rafmacalaba/datause-encoder-data huggingface dataset:
annual labor force surveys conducted in Lima, Saavedra (1998) and Saavedra and Maruyama (1998) show that the rate of return to education estimated from a standard Mincerian regression fell noticeably during the crisis (from .11 in 1987 to .08 in 1991), and rose steadily thereafter (to about .13 in 1995). It therefore seems unlikely that changes in access to credit or changes in the marginal benefit to education can explain the patterns in school attendance and completion rates I observe.

The effect of the crisis in Peru on education outcomes appears to have operated through a substantial reduction in forgone income. Real wages in urban areas dropped precipitously during the crisis. The results in Saavedra (1998) for Lima suggest that the mean real monthly wage for informal sector workers fell from about 600 soles in 1987 to 200 soles in 1991, before recovering partially to about 400 soles in 1987. The estimates for formal sector workers are even more staggering, indicating a drop from 1200 soles to 200 soles between 1987 and 1991, and a recovery to 800 soles by 1997. These results suggest that there was a massive decline in the opportunity cost of schoolinig in Lima during the crisis.

Children who hold jobs in Peru in "normal" years may have been less willing and able to combine school and work during the 1988-92 crisis. The extent to which a reduction in child employment frees up time for sclhooling or child leisure is a hotly contested topic in the literature on child labor (see, for example, Akabayashi and Psacharopoulos 1999; Basu 1999; Basu and Van 1998; Jensen and Skyt Nielsen 1997; Psacharopoulos 1997; Ravallion and Wodon 2000; Rosenzweig and Evenson 1977). The three Peru LSMS have no time allocation data, and can therefore not be used to answer this question conclusively. But the results I present in this paper are consistent with some substitution between employment and schooling: During the 1988-92 crisis, children in Peru were less likely to combine school with work, and they were more likely to make adequate grade progress. Arguably, children who were not working could expend more "effort" in school.

Some possible concerns with interpretation of my results are the effects of late school entry; possible changes in public expenditures on education; changes in the quality of education; and migration patterns, specifically migration from rural to urban areas. I discuss each of these in turn.

some tertiary education (-25%). The recovery in incomes between 1991 and 1997, by contrast, clearly favored households with more schooling: The incomes of households with heads with primary education or less increased by 2% between 1991 and 1997, by 19% for households with some secondary education, and by 76% for households with some tertiary education. Note that long-term trends in the marginal benefits to schooling are unlikely to account for my results because education attainment improved between 1985/86 and 1991, and _worsened_ between 1991 and 1997.

14
|**Study reference**|**Incidence rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trialpopulation**|**Region**|
|---|---|---|---|---|
|Abdool et al.,<br>AIDS 2011|3.57|2005-2009|Women in microbicide study<br>(control)|Lusaka|

# **Zimbabwe**

||**Prevalence amo**<br>**National Antenatal Sentinel**<br>**Surveillance Surveys **|**ng females 15-24(%)**<br>**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Spectrum**<br>**Incidence in**<br>**adults 15-49(%)**|**estimates**<br>**Antiretroviral**<br>**coverage (%)**|
|---|---|---|---|---|
|**1990**|-|-|4.03|-|
|**1991**|-|-|4.89|-|
|**1992**|-|-|5.49|-|
|**1993**|-|-|5.84|-|
|**1994**|-|-|5.78|-|
|**1995**|-|-|5.47|-|
|**1996**|-|-|4.86|-|
|**1997**|-|-|4.16|-|
|**1998**|-|-|3.48|-|
|**1999**|-|-|2.87|-|
|**2000**|29.0|-|2.38|-|
|**2001**|25.2|-|1.98|-|
|**2002**|20.8|-|1.72|-|
|**2003**|-|-|1.56|-|
|**2004**|17.4|-|1.45|1|
|**2005**|-|-|1.35|4|
|**2006**|12.5|10.9|1.31|11|
|**2007**|-|-|1.29|18|
|**2008**|-|-|1.28|-|
|**2009**|11.6|-|1.25|37|
|**2010**|-|-|1.22|54|
|**2011**|-|7.3|1.16|77|
|**2012**|-|-|0.96|79|

<u>ANC data sources: Zimbabwe UNGASS Report 2008, Zimbabwe GARPR 2012 National DHS or AIS data sources: Zimbabwe DHS 2005-2006 and 2010-2011</u>

|**Study reference**<br>|**Prevalence**<br>**among females**<br>**15-24(%)**|**Incidence**<br>**rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Gregson et al.,<br>AIDS1997|Ages 15-29: 21.8||1993-1994|Adult general population|Manicaland|
|Boerma et al.,<br> Sex Transm Dis2003|11.67||1998-2000|Adult general population|Manicaland|
|Kumwenda et al.,<br>Sex Transm Dis 2006||4.78|1999-2001|Women attending<br>postnatal or family<br>planning clinics|Harare|
|Brown et al.,<br>AIDS2007||4.1|1999-2004|Women (18-35)|Harare and<br>Chitungwiza|
|Marsh et al.,<br>J Int AIDS Soc 2011|||1998-2005|Women from antenatal<br>clinics and in general<br>population|Manicaland|
|Corbett et al.,<br>AIDS 2007||1.2|2002-2004|Employees from<br>businesses included in<br>VCTstudy|Harare|
|Mavedzenge et al.,<br>J Acquir Immune Defic<br>Syndr 2011||2.72|2003-2005|Women in prevention trial|Harare|
|Abdool et al.,<br>AIDS 2011||2.71|2005-2009|Women in microbicide<br>study (control)|Harare|
|Abdool et al.,<br>AIDS2011||3.29|2005-2009|Women in microbicide<br>tdtl|Chitungwiza|
|||||suy (conro)||
|ALPHA network study||||||

66 **ANNEX 2: Changes to Spectrum data curves from 2012 to 2013**
increases their ability to obtain credit with a low maturity (credit card). To secure credit for a long period is more difficult; for this reason we believe that their ability to obtain long-term credit to finance human capital investments is not enhanced and the credit constraint is binding for poor households.

||Fuzzy <br>Lin|RDD<br>ear|
|---|---|---|
|Nr. of hh members<br>|-0.29<br>|(0.18)<br>|
|Nr. of women|-0.29|(0.14)|
|Female hh head|0.066|(0.06)|
|Black hh head|-0.046|(0.037)|
|Asian hh head|0.0042|(0.0058)|
|Pardo hh head|-0.049|(0.061)|
|Indigenous hh head|0.019|(0.011)|
|HH head illiterate|0.063|(0.054)|
|Universal religion|-0.0043|(0.0084)<br>|
|Catholic religion|0.09|(0.048)|
|Batista religion|-0.0084|(0.01)|
|Evangelical religion|-0.019|(0.012)<br>|
|Assembly of god religion|-0.021|(0.025)|
|Lutheran religion|-0.00029|(5e-04)<br>|
|Nb. of whites in hh|-0.22|(0.19)<br>|
|Nb. of blacks in hh|-0.11|(0.14)|
|Nb. of asians in hh|-0.02|(0.022)|
|Nb. of pardo in hh<br>|0.066|(0.25)<br>|
|Nb. of indigenous in hh|0.027|(0.039)|
|Child or teen pregnant (10-17)|0.0034|(0.014)|
|Young adult pregnant (18-25)|-0.012|(0.016)<br>|
|Bank account|0.58|(0.033)|
|Credit card|0.063|(0.055)|
|Socio-economic controls|No||
|Regional dummies|549||
|Standard errors in parentheses.|Clustering|applied|
|to all models at the level of (55|0) sampling|strata.|

Table 11: Testing for balanced predetermined characteristics

# **Robustness tests: What do we take from this?**

Before we proceed further, we briefly summarize the evidence of the robustness tests. Overall, the fuzzy RDD seems a valid approach to identification and also the assumptions taken in our model appear reasonable. The null hypothesis of balanced characteristics around the threshold cannot be rejected which is an indication that “the local randomization around the threshold”
# **I. Introduction**

1. Governments use a variety of instruments to provide direct support to private enterprises. These include the provision of finance (subsidized and/or directed credit) and business development services (management and marketing advice to small businesses, agricultural extension services, support for enterprise level training and support to technology development).  These interventions are distinct from those that support enterprises indirectly by establishing a policy and legal environment conducive to enterprise development.

2. How effective have these direct support schemes been? This paper attempts to provide some answers to this question by surveying the available literature on the effectiveness of direct support interventions. The surveyed literature covers both academic papers and project/program evaluations. Several, but not all, of the projects and programs evaluated are that of the World Bank Group.

3. Evaluations of direct support programs vary in their methodologies. Detailed costbenefit analyses are rare. Where available, impact evaluations suggest that the performance has been mixed at best. The evidence indicates that active intervention does not work unless the basic environment for private sector development is sound. Public policy thus needs to focus on creating an enabling environment, key elements of which include a sound legal and judicial system which supports low-cost contract enforcement, good infrastructure, a policy playing field which is level in terms of ease of registration, taxes and investment incentives for all enterprises—large and small, domestic and foreign.

4. The rest of the paper reviews the evidence for direct support of the following types: a) credit lines (non-agricultural), b) micro-finance, c) rural finance, d) technical assistance to private firms, e) interventions to increase private sector training and f) support to technology development.

# **II. Provision of Finance**

# **A. Credit lines (non-agricultural)**

5. Many governments have programs to deliver subsidized long-term credit to private firms through financial intermediaries. Often supported by donors, these programs aim at promoting industrial growth and are based on the premise that lack of long-term credit is a major constraint to industrial investment and capacity utilization. Evidence suggests that such programs often do not achieve their aims. A recent study of firms in thirty countries (nineteen developed and eleven developing) shows that while term finance is positively associated with firm growth, government intervention in providing such finance has not been successful when there were weaknesses in the underlying 3
Pakistan: Sunmary and Timeframs for Structural and Policy **Reformn** in Agriculture, continued

|PolicyArea|objective|Measures|Proposed<br>Timing<br>of<br>Initiating Measures and<br>Expected Implementation<br>Period|
|---|---|---|---|
|Input Markets|Private sector<br>production and<br>distribution of<br>commercial inputs|*Privatize urea production<br>and distribution<br>Divest National Fertilizer<br>Corporation, including<br>plants operated by its<br>subsidiaries|1994/95, short-run<br>1994/95, short- to<br>medium-run|
|||*Expedite privatization of<br>phosphate imports|1994/95,<br>short-run|
|||*Level playing-field between<br>public and private sector in<br>input markets|1995/96, short-run|
|||'Commercialize seed<br>corporations|1994/95, short-run|
|||Privatize seed corporations|1995/96, medium-run|
|||Strengthen seed<br>certification process|1994/95, medium-run|
||Market-determined<br>input prices<br>Reform the irrigation<br>system|Remove subsidy on<br>electricity<br>'Decentralize irrigation<br>system based on water user<br>associations,<br>public<br>utilities, and marketin<br>water rights|1995/96, medium-run<br>1995/96, medium-run|
|||*Raise irrigation charges|1994/95, short-run|
||Remove distortions in<br>land markets and<br>reform credit<br>provision|Initiate a study of land<br>reform<br>'Improvement and<br>computerization of land<br>records|1994/95, short-run<br>1995/96, medium-run|
|||Clamp down on delinquent<br>loans<br>and end cheap loans<br>for machinery<br>purchase<br>End directed<br>credit|1994/95, short-run<br>1994/95,<br>short-run|

Note: For implementation periods, short-run implies 1 to 2 years, medium-run, 3 to 5 years, and long-run, 5 to 7 years.

* Element of high priority

iv
# **Tables**

**Table 1: Trends in the Stock of Durables Owned by Bulgarian Households** <u>(Average number of the durable good per household)</u>

||**1995**||||**1997**|_% change_<br>_(to 1995)_|**2001**|_% change_<br>_(to 1995)_|**2003**|_% change_<br>_(to 1995)_|
|---|---|---|---|---|---|---|---|---|---|---|
|Asset(durbalegood)|mean|std. err.|95% conf|. interval|Mean||Mean||Mean||
|Gas stove|0.108|0.006|0.095|0.121|0.125|_16.2_|0.125|_10.8_|n/a||
|Electric stove|0.940|0.010|0.920|0.959|0.916|_-2.6_|0.882|_-9.1_|n/a||
|Gas/electr. stove|1.047|0.012|1.023|1.072|1.041||1.007|_-7.0_|0.868|_-17.1_|
|Refrigerator|0.949|0.007|0.934|0.964|0.946||0.899|_-7.8_|0.928|_-2.2_|
|Freezer|0.137|0.007|0.123|0.151|0.219|_60.4_|0.264|_85.2_|0.303|_121.7_|
|Auto washing machine|0.370|0.010|0.350|0.389|0.402|_8.8_|0.454|_17.2_|0.523|_41.6_|
|Manual washing machine|0.491|0.011|0.470|0.513|0.458|_-6.9_|0.307|_-38.7_|n/a||
|Dryer|0.004|0.001|0.002|0.007|0.007||0.003||n/a||
|Dish washing machine|0.004|0.001|0.002|0.007|0.006||0.008|_96.5_|0.015|_274.2_|
|Manual sewing machine|0.360|0.010|0.341|0.380|0.372||0.295|_-22.0_|0.325|_-9.9_|
|Electric sewing machine|0.123|0.007|0.110|0.136|0.124||0.111|_-13.8_|n/a||
|Color TV|0.750|0.012|0.727|0.772|0.786|_4.8_|0.894|_16.0_|1.030|_37.5_|
|VCR|0.263|0.009|0.246|0.281|0.277||0.237|_-13.1_|0.251||
|Satellite antenna|0.026|0.003|0.019|0.032|0.067|_161.3_|0.049|_85.7_|0.068|_167.3_|
|Stereo system|0.105|0.006|0.093|0.117|0.092||0.114||0.128|_21.7_|
|Radio|0.664|0.011|0.642|0.686|0.638|_-4.0_|0.508|_-25.9_|n/a||
|Personal computer|0.015|0.003|0.010|0.020|0.012||0.041|_156.1_|0.103|_569.7_|
|Car|0.346|0.010|0.326|0.366|0.367||0.356||0.421|_21.5_|
|Motorcycle|0.030|0.003|0.023|0.036|0.035||0.016|_-48.7_|0.025||

Source: Author’s calculations.

**Table 2: Regression Results from the Fixed Effects Model**

|Dependentvariable|Ln(N of<br>freezers per<br>100 HHs)|Ln(N of<br>AWMs per<br>100 HHs)|Ln(N of<br>CTVs per<br>100 HHs)|Ln(N of cars<br>per 100<br>HHs)|
|---|---|---|---|---|
|Explanatoryvariables||Coefficient|(std.err.)||
||1|2|3|4|
|Ln(real GDP index)|1.112 **|0.826 ***|1.023 ***|0.405 *|
||_0.547_|_0.278_|_0.166_|_0.244_|
|Ln(avg.consumption of the|||||
|group/avg. consumption)|-2.982 **|-1.048 *|-0.959 ***|-1.095 **|
||_1.211_|_0.614_|_0.367_|_0.540_|
|Constant|11.352 **|4.726 *|4.193 **|6.628 ***|
||_5.674_|_2.878_|_1.718_|_2.530_|
|N of observations|80|80|80|80|
|N of groups|20|20|20|20|
|F(2,58)|4.60|5.39|20.93|3.10|
|Prob > F|0.014|0.007|0.000|0.053|
|F (fixed effects are jointly zero)|1.34|2.87|1.80|2.77|
|Prob >F|0.194|0.001|0.045|0.002|

Source: Author’s estimates.

- 29 -
is the case in other Sub-Saharan countries, the rate of fostering of children is high in Tanzania; while fostering is associated with orphanhood, many non-orphaned children are not residing with one or both surviving parents. A quarter of the children in the sample were not residing with their father or not residing with their mother at baseline. Fostering prior to parental death could potentially reflect a strategic decision on the part of families. However, taking into consideration that an average length of illness before adult deaths is reported at about one year and the large span of the panel survey, we suspect that baseline fostering does not reflect _ex ante_ adjustments in living arrangements in expectation of a death. Neverthless, fostering status of the children (all of whom are non-orphans at baseline by sample definition) is included as one of the controls for baseline individual characteristics.

# **Results**

The main findings are presented in Table 2. The first column shows the results for the whole sample; the sample is then split by gender in the second and third columns. Overall, while neither the death of a prime-age male household member nor parental death appear to have any affect on the probability of early marriage for the whole sample, female death reduces this probability by 14 percentage points. However, results presented in columns 2 and 3 reveal a very different pattern when the sample is split by gender. The loss of a father before the age of 15, results in a 13 percentage point increase in the probability of being married among women age 17-23. This effect is both extremely large and statistically significant. At the mean, this translates into a 25 percent increase in marriage probability. In contrast, neither paternal nor maternal orphanhood have any impact on transition into marriage among men. For men, marriage probability responds to the death of non-parental adult females in the household; men have a higher probability of postponing marriage in this instances. The probability of marriage declines by a large and significant amount of 31 percentage points.

Among other covariates, there is some indication that marriage among young women from wealthier households is delayed compared to their less-wealthy counterparts, where wealth is measure by presence of permanent flooring in the dwelling. While there is no significant relationship between wealth and probability of marriage 8
Table 3: **Effect of education on each element of pro-climate outcome indices** .

|||Clima|te Outcomes|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A: pro-climate_|Think the|CC has bad|Worried about|Pro-|Too de-|
|_beliefs_|world’s climate|(not good)|CC|clean|pendent|
||is changing|impact across||energy|on fossil|
|||world||beliefs|fuels|
|Years of education|0.045|0.024|0.052|0.025|0.048|
||(0.016)|(0.014)|(0.010)|(0.009)|(0.015)|
||[0.005]|[0.091]|[0.000]|[0.007]|[0.001]|
|Observations|16926|15986|16473|16842|16517|
|Clusters|36|35|36|36|36|
|Mean|0.542|0.566|0.760|0.717|0.654|
|Percent change|8.2 %|4.2 %|6.8 %|3.4 %|7.4 %|
|_Panel B: pro-climate_|Thought about|Likely to buy|How often do|||
|_behaviors_|CC before|most efcient|things to reduce|||
||today|appliance|energy use|||
|Years of education|0.071|0.060|0.041|||
||(0.010)|(0.014)|(0.016)|||
||[0.000]|[0.000]|[0.012]|||
|Observations|17240|16888|17059|||
|Clusters|36|36|36|||
|Mean|0.688|0.664|0.706|||
|Percent change|10.3 %|9.0 %|5.8 %|||
|_Panel C: pro-climate_|Favor increase|Favor|Favour ban sale|Green||
|_policy preferences &_|taxes on fossil|subsidise|of inefcient|Voting||
|_voting_|fuels to reduce|renewable|household|||
||CC|energy|appliances|||
|Years of education|-0.008|0.024|0.022|0.036||
||(0.017)|(0.011)|(0.010)|(0.013)||
||[0.631]|[0.028]|[0.037]|[0.005]||
|Observations|16417|16734|16611|52493||
|Clusters|36|36|36|39||
|Mean|0.544|0.748|0.589|0.103||
|Percent change|-1.5 %|3.3 %|3.7 %|35.0 %||

_Notes:_ This table shows point estimates for each of the elements of the indices. Panel A shows the elements of the beliefs index, Panel B the behaviors index, and finally Panel C shows both the policy preferences index and green voting. Outcomes are binary, so multiplying the point estimate by 100 yields the percentage point increase in the likelihood of having a pro-climate stance on the given outcome from an additional year of education. Standard errors clustered at the country _×_ law level in parentheses and p-values in brackets. Estimates include country fixed effects and country-specific linear time trends. “CC” means “climate change”.

11
matter of serious concern.

Theoretical research from transport economics (Vickery 1969; Mohring 1972) postulates the existence of two counteracting effects on air pollution from introducing a new mode of public transportation. On the one hand, the introduction of the new mode could increase overall economic activity, which could in turn generate _new_ demand for intracity trips. New demand for travel could also be created if the availability of rapid public transport results in a relocation of residents away from the city-center, for example, if real estate is cheaper in the suburbs leading to longer commutes to work. Such demand that did not exist before the new mode was introduced is referred to as the traffic creation effect. If part of the new demand is met by private means of transport, then ceteris paribus this should add to existing levels of vehicular emissions and increase air pollution. On the other hand, with the introduction of a new mode of public transportation, commuters who had earlier relied on private means may now switch to the new mode.<sup>11</sup> This substitution away from private to public mode of travel is called the traffic diversion effect. Ceteris paribus, the traffic diversion effect should reduce the overall level of vehicular emissions and consequently reduce air pollution. In reality both effects are likely to operate. We hypothesize that the traffic diversion effect is likely to dominate the traffic creation effect in the short run. This is because the processes involved in creating new demand for travel are likely to unfold slowly and over a longer period of time, while the traffic diversion effect can occur almost immediately after the new mode is introduced. Nonetheless, it is

> two-wheelers in Delhi in 2011; 59 percent and 42 percent, respectively, are in-use. If we make a very conservative estimate of vehicles in use and assume that only 42 percent of all registered motor vehicles are in use, we get a figure of 3.1 million for in-use vehicles in Delhi. In 2013, the number of in-use vehicles in New York City was 2 million (Department of Motor Vehicles, New York State website; source url: http://dmv.ny.gov/org/aboutdmv/statistical-summaries, last accessed on March 31, 2015). Thus, Delhi has at least 55 percent more in-use vehicles than New York City.

> 11. According to a report by the Delhi Metro Rail Corporation (DMRC, 2008), the DM has already taken the share of 40,000 vehicles.

6
goods, a household bargaining effect. The net effect – between the income effect, the substitution effect, the flypaper effect, and the household bargaining effect – is unclear theoretically: This paper seeks to characterize it empirically.

A large literature has examined the impact of cash transfers on consumption, with a few studies explicitly contrasting transfer income with earned income. For example, Schady and Rosero (2008) show that food expenditures were much higher for transfer recipients than nonrecipients in the Ecuador program, even when controlling for per capita expenditures (i.e., the income effect of cash transfers). This finding is contrary to Engel’s law, which states that “the proportion of income spent on food declines as income rises” (Houthakker 1957) and which has been empirically identified across many countries. In Nicaragua, Macours, Schady and Vakis (2012) use a similar strategy and find that cash transfer recipients shifted the composition of food expenditures to more expensive foods (i.e., more protein, fruits, and vegetables; fewer staples), even though total food expenditures were not different from other households with similar per capital expenditures. Case & Deaton (1998) demonstrate that pension income in South Africa increased food consumption and may have reduced alcohol and tobacco consumption, depending on the specification. These studies suggest that households may indeed treat transfer income differently from earned income.

# **Methodology**

In this section we describe the criteria used to define the universe of literature relevant to this systematic review, as well as the search strategy employed to find papers conforming to these criteria.

## **Scope of the review**

First, we describe the scope of the review in terms of the types of interventions, studies, and outcome variables of primary interest. We restrict our analysis to conditional cash transfers (CCTs) and unconditional cash transfers (UCTs) implemented in low and middle-income countries (as defined by the World Bank), with no other explicit population exclusion criteria. Since both CCTs and UCTs generally target poor and vulnerable households (often including school-aged children or pregnant women), the entire set of eligible interventions is largely targeted at disadvantaged populations.

The review focuses on studies from 1997 to early 2014, which corresponds to the period following the onset of PROGRESA/Oportunidades, allowing for a relatively comparable group of cash transfer interventions, as in Baird et al. (2014). Eligible studies include both experimental and quasi-experimental designs. We limit the review to papers that compare cash transfer 5