rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7:** **<u>Summary of estimated poverty impacts</u>**

||Round<br>|1<br>% below p<br>|Roun<br>overty line<br>|d 2<br>|
|---|---|---|---|---|
||Participants<br>only|Population|Participants<br>only|Population|
|**Pre-intervention**|||||
|Estimated by deducting gross earnings<br>gains from BREGS|63.0|51.7|56.4|43.2|
|Estimated by deducting net earnings<br>gains from BREGS|62.2|51.4|52.6|42.3|
|**Post-intervention**|||||
|Observed withBREGS|56.8|50.0|50.2|41.8|
|Counterfactuals for same spending:|||||
|Basic-income scheme (BIS)|60.8|49.5|50.9|39.1|
|BIS with 10%leakage|61.1|49.7|51.0|39.6|
|Transfers based on ration cards|60.5|49.8|50.1|40.0|
|Ration card transfers with 10% leakage|60.6|49.9|50.7|40.2|

_Note_ : The poverty line is set at the median for R1 as observed (post-intervention).
particularly focussed form of the transfer of the benefits of education by (particularly, non-market) -ssociat0.on.4/ He argues that these benefits may arise in at least three ways:

- (1) by providing a close substitute for a person's own formal education by extending infDrmation and advice,

- (2) by helping the person acquire specific skills, and

- (3) by helping the person acquire general skills related to information acquisition and assimilation and coping with change.5/

Marriage of course offers both greater opportunities for sharing in the pecuniary benefits of association and, therefore, a larger incentive for doing so, than are available in other forms of non-market association.6/ But are there particular labor market contexts in which we might fail to observe such positive cross-productivity effects of wife's education on husband's earnings? That this may be the case is suggested by the results of a recent application of this approach to small family businessses. Using data for Hong Kong, Wong argues that cross-productivity effects are stronger within entrepreneural families (when both spouses work in the family business) than for wage-employed families. Would cross productivity effects also differ across broad occupational categories? The purpose of this paper is to probe some of these issues using israeli data.

2
years of age, but with 24 months of program exposure the illness rate among the treatment group was about 40 percent lower than the rate among the control group, a difference that is significant at the 1 percent level.

The interaction of age effects and the duration of exposure has been examined also by Pitt, Gibbons and Rosenzweig (1993) and Duflo (2001) in Indonesia and by Chin (2005) in India in their evaluations of schooling programs. These studies use information on the region and year of birth of children, combined with administrative data on the year and placement of programs, to measure duration of program exposure. Duflo (2001), for example, examines the impact of a massive school construction program on the subsequent schooling attainment and on the wages of the birth cohorts affected by the program in Indonesia. From 1973 to 1978, more than 61,000 primary schools were built throughout the country, and the enrollment rate among children aged 7–12 rose from 69 percent to 83 percent. By linking district-level data on the number of new schools by year and matching these data with intercensal survey data on men born between 1950 and 1972, Duflo defines the duration of exposure of an individual to the program. The impact estimates indicate that each new school per 1,000 children increased years of education by 0.12– 0.19 percent among the first cohort _fully_ exposed to the program.

Chin (2005) uses a similar approach in estimating the impact of India’s Operation Blackboard. Taking grades 1–5 as the primary school grades, ages 6–10 as the corresponding primary school ages, and 1988 as the first year that schools would have received program resources, Chin supposes that only students born in 1978 or later would have been of primary school age for at least one year in the program regime and therefore potentially exposed to the program for most of their schooling. The evaluation compares two birth cohorts: a younger cohort born between 1978 and 1983 and was therefore potentially exposed to the program and an 26
aversion is obscuring the economic stake that all countries have in open global services markets. While developing countries like India have seen rapid export growth, by far the largest exporters of these services are the US and the countries of the European Union. The EU and US account for 65 percent of world services exports; China and India for 6 percent.<sup>17</sup> The US and EU have both consistently run a huge annual surplus on services trade, currently nearly $160 billion for the US and $220 billion for the EU countries. While US services imports from India and China have indeed grown to around $22 billion in 2008, US exports to these countries have expanded even faster, to over $26 billion. Even during the crisis US exports of key services are growing faster than its imports.

The US and EU have been powerful advocates of open services markets all over the world. Many developing countries have begun to reform their markets for communications, transport, financial, distribution and other business services. A retreat from openness in services in industrial countries could undermine reform efforts in developing countries, and even trigger a costly spiral of retaliatory protection.

> 17 Data in this paragraph is from the World Trade Organization.

20
3.1) that is known to be a male dominted industry (see Cumming et al. 2015). For instance, using the ILO data for the 31 mostly developing countries for which information is available over 20102021, the manufacture of basic metals industry accounts for 0.07 percent (Uruguay, 2020) to 1.3 percent (Bangladesh, 2010) of all formal sector female employment in the country. For the median country, the industry employs 0.17 percent of all formal sector female workers. For robustness we also look at the manufacture of food and beverages industry and the retail sector. For the median country in the ILO data mentioned above, 3.5 percent of all formal sector female workers are employed in the manufacture of food and beverages industry and 11.3 percent in the retail industry.<sup>11</sup> As discussed above, presence of reverse causality implies a larger (more positive) relationship between female workers and maternity as we move from the industry with the smallest to the largest share of female workers (manufacture of basic metals industry to food and beverages industry to the retail sector).

Regression results for the three industries are provided in table 7. For brevity, only some of the speicfications are shown. Columns 1-2 show the results for for the manufacture of metals, columns 3-4 contain results for food and beverages industry, and columns 5-6 provide results for the retail sector. We find a large, positive, and statistically significant relationship between maternity leave and female workers in all the three industries. Further, there is no evidence that this relatonship becomes stronger (more psotiive) as we move from metals manufacutring to retail sector. In fact, quite the oppsitie holds. For the final baseline specification, the estimated coefficient value of maternity leave equals 3.5 in metals manufacutirng, 3.7 in food and beverages manufacutirng, and a much lower 2.3 in the retail sector. Thus, the endogeneity test is passed.

> 11 The ES data can be used to estimate the proportion of all formal sector female workers that are employed in an industry. For robustness, we used the ES to identify industries that account for very small shares of all formal female workers in the country’s private sector covered by the ES. Regression results for these industries are like the ones reported below for the manufacture of basic metals. These results are available on request from the authors.

35
<!-- Start of picture text -->
[m]<br>Baruria _—-Bheiseb Bazar<br>600000 ~<br><!-- End of picture text --> <!-- Start of picture text --> 500000 re ya a<br>400000 __-f\, }SI sO<br>, = — Bathymetry [m]<br>300000 }) HM Above 20<br><!-- End of picture text --> <!-- Start of picture text --> 200000 ; f a ieme 10-(o- 20610<br>E . {|} 6- 0<br>100000; =}& D>( =MM 10-15--20- -10-155<br><!-- End of picture text -->

<!-- Start of picture text --> of y Bay of Bengal ’ Mm -50- -25<br>-100000 <4" j —MM -1000--500--200- -200-500-50<br>: HB -2000 - -1000<br><!-- End of picture text --> <!-- Start of picture text --> “ p HE -3000 - -2500<br>HE Below -3000<br>-300000 | Undefined Valu¢<br>-200000 0 200000 400000 600000 800000<br>[m]<br><!-- End of picture text -->
demonstrates the absence of a link between increased number of judges and efficiency of courts in Peru.

Smith et al (2021) analyze whether performance measurement improves efficiency, by reviewing performance measurement systems in Australian state-level jurisdictions. The overall conclusion is that performance measurement, at least in the judiciary, does not lead to better efficiency. In contrast, de Figueiredo, Lahav and Siegelman (2020) use the complete record of U.S. federal civil cases between 1980 and 2017 to document that judges close substantially more cases and decide more motions in the week immediately before they report their outputs as part of semi-annual evaluation cycles. Chemin et al (2023) find even stronger supportive evidence of performance measurement improving court effectiveness in the case of Kenya.

Efficiency of the court system is related to the workings of existing ADR mechanisms, as these have the potential to reduce the caseload of courts. Court level data of conciliation activities in French civil magistrate courts between 2010 and 2017 show that efficiency is positively related to factors that foster the demand for ADR settlement. More efficient courts are more prone to involving conciliation in the ADR (Belarouci 2021). A study using data on 8,038 trials provides mixed support for ADR programs being able to reduce time or cost of proceedings but indicates that participation in an ADR program correlates with an increased likelihood of out-of-court settlement (Heise 2010).

In contrast, Genn et al (2007) find that out-of-court mediation is associated with similar delays and additional costs in London, the United Kingdom. They study the impact of ADR programs, using a quasi-compulsory mediation program which ran in the Central London County Court between April 2004 and March 2005. During the ARM pilot, 1,232 defended civil cases were randomly referred to mediation, of which 82% were personal injury cases. By the end of the evaluation (10 months after termination of the pilot), only 14% of the cases originally referred to mediation had been settled. Also, the study found that the additional cost of cases that did not settle and went back to litigation was between 1,000 and 2,000 pounds. The presence of mediation did not significantly affect the overall likelihood of earlier closure, that is, mediation showed little effect on case duration.

20
opposite effects are magnified, consistently with the idea that trade openness results in local benefits for places that export but negative impacts for places that are exposed to competition with imported goods.

Table 10: The effect of road infrastructure on employment

||Total emp|loyment|Manufa|cturing|Serv|ices|Com|merce|
|---|---|---|---|---|---|---|---|---|
|Variables|ols|iv|ols|iv|ols|iv|ols|iv|
|Road Effciency|5.501***|6.633***|3.570***|4.301***|5.007***|6.044***|5.833***|7.061***|
||(0.0941)|(0.0911)|(0.144)|(0.141)|(0.0793)|(0.0719)|(0.110)|(0.104)|
|Min travel cost to border|-3.271***|-0.404|-3.808***|-1.887***|-3.354***|-0.730**|-4.242***|-1.222***|
||(0.431)|(0.404)|(0.669)|(0.634)|(0.363)|(0.318)|(0.470)|(0.437)|
|Min travel cost to port|1.522***<br>(0.467)|0.493<br>(0.406)|2.242***<br>(0.737)|1.509**<br>(0.660)|1.683***<br>(0.383)|0.743**<br>(0.333)|1.567***<br>(0.527)|0.573<br>(0.468)|
|Above median education|0.224***<br>(0.0460)|0.238***<br>(0.0416)|0.339***<br>(0.0681)|0.348***<br>(0.0630)|0.159***<br>(0.0363)|0.171***<br>(0.0325)|0.130***<br>(0.0475)|0.145***<br>(0.0438)|
|Above median population|0.427***<br>(0.0448)|0.404***<br>(0.0405)|0.314***<br>(0.0782)|0.304***<br>(0.0666)|0.415***<br>(0.0419)|0.394***<br>(0.0363)|0.420***<br>(0.0575)|0.396***<br>(0.0510)|
|Constant|-61.14***||-38.67***||-55.82***||-65.98***||
||(1.341)||(1.978)||(1.139)||(1.609)||
|Observations|11200|11200|10619|10614|11184|11184|10583|10575|
|Adj. R-squared|0.549|0.537|0.210|0.206|0.607|0.594|0.497|0.486|

_Note_ : * denotes significance at the 10% level, ** denotes significant at the 5% level, and *** denotes significance at the 1% level. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 150 and 300 km radii.

Finally, looking at the effects of road infrastructure on specialization, Table 11 shows that better local accessibility has a positive and statistically significant causal effect on specialization. A 1 percent increase in efficient road length leads to a 5.9 percent increase in the locality’s output specialization, and a 4.5 percent increase in the locality’s employment specialization. Interestingly, reducing travel costs to u.s. borders or ports has not significant impact on specialization, neither in terms of output nor in terms of employment.

26
different groups of households. Since the data used is a panel, the growth in real incomes reflects the growth for the average income of the same households from 1993 to 1998. The different categories presented refer to the households that were in that specific group in 1993. Table 7 illustrates that the income of an average Vietnamese household has increased by 59% between 1993 and 1998. A look at the sub-categories show that rural households’ income increased (63%) more than their urban counterparts (49%). Similarly net buyer households experienced a slightly higher increase in their income compared to net seller households. This is in contrast with the standard literature result, which only take into account the direct first round effects. Moreover, contrary to popular view that net-buyer households will lose in response to an increase in prices, when we examine the income changes in net buyer households according to their intensity, we find that large net buyers (for which the amount of net buy is more than 30% of their total expenditure) experience the highest increase (80%). Across the regions, North Central Coast and Central Highlands seem to have gained the most at 94%, and more than double (101%) respectively. However, these regions are the poorest to begin with which could explain the higher than usual growth rates for income.

**Table 7. Changes in Real Income for Different Groups of Households.**

|**Households**|**% Change in real income**|
|---|---|
|_All Vietnam_|59%|
|Rural|63%|
|Urban|49%|
|_Seller-buyer category_||
|Net buyer|60%|
|Net seller|56%|
|_Net buying intensity_||
|Marginal buyer|52%|
|Medium Buyer|64%|
|Large Buyer|80%|
|_Regions_||
|Northern Uplands|51%|
|Red River Delta|42%|
|North Central Coast|94%|
|South Central Coast|79%|
|Central Highlands|101%|
|South East|53%|
|Mekong Delta|58%|

Source: Author’s own calculations using VNLSS 93 and 98.

21
and dependence on the brick and mortar model is a deterrent to the expansion and uptake of digital financial services.

# **3.2. Methodology**

Having observed the current status of financial inclusion as covered by the NFI Survey in the previous section, we now use several Probit models to understand how certain characteristics are related to financial inclusion. This approach is similar to comparable studies by several authors (Cámara, Peña and Tuesta (2014), Fungacova and Weill (2015), Tuesta et.al (2015), Zulfiqar et.al (2016), Zins and Weill (2016)) who too used probit estimations<sup>20</sup> stemming from the basic formula: ∗ Where is a latent variable, which depends on _x_ being a vector of exogenous variables, **_β_** is a vector of parameters, the subscript ii _i_ represents individuals and _u_ is a normally distributed error yy term with mean 0 and variance 1.

Based on this, a formula popular among previous authors to analyze the determinants of financial inclusion is as follows:

**_fin.Inc.i= α+β*incomei+γ*educationi+ δ*agei+ σ*genderi +_** ii uu

where _finInc_ denotes one of three indicators of formal financial inclusion (i.e. having an account at a formal financial institution (FI), saving at a formal FI, and borrowing from a formal FI) and _i_ is the index for individuals. Given that the endogenous variable is a binary response – i.e. whether the individual uses formal financial service – it can only take the values 0 or 1. It is assumed that the decision to use formal financial services depends on a latent variable, which is determined by

> 20 ⋅ In Probit regression, the cumulative standard normal distribution function Φ( ) is used to model the regression function when the dependent variable is binary. https://www.econometrics-with-r.org/11-2-palr.html This model is adopted from “Clamara, N., Pena, X. and Tuesta, D. (2014).” And Tuesta, D. Sorensen,G. Haring,A. and Cámara, N. (2015) and amended as per the requirements of the present study.

13
Disaggregated estimates of intangible capital (such as our industry-level estimates) are essential to such research. Here, we present evidence that our estimates of intangible investment are meaningful, in the sense that they are empirically related to ̳productivity‘ growth – first presenting the correlation with export growth with a larger set of sectors, and then presenting evidence with TFP but with a more restricted set of sectors.

Taking export growth as a proxy for traditional TFP growth, we show that intangible investment is significantly correlated with export growth in Brazilian manufacturing industries between 2000 and 2008. Specifically, our proxy for TFP growth is the industry level growth in the index of revealed comparative advantage (RCA). RCA of an industry i in year t is calculated as the share of industry i in Brazil‘s total exports in year t divided by the share of industry i in total world exports in year t. An increase in RCA of an industry during 2000-08 indicates Brazil‘s rising comparative advantage in that industry, which in turn suggests rising productivity in that industry. Figure 14 below shows that industry-level growth in RCA during 2000-08 is positively related to industry-level intangible investment during this period. This relationship is statistically significant.

# **4.2 Evidence from TFP patterns**

We rely on parametric estimates of traditional TFP from residuals of the regression of production values against labor and tangible capital based on a Cobb Douglas production function. The estimates are at a broad industry level, using 2-digit industrial data for 22 manufacturing sectors over the years 2000-08<sup>25</sup> . We present results for two different estimates of TFP, based on two different assumptions for calculating the values of the initial capital stock.<sup>26</sup> Table 2 reports results from regressing our intangible capital measure against both TFP estimates. Results are shown for models with and without industry fixed effects (which allow for unobserved sources of

> 25 We had to restrict the number of sectors to only 22 manufacturing sectors due to the available data for capital investment.

> 26 The estimates for initial capital stock are based on the equation, _K j_  0  _I j_  0 /  _g j_  _j_  where g is the average growth of investments in the first 5 years of the series and δ is the depreciation rate for the j<sup>th</sup> good (De Mello 2003). For TFP1, we let the sum (gj + δj) vary for each sector, either 10, 15 or 20% depending on which produces a more stable capital/product ratio. For TFP2, we set the sum (gj + δj) to be equal to 10% for all sectors.

32
<!-- Start of picture text --> 25 m1983. 1987<br>20 m1993 1999<br>m 2004<br>15<br>10<br>5<br>0<br>Male Female All Male Female All Male Female All<br>weer Rural Urban All<br>wer 7323/48 [as [38 ma2 | maas2 [a7200 wwess [ a s) | ae76 |<br>vo 7536) 56 | 200 aa) BO tn<br>wo 9737 | 77) «68 | A) 2582) S| 4A) DO<br>Orissa<br>Kerala, — Bihar<br>Himachal Pradesh 6.5— Tsai Pradesh<br>foJ oN on<br>Haryana ~~ / SSf \ \ Assam<br>x ><br>Tamil Nadu [;-——_ ee I\_—} Rajasthan me 2004<br>| m1993<br>Maharashtra | | _ m 1983<br> X Seyif y Uttar Pradesh<br>_. ~ /<br>Jammu & kashY ‘“ \ ‘daY~TMvast Bengal<br>Se a \ ZL<br>Punjab \ ——~ Gujarat<br>Andhra Pradesh Karnataka<br><!-- End of picture text -->
deceptive when scaled up, given that the structure of returns to schooling will alter.<sup>59</sup> To give another example, a small pilot wage subsidy program such as implemented in the _Proempleo_ experiment may be unlikely to have much impact on the market wage rate, but that will change when the program is scaled up. Here again the external validity concern stems from the contextspecificity of trials; outcomes in the context of the trial may differ appreciably (in either direction) once the intervention is scaled up and prices and wages respond.

Contextual factors are clearly crucial to policy and program performance; at the risk of overstating the point, in certain contexts anything will work, and in others everything will fail. A key factor in program success is often adapting properly to the institutional and socio-economic context in which you have to work. That is what good project staff do all the time. They might draw on the body of knowledge from past evaluations, but these can almost never be conclusive and may even be highly deceptive if used mechanically.

The realized impacts on scaling up can also differ from the trial results (whether randomized or not) because the socio-economic composition of program participation varies with scale. Ravallion (2004) discusses how this can happen, and presents results from a series of country case studies, all of which suggest that the incidence of program benefits becomes more pro-poor with scaling up. Trial results may well underestimate how pro-poor a program is likely to be after scaling up because the political economy entails that the initial benefits tend to be captured more by the non-poor (Lanjouw and Ravallion, 1999).

**_What determines impact?_** These external validity concerns point to the need to supplement the evaluation tools described above by other sources of information that can throw light on the processes that influence the measured outcomes.

One approach is to repeat the evaluation in different contexts, as proposed by Duflo and Kremer (2005). An example can be found in the aforementioned study by Galasso and Ravallion in which the impact of Bangladesh’s _Food-for-Education_ program was assessed across each of 100 villages in Bangladesh and the results were correlated with characteristics of those villages. The authors found that the revealed differences in program performance were partly explicable in terms of observable village characteristics, such as the extent of intra-village inequality (with more unequal villages being less effective in reaching their poor through the program).

> 59 Heckman et al., (1998) demonstrate that partial equilibrium analysis can greatly overestimate the impact of a tuition subsidy once relative wages adjust; Lee (2005) finds a much smaller difference between the general and partial equilibrium effects of a tuition subsidy in a slightly different model.

51