rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 7: Effects of participation in the RNFE on quintiles of HHPCE**

|VARIABLES|p10|p20|p25|p50|p75|p80|p90|
|---|---|---|---|---|---|---|---|
|Wage employment participation|2.611+|2.634+|4.639***|6.198***|6.386**|6.214*|2.709|
||[0.114]|[0.138]|[0.006]|[0.005]|[0.016]|[0.086]|[0.714]|
|Self-employment participation|2.347**|4.209***|4.303***|6.503***|10.804***|13.041***|16.227**|
||[0.018]|[0.001]|[0.000]|[0.000]|[0.000]|[0.000]|[0.016]|
|Age of the household head|-0.042|0.005|-0.013|-0.079|-0.161|-0.192+|-0.392*|
||[0.524]|[0.939]|[0.829]|[0.341]|[0.198]|[0.144]|[0.057]|
|Male-headed household (0/1)|1.870|2.858*|1.598|2.211|6.659*|6.856*|2.388|
||[0.268]|[0.063]|[0.335]|[0.399]|[0.060]|[0.091]|[0.740]|
|Highest level of formal education acquired by household head||||||||
|PSLC|-0.349|-1.262|-0.916|0.929|9.709*|10.191*|6.786|
||[0.828]|[0.423]|[0.658]|[0.717]|[0.055]|[0.070]|[0.466]|
|JCE|5.945**|8.658***|7.995**|9.316**|21.622***|18.744**|20.544|
||[0.011]|[0.004]|[0.011]|[0.033]|[0.006]|[0.034]|[0.210]|
|MSCE|2.274|4.883|5.615|16.777**|36.827***|43.716**|28.356|
||[0.479]|[0.444]|[0.348]|[0.014]|[0.009]|[0.016]|[0.424]|
|Maximum level of formal education acquired in the household||||||||
|PSLC|0.626|0.779|0.259|-0.171|-4.342|-6.652|-1.599|
||[0.734]|[0.693]|[0.915]|[0.943]|[0.316]|[0.223]|[0.829]|
|JCE|0.325|1.269|0.372|1.340|-5.894|-4.363|-1.536|
||[0.887]|[0.539]|[0.885]|[0.703]|[0.265]|[0.521]|[0.881]|
|MSCE|2.153|1.823|-0.258|-0.767|-15.890+|-10.826|-0.373|
||[0.397]|[0.678]|[0.957]|[0.896]|[0.135]|[0.405]|[0.983]|
|Number of infant (<5yo) in HH|-2.766***|-4.092***|-4.592***|-6.785***|-9.154***|-10.031***|-9.751***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Number of children (5-14yo) in the household|-3.433***|-4.063***|-4.556***|-5.737***|-6.848***|-7.026***|-8.640***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Number of prime adults (15-60yo) in HH|-2.939***|-3.856***|-4.221***|-4.003***|-4.337***|-5.659***|-8.076***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Number of elderly (60yo+) in HH|<br>-3.744**|<br>-7.800***|<br>-6.678***|<br>-4.867**|<br>-7.138*|<br>-6.759|<br>-1.831|
||[0.022]|[0.000]|[0.001]|[0.024]|[0.086]|[0.199]|[0.780]|
|Household access to loan (0/1)|<br>2.727**|<br>1.593|<br>1.089|<br>0.723|<br>0.871|<br>0.757|<br>2.754|
||[0.048]|[0.318]|[0.452]|[0.728]|[0.632]|[0.784]|[0.549]|
|Normalized TLU index|32.917|12.544|48.236|89.698*|99.853+|19.958|13.738|

52
are reduced, and the final outcome depends on the relative change in the (utility) value of the two. If the transfer exceeds the monetary cost of attending school, the budget constraint shifts upwards (for instance, to the dashed line IJF). Child labor should unambiguously decrease if the household does not invest the transfer in productive assets. However, in both cases, the effect on child labor also depends on whether the household decides to invest (part) of the transfer in productive assets, which may increase the returns to child labor.

# **<<B>> 1.2. Identifying Relevant Studies**

To identify potentially relevant studies that evaluate the impact of cash transfer interventions on children’s work outcomes, we began with a literature search covering Google Scholar’s electronic bibliographical database, the World Bank Development Impact Evaluation Initiative (DIME) database, the Poverty Action Lab, the Social Science Research Network (SSRN), Network of Networks for Impact Evaluation (NONIE), and the International Initiative for Impact evaluation (3IE).<sup>11</sup> To determine which papers to include in our discussion, we split the identified papers into two subgroups: peer-reviewed papers and non-reviewed papers. We included all of the peer-reviewed papers in our discussion. We included non-reviewed studies if they applied a plausible strategy to address endogenous program placement and self-selection into the program. We did not limit our overview to randomized controlled trials (RCTs) and considered other methodologies, such as regression discontinuity designs, natural experiments, and propensity score matching studies.<sup>12</sup> Table 1 provides an overview of the studies included in our primary analysis: seven studies of unconditional cash transfer schemes and 23 studies of conditional cash transfer schemes. The majority of studies, 23 in total, focus on cash transfer programs implemented in Latin America and the Caribbean (column 1). Five studies focus on Mexico’s flagship conditional cash transfer scheme, called Oportunidades.<sup>13</sup> Over half of the studies appeared in a peer-reviewed journal (column 4). Fourteen studies are based on an RCT (column 5). The age range covered differs 6
level data, and above all, aggregate data are simply not adequate to address the important policy issues.

To illustrate this, our paper examines whether infrastructure investment has, indeed, contributed to East Asia’s economic growth using both a growth accounting framework and cross-country regressions. Our results are then contrasted with the results of Seethepalli, Bramati, and Veredas (2007), who use a production function specification to examine the impact of infrastructure on East Asia’s growth. With all three methodologies focused on the same region and the same time-frame, any significant findings that recur across methodologies would shed light on whether infrastructure investment has indeed been a cause of economic growth in East Asia.

Two main conclusions emerge. First of all, for most of the variables used both the growth accounting exercise and cross-country regressions fail to find a significant link between infrastructure, productivity and growth. When they do, they produce rather contradictory conclusions, as growth accounting indicates no contribution of infrastructure to productivity in the richer countries (South Korea and Singapore), and some contribution in the relatively poorest countries (of telecommunications in Indonesia and Philippines, and of roads in Thailand), while cross-country growth regressions tend to indicate that the effects are generally negative for low-income countries and positive only for the high-income ones.

Second, these conclusions contrast strongly with those of Seethepalli, Bramati and Veredas (2007), who find positive and significant effects for all infrastructure variables in the context of a production function study. This leads us to conclude that results from studies using macro-level data should be considered with extreme caution. Given that macroeconomic data give only limited support to the notion that infrastructure investment has driven growth in East Asia, we conclude by speculating on other aspects, in particular the idea that infrastructure investment may have had the primary function of relieving constraints and bottlenecks as they arose.

The structure of the paper is as follows. Section 2 presents the growth accounting exercise. Section 3 turns to cross-country growth regressions. Finally, Section 4 discusses the results, compares them to other related studies and concludes.

7
starting from a random point.<sup>16</sup> Because we needed a larger sample for the interventions, in October 2008 we selected a set of GNs neighboring those in the original panel survey. We used a similar screening survey to identify male self-employed workers with fewer than 2 paid employees, boosting the sample by 816 individuals. Because of the way they are constructed, both subsamples are representative of the areas from which they are taken. However, there are some differences in the manner of constructing them, so we add a control for the enterprises in the booster sample in each of the regressions.<sup>17</sup>

# **Appendix 3: Details of Supplementary Interventions**

_Savings Intervention:_ In November 2008, we notified those assigned to the savings treatment that they had been selected to participate in a program designed to encourage them to build savings balances. The participants were not told about the other two interventions in November even if they had been assigned to one of the other two treatments. As a part of the savings incentive program, we offered to make the initial deposit in a savings account at the National Savings Bank (NSB) and then to match deposits made into that account up to a certain limit each month and at a pre-announced match rate. The account would remain ‘locked’ until August 1, 2009. The initial match rate was set at 50 percent for deposits of up to 1,000 Sri Lankan Rupees (LKR)<sup>18</sup> made by the end of December. The match rate was kept at 50 percent through July, but the maximum amount we would match was increased to 2,000 LKR in January and to 4,000 LKR in May 2009. In July, we raised the match rate to 100% and the maximum to 5,000 LKR. Finally, just before the accounts were unlocked, we added 5,000 LKR to every account, regardless of previous deposit patterns. The participants received regular passbooks for the accounts, and deposits could be made at any NSB branch. But the accounts were all opened through a single branch in Gampola so that the branch manager there was able to ensure that money was withdrawn before August 1 only if the participant faced an emergency situation. After the accounts were unlocked on August 1, the participants were free to move the accounts to any NSB branch, or to withdraw the money. At that

> 16 The G.N. Division is the smallest of the four administrative levels in Sri Lanka: Provinces (9), Districts (25), Divisional Secretariat (DS) Divisions (324), and Grama Niladhari (GN) Divisions (14,008).

> 17 We find no differences in the operating characteristics of the enterprises (sales, profits, etc.) but the owners in the original sample have about a half year less schooling and have been in business for about three-quarters of a year longer.

> 18 1,000 LKR was approximately US$8.75 in mid-2009, $8.85 in mid-2010, $9.14 in mid-2011, and $7.49 in mid2012.

46
# **<mark>Table 6: Land Acquisition</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

|||Pooled|Mean|Impact|byRound|(OLS)|Pooled|
|---|---|---|---|---|---|---|---|
|||N|R3|R1|R2|R3|Impact|
|||(1)|(2)|(3)|(4)|(5)|(6)|
|**_Panel A: Plots Contr_**|**_olled by Women_**|||||||
||Purchase|1,738|.212|.103***|.025|.107***|.075***|
|||||(.039)|(.037)|(.039)|(.028)|
|Mode through|Inheritance|1,738|.246|-.011|.018|.013|.006|
|<br>which plot was||||(.045)|(.041)|(.042)|(.030)|
|<br>obtained,|Renting|1,738|.123|-.028|-.016|-.031|-.024|
|conditional on||||(.028)|(.028)|(.032)|(.019)|
|female plot<br>|Sharecropping|1,738|.084|-.027|-.023|-.061**|-.036*|
|existing [yes=1]||||(.025)|(.025)|(.025)|(.019)|
||Free allocation|1,738|.260|-.044|-.005|-.026|-.023|
|||||(.036)|(.037)|(.044)|(.025)|
|**_Panel B: Plots Contr_**|**_olled by Men_**|||||||
||Purchase|2,523|.339|.097**|.095**|.023|.071**|
|||||(.037)|(.039)|(.060)|(.030)|
|Mode through|Inheritance|2,523|.112|.031|-.040|.100***|.030|
|<br>which plot was||||(.029)|(.042)|(.030)|(.027)|
|<br>obtained,|Renting|2,523|.133|-.035|-.048|-.023|-.036*|
|conditional on||||(.025)|(.027)|(.025)|(.020)|
|male plot existing<br>|Sharecropping|2,523|.085|-.032|-.029|-.051**|-.037**|
|[yes=1]||||(.025)|(.027)|(.023)|(.014)|
||Free allocation|2,523|.306|-.060|.020|-.038|-.025|
|||||(.041)|(.031)|(.059)|(.025)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on the existence of a female (male) plot. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three different dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

28
has been based on standard Cobb-Douglas two-factor production functions which assume that output growth is exclusively explained by capital and labor as follows: Under this standard representation, is the aggregate output, is total factor productivity (TFP), is capital input, is labor input. The coefficient is the elasticity of output with respect to the capital input and is the labor’s share of income. Increasingly, attempts are made to infer from growth accounting frameworks on the basis of expanded versions of standard Cobb-Douglas production functions. The expansion of these production functions is carried out either to account for other relevant factor inputs or to decompose existing ones to take into account the quality and sources of labor, demographic factors, or other drivers of capital accumulation [Collins and Bosworth (1996), Madsen and Ang (2009)].

Although this paper is in line with recent attempts to account for other sources of growth from growth accounting, the underpinning framework emphasizes gender production, reflecting the fact that the paper is primarily interested in quantifying the relative contribution of women to economic growth. At the same time, the productivity of labor is affected by labor quality and other productivity enhancing factors such as infrastructure, level of education and qualitative skills. In order to account for this complexity of the growth process, the present section builds on the framework proposed by Mankiw et al. (1992). More specifically, the operating framework is specified as follows: where . The main difference with most existing frameworks [Mankiw et al. (1992), Collins and Bosworth (1996)] is the inclusion of gender differentiated land ownership and quality adjusted labor differentiated on the basis of gender. In this line, the additional factor inputs are quality-adjusted male and female labor, respectively. Similarly the variables are shares of productive land owned by men and women, respectively at time _t_ . These additional factors of production are introduced to account for possible changes in land ownership and labor quality over time.

The inclusion of land in the production function is consistent with Africa’s production parameters in recent years—land and labor surplus [Fofack (2012)].<sup>17</sup> Aggregate output in these economies continues to be overwhelmingly dominated by agricultural production, especially in rural areas; as such it is contingent upon household’s ownership of or access to land. Furthermore, despite the fact that the distribution of land ownership continues to be heavily skewed in favor of men – reflecting the persistent gender bias— steps have been taken by a number of governments in recent years to equalize the distribution of bequests across gender [World Bank (2012)].

> 17 While land surplus is the rule in Africa during pre-colonial and colonial times, rising life expectancy and rapid population growth have resulted in significant increase in labor force participation in recent years.

9
of impacts with results from a social experiment on the same program. They found large discrepancies in some cases, which they interpreted as being due to biases in the NX estimates.

Using a different approach to testing NX methods, van de Walle (2002) gives an example for rural road evaluation in which a naïve comparison of the incomes of villages that have a rural road with those that do not indicates large income gains when in fact there are none. Van de Walle used simulation methods in which the data were constructed from a model in which the true benefits were known with certainty and the roads were placed in part as a function of the average incomes of villages. Only a seemingly small weight on village income in determining road placement was enough to severely bias the mean impact estimate.

Of course, one cannot reject NX methods in other applications on the basis of such studies; arguably the lesson is that better data and methods are needed, informed by past knowledge of how such programs work. In the presence of severe data problems it cannot be too surprising that observational studies perform poorly in correcting for selection bias. For example, in a persuasive critique of the Lalonde study, Heckman and Smith (1995) point out that (amongst other things) the data used contained too little information relevant to eligibility for the program studied, that the methods used had limited power for addressing selection bias and did not include adequate specification tests.<sup>10</sup> Heckman and Hotz (1989) argue that suitable specification tests can reveal the problematic NX methods in the Lalonde study, and that the methods that survive their tests give results quite close to those of the social experiment.

The 12 studies used by Glazerman et al. (2003) provided them with over 1,100 observations of paired estimates of impacts — one experimental and one NX. The authors then regressed the estimated biases on regressors describing the NX methods. They found that NX methods performed better (meaning that they came closer to the experimental result) when comparison groups were chosen carefully on the basis of observable differences (using regression, matching or a combination of the two). However, they also found that standard econometric methods for addressing selection bias due to unobservables using a control function and/or instrumental variable tended to increase the divergence between the two estimates.

These findings warn against presuming that more ambitious and seemingly sophisticated NX methods will perform better in reducing the total bias. The literature also points to the

> 10 Also see the discussion in Heckman et al. (1999).

10
The Rama index is constructed by averaging the normalized values of eight labor-related variables, namely, number of days of annual leave with pay, number of days of maternity leave, social security contribution as a percentage of wages, government employment as a percentage of labor force, minimum wage as a percentage of average wage, severance pay, number of ILO conventions ratified, and percentage of labor force in unions. Rama calculated this index for 31 countries in Latin America and the Caribbean for the period 1980-92, when labor regulations remained basically unchanged. The third causal variable considered in the model is the strength and efficiency of government institutions proxied by an average of three subjective indicators reported by International Country Risk Guide (ICRG), namely, quality of the bureaucracy, corruption in government, and rule of law."<sup>4</sup> Restrictions on labor markets were not explicitly included in the theoretical model; however, they can be understood as a form of tax burden in the model, particularly the portion of tax burden that is not transformed into productive public services. As discussed in section II, restrictions on labor markets are important factors in the formation of informal economies, particularly in Latin America where these regulations are some of the most stringent in the developing world. The variable strength and efficiency of government institutions is directly related to two variables in the theoretical model, namely, the strength of enforcement (X) and the quality of government institutions (q).

We expect that, in the sense of multiple regression analysis, the variables proxying for tax burden and labor market restrictions be positively related to the size of the informal sector, and that the variable proxying for the strength and efficiency of government institutions be negatively related to it.

-21-
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where ej is the per capita e¤ort input of a worker in sector j, and ejLj may be interpreted as e¤ective labor input. We assume workers in the same sector are homogenous. The production function Fj(�; �) is homogenous of degree one in Kj and ejLj. Per capita output and per capita capital in sector j are respectively yj = L<sup>Yj</sup> j<sup>andkj=K</sup> Lj<sup><u>j</u>.</sup> Total capital balance constraint implies that �k1 +(1 � �)k2 = k. For per capital output, we have It is obvious that fj(kj; ej) is homogenous of degree one in kj and ej. Consumption goods 2 can be used only for consumption and are not preservable, and capital goods 1 can be used for either consumption or investment.

Though economic agents in both sectors are assumed to have the same preference, they are heterogenous in essence since they are in di¤erent sectors, having di¤erent skills, and labor is not intersectoral transferrable. The utility function of agents in sector j is where xij is the amount of goods i consumed by an agent in sector j, and ej is his e¤ort input. u(�; �) is strictly concave in (x1j; x2j) and satis...es the Inada conditions, and the disutility of e¤ort, C(ej), is strictly convex satisfying the conditions that C<sup>0</sup> (0) = 0 and C<sup>0</sup> (1) = 1.

# 2.2 Optimal Allocation

For later comparison with distorted allocation under catching-up strategy, we ...rst study the optimal allocation in the benchmark case of no distortion. The social welfare function is standard and de...ned as 8