms because this shed empirical light on the distributional effects of financial development. Future research should seek to link findings on the impact of finding on firms of different sizes to the broader concept of economic opportunity.

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with information from the credit bureau slightly boosts performance, suggesting that the information gathered by the bureau is only slightly complementary to that collected by our approach.

A robustness check assesses models trained instead with 10 fold cross validation in Table S2, which exploits more of the sample but is more computationally demanding. This improves performance of the CDR models in particular, which are more complex and thus data hungry. Their performance is also likely to improve with additional tuning.

Our method also performs better than credit bureau models when assessed with alternate metrics of performance that are informative for our decision problem; in particular, the H-measure designed to overcome some weaknesses of AUC (Hand, 2009) and out of sample R<sup>2</sup> (see Table S3). ROC curves are also presented in Figure S4 and comparisons of scores to actual repayment in Figure S5, for the main models.

# _Out of Time_

When implemented, a model trained on past data will be used to predict future repayment. As a robustness check, we assess the out-of-time performance of all models by training and testing on different time periods. To do this, we construct an offset version of the dataset. The sample of individuals is split into two; the early group that was transitioned before the median date, and the late group after the median. Then, the phone data is evenly divided, into an early and late period. We construct offset versions of our indicators using only transactions occurring in that half of the data (up to the date of each transition). Because these offset indicators are constructed on a shorter panel, they capture less information than our full indicators. The model is trained on the early group, with phone indicators derived from the early period of phone data, and test it on the late group, with indicators derived from the late period of phone data, with results in the last column of Table 3. Because there is only one late period to test on, out of time results are exposed to much more noise than the within time results (for which performance can be assessed across 16
on welfare and the relativist gradient is attributed entirely to social norms. At the upper bound, the relativist gradient is due entirely to social effects, and an absolute welfare comparison calls for a weakly relative poverty line.

The paper provides a new schedule of weakly relative lines that anchors the relative lines to the means from the same surveys used to measure poverty. This is more consistent with past practice in measuring relative poverty than the method used in Ravallion and Chen (2011). It is also means that we are better able to use sub-national data; for example, we can allow for a relative line that is higher in urban areas than rural areas. Second, the paper implements both this approach and the absolute approach on a larger data set than has been used before, adding results from almost 200 household surveys to bring our data base up to 850 surveys, spanning 1979-2011 and 125 countries. The paper presents summary results for the developing world as a whole and by region back to 1981, and up to 2008. Along with this paper, a substantially revised and updated version of the Bank’s website _<u>PovcalNet</u>_ has been produced, which provides public access to the primary data, to replicate our estimates, and to make estimates for selected countries and alternative poverty lines.

The following section describes both our absolute and relative lines, while section 3 describes the data base of household surveys and other data inputs. Section 4 then presents our absolute poverty measures while Section 5 presents the relative measures. Section 6 looks at how our poverty measures compare to a measure of inequality. Section 7 concludes.

# **2. Poverty lines**

_Absolute poverty:_ In setting an international absolute line we follow the same approach used in our past work, namely that the line should be representative of the national lines found in the poorest countries—in the spirit of the original ―$1 a day‖ line (Ravallion et al., 1991; World Bank, 1990). For this purpose, Ravallion, Chen and Sangraula (RCS) (2008) compiled a new set of national poverty lines for developing countries drawn from the World Bank’s country-specific _Poverty Assessments_ and the _Poverty Reduction Strategy Papers_ done by the governments of the countries concerned. While the Ravallion, Datt and van de Walle (1991) data set on national poverty lines was drawn from sources for the 1980s, the new and larger compilation produced by RCS are all post-1990, such that in no case do the proximate sources overlap.

5
# A 6.4 Constructing the IRR indicators

The following is a demonstration of how an IRR was constructed by using the estimated coefficients-which were reported in the previous section.

Based on the following formula, which was explained in section 7, particularly for policy consistency, two pieces of informnation were required to construct a specific IRR.

# A _performance / Apolicy consistency_

_IRRpolicyconsistency performance_ For instance, to construct Results-focus IRRs

- (1) we need estimated partial derivatives of the results focus with respect to particular potential reform interventions-i.e., the coefficient of the related variable from Model III. Yet, for statistical significance of the constructed IRR, only the estimated coefficients, which were statistically significant for one-tailed test, were used in the construction of the indicators.

- (2) we need the initial level of the results-focus indicator.

As the reported regression results show, four of the estimated coefficients were statistically significant for one-tailed test. The initial level of the result focus-indicator was 4.481. Thus, the IRRs were constructed as shown in the following table.

# **Statistical Table 9 Calculating the IRRs (Bolivia)**

|_Institutional_<br>_environmentk_|8_(results focus)_<br>_(_<br>_a(institutional_<br>_environment,,)_|_Resultsfocuso_|_Results focus-_<br>_IRRk_|
|---|---|---|---|
|Rules in evaluation|0.603|4.481|13.4%|
|Rules inrecording|0.599|4.481|13.4%|
|Fairtreatment|0.398|4.481|8.9%|
|Policy support|0.755|4.481|16.9%|

63
**Table 8. Tobit Regressions - "Contribution Density"** (Men and Women of Working Age who Responded to the PRIESO)

||**Men,aged **|**14- 65**|**Women, aged**|**14- 60**|
|---|---|---|---|---|
|CONDEN|Coefficient|Std.Err.|Coefficient|Std.Err.|
|AGE<br>|0.256|(0.031)***|0.310|(0.049)***|
|AGE<sup>2</sup><br>|**-0.006**|**(0.001)*****|**-0.008**|**(0.001)*****|
|AGE<sup>3</sup>|**4.7E-05**|**(0.000)*****|**6.9E-05**|**(0.000)*****|
|HEAD|**0.084**|**(0.046)***|-0.018|(0.056)|
|MARRIED|0.017|(0.035)|**-0.085**|**(0.046)***|
|YEDU|**0.024**|**(0.004)*****|**0.026**|**(0.005)*****|
|WORKED|0.289|(0.050)***|0.419|(0.038)***|
|SPCONT|0.002|(0.036)|0.047|(0.043)|
|PAYGREG|-0.140|(0.054)***|0.031|(0.077)|
|CREDCON|**-0.126**|**(0.030)*****|**-0.114**|**(0.038)*****|
|EXKIDS|0.016|(0.010)*|-0.050|(0.014)***|
|LEDKIDS|0.004|(0.003)|0.002|(0.004)|
|FAMEX|0.016|(0.026)|-0.033|(0.036)|
|GIVES|0.043|(0.028)|0.028|(0.038)|
|GETS|-0.046|(0.035)|0.031|(0.044)|
|LRENT|-0.006|(0.003)**|-0.006|(0.004)|
|COLATT|0.030|(0.032)|0.051|(0.042)|
|FINASST|0.031|(0.108)|0.030|(0.165)|
|WRKASST|-0.096|**(0.031)*****|**0.119**|**(0.051)****|
|SLIFEX|0.003|(0.001)***|-1.8E-04|(0.001)|
|LJUB|**0.012**|**(0.004)*****|0.001|(0.005)|
|LPASIS|0.002|(0.007)|-0.011|(0.010)|
|Intercept|-3.580|(0.362)|-3.924|(0.556)|
|Numberofobs|935||982||
|leftcensored|198||449||
|uncensored|737||533||
|Log likelihood|-476.391||-648.5207||
|LR**;2**(22)|469.95||361.410||
|Prob>**x2**|**0.000**||0.000||
|PseudoR<sup>2</sup>|0.330||0.218||

statistically significant at 1%; "**" at 5%; and "*" at 10% 47
||**Y= RIF es**|**timate of Lo**|**g [ Self-repor**|**ted Producti**|**vity (XOF/ha**|**)]-Food crop**|**s**|||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
||<br>(0.028)|<br>(0.019)|<br>(0.015)|<br>(0.015)|<br>(0.015)|<br>(0.015)|<br>(0.016)|<br>(0.017)|<br>(0.029)|
|Household Male Labor Use|0.231**|0.234***|0.146***|0.148***|0.117**|0.059|0.078|0.105*|0.013|
|(Number)|(0.111)|(0.069)|(0.052)|(0.053)|(0.055)|(0.053)|(0.055)|(0.061)|(0.089)|
|<br>Household Female Labor Use|-0.005|0.051|0.033|-0.001|0.000|-0.008|-0.001|0.003|0.052|
|(Number)|(0.054)|(0.038)|(0.029)|(0.030)|(0.029)|(0.030)|(0.031)|(0.033)|(0.048)|
|<br>Household Child Labor Use|0.005|0.006|0.005|0.006|0.001|0.004|-0.000|0.001|-0.016|
|(Number)|(0.015)|(0.009)|(0.008)|(0.009)|(0.008)|(0.008)|(0.009)|(0.010)|(0.016)|
|<br>Hired Labor Use (Number)|0.008|0.008|0.006|0.005|0.006|0.005|0.004|0.003|0.003|
||(0.012)|(0.011)|(0.009)|(0.007)|(0.009)|(0.008)|(0.006)|(0.005)|(0.005)|
|Number of Fields Managed by the|<br>0.293***|<br>0.196***|<br>0.128***|<br>0.151***|<br>0.139***|<br>0.120***|<br>0.125***|<br>0.133***|<br>0.193***|
|<br>household|(0.090)|(0.054)|(0.041)|(0.034)|(0.033)|(0.033)|(0.032)|(0.035)|(0.057)|
|Total number of crops produced|-0.106|-0.060|-0.001|-0.048|-0.031|-0.016|-0.020|-0.049**|-0.048|
||(0.088)|(0.054)|(0.037)|(0.029)|(0.026)|(0.024)|(0.023)|(0.024)|(0.044)|
|Household used any purchased seed|0.000|-0.000|-0.000|-0.000|0.000|-0.000|0.000|0.000|0.001|
||(0.002)|(0.003)|(0.002)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|(0.005)|
|Plots that Use Chemical Fertilizer|-0.009|0.029*|0.018|0.018|0.022*|0.038***|0.034**|0.040**|0.039|
|<br>(% of total)|(0.025)|(0.017)|(0.012)|(0.012)|(0.013)|(0.014)|(0.014)|(0.016)|(0.025)|
|<br>Plots that Use Pesticide (% of total)|0.030|0.031|0.039**|0.026|0.039**|0.034*|0.032*|0.008|0.053*|
||(0.034)|(0.023)|(0.018)|(0.017)|(0.018)|(0.018)|(0.017)|(0.018)|(0.031)|
|**unexplained**||||||||||
|<br>Log [Total land area cultivated in|0.129|0.073|0.076*|0.174***|0.164***|0.178***|0.193***|0.203***|0.388***|
|<br>ha]|(0.114)|(0.056)|(0.046)|(0.051)|(0.050)|(0.058)|(0.064)|(0.074)|(0.141)|
|<br>Household head age (years)|0.961|0.275|0.183|-0.200|-0.153|-0.258|-0.698**|-0.739**|-0.807*|
||(0.975)|(0.392)|(0.335)|(0.348)|(0.327)|(0.320)|(0.325)|(0.329)|(0.454)|
|Married|-0.038|0.121|0.235|0.331**|0.363**|0.298*|0.371*|0.350*|-0.002|
||(0.550)|(0.217)|(0.181)|(0.167)|(0.162)|(0.170)|(0.192)|(0.208)|(0.329)|
|Divorced/Separated|0.052|-0.025|0.033|0.083**|0.073**|0.074*|0.060|0.071|0.046|
||(0.080)|(0.049)|(0.040)|(0.039)|(0.037)|(0.041)|(0.040)|(0.045)|(0.084)|
|Widowed|<br>0.125|<br>0.157|<br>0.215*|<br>0.317***|<br>0.364***|<br>0.349***|<br>0.364***|<br>0.401***|<br>0.236|
||(0.340)|(0.144)|(0.124)|(0.115)|(0.109)|(0.117)|(0.131)|(0.149)|(0.263)|
|Household head has Primary|<br>0.274**|<br>0.131**|<br>0.062|<br>0.086*|<br>0.119***|<br>0.137***|<br>0.075|<br>0.059|<br>0.015|
|<br>education|(0.134)|(0.060)|(0.048)|(0.049)|(0.045)|(0.051)|(0.048)|(0.043)|(0.086)|
||<br>-0.017|<br>0.002|<br>-0.013|<br>0.013|<br>0.041**|<br>0.029*|<br>0.013|<br>-0.002|<br>-0.021|

48
**Figure 4: Awareness of Climate Change Causes and Mitigation Measures**

**Personally, do you think you are well informed or not about...**

<!-- Start of picture text --> (a) The different causes of climate change<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>9 8<br>13<br>90<br>80<br>33 36<br>70<br>60 51<br>50<br>40<br>48<br>30 54<br>20 31<br>10<br>9<br>0 2 4<br>EU27 countries Kazakhstan Tajikistan<br>Very well informed Fairly well informed Not very well informed Not at all informed<br>(b) Ways in which we can slow down climate change<br>100<br>10<br>90 18<br>26<br>80<br>70 36<br>60<br>54<br>50 50<br>40<br>30 45<br>20<br>22<br>10 23<br>0 8 1 5<br>EU27 countries Kazakhstan Tajikistan<br>Very well informed Fairly well informed Not very well informed Not at all informed<br>Percent of respondents<br>Percent of respondents<br><!-- End of picture text --> Source: Authors calculations based on P-LITS 2 data. Special EU Barometer 300/wave 69.2

# **Climate Change Impacts of Greatest Public Concern**

18. Respondents were presented several possible adverse impacts of climate change, and were asked to choose (a) which of these were of concern to them, as well as (b) which of these was of greatest concern. “Increase in natural disasters” was cited as a concern by the highest share of respondents (78 percent), and as the most important concern by the second-highest share of respondents (25 percent), while “dangers posed by extreme hot/cold seasons that are harmful for health” were cited as a concern by the second-highest share of respondents (69 percent), and as the single-most 10