rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Empowerment Outcomes

||Wor|k Outco|mes|Financi|al & Per|sonal A|gency|Agre|ement wi|th Conser|vative No|rms|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Works<br>outside<br>home|Hours<br>on<br>business|Hours<br>on<br>chores|Spending<br>on<br>self|Has<br>Savings|Has<br>Loans|Locus<br>of<br>Control|Women<br>better<br>home|Children<br>sufer|Husband<br>can’t<br>provide|Reserve<br>Jobs<br>to Men|Norms<br>Index<br>8-11|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Treatment|0.01|0.02|-0.16|4.71|0.02|-0.03<sup>_∗_</sup>|0.21|-0.05<sup>_∗_</sup>|-0.05|-0.03|0.01|-0.11|
||(0.02)|(0.21)|(0.18)|(3.88)|(0.02)|(0.02)|(0.13)|(0.03)|(0.03)|(0.03)|(0.03)|(0.08)|
|RI p-values|.63|.94|.43|.28|.27|.11|.07|.09|.16|.51|.65|.2|
|Control Mean|.2|3.89|4.69|51.92|.1|.93|7.52|.4|.77|.55|.59|2.22|
|Obs|983|971|985|985|945|944|828|952|956|952|952|969|

* ** *** _Notes_ : This table estimates specification (1) in the paper. p _<_ 0.1, p _<_ 0.05, p _<_ 0.01. Robust standard errors in parentheses. All regressions have strata fixed effects and control for variables chosen using lasso.
SHG, like those provided by LEAP and a stand-alone savings group such as a tontine. We observed during field implementation that both our enumerator and the respondents seemed to be confused by this distinction despite our repeated explanations and so we are not confident that the links in these groups were recorded properly. This may explain the large increase in non-SHG savings group participation in LEAP communities since subjects may have been conflating their participation in an SHG with participation in a traditional savings group. This misunderstanding in implementation is unfortunate because we had hoped to assess the extent to which LEAP caused a substitution out of more traditional saving arrangements like tontines, a question we can now not confidently answer. Regardless both effects are substantively large and highly significant statistically suggesting that LEAP had a large effect on SHG membership and in participation in savings group schemes either through LEAP SHGs or some other group. Interestingly LEAP villages even saw a significant increase in SHG membership among the non-poor even though the program was targeted to the poor and LEAP SHGs were made up overwhelmingly of poor members. As shown in column 3 the standardized mean effects of these two indicators are substantial and highly significant statistically. In the full sample the program caused an estimated increase of 0.74 standard deviations in financial network linkages. Among the poor the effects are very large—over one standard deviation increase. Non-poor saw an estimated 0.46 standard deviation in financial network linkages.

# **_4.2 Livelihood and household expenditure outcomes_**

Tables 3 through 5 exhibit our results of the impact of LEAP on livelihood and household expenditure outcomes. The first and second columns of Table 3 show that meat and fish production increased substantially and statistically significantly as did sales of meat and fish. This increase in incomes from meat and fish sales is consistent with the observation above that 45 of the 52 producers groups created by LEAP SHGs involved raising livestock (38 chicken-raising and 7 pig-raising groups).

Several factors raise questions about the veracity of this estimated increase, however. First, the non-poor, who were not targeted by the program, enjoyed large (indeed the largest) increases in sales of meat and fish. Second, increases in meat and fish production were not 15
# **Table A.9. The effect of welfare definition, RLMS 1994-2017**

|_Dependent variable_|_Satisfaction wit_|_h life_|_Satisfaction with ec_<br>_conditions_|_onomic_|
|---|---|---|---|---|
|_Main variables_|**Pooled Ordered**<br>**Logit**|**BUC**|**Pooled Ordered**<br>**Logit**|**BUC**|
|L f hhld i|0.606***|0.431***|0.863***|0.719***|
|og o ouseo ncome|_(0.010)_|_(0.077)_|_(0.013)_|_(0.112)_|
|||||-|
|Log of household size|-0.462***|-0.284***|-0.790***|0.569***|
||_(0.022)_|_(0.035)_|_(0.025)_|_(0.046)_|
|Children*Log of household|0.071***|0.018*|0.069***|0.015|
|size|_(0.007)_|_(0.011)_|_(0.008)_|_(0.012)_|
|_Number of observations_|240,383|525,832|211,032|446,937|
|_Log pseudolikelihood_|-329,968|-196,405|-286,373|-169,660|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

44
in each grade is used instead of the maximum to avoid the extreme values. The reference salaries for each pay grade under SC3 and SC4 are taken directly from the Government’s study.

In estimating the fiscal impact, the following assumptions are made under all options.

- (i) The number of civil service employees will grow by 10 percent per year in the priority sectors (education and health), and 0 percent per year in other sectors;

- (ii) Distribution across the civil servants will remain the same, even after new hirigng;

- (iii) After the initial salary adjustment (at _t_ +1), the annual automatic salary increase of 6 percent is maintained (as assumed in the Government’s study).

In addition, the following assumptions are made about the adjustment process.

- Under Option 1 and Option 3, we assume the costs of adjustments are evenly spread over 3 years and 2 years, respectively. For example, under Option 1, a third of the existing employees are brought under the new salary scale in the first year (t+1), another third in the second year (t+2), and all existing employees in the third year (t+3). Under Option 3, a half of the existing employees are brought under the new salary scale in the first year (t+1), and the remaining half in the second year (t+2).

- During the adjustment period, there will be no automatic adjustment of salaries.

- Newly hired employees are paid according to the new salary scale.

36
- 70 -

utilities studied had more than 150 consumers per employee, a fair performance for an average utility Involved In distribution activities and not carrying out significant force account work.

# Maintenance

A review of appraisal reports produced In the 1980s Indicated that the level of maintenance varied considerably from one utility to another. Good maintenance Is a contributing factor to achieving higher plant availability and efficient distribution and, therefore, more reliable service. Of course, poor maintenance may not necessarily denote only Inefficiency on the part of the utility, for It could also result from lack of funds or foreign exchange for spare parts, and other factors which may be outside the control of the utility.

Quantitative measures of the adequacy of maintenance are difficult to establish; hence, i was necessary to accept the evaluations given in the appraisal reports. These evaluations were ranked on a five point scale from very good to very poor. For projects approved In the 1980s, twelve out of 26 utilities fell Into the poor to very poor range - four In Africa, threa In Latin America and three In S. Asia. On the other hand, nine utilities fell Into the good to very good range - three In E. Asia, two In LAC and two In S. Asia. Most of th'ese utilities had been established for many years so that'there had been the opportunity to gain experience and Improve maintenance over the years.

# Overall Utility Efficiency

A similar rating of the overall efficiency of utilities was carried out as above for a sample of projects Implemented In the 1970s and 1980s. The evaluation was based on the appraisal and audit reports' assessment of Institutional performance, which may not be entirely consistent given the lack of precise criteria and the need for judgement on the part of Bank staff.
policy, reducing spending in a downturn, while high-income countries pursue countercyclical fiscal policy, raising expenditures in a downturn. The latter reflect automatic stabilizers in place as well as discretionary spending to stimulate the economic recovery (Aghion and Marinescu (2007), Talvi and Végh (2000), Perotti (1999), Darby and Melitz (2008), Kaminsky, Reinhart and Végh (2004)).

What it is interesting about this crisis is that developing countries have become more sophisticated at fiscal management, and implemented countercyclical spending by temporarily expanding safety nets, protecting social sector spending, and redirecting fiscal resources to retain social services. This is in sharp contrast to the reaction of developing countries to past crises, in which many countries pursued across the board cuts in government expenditures with little concern for the incidence of fiscal allocation. Therefore, although poverty has increased as a consequence of the crisis, it has increased less than would have been the case otherwise. (World Bank (2010a) and Lewis and Verhoeven (2010)).

Moreover, in contrast to previous crises, the 2008-09 crisis found many developing countries with the required credibility and fiscal breathing space to conduct countercyclical fiscal policy. Among the factors that helped developing countries achieve resiliency to the ongoing economic and financial crisis and be much better prepared to respond to this crisis than more developed countries were higher fiscal surpluses, a better build-up of reserves, lower inflation, and avoidance of currency mismatches which could have led to exchange rate speculation and insolvency (Didier, Hevia and Schmukler (2010a, b) and Frankel and Saravelos (2010)).

Finally, unlike previous crises, developing countries generally fared much better and are recovering more quickly than developed countries. This can be seen in Figures 2a, Figure 3, and Figure 4. Figure 2a shows that developing country growth rates fell less and recovered more quickly than developed countries. The projected growth rates, prepared by the World Bank Prospect Group within Research, also show systematically higher growth rates for 2011 and 2012 in developing countries. Figure 3 documents the evolution of trade before and after the crisis. Figure 3 shows that developing countries are leading the recovery in trade, with their import demand rising at twice the rate of that in high-income countries in volumes and values.

7
# **_Table A3.2 First-step regression used for calculating expected IEG Outcomes, by which value-added measure of CD quality is generated_**

|**Variables**|**(1)**|
|---|---|
|CPIA at Approval Calendar Year|0.267**<br>(0.131)|
|CPIA Change Over Project Lifetime|0.354***<br>(-0.11)|
|(Log) GDP per capita PPP $ (at approval Fiscal Year)|-0.282***|
||(-0.086)|
|(Log) Population (at approval Fiscal Year)|-0.416<br>(-0.258)|
|(Log) Original Commitment Amount (in millions [constant 2015 $])|0.030<br>(-0.024)|
|Human Development Sector Group|0.171***<br>(-0.061)|
|Sustainable Development Sector Group|0.176***<br>(-0.0634)|
|Constant|12.17***|
|Observations|3,498|
|Adjusted R-Squared|0.084|

Note: Robust standard errors are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). Regression estimated using OLS on full 4,348 data set. All specifications include country fixed effects.

47
Since 2014, the data show increases in the participation rate (Figure 1), which is known to be countercyclical, and employment-to-population ratios. The data also show a clear deterioration of the quality of employment. This can be seen in terms of a falling rate of _adequate_ employment among the working-age population (Figure 2, top), and the sharp increase in informality (bottom). Last, the data also shows a reduction in monthly earnings since 2015 (Figure 3, top) and stagnating hourly wages in 2015-2017 followed by a sharp reduction in 2017-2019 (bottom).

# **4.2 Mobile Phone Records:** **_Telefonica Ecuador_**

The main challenge to carry out the analysis was the need to obtain information on the size and geographic distribution of the Venezuelan immigrants in Ecuador. Prior to our study the existing information regarding the size of the Venezuelan population in Ecuador was mainly based on official records of net migration flows on a monthly basis. These data have two important limitations. First, what is being measured are the entries and exits, not the actual count of people. More importantly, the net migration estimates miss the foreign population that enters the country without registration at an official point of entry. Furthermore, no data were available at the time on the magnitude of unregistered entry.<sup>14</sup> The knowledge of the geographic distribution of the Venezuelan migrant population was also severely limited. Characterizing this distribution is complicated by the high geographic mobility of this population and the lack of baseline information in the traditional datasets, such as the Population Census.

Our approach to address these challenges has been to rely on mobile phone records. More specifically, we contacted the two main cell phone providers in the country to request their collaboration. However, only _Telefonica Ecuador_ had the technical capabilities to implement the data collection we proposed. In addition, this company was the only one with a large presence in the Venezuelan mobile phone market and, thus, could be expected to attract the majority of Venezuelans seeking to obtain cell phone service in Ecuador.

> _Telefonica Ecuador_ keeps millions of geocoded records of phone calls and data use in the corresponding denominators. All canton-year aggregates have been computed using the survey weights.

> 14On July 25 2019, the Government of Ecuador issued the Executive Decree 826 which establishes the implementation of a migration census and the requirements to access a humanitarian visa for migrants that entered legally but have overstayed their permits.

9
<!-- Start of picture text --> Avg. Change in time to trade (%) CEE CWA EAS MEA __ SAS SSF<br>Central and Eastern Europe 0.7<br>Central and Western Asia 2.9 19.4<br>East Asia & Pacific 2.9 5.6 5.2<br>Middle East & North Africa 0.4 2.3 4.0 0.6<br>South Asia 14 1.9 6.0 1.5 2.9<br>Sub-Saharan Africa 3.5 4.0 7.8 4.0 4.9 5.8<br>Regional Average 1.9 6.0 5.3 21 3.1 5.0<br><!-- End of picture text --> <!-- Start of picture text --> Avg. Change in time to trade (%) CEE CWA EAS MEA _ SAS SSF<br>Central and Eastern Europe 1.1<br>Central and Western Asia 3.8 20.8<br>East Asia & Pacific 3.7 9.1 8.6<br>Middle East & North Africa 3.1 4.2 5.1 0.8<br>South Asia 3.5 2.9 7.0 2.9 3.6<br>Sub-Saharan Africa 5.8 8.9 8.6 4.4 5.4 6.3<br>Regional Average 3.5 8.3 7.0 3.4 4.2 6.6<br><!-- End of picture text -->

|Avg.Change intimetotrade(%)|CEE|CWA|EAS|MEA__|SAS|SSF|Avg.Change intimetotrade (%)|CEE|CWA|EAS|MEA_|SAS|SSF|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|CentralandEastern Europe|0.7||||||CentralandEastern Europe|1.1||||||
|Central and Western Asia|2.9|19.4|||||Centraland WesternAsia|3.8|20.8|||||
|EastAsia& Pacific|2.9|5.6|5.2||||East Asia& Pacific|3.7|9.1|8.6||||
|Middle East& NorthAfrica|0.4|2.3|4.0|0.6|||Middle East& NorthAfrica|3.1|4.2|5.1|0.8|||
|SouthAsia|14|1.9|6.0|1.5|2.9||South Asia|3.5|2.9|7.0|2.9|3.6||
|Sub-SaharanAfrica|3.5|4.0|7.8|4.0|4.9|5.8|Sub-SaharanAfrica|5.8|8.9|8.6|4.4|5.4|6.3|
|RegionalAverage|1.9|6.0|5.3|21|3.1|5.0|RegionalAverage|3.5|8.3|7.0|3.4|4.2|6.6|

ijg = 100 * B, *
Lin, Justin Yifu, 2010. "New Structural Economics : A Framework for Rethinking Development," Policy Research Working Paper 5197, World Bank, Washington, D.C.

Lipsey, R. G., and Kelvin Lancaster. 1956. “The General Theory of Second Best.” _Review of Economic Studies_ 24 (1): 11–32.

Love, Inessa, and Nataliya Mylenko. 2003. “Credit Reporting and Financing Constraints.” Policy Research Working Paper 3142, World Bank, Washington, DC.

Rajan, Raghuram, and Luigi Zingales. 1998. “Financial Dependence and Growth.” _American Economic Review_ 88 (3): 559–86.

Reinikka, Ritva, and Jakob Svensson. 2002. “Coping with Poor Public Capital.” _Journal of Development Economics_ 69 (1): 51–69.

World Bank. 2009. _Africa Competitiveness Report 2009._ Washington, DC: World Bank.

20
children of age 10-15 years. This result is in the ballpark of the actual 3 percent increase in enrolment for the same cohort of boys between 1999 and 2003 estimated from PNAD 2003, which is _not_ a result from an impact evaluation. The forecast from the BFL model is also close to the results from a quasi-experimental evaluation of the Bolsa Escola program by Cardoso and Souza (2004), in which a matching estimator suggests a 3.1 percent increase in the enrollment rate in boys and 3.0 percent increase for girls aged 10-15 years associated with the Bolsa Escola. De Janvry and Sadoulet (2006) also use _PROGRESA_ data to develop a predictive model. They analyze the potential for increase in school attendance in order to help increase the efficiency of such transfers, concentrating on secondary school attendance and the extent to which it can be affected by a conditional transfer.

Lise et al (2003) work with data from the Canadian Self-Sufficiency Project (SSP) and construct a dynamic, partial equilibrium model to simulate the effects of the program in terms of labor market behavior and compare these to observed treatment effects on individuals. The SSP is a social program providing time-limited earnings supplements to Income Assistance recipients who are able to obtain full time employment within a 12 month period. Recipients (i.e. the treatment group) of SSP were picked at random from among those eligible for these benefits. Lise et al restrict their sample to single mothers only, with around 2,300 women in the control and treatment groups alike.<sup>5</sup> Their _ex ante_ model is calibrated to control group data and the experiment is simulated within this model to imitate the “welfare-to-work transition of the treatment group.”<sup>6</sup> The results are then compared with those of an _ex post_ evaluation of the SSP, which produces similar results.

An earlier body of literature, including LaLonde (1986), Heckman and Hotz (1989) and others, use experimental results as benchmarks for evaluating the predictive power of partial equilibrium non-experimental estimators, mainly in the context of labor market programs. These papers concluded that non-experimental methods were not effective in evaluating program impacts. In more recent literature, propensity score matching was extended with

> 5 Lise et al’s calibrated search-matching model incorporates three segments of the market: employed individuals, unemployed individuals receiving unemployment benefits, and individuals receiving income supplements through IA. The model expands on Davidson and Woodbury (1993)’s equilibrium search model by assuming that expected lifetime income is maximized by individuals when they choose their employment state and the intensity with which they seek work if unemployed.

> 6 For further details, please see Lise, Seitz and Smith (2003)

7
market =G (F(yzterter)) disposable -G¢ (F(yisroe?"*)) t disposable -—G¢ (F(yirarket :)) byt1~—*0 = [Ginimer*et1 — Ginimarket]0 — [Gini Z'sP290?"*1 — Ginis's?(0) 59?" t-te = [6 (F(virarker)) —G (F(vimar*er))| _ [6 (f(ymartet mm) —G (F(yimarket to))| (5)
or rail). Public transport is not an issue in the pilot provinces in Cambodia as it is rarely present and only serves for long-distance trips (for example, to the capital); however, if this method is to be applied in other regions (especially in the urban setting), this limitation may be important. At the same time, inland waterways are relevant in the pilot areas as there is evidence that villages depend on that mode of transport especially during rainy and flooding season. As data on inland waterways were not available, it was not possible to include it in the model at this point.

2. **Accessibility is estimated based on the shortest paths to the point of interest.** The model considers accessibility as the availability to reach the closest point of interest. For example, for agricultural production, it uses the provincial market as its destination regardless of the type of crops. This may not be always true as some products are sold locally, and some may be transported to the capital for international exports. This is a simplification as there are no available data on actual origin and destination or household surveys that would allow for that level of detail. Similarly, the model used assumptions of travel speeds rather than actual travel speeds (in the exception of the additional use case study in Section 3.4). This would require an intense data collection campaign that is beyond the scope of this study.

3. **Agricultural data have low resolution and have no distinction between seasons.** The values of agricultural production are taken from an open-source global model developed by IFPRI.<sup>16</sup> Its resolution is 10 km by 10 km, and it provides an indication of high-productivity areas. The values used in our model are the estimated annual value of production. As such, we did not differentiate between crops that are grown during dry or rainy seasons nor if they are perishable or not. Therefore, the estimation for climate impacts may be over- or undercalculated. With more detailed information about crops, one could model the specific crops that would be affected by disruption during the rainy season, and it would allow for a more accurate sense of economic impacts.

# **4. Conclusion and Next Steps**

Focusing on three pilot provinces, Kampong Cham, Kratie, and Tboung Khmum, the study has highlighted the urgent need to invest in resilient accessibility. For example;

- **Only 32 percent of its population can reach a referral hospital and emergency services in half an hour** . Even though there is an extensive network of community health centers in the rural areas, accessibility to a referral hospital with emergency services such as intensive care bed

> 16 <u>https://www.mapspam.info/.</u>

17
**Figure 3 Earnings Distribution, 2001**

<!-- Start of picture text --> .002<br>.0015<br>.001<br>.0005-<br>0 500 1000 1500 2000<br>Monthly<br>Kernel Density Estimate<br><!-- End of picture text --> _Source: Labor Force Survey, May 2001, Bank staffcalculations._ Which firms provide low-paying jobs and which provide well-paying jobs? Not surprisingly, in the private sector low-paying jobs are more prevalent than in the public sector (27 percent and 16 percent, respectively). Contrary to common perception, however, well-paying jobs are concentrated in the public sector, rather than in the private sector (22 percent and 14 percent, respectively). This is not untypical for transition economies, what is striking is that the incidence of well-paying jobs in the private sector is in Lithuania so much lower than in the public sector. A couple of factors can account for this situation. First, the public sector may be located in more skill-intensive industries (such as education and health care) and thus have higher proportion of highly skilled labor. Second, there may be some rents in the public sector, which are appropriated by workers and which lead to wages higher than the equilibrium level. Determining which factor is at work requires further investigation, which goes beyond the scope of this paper. The upshot is, that the private sector in Lithuania provides a large number of lowpaying jobs while offering relatively few opportunities for high pay, which can be 61