rafmacalaba/datause-encoder-data huggingface dataset:
The inequality performance in SSA also raises questions regarding development theory, the application of which would predict a systematic increase in inequality in SSA over the past two decades <mark>(Spilimbergo et al. 1999; B</mark> eegle et al. 2016). Literature on the topic ( _inter alia_ Lewis 1955; Gollin et al. 2014) has noted that, even during the recent economic take-off, large productivity gaps between agriculture and nonagricultural sectors have persisted (MacMillan et al. 2014). According to the Kuznets curve theory (Kuznets 1955; Kanbur 2017), inequality first increases and then declines as a country develops. In SSA, the low initial level of development as well as persistent sectoral and spatial productivity gaps would predict an increase in income differentials during the early growth spurt. However, as mentioned, inequality figures from SSA fail to provide clear evidence for a Kuznets-type trajectory (Ravallion 2005). Also, the existing significant positive relationship between the level of GDP and inequality (Bhorat et al. 2016; Beegle et al. 2016) is in fact driven almost entirely by the Southern Cone.

This puzzle induced scholars like Jerven (2015), to question the veracity of numbers and accuracy of measures used. On one side, many have questioned the extent to which the GDP growth has been driven by structural transformation of African economies. They maintain that much of the recent performance seems to be due to temporary boosts: advantageous external context and making up of lost ground after a long period of economic decline. In other words, describing a growth model mainly due to fortuitous and potentially temporary favorable external conditions (Rodrik 2016a; Bhorat et al. 2017; Diao et al. 2018), they underline that the traditional engines behind rapid GDP growth, structural change and industrialization operated at less than full power, implicitly resizing the extent of the African miracle, especially from a qualitative viewpoint.

All of these explanations have several shortcomings. First, they do not take into adequate account the fact that inequality as it is currently measured in SSA could be simply underestimated. The limitations of using consumption as the widely preferred welfare measure to investigate inequality in SSA, we argue, could be partly able to explain this conundrum. Our belief is that consumption as a proxy for well-being is well-suited to measuring poverty or in general the well-being of the bottom deciles (Meyer and Sullivan 2011), but its appropriateness as a proxy for well-being fades as one moves up the consumption distribution (Chancel et al. 2019). This is because the basket of goods and services commanded by top deciles is not well represented in standard consumption surveys, which, after all, are designed primarily for poverty measurement and hence focus on a fairly basic basket of goods.

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# The Risk of Polygamy and Wives’ Saving Behavior

_Marie Boltz and Isabelle Chort_ JEL codes: J12, D13, D14, O12 Marie Boltz is with the Department of Business Administration, Pontificia Universidad Javeriana, Bogotá, Colombia; the Centre for Research in the Economics of Development, University of Namur, Belgium; e-mail <u>marie_boltz@javeriana.edu.co. Isabelle Chort is with the Université Paris-Dauphine,</u> PSL Research University, IRD, LEDa, DIAL, 75016 Paris, France; e-mail: <u>isabelle.chort@dauphine.fr. We would like to thank the Editor Andrew Foster, three</u> anonymous referees, Luc Arrondel, Jean-Marie Baland, Xavier Gine, Laurent Gobillon, Marc Gurgand, Sergei Guriev, Kenneth Houngbedji, Elise Huillery, Alain de Janvry, Sylvie Lambert, Karine Marazyan, Joan Monras, Pauline Rossi, Elisabeth Sadoulet, and Denni Tommasi for precious comments and suggestions, and Philippe De Vreyer and Sylvie Lambert for providing us with the PSF data. We are also grateful to participants to the 2015 DIAL Development Conference at the Université Paris-Dauphine, the 2015 German Economic Association, the Research Group on Development Economics Conference in Kiel, the 2015 Journées de Microéconomie Appliquée in Montpellier, the 2015 Annual Conference of the Royal Economic Society in Manchester, the 2015 CSAE Conference in Oxford, the 2015 Annual Doctoral Conference of the Association for the Development of Research in Economics and Statistics at the Centre d’Economie de la Sorbonne in Paris, the 2014 Northeast Universities Development Consortium Conference at Boston University, the 2014 Jamboree conference of the European Doctoral Program in Quantitative Economics at the Paris School of Economics, the 2014 5th GRETHA International Conference on Economic Development in Bordeaux, and the audience to seminar presentations at the Paris School of Economics. This research is part of the NOPOOR project, which is funded by the European Union under the 7<sup>th</sup> Research Framework Programme (Theme SSH.2011.1) - Grant Agreement No. 290752.

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similar, although the statistical significance of the results drops somewhat. This is not surprising given that the correlation between the U.S.-based and Mexican measures of incorporation intensity are is quite high, around 50 percent. These results suggests that to enhance growth it is important to protect the returns to investments, and that this is particularly important for (small) proprietorships that operate under unlimited liability.

# **Section 5: Conclusions**

The average size of firms in Mexico varies with the quality of legal enforcement in the state in which the firm operates. States with more effective legal systems have larger firms. A one-standard deviation improvement in the quality of the legal system increases the average firm size by about 10-15 percent. This finding suggests that the administration of the legal system is an important determinant of the prospects for firm growth. All states in Mexico have the same legal origin, and firms across the country are governed by the same legal code. What varies is the administration of laws and the enforcement of court verdicts.

The relationship between the quality of legal enforcement and firm size may be the result of any one or a combination of several effects. Better contract enforcement may allow trade with a broader set of trading partners, expanding a firm’s market. Courts may also lead to an increase in the availability of external finance. Our empirical results provide some evidence, though, that one channel through which the legal system operates

> from the census with data from Mexico’s National Employment Survey (a household-based survey) indicates that in most of these sectors, a majority of the self employed were included in the census. This is not the case with the construction census, however, which appears to miss almost all of the self employed. Since about 25% of the employees in Mexico’s construction sector work by themselves, we exclude the construction industry from our analysis. Note the especially large difference between Mexico and the United States in the proportion of proprietorships in the construction industry (see Table 5).

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vs. employees in 152 surveys and 42 countries. Over 50% of these surveys are from Europe and Central Asia (33% from high income countries ECA countries, 18% from developing ECA countries) and 34% are from Latin America.

There may also be substantial differences between formal and informal employees, especially in developing countries. We identify formal employees as those who exhibit one or more of the following characteristics: union membership, employer pays for social security or health insurance, or has a formal work contract. Employees who are not identified as formal are classified as informal. We are able to examine differences between formal and informal employees, and between self-employed workers vs. informal or formal employees in 34 countries (190 surveys). The sample of countries where we can identify formal from informal employees is dominated by Latin America; 90% of surveys where we can identify formal and informal employees are from Latin America.

# **IV. Methodology: Estimating the Self-Employment Earnings Penalty/Premium**

We estimate the earnings premium/penalty in each survey using individual worker-level (i) data to estimate the following earnings equation for each country(c)/year(t) combination:

[1]

# Where

- Yict is the dependent variable, self-reported monthly earnings of worker i in country c in year t.

- Xict is a vector of eight variables that partially control for observed differences between workers and industries. These are: years of education, years of education squared, age, age squared, a gender dummy variable, an urban/rural dummy variable, a set of dummy variables for one-digit industry code, and a set of dummy variables representing the frequency of wage payments.<sup>6</sup>

- ���� is the error term

- ����� is a dummy variable indicating whether the worker is self-employed (1) or a wage and salary worker (0)

- EPct is the average self-employment earnings premium, estimated separately for each survey (country(c)/year(t) combination).

The first set of earnings differential estimates that we present use this specification to estimate the earnings premium or penalty between self-employed workers and all employees. In a second set of estimates we re-estimate equation 1 and include two selfemployment dummy variables: one that identifies non-professional own-account workers and the other that identifies employers and own-account professionals (the reference

> 6 The frequency of wage payments is included as a control in order to guard against errors in the coding of wage payment frequencies across surveys, which could otherwise severely distort the results.

10
costs for default in the credit plus insurance product, leading that product to have lower take-up.

# **<u>6. Conclusion</u>**

A large body of theory and empirical work in development economics argues that technology adoption (and income-maximizing production choices more generally) may be hindered when returns are risky and insurance or other financial markets are imperfect. This paper reports the results from an experimental study that tested whether reducing risk fosters technology adoption. Nearly 800 maize and groundnut farmers in Malawi (where by far the dominant source of production risk is the level of rainfall) were offered credit to purchase high-yielding hybrid maize and improved groundnut seeds in advance of the planting season. Farmers were randomized into two groups that differed in whether the loan was insured against poor rainfall. Take-up was 33.0 percent for farmers who were offered the uninsured loan. Contrary to expectations, take up was lower, by 13 percentage points, among farmers offered insurance with the loan.

These surprising results help underscore the difficulties inherent in designing effective approaches to reducing the consequences of environmental risks for farmers so as to encourage adoption of income-raising technologies. We provide suggestive evidence that reduced take-up of the insured loan was due to the high cognitive cost of evaluating the insurance: insured loan take-up was positively correlated with farmer education levels. By contrast, take-up of the uninsured loan was uncorrelated with farmer education. This suggests that marketing efforts devoted to reducing the complexity of the insurance from the farmer perspective can help ease the acceptance of such insured or contingent loans. However, a number of other additional factors may have also contributed to lower take-up of the insured loan, and we view investigation of such factors as important avenues for future research.
Table 9. Estimated impact of intervention on reading domains in standard deviations per 100 USD cost per student

||Domains t<br>the inte|argeted by<br>rvention|||Other read|ingdomains||Oral|
|---|---|---|---|---|---|---|---|---|
||Initial<br>sounds|Familiar<br>words|Letter<br>names|Unfamiliar<br>words|Reading<br>comp.|Oral<br>comp.|Dictation|read.<br>fluency|
|Effect per 100 USD|1.069***|0.519|‐0.052|0.206|0.417|0.826*|0.107|0.204|
|males|(0.317)|(0.465)|(0.298)|(0.339)|(0.485)|(0.453)|(0.384)|(0.432)|
|Effect per 100 USD|1.264***|1.61***|0.298|1.09***|1.19***|1.343***|0.656**|1.343***|
|females|(0.314)|(0.388)|(0.291)|(0.322)|(0.403)|(0.405)|(0.294)|(0.416)|
|Average effect per 100|1.156***|1.044***|0.117|0.611**|0.778*|1.074***|0.365|0.759*|
|USD|(0.278)|(0.373)|(0.261)|(0.27)|(0.407)|(0.359)|(0.301)|(0.384)|

Table presents average effects of the intervention in standard deviations per 100 USD; this calculated using the average effects of Table 7 and dividing by the cost per student in 100s USD (58.65 USD). Statistical significance at the 1, 5, and 10 percent levels denoted by ***, ** and *, respectively.

Compared to the data compiled by the Poverty Action Lab, this intervention’s cost effectiveness lies towards the bottom end of the distribution. However, it is not clear how this intervention will affect test scores later in the students’ schooling. The impact of the program may be amplified over time, as early reading skills are crucial to a child’s literacy and future learning.

# <u>6. Conclusions</u>

These findings provide evidence that a teacher training approach providing highly scripted lesson plans can improve basic reading skills in a low-cost, remedial course format, especially for girls. The Papua New Guinea curriculum provides teachers with flexibility over how they use their instructional time for language; this flexibility permitted the piloting and evaluation of the program. The Reader Booster Programme diverges from the curriculum’s approach by providing teachers with a very specific teaching method and scripted lesson plans that they apply within the time allocated to language instruction.

A natural question is how much flexibility should teachers have within the curriculum in a developing country context? Highly structured approaches are appealing in contexts where teacher qualification and ability vary considerably. While in developed countries, the use of highly 17
Table A30: Robustness of impact on violence index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimming|treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|0.05|0.13|0.04|0.05|0.05|0.05|0.04|
|||(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|T1|+Leaders|-0.03|0.28***|-0.10|-0.05|-0.05|-0.05|-0.03|
|||(0.11)|(0.08)|(0.12)|(0.11)|(0.11)|(0.11)|(0.11)|
|T2|(Safe Spaces + IGA)|-0.02|0.22***|-0.05|-0.03|-0.03|-0.03|-0.02|
|||(0.08)|(0.07)|(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|
|T2|+Leaders|0.08|0.36***|0.03|0.06|0.06|0.06|0.08|
|||(0.07)|(0.06)|(0.08)|(0.07)|(0.07)|(0.07)|(0.07)|
|T3|(Safe Spaces + Husbands Clubs)|0.14<br>(0.09)|0.40***<br>(0.06)|0.09<br>(0.10)|0.11<br>(0.09)|0.11<br>(0.09)|0.11<br>(0.09)|0.14<br>(0.09)|
|T3|+Leaders|0.02|0.14*|0.00|0.01|0.01|0.01|0.01|
|||(0.09)|(0.08)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|T4|(control spillover)|0.05|0.06|0.05|0.05|0.05|0.05|0.04|
|||(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|N||2931|2930|2930|2932|2932|2932|2931|
|Con|trol Mean|0|0|0|.001|.001|.001|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

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solution and its estimation. Section 7 presents the estimation results and the model fit assessment. The robustness of the model and the quantification of Azteca’s impact are examined in Sections 8 and 9. The policy experiment is discussed and evaluated in Section 10. Finally, Section 11 concludes.

# **2 Background of Banco Azteca**

Banco Azteca is owned by Grupo Elektra, Latin America’s leading retail company. Elektra stores have operated in Mexico since 1957 and specialize in selling consumer electronic products, household appliances, and furniture through sales on credit schemes. Its in-store credit schemes have consisted of weekly payments at high interest rates that appeal to customers lacking alternative credit options (mainly people from the informal sector). By 2001, Grupo Elektra applied for a license to open a bank, which was approved in March 2002. In October 2002, Banco Azteca started operations inside all Elektra branches that existed in the country. After some months of operation, several Azteca branches began relocating independently of the Elektra stores.

Banco Azteca’s business model closely replicates Elektra’s. Its loans are collateralized, they range from 2,000 to 20,000 Mexican pesos and their average maturity term is one year. Different from Elektra’s credit, Azteca’s loans are in the form of cash, and are not tied to the purchase of any item in the store. To obtain a personal loan, applicants visit a branch where, among other things, they state a list of belongings to be pledged as collateral for their loan. Household appliances such as refrigerators or televisions are common collateral accepted by the bank<sup>2</sup> . To assess their value and to obtain more information about the applicant, a loan agent mounted on a motorcycle visits the applicant’s house shortly after the application is processed. After this visit, the bank decides whether or not to approve the loan. Once a loan is approved, Azteca’s clients begin making weekly payments, . and those who delay receive weekly visits from loan agents requesting their payment<sup>3</sup> As stated by Azteca, its default rate is as low as that of traditional banks, plausibly due to its crude collection and . repossession mechanisms<sup>4</sup>

> 2In case of default, collateral is repossessed and resold at Elektra’s Bodega de Remates outlet stores.

> 3Annual Report of Grupo Elektra, 2002.

> 4According to Rhyne (2009), "Azteca has reportedly fired agents for crossing the line between peer pressure and public humiliation".

6
day after the initial bank visit, pre-transfer expenditures come from the survey conducted at the initial visit, on day _t_ = _−_ 2 as indicated in Figure 2. Spending in the week after the transfer (including day of the transfer) is measured in Survey 1, conducted on day _t_ = 7 for the immediate-transfer group and day _t_ = 8 for the one day delay group. For the eight day delay group, Survey 1 measures spending in the week after the announcement, but before the transfer was made. As we will discuss, households may spend in anticipation of receiving a large transfer. Survey 2, conducted on day _t_ = 15, measures post-transfer expenditures for this group. The only exception to a one-week recall period is this survey, which includes an eight day recall period to capture spending on the day of the transfer.

Transfers were implemented in March and April 2014. They were timed to coincide with the end of the lean season, just before many households harvest and sell crops.

# **3 Data**

The household survey described above is adapted from Malawi’s Third Integrated Household Survey (IHS-3) and contains its detailed expenditure module. In addition to asking about the quantity purchased and total paid for 218 consumption goods, durable goods and services, we also ask whether each purchase was planned before the respondent arrived at the store or market, or was made on the spot.

We also use survey data collected between June and Aug 2013, before bank accounts were opened. These data include information about household demographics, expenditures, asset ownership and time preferences.

Panels A and B of Table 1 provide summary statistics for variables collected during baseline in 2013 and during round 0 of the midline interviews just prior to the transfer experiment. The majority of respondents are male. Sixty-three percent are married. Households have 4.7 members on average. Households own 1.5 acres of land on average and non-fixed assets worth MK 177,697 (about USD 555). During the 7 days leading up the round 1 interview, households spent a total of MK 11,500 (USD 36) on average, with MK 4,593 (USD 14) spent on food.<sup>5</sup> Respondents reported spending an average of MK 422 on unplanned food purchases, and MK114 on unplanned non durable items.

Our outcomes of interest are computed using administrative data from NBS and expenditures measured in the household surveys. We use NBS data to measure the saving balances at different points in time. Savings defaults could change spending patterns through mental accounting even if all money was immediately withdrawn, as long as direct deposit recipients still treated the transfer as funds to be saved for the future. More likely, though, savings

> 5Using the March 2013 exchange rate of MK 320 to USD 1.

7
# **Results on Mechanisms and Secondary Outcomes**

## Table A9: Impact on SRH knowledge (15yo+)

||Knows<br>modern contraception<br>method|Heard of<br>HIV|Knows<br>condom<br>reduces HIV<br>risks|Maternal<br>care<br>attitude|Knows<br>modern<br>MH products|Declares<br>advantages<br>of modern<br>MH producs|Agrees that<br>girls should<br>attend school<br>on their period|
|---|---|---|---|---|---|---|---|
|T1 (safe spaces)|0.089**|0.079|0.090|0.225***|0.100|0.074|0.056|
||(0.042)|(0.053)|(0.056)|(0.085)|(0.061)|(0.074)|(0.037)|
|T1 + Leaders|0.046|0.088*|0.060|0.149*|0.067|0.097|-0.010|
||(0.042)|(0.045)|(0.064)|(0.088)|(0.053)|(0.066)|(0.048)|
|T2 (safe spaces + IGA)|0.061|0.069|0.163***|0.287***|0.157***|0.158***|0.027|
||(0.037)|(0.043)|(0.057)|(0.088)|(0.049)|(0.049)|(0.034)|
|T2 + Leaders|0.054|0.082*|0.078|0.089|0.035|0.084|-0.048|
||(0.042)|(0.044)|(0.053)|(0.078)|(0.058)|(0.052)|(0.050)|
|T3 (safe spaces + husbands’ clubs)|0.117***|0.094**|0.064|0.239***|0.160***|0.169***|0.095***|
||(0.036)|(0.044)|(0.057)|(0.082)|(0.046)|(0.043)|(0.032)|
|T3 + Leaders|0.077*|0.054|0.117|0.109|0.051|0.007|0.092**|
||(0.040)|(0.062)|(0.074)|(0.100)|(0.068)|(0.074)|(0.044)|
|T4 (control spillover)|0.015|0.002|0.018|-0.053|0.041|-0.002|0.021|
||(0.037)|(0.045)|(0.054)|(0.083)|(0.052)|(0.049)|(0.030)|
|T1 = T1 + L|.356|.879|.679|.393|.623|.782|.222|
|T2 = T2 + L|.859|.792|.195|.0282|.0469|.158|.158|
|T3 = T3 + L|.273|.519|.519|.203|.107|.021|.943|
|T1 = T2|.511|.855|.286|.513|.374|.25|.491|
|T2 = T3|.115|.598|.161|.609|.96|.805|.0679|
|T1 = T3|.49|.784|.694|.876|.333|.177|.334|
|T1 + L = T2 + L|.869|.897|.8|.494|.607|.849|.539|
|T2 + L = T3 + L|.587|.654|.618|.838|.831|.302|.0206|
|T1 + L = T3 + L|.479|.592|.513|.711|.824|.295|.0782|
|N|2,953|2,931|2,094|2,931|2,871|2,871|2,995|
|Control mean|.831|.785|.529|3.35|.639|.463|.811|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p- values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

42