rafmacalaba/datause-encoder-data huggingface dataset:
pact on the overall consumption level of recipient households. Angelucci et al. [2012] evaluate the impact of Oportunidades on consumption, savings, ownership of different assets, and transfers of households in Mexican urban areas. They found a significant impact on non-durables, especially food, which is consistent with other findings in the literature (similar results were found by Attanasio et al. [2011] for Familias en Acci ́on in Colombia and Schady and Rosero [2008] for Bono de Desarollo Humano in Ecuador), but they found no impact on savings. Interestingly, Angelucci and De Giorgi [2009] found spillover effects on expenditures of non-eligible households that were living in the same villages as Oportunidades’ beneficiaries.<sup>18</sup> Until today, there are relatively few studies on Bolsa Fam ́ılia’s impact on household expenditures. Resende and Oliveira [2008] used the Brazilian household expenditure survey (POF, 2002-2003) to analyze the impact of Bolsa Escola (which was later incoporated by BFP). They found higher levels of consumption among beneficiaries, especially concentrated on education, hygiene products, clothing and food. Duarte et al. [2009] used survey data of 838 households living in a rural area in the Northeast of Brazil and found a positive impact of BFP on food consumption. Santos and Oliveira [2012] used POF 2008-2009 to analyze the impact of BFP on health expenditures of households from the state of Minas Gerais finding no evidence of such impacts. The above studies either relate to Bolsa Fam ́ılia’s predecessor Bolsa Escola [Resende and Oliveira, 2008] or analyze the impact of Bolsa Fam ́ılia in particular regions [Duarte et al., 2009, Santos and Oliveira, 2012]. Given the great regional heterogeneity of Brazil (see section 4), there is a need for further research which covers all Brazilian regions.

In conclusion, there is a large and growing literature on the impacts of CCTs.<sup>19</sup> Overall, CCTs have been found to be effective in ensuring compliance with the conditionalities. As a result, school attendance and the use of health services has increased among the beneficiary population. The literature has also found evidence that CCTs, through the cash transfer, have increased the consumption level of the beneficaries, reduced poverty and inequality. Nonetheless, open questions remain with respect to the mechanisms through which CCTs affect household choices. In particular, more research is needed to investigate how the interaction between the conditionalities and the cash transfer household choices.

Our essay aims to fill this gap in the literature by providing further evidence on the impact of CCTs on beneficiary households’ expenditure decisions. Changes in expenditure choices can be crucial in counteracting or fostering the CCT’s objectives since the public provision of services

> 18Apart form being interesting this finding challenges some program evaluation methods that assume no impact on untreated.

> 19For a good summary about this discussion see Schady et al. [2009].

8
The impact of fertility on child st'rvival has been a topic of a substantial body of empirical research during the past decade. Micro-level studies, many of them u lizing WFS data, have examined the impact on child survival of maternal age, maternal parity, and the timing of preceding and subseqiuent pregnancies (eg. Winikoff, 1983; Cleland and Sather, 1984; Hobcraft, et al., 1985; Palloni and Millman, 1986). Each of these three aspects of reproduction - maternal age, parity, and interval lengths - have been demonstrated to influence the likelihood of a birth surviving infancy and early childhood, with the impact of preceding and subsequent pregnancy (or birth) intervals being particularly large.

Previous analyses of Egyptian data have considered the relationship between reproductive behavior and child survival. A set of analyses of the Egyptian Fertility Survey (1980) have examined the micro-level effects. With respect to the impact of child survival on fertility, Callum, Farid, and Moussa (1988) show that birth intervals following infant deaths are substantially shorter than intervals following births that survive infancy. In addition to involuntary biological effects, behavioral (eg. contraceptive use patterns) and attitudinal (eg. desire for additional births) responses to infant and child mortality are both evident. With respect to the impact of fertility on child survival, maternal age, parity, and the length of the preceding birth interval have statistically significant and large net effects on infant mortality: births to wome- under age 20 or over age 39, births of parity seven and greater, and births following a preceding birth by less than 24 months are all distinctly disadvantaged (Eid and Casterline, 1988). Parity and length of the preceding birth interval, but not maternal age, also influence survival between age 1 and 5. A more detailed study of the birth spacing effect demonstrates a significant 2
- 33 -

excessive insurance coverage on the population simply to develop contractual savings may have negative welfare implications.

Although funding for contingent liabilities generates positive externalities through capital market development, this does not mean that forcing a given level of funding through mandatory retirement schemes coincides with the social optimum. In other words, there is an argument for a minimum level of mandated funding to provide a minimum level of benefits, leaving the provision of additional benefits to voluntary arrangements. This minimum funding would be sufficient to address the market failures existing in a fully voluntary scheme. These failures derive from myopia of individuals, who do not necessarily save enough for retirement needs or other contingencies (e.g., death, disability); from the moral hazard of individuals relying on Government retirement income guarantee schemes; and from the adverse selection implicit in the different life expectancy of individuals. Hence, a fully funded mandatory pension system that ensures a minimum level of benefits would maximize social welfare, whilst a mandatory pay-asyou-go system (PAYG) that precludes the development of stock markets would not.

The design of pension reform is likely to affect social welfare through this and other channels. For instance, regulations imposed on the portfolio composition of pension funds can severely affect the quantitative impact of contractual savings development on capital markets. As an extreme example, if pension funds were restricted to holding only government bonds, the development of contractual savings would have minimum or no effect on stock markets and social welfare would be lower. Furthermore, the availability of contractual savings as captive funds may reduce fiscal control.

Finally, the development of a sound contractual savings and non-life insurance sector requires an enabling macroeconomic environment, a sound banking system, a reliable financial sector regulation and supervision, and appropriate tax treatment, accounting, auditing and disclosure standards.

# **VI. References**

**Arrau P. and K. Schmidt-Hebbel** (1993) Macroeconomic and Intergenerational Welfare Effects of a Transition from Pay-As-You-Go to Fully Funded Pensions. Background Paper for the World Bank Study on Old-Age Security, Washington, D.C. (mimeo).

**Bailliu J. and H. Reisen** (1997) Do Funded Pensions Contribute to Higher Aggregate Savings? A Cross-Country Analysis., OECD Development Centre Technical Papers Nol130.

**Baltagi B. H.** (1995) Econometric Analysis of Panel Data., John Wiley & Sons, Chicester.

**Bodie Z.** (1990) Pensions as Retirement Insurance., Journal of Economic Literature, 28(1): 28-49.
Table B2: Effect of Self-interviewing (SI) on IPV Reporting in Individual Questions, no individual controls

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _individual_ q|uestion in the fo|llowing category:|All<br>|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|questions<br>pooled|
|**Panel A. Malawi**||||||
|SI|0.09***|0.06***|0.01*|0.03***|0.05***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|FTFI mean|0.11|0.07|0.03|0.04|0.07|
|Number of individuals|1,715|1,711|1,712|1,709|1,716|
|Observations|11,887|6,802|10,181|5,095|33,965|
|**Panel B. Liberia**||||||
|SI|0.05***|0.00|0.00|0.03***|0.02**|
||(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|
|FTFI mean|0.20|0.19|0.09|0.04|0.14|
|Number of individuals|1,259|1,259|1,259|1,259|1,259|
|Observations|8,752|5,006|7,508|3,758|25,024|

Note: Regressions are at the respondent- _question_ level (violence is not aggregated into indexes). See Table B3 for results in which IPV questions are aggregated into indices. Regressions include question-level fixed effects. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

Table B3: Effect of Self-interviewing (SI) on IPV Indices, no individual controls

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _at least one_|question in the f|ollowing category:|Any|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|IPV|
|**Panel A. Ma**|**lawi**|||||
|SI|0.18***|0.11***|0.05***|0.06***|0.14***|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.02)|
|FTFI mean|0.38|0.16|0.08|0.07|0.20|
|Observations|1,737|1,737|1,737|1,737|1,737|
|**Panel B. Lib**|**eria**|||||
|SI|0.07**|0.03|0.01|0.07***|0.03|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.03)|
|FTFI mean|0.57|0.34|0.23|0.07|0.38|
|Observations|1,261|1,261|1,261|1,261|1,261|

Note: IPV measures are indexed by category; index is set equal to 1 if the respondent answered “yes” to any question in the category. “Screen Pass” is defined by selecting “yes” to all questions in Table 2. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

32
39

||[0.0441]|[0.0439]|[0.0463]|[0.0447]|[0.0305]|[0.0420]|[0.0439]|[0.0419]|[0.0552]|[0.0631]|
|---|---|---|---|---|---|---|---|---|---|---|
|Young child|0.0473|0.00798|0.0444|0.0102|0.0182|0.0472|0.00240|0.0172|0.0268|0.0104|
||[0.0406]|[0.0435]|[0.0405]|[0.0434]|[0.0297]|[0.0404]|[0.0429]|[0.0330]|[0.0437]|[0.0481]|
|HH Income|0.0528***|0.0743***|0.0570***|0.0788***|0.0632***|0.0529***|0.0730***|0.0713***|0.0549***|0.0835***|
||[0.00911]|[0.00957]|[0.00880]|[0.00958]|[0.00647]|[0.00898]|[0.00947]|[0.00718]|[0.00973]|[0.0107]|
|Num cars|-0.00857|0.0161|-0.00305|0.00263|0.00998|-0.00475|0.0269|0.000468|0.00402|0.00797|
||[0.0319]|[0.0347]|[0.0315]|[0.0346]|[0.0234]|[0.0313]|[0.0343]|[0.0264]|[0.0345]|[0.0400]|
|Female|||||0.0120|||0.0576**|||
||||||[0.0275]|||[0.0289]|||
|Observations|558|560|554|560|1,044|559|485|972|493|479|
|Adjusted R-|||||||||||
|squared|0.293|0.176|0.285|0.141|0.242|0.306|0.202|0.200|0.302|0.129|

Notes: Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1. The outcome variable is always whether the woman (respondent or counterpart) is working. Columns 1 and 2 shows regressions for the questions we had under the theme women working (WW). Columns 3 and 4 show regressions for questions we had under publicness and mixing (PM), Columns 5-7 show regressions for questions related to gender roles (GR), and columns 8-10 show regressions for questions under the private sector theme (PR). All regressions are shown separately for male respondents and women respondents. We also show the gender roles (GR) and private sector (PR) regressions for everyone in columns 5 and 7.
schools that became Single-Shift in 2011 observed a positive increase in the order of 0.784 standard deviation compared to the control group.

The greater effects in the first group of elementary Single-Shift schools can have three possible interpretations. First, schools that were initially selected into the program had particular unobserved characteristics that were important for the effect, which we show in Table 7 that this does not seem to be the case. Second, the program was better implemented in the first group of schools. Third, the program loses impact when it is expanded. However, if we can say that the policy had an effect in elementary school students, the fact that we observe positive and significant results for those schools that entered in 2012 and 2013 indicates that this impact also happened in its “expanded version.”

||**Table 9: Single-S**<br>(1)|**hift & IDEB Eleme**<br>(2)|**ntary**<br>(3)|(4)|
|---|---|---|---|---|
||IDEB Elem(SD)|IDEB Elem(SD)|IDEB Elem(SD)|IDEB Elem(SD)|
|Single-Shift * Post|0.432|0.264|0.333|0.127|
||[0.064]***|[0.062]***|[0.131]**|[0.104]|
|School of Tomorrow|-0.006|-0.043|0.096|0.021|
||[0.072]|[0.067]|[0.151]|[0.137]|
|More Education|0.025|0.003|0.002|0.011|
||[0.037]|[0.035]|[0.093]|[0.056]|
|Mean items in the house||0.074|0.112|0.037|
|||[0.019]***|[0.048]**|[0.029]|
|% Mothers w/ HE||0.341|0.621|0.416|
|||[0.139]**|[0.333]*|[0.201]**|
|Participation rate-Prova Brasil|||||
|Elem||0.045|-0.011|0.055|
|||[0.020]**|[0.071]|[0.035]|
|Age-grade distortion||-0.027|-0.016|-0.032|
|||[0.003]***|[0.007]**|[0.006]***|
|Constant|-0.903|-0.802|-1.175|-0.736|
||[0.021]***|[0.154]***|[0.387]***|[0.239]***|
|Observations|3114|2690|368|1430|
|Number of schools|519|538|92|286|
|R-squared|0.61|0.53|0.54|0.55|
|School FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brackets|No|No|Yes|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: IDEB Elementary Schools 2005, 2007, 2009, 2011, 2013 and 2015. Columns (2) and (4) refer to years 2007 through 2015. Column (3) refers to only schools that eventually become Single-Shift until 2015, in years 2007 through 2013. Column (4) refers to only schools with low age-grade distortion in 2010 (prior to the program). Single-Shift includes CES in this model. By 2013, there were 77 non-certified Single-Shift schools and 2 CES (with complete information). Clustered standard errors in school level.

28
household consumption or expenditures.

The lack of beneficial effects of the lean season program variant on survey round 3 outcomes also undermines the idea that a more generous program would transform the effects. Treated households in villages selected for the lean season treatment were eligible for 24 additional days of work in March (for a total of 48 days from November to March). Yet in surveys conducted the following month, their food security is no better than either households in control villages or treated households in the harvest season villages (offered 24 days from November to January).

# **6.2.2 Timing**

A second hypothesis related to the design of the PWP program concerns timing of the program. First, the program covers periods where the opportunity cost of time is potentially high. Perhaps work on PWP activities crowded out labor supplied to the household’s own farm or to the wage labor market. We do not have data about hours of work in household agriculture, but note that since survey round 2 is conducted _before_ the harvest, any reduction in food security due to reduced harvests from households’ own plots cannot explain the results and would instead exacerbate the negative effect on food security measured later in the year.

We report the effect of the public works program on labor supply in daily wage markets(“ganyu”) in Table 8 (columns 3 and 4). Both the direct and indirect effects of the program are small and not statistically significant at the extensive margin of participation; at the intensive margin, PWP if anything crowds in wage labor, though standard errors are large. It appears that households have excess supply of labor, a finding consistent with Goldberg (Forthcoming).

The planting season begins earlier in the South than in the North, and the Government activated PWP activities earlier in the South. PWP and the fertilizer subsidy program are administered separately and are not perfectly synchronized. In three study districts, fertilizer subsidy coupon distribution took place between the first and second 12-day waves of PWP activities and in the remaining nine districts, fertilizer coupon distribution overlapped with PWP work and payment. The three districts without overlap were Blantyre (South); Dowa (Center); and Karonga (North). The North accounts for a smaller fraction of total population and therefore of our sample, so discordant timing in one study district represents a larger share of beneficiaries in that region than in the Center or South.

# **6.2.3 Targeting**

Regressive or ineffective targeting potentially explains both lack of direct effects and negative indirect effects. PWP is intended for the able-bodied poor, and uses a combination of 21
# **Appendix: Supplementary Tables and Figures**

# **_Tables_**

**<u>Table A1: Descriptive Statistics Based on Administrative Data, 1981–2019</u>**

||<br>**Total **|<br>**1981–99**|**2000–09**|**2010–14**|**2015–19**|
|---|---|---|---|---|---|
|**Initial registration**||||||
|<br>No. of leases issued|58,582|6,641|4,845|39,189|7,907|
|per year|1,502|350|485|7,838|1,581|
|Plot area (m<sup>2</sup>)|1,060|1,494|1,025|972|1,173|
|Sporadic|24,160|6,641|4,845|6,111|6,563|
|Systematic|34,422|||33,078|1,344|
|<br>Sporadic/year|619|350|485|1,222|1,313|
|Systematic/year|883|||6,616|269|
|_Approach and land use type_||||||
|Sporadic registration|0.412|1.000|1.000|0.156|0.830|
|Systematic registration|0.588|0.000|0.000|0.844|0.170|
|<br>Residential land|0.892|0.853|0.924|0.889|0.921|
|Commercial land|0.046|0.115|0.055|0.035|0.034|
|Agricultural land|0.002|0.001|0.001|0.001|0.013|
|<br>Other Use|0.059|0.030|0.020|0.075|0.032|
|_Subject of registration_||||||
|<br>Ownership male/female joint|0.399|0.002|0.063|0.515|0.365|
|_of which_systematic reg.|0.777|0.000|0.000|0.866|0.235|
|Ownership by females only|0.303|0.215|0.287|0.324|0.282|
|_of which_systematic reg.|0.633|0.000|0.000|0.855|0.174|
|Ownership by males only|0.251|0.709|0.602|0.147|0.170|
|_of which_systematic reg.|0.322|0.000|0.000|0.789|0.151|
|Ownership by companies or others|0.046|0.074|0.048|0.014|0.182|
|<br> _of which_systematic reg.|0.101|0.000|0.000|0.362|0.051|
|<br>**Transfers**||||||
|No. of transfers|8,204|972|927|1,950|4,355|
|per year|210|51|93|390|871|
|Transferred by male owners|0.384|0.614|0.662|0.363|0.283|
|Transferred by female owners|0.309|0.180|0.282|0.321|0.338|
|Transferred by males/female jointly|0.263|0.002|0.008|0.296|0.361|
|Transferred by companies|0.044|0.204|0.049|0.021|0.018|
|<br>Days to get consent|93|190|395|79|28|
|<br>Days to complete registration|120|160|148|121|104|
|<br>Days since approval of consent|77|85|73|74|77|
|<br>Purchase price (US$)<sup>a</sup>|11,394|13,335|15,497|12,386|9,701|
|<br>Plot area (m<sup>2</sup>)|<br>1,005|<br>1,204|<br>1,411|<br>938|<br>907|
|Price (US$/ m<sup>2</sup>)|15|15|17|16|14|
|**Mortgages**||||||
|No. of registered mortgages|6,542|2,227|872|1,445|1,998|
|<br>per year|168|117|87|289|400|
|Mortgage value (US $)|51,573|37,372|59,329|71,471|49,620|
|Plots reg. to male owners|0.529|0.732|0.672|0.461|0.296|
|<br>mortgage value (US $)|42,626|31,279|43,427|63,503|49,338|
|<br>Plots reg. to female owners|<br>0.209|<br>0.175|<br>0.222|<br>0.220|<br>0.234|
|<br>mortgage value (US $)|37,447|21,333|40,483|50,632|40,520|
|<br>Plots reg. by males & female jointly|0.183|0.009|0.032|0.245|0.395|
|<br>mortgage value (US $)|41,917|19,975|39,425|45,082|41,130|
|Plots reg. by companies|0.078|0.085|0.073|0.075|0.075|
|<br>mortgage value (US $)|175,531|131,375|255,818|266,687|131,479|
|Days for registration process|69|136|86|24|20|
|Days to get ministerial consent|94|99|79|||

_Source:_ LAA, land administration database and digitized records, 2020.

aNote that because the number of observations for purchase price and price per unit of land differ slightly due to missing plot area, unit prices cannot be computed from the mean values.

31
# **Introduction**

Price information plays an important role in arbitrage behavior and market efficiency. Access to such information has improved significantly in developed countries, especially with the introduction of online databases (Autor 2001; Anderson and Magruder 2012). In sub-Saharan Africa, long distances and limited infrastructure have historically made obtaining such information costly. The spread of mobile phone coverage over the past decade has significantly reduced the costs of obtaining such information, thereby enabling consumers, traders, and producers to send and receive information more quickly and cheaply (Aker and Mbiti 2010). Although a growing body of evidence suggests that this increased access to information has improved consumer market efficiency, there is more limited evidence on its impact on producer markets, which form an important segment of the population, especially in developing countries.

Between 2001 and 2008, mobile phone networks were phased in throughout Niger. By 2008, more than 44 percent of the population and 90 percent of the agricultural markets in our sample had mobile phone coverage (Aker 2010; authors’ calculations from GSMA). As coverage remained relatively limited in rural areas by 2008, the introduction of mobile phones primarily reduced traders’ costs of obtaining market information (Aker 2010), and, to a lesser extent, farmers who transported their output to local markets for sale.

In earlier work, Aker (2010) finds that the rollout of mobile phone coverage in Niger reduced millet consumer price dispersion by 10-16 percent. Building upon that work, this paper estimates the impact of mobile phone coverage on the spatial producer price dispersion for three agricultural commodities in Niger. We first develop a simple model showing how the circulation of information among traders is likely to improve spatial arbitrage and reduce price dispersion in producer markets. These effects should be strongest for perishable and semi-perishable commodities when inter-temporal arbitrage is largely absent. However, the theoretical predictions related to producers’ prices and traders’ gross margins are ambiguous, as they depend on the market structure and relative elasticities of supply and demand for a particular commodity.

2