rafmacalaba/datause-encoder-data huggingface dataset:
continuing having access to it increase by about 0.5 (column 1) and 0.4 percentage points (column 2) for both women and men in a governorate with average per capita expenditure in communications in 2010 (235 JOD in 2010 prices). In all cases, the F statistic surpasses 10, the rule for rejection of the hypothesis of weak instruments with one endogenous variable.

The assumption of similar pre-treatment trends is crucial for our identification strategy. We provide additional evidence on the validity of distance to 3G broadband towers as an instrument in Figure 4 and Table 3. We use data from the Jordan Population and Family Health Survey (JPFHS) 2002, 2007 and 2009 which provide us with information on employment previous to the deployment of 3G technology in the country. We calculate the average employment rate of women aged 15-49 in each subdistrict and we group them according to whether their distance to the nearest 3G cell tower in 2016 is above or below the average distance across subdistricts.<sup>4</sup> Figure 4 shows that the average women’s employment rate followed the same trend in both groups of subdistricts. The same conclusion is obtained when estimating OLS models at the subdistrict level where the female employment rate is regressed on year dummies, an indicator for whether the distance to the nearest 3G tower in 2016 is below or equal to the average distance across subdistricts, and the interaction between these two variables. Results appear in Table 3 and show that the indicator of distance and the interaction terms are not statistically significant.

# **5 Effect of internet adoption on labor participation**

Our main results are presented in Table 4. They show an increase in women’s labor force participation associated with internet access under OLS and IV estimations, and no statistically significant effects for men. IV results indicate that a 1 percentage point increase in internet adoption or continuation between 2010 and 2016 led to an increase in female labor force participation of around 0.7 (column 5) and 0.8 (column 6) percentage points.

> 4The JPFHS interviews ever-married women age 15 to 49 and has information on employment but not on labor force participation. We were able to construct a balanced panel of 60 subdistricts for the period under analysis.

9
correct this, program administrators launched a large-scale campaign to stress the importance of compliance.

Under BDH, beneficiary households (again the mothers) receive grants of US$15 per month on the condition that children of ages 6-16 years are regularly enrolled, with at least a school attendance rate of at least 80 percent per month. Poor households with children of ages 0-5 years, additionally, have to fulfill the condition that they make scheduled visits to health centers for growth and development checkups and immunizations.<sup>23</sup> According to Schady and Araújo (2006), the monthly US$15 transfers account for only 7 percent of household expenditure. BDH coverage reached approximately one million households (5 million people), covering 40 percent of the Ecuadorian population with an estimated cost of 0.6 percent of GDP in 2005.

The baseline survey, collected between June and August 2003 for the evaluation of the BDH program, was drawn from four of 22 Ecuadorian provinces around the country.<sup>24</sup> Within all four provinces, “paroquias” (parishes) were randomly assigned. For each selected paroquia, a random household sample was selected, generating a total sample size of 1,488 households. Around 90.5 percent of the household sample had at least one child aged 6 to 17 years at the time of the follow-up survey. None of the households in the sample were enrolled in BDH or _Bono Solidario_ prior to the sample selection. Thereafter, some of the sample households were selected for immediate participation in the program while others were not eligible to receive any transfer in the first two years. The follow-up survey took place between January and March 2005 and collected information on households after program implementation, generating a panel sample of 1,306 households (2,875 children between six and seventeen years of age).

According to Schady and Araújo (2008), around 27 percent of households in both treatment and control groups had stated that school attendance was a prerequisite for receiving BDH grants, despite the fact that the program administrator had never enforced any conditions. BDH was found to have a positive impact on school enrollment and a negative impact on child work; the

> 23 The transfers can be collected at any office of the Banred (the largest network of private banks in Ecuador) or from the National Agricultural Bank. 24 The 4 provinces were Carchi, Imbabura, Cotopaxi, and Tungurahua.

17
vi The absenteeism findings have received much attention and have led to calls for increased teacher accountability and incentives to improve attendance. But absenteeism is only one of the reasons for limited instruction. School closings and poor instructional time use inside the class are important ones as well. Wastage in small amounts constitutes significant loss of time in the long run. For this reason, an instructional time loss model was developed from research evidence, and some parameters were estimated.

|Intended class time as allotted by a government (e.g., 200 days, about 1000 teaching hours)|
|---|
|Remaining after school closures (strikes, weather, inservice training, extra<br>holidays,weather)<br>Remaining after teacher absenteeism and tardiness|
|Remaining after student absenteeism|
|Class time devoted to any learning<br>task|
|Learning time relevant<br>to curriculum|

_How much of the theoretically available school time is in fact used to engage students in learning activities?_ The study studied instructional time loss in four countries: Ghana, Morocco, Tunisia, and the Brazilian state of Pernambuco. A methodology was developed in 2003, and data were collected in 2004-2005 through surprise school visits, classroom observations, and surveys. Instructional time losses varied by country. In Tunisia, the country with the most efficient use of time, students were engaged in learning 79% of the available time. In Ghana, however, students were engaged in learning only 39% of the time, and in Pernambuco 63% of the time.<sup>1</sup> Morocco had indicators somewhat lower than those of Tunisia (using 71% of time for learning), but school closures may have been underreported. Translated into number of days effectively available for learning, losses are palpable. For example, only 76.3 days were devoted to learning tasks of the 197 officially available to Ghanaian students, while for Tunisian students 148.1 of the 190 days officially available were devoted to learning tasks. In effect, Tunisian students get twice as much of the intended classroom time as Ghanaian students.

Within each country, time use among schools showed substantial variability. Most of them used the time rather well, but a minority had substantial losses and skewed the averages. Across countries, teacher delays and absences (legitimate or otherwise) resulted in losses of 11.5 to 43 days during the school year. Classes lost were rarely recuperated. The number of days schools were closed for various reasons during the school year was reported as low, from 1.4 day in Morocco to 4.8 days in Pernambuco. However, no details were asked about the timing and reasons for closures or about partial cancellations of classes, so these figures may be underestimates. For example, in Morocco schools may cancel classes for several days so that students can study for final exams.

1. In the summary and abstract percentage of time loss figures have been rounded for brevity.
Figure A.6: Event study: the effect of foreign acquisition on firm-level employment (common treatment) <!-- Start of picture text --> (a) French ownership (b) British’s ownership<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Coefficient Coefficient<br>-.1 -.1<br>-.2 -.2<br>-.3 -.3<br>-.4 -.4<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The plot is created by regressing the log of employment on a full set of event time indicators interacted with the a dummy variable indicating whether a firm was acquired by a Foreign (either French or British) shareholder after 2004 and controlling for firm and year fixed effects. The estimation includes only firms established before 2004. The omitted baseline is the last pre-treatment year, 2003.

Figure A.7: Event study: effect of German acquisition on employment based on doubly robust estimator <!-- Start of picture text -->
-4 -3 -2 -1 0 1 2 3 4<br>Years from aquisition<br>.2<br>.1<br>ATT<br>0<br>-.1<br>-.2<br><!-- End of picture text -->

Notes: Authors’ calculation based on the Orbis database. The plot is obtained by implementing the doubly robust estimator of Sant’Anna and Zhao (2020). The estimation includes only firms established before 2004. The omitted baseline is the last pre-treatment year, 2003. The vertical bars indicates 95% confidence interval.

29
reform we examine here. In particular, Vietnam could expect an increase in real GDP by more than 30 percent<sup>21</sup> in Case 1. Russia, Philippines and Thailand’s GDP and welfare would rise substantially, as well. The benefits to Malaysia and China would be almost one year’s growth. The estimated global benefits here with transparency reform, US$406 billion in Case 1 and US$290 billion in Case 2 in the 2006 prices are larger than those reported in previous work on trade facilitation. For example, Francois et. al. (2003) assessed the welfare gains from trade facilitation to cut 1.5% of importing costs under the WTO new round, estimating the gains at about $US 63 billion for the world<sup>22</sup> . The base year of our estimates, 2006, is nine years after their study, and meanwhile, the real GDP in East Asia and Pacific, for example, grew nearly double. Even after the base year of the estimates is adjusted, our estimates of the gains with transparency reform are significantly larger. This means the initial impacts of our scenario is a great challenge to the total wide-ranged profound institutional reform, including the elimination of corruption, but the expected gains are huge. The estimated effects may receive the values of parameters. Appendix D summarizes the results of the systematic sensitivity analysis.

Second, the benefits materialize in these estimations only in the reforming economies. Virtually no spillover effects of the welfare gains are seen in other economies. This suggests a strong incentive to implement these reform measures to improve transparency. However, the potential gains require large-scaled industrial adjustment. Technical progress from improvements in the quality of labor force, for example, would lead to economic growth without serious industrial adjustment. However, when the technical progress takes place in specific industrial sectors, or across-the-board importing sectors (as in our case), domestic resources would shift to specific favored sectors<sup>23</sup> . This is the source of the welfare and real GDP gains. This finding requires further investigation of the sector base impacts which follows in the next sub-section.

Third, non-APEC economies do not gain in a significant manner with the specifications

> 21 As a general tendency, EV in terms of national income amount to similar magnitude to GDP. 22 The scenario assumes the liberalization of 50% of border measures. Their study is based on the 1997 dollar value.

> 23 In our case, the production resources are expected to shift from importing sectors to exporting and/or non-traded goods sectors.

26
correlations, intergenerational correlations are stronger for women than men in both the years. The higher intergenerational persistence observed for women compared with men in India is consistent with the recent findings on intergenerational economic mobility from other developing countries. For instance, Emran and Shilpi (2011) find occupational immobility (as measured by correlations between parents and children) to be much higher for women in Nepal and Vietnam.

As discussed in the conceptual framework above, the square of intergenerational correlation provides an estimate of the share of total variance in schooling that can be explained by parent‘s education alone. The estimates (5<sup>th</sup> row in Table 3) show that parent‘s education alone can explain between 27 to 29 percent of variations in years of education for men (brothers) and 31 to 39 percent variations for women (sisters). In contrast, for developed countries, parental education explains only 10 to 20 percent of total variations in schooling years (see Bjorklund and Salvanes (2010)).

# **(4.1.2) Role of Caste and Religion**

A potentially important determinant of educational attainment in India is the caste and religious identity of a household. Studies on education in India show that the average level of education is much lower among children from socially disadvantaged scheduled caste (SC) and scheduled tribes (ST) (Jalan and Murgai (2008), Kajima and Lanjouw (2006), Aslam et al. (2011)). In the next specification of our regressions, we include dummies for SC, ST and other backward castes. We also include a dummy for households whose head is a Muslim, as Muslim are among the most economically lagging groups in India (Sachar Committee (2006), World Bank (2011)). The effects of the caste and religion dummies on the estimated sibling correlation is minimal; the estimates in panel B of Table 3 are only slightly smaller compared with those reported in panel A. The inclusion of caste and religion dummies also does not affect the magnitudes of intergenerational correlations in any significant way. The results thus suggest that sibling and intergenerational correlations do not vary across caste groups in any significant way in both of the survey years, 1993 and 2006. This is consistent with the findings in Hantskovska et al. (2011) which reported a convergence of intergenerational persistence in education across castes during last three decades in India. The conclusion that the sibling and intergenerational correlations do not depend in any significant way on caste or religious identity is also supported by the estimates from the sub-samples based on caste and religion. The only exception is the urban women, where lower caste women experienced significantly higher mobility compared to the upper caste women (see section 4.2.3 below).

17
U.S. As part of a Latin American Regional study on SME finance, the World Bank (2007a) developed its own survey. While the initial study focused on Argentina and Chile, similar studies were done for Colombia (Stephanou and Rodriguez 2008) and Serbia (World Bank 2007b). Finally, De la Torre, Martinez Peria, and Schmukler (2008) combine the data from the IFC and World Bank case studies, along with data from SME surveys in Latin America, to show that the conventional wisdom that relationship lending by small and niche banks is at the heart of SME finance is misguided, at least in the countries they consider in their sample.

Though the questionnaire developed as part of this paper has elements of the surveys used by the IFC and World Bank in previous studies, there are a couple of important differences between our paper and the aforementioned studies. First, we cover a lot more countries than both other studies. In particular, we try to compare findings for developed and developing countries. Second, our questionnaire and this study places a lot more emphasis on obtaining and analyzing quantitative data on the extent, type, and pricing of bank financing to SMEs than the other studies. Finally, wherever possible, this study tries to compare SME financing to large firm financing, which the other studies do not do.

Our data shows that banks perceive the SME segment as being very profitable. Banks’ reaction to government programs supporting SME finance is positive and prudential regulations are not considered a hurdle. Instead, banks perceive macroeconomic instability in developing countries and competition in developed countries as the main obstacles impeding SME finance. To serve SMEs, banks have set up dedicated departments and have decentralized the sale of financial products to the branches. However, the loan approval, risk management, and loan recovery functions remain centralized. Relative to large firms, banks are less exposed to small firms, charge them higher interest rates and fees, and, in relative terms, experience more nonperforming loans from lending to them. Compared to other bank types, foreign banks are more likely to engage in arms-length financing - they tend to rely more on scoring, are more likely to make loan decisions based on quantifiable information, and prefer to use real estate as collateral. Despite some differences in SME financing across bank ownership types, the most significant differences are found comparing banks in developed and developing countries. In particular, banks in developing countries are less exposed to SMEs, tend to provide a smaller share of investment loans, and charge higher fees and interest rates to SMEs relative to banks in 3
consistent reports of underspending on the demand side activities. There may be a number of origins of this problem. One is that software, in general, seems to receive less attention during supervisory missions than construction or other hardware. A second is that the ministries of health which are usually the lead agencies in Bank projects have little expertise in or commitment to demand generation activities and efforts to bring other agencies into the process have not always been successful.

Bank projects have made little use of the marketing skills of private sector agencies that have been central to the contraceptive social marketing activities supported by USAID.

# b. Acceptance of Family Planning and Contraceptive Prevalence

Measures of the utilization of services are often used as indicators of project effectiveness, but their limitations should be recognized. First, since Bank projects are directed at middle and long range objectives, their immediate impact may be more on system capacity than on the level of service utilization. Second, there are many factors other than project activities that influence the initial acceptance or continued use of services. Therefore, the simple statistics of acceptance are not sufficient evidence of project effectiveness. Third, the indicators themselves can be misleading. In Indonesia, for example, thanks in part to a special Bank mission report, it is now clear that contraceptive prevalence estimates based on service statistics may have been significantly overstated by BKKBN statistics. In Bangladesh there has been substantial controversy concerning service statistics related to condoms. Thus, service statistics, while part of a system of information on effectiveness are incomplete in themselves and should be supplemented both by detailed analysis and by the use of other measures of performance.

Measures of contraceptive prevalence have proven particularly useful for assessing program effectiveness. Measures of prevalence do not resolve the problems of lags between project inputs and effects or of complex causality, but they do provide an alternative measure of effect that does not depend on service delivery statistics. Moreover, contraceptive prevalence surveys have often been able to supplement information on current use with information about knowledge, accessibility and sources of supply, all of which permit a more complete assessment of program impact. Information from regular and effective prevalence surveys may help amid the kind of problems that arise with service statistics. Bank support for prevalence surveys has varied from country to country. Support for these surveys seems to have come from USAID in most cases. This is an appropriate division of labor, but it is important that Bank supervisory missions make use of this data and emphasize its importance to leaders.

49
39

# P92 - P85 **= S(P92** - **Pi85)ni85** + **S(n92** - **n 85)P 85** + **S(Pi 9 2** - **Pi85)(ni 92** - **ni85)**

Total change = Intra-sectoral + Population Shift + Interaction Effect The interaction effects arise from the possible correlation between sectoral gains and population shifts, with the sign of the effect indicating whether people tended to switch to the sectors where poverty was falling or not. The finding can be summarized as follows: the decline in national poverty as expressed by the head count, between 1985-92 was due mainly (97.8 percent) to intra-sectoral effects while 3.4 percent was due to population movements within the country. The interaction effect for the headcount shows that people tended to move to sectors where poverty was falling. For the depth P1 measure, the intra-sectoral, population shifts and interaction effects, all contributed to reducing the depth of poverty. However, for the P2 intensity measure the interaction effect increased poverty by 8.3 points, but the effects were balanced by the more than proportional decrease in the severity caused by the intra-sectoral and population shift effects. We have seen that the decline in rural poverty was mainly due to a decline in agricultural households. 1986 was not a very good agricultural year in Nigeria, the improvements in agriculture were due to the rather favorable terms of trade for cocoa farmers and the abolition of the cocoa board. It is no surprise therefore that the intra-sectoral effects have been so strong in reducing poverty. It is also believed that there was reversed migration with families moving from urban to rural areas, thereby causing a reduction in rural poverty (CBN/NISER, 1992).

**Table 4.27: Decomposition of Change in Poverty (Pa, 1985-93) Into Intersectoral, Population Shift and Interaction Effects**

|**P. Indicator**|**Total Change**|**Intra-Sectoral**|**Population Shift**|**Interaction Effect**|
|---|---|---|---|---|
|**P0**|-0.089|-0.087|-0.003|0.001|
||100.0|97.8|3.4|1.2|
|P1|-0.004|0.001|-0.002|-0.003|
||100.0|-25.0|50.0|75.0|
|P2|0.012|0.001|-0.001|0.012|
||100.0|-8.3|8.3|100.0|

Source: NCS, 1985/86 and 1992/93.

## 4.5 **Implications for Targeting**

From various aspects of the inter-temporal poverty profile presented thus far, one can draw out the implications for targeting, by using two targeting indices. The indices relate to how much impact on aggregate (national) poverty can be expected from a given transfer across different groups defined by a particular household indicator or characteristic. This paper focuses on two benchmark criteria. These correspond to the additive (or uniform) and multiplicative or (proportional) transfers. Additive transfers are generally defined as those transfers where the amount transferred is the same for all persons within the group. These transfers are progressive if it translates into increased or higher proportion of expenditure for the relatively poor. In the second case of multiplicative transfers, the amount received is proportional to the recipient's income or expenditure, these are distributionally neutral transfers. As shown in Kanbur (1987)