rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 2. Impact on enrollment outcomes for the younger cohort</u>

|<br>**Outcome**|<br>**Survey**<br>**Year**|<br> <br>|<br>**All**<br>(1)|<br>**Poor**<br>(2)|**Non-poor**<br>(3)|**Low**<br>**Parenting**<br>**Score**<br>(4)|**High**<br>**Parenting**<br>**Score**<br>(5)|
|---|---|---|---|---|---|---|---|
|||Coeff.|-0.199|0.068|-0.516|-0.151|-0.273|
||2013|(S.E.)|(0.436)|(0.505)|(0.496)|(0.479)|(0.513)|
|||Comp. mean|4.983|3.777|5.859|4.555|5.453|
|Months enrolled in<br>||Obs.|2,778|1,201|1,577|1,540|1,238|
|kindergarten until<br>re ear||Coeff.|-1.012|-0.589|-1.326|-1.365|-0.692|
|suvy y|2016|(S.E.)|(0.663)|(0.844)|(0.683)|(0.722)|(0.767)|
|||Comp. mean|10.24|9.008|11.16|10.06|10.43|
|||Obs.|2,894|1,289|1,605|1,834|1,060|

p<0.001***; p<0.01**; p<0.05* Note: Each cell-block is the result of a separate regression. Standard errors clustered at the village level are in parentheses. “Comp. mean” refers to the comparison group mean for the outcome variable. Column (1) regressions control for child characteristics (age, household size, household wealth asset index, parenting practices, mother's education and gender) and baseline child development measures (BMI, cognitive, fine motor, gross motor, and language). Columns (2) and (3) regressions use the same controls as column (1) except they exclude household wealth. Columns (4) and (5) regressions use the same controls as column (1) except they exclude parenting practices.

Next, we turn to results on child development outcomes. Table 3 presents the impact estimates on the EDI at age 5 (2013) and age 8 (2016). Overall, we find a few positive impacts of the project on children’s developmental outcomes at age 5 but no positive impacts at age 8. At age 5, we estimate a 0.208 S.D. increase in scores on the physical health and well-being domain and a 0.115 S.D. increase in scores on the emotional maturity domain for the overall sample. However, these effects do not persist to age 8. We generally observe null effects in 2016, with one negative impact on the communication skills and general knowledge domain (- 0.136 S.D). These results are largely consistent with the fade-out literature in early childhood education, where interventions aimed to reduce early childhood inequalities have large initial impacts but these dissipate over time (see for instance the studies included in Tanner et al., 2015). Our results suggest that the impact on emotional maturity may be concentrated among those classified as poor in our data.<sup>13</sup> There are no other statistical differences across subgroups – either by wealth or by parenting practices.

One note of caution in interpreting our results is warranted. The data suggest that measurement error may contribute to the null effects on the EDI that we observe in 2016. In the raw histograms of the EDI domains presented in Appendix Figure 1, we find evidence of ceiling effects in 2016 that are not present in 2013. The exception in 2013 was ceiling effects on the communication skills and general knowledge domain, which may also explain the negative impact on this domain identified in 2016.

13 See Appendix Table 5.

12
of farmers could in fact crowd-out productive investments (especially in contexts with deficient credit markets). Second, the question is well-tailored to the current policy debate, as demand for index insurance is highly price sensitive and particularly low at market prices. The optimal level of subsidies remains unclear and depends on the size of the productivity gains at stake. Third, although the overall evidence suggests that access to index insurance can stimulate productive investments, some heterogeneity in treatment effects exists across studies and deserves to be investigated more systematically. For example, farmers with access to index insurance increased total cultivation expenditures by 23% in Bangladesh (Hill et al., 2019), by 13% in Ghana and Kenya (Karlan et al., 2014; Bulte et al., 2019), but in India researchers found small and non-significant effects (Cole et al., 2017).

# **2.2 Search process**

We found ten studies satisfying our inclusion criteria. In order to identify eligible studies, we relied first on the studies listed in the review by J-PAL, CEGA, and ATAI (2016). We found an initial set of four eligible papers: Mobarak and Rosenzweig (2013); Elabed and Carter (2014); Karlan et al. (2014); Cole et al. (2017). We then relied on Google Scholar searches to screen all studies citing at least one of these four initial papers and we further assessed their eligibility by reviewing their title, abstract, and empirical analysis. This resulted in the inclusion of three additional papers: Berhane et al. (2015); Ahmed et al. (2020); Stoeffler et al. (2022). We completed this search process in April 2018. Three additional papers (Belissa and Marr, 2018; Bulte et al., 2019; Hill et al., 2019) reached our attention before registering our study, bringing the total number of papers to ten. We stopped screening potential papers to include in August 2020, following the registration of our pre-analysis plan.<sup>13</sup> To confirm that this search process did not miss eligible studies, we conducted two independent literature searches on Google Scholar: (i) using the keyword “index insurance” and any of the terms “evidence”, “randomized”, or “experiment”; (ii) using the keyword “index insurance” and any of the terms “production decision”, “investment”, “risk”, or “ex-ante”. These searches yielded 116 and 327 results respectively. However, after reviewing these papers, we did not find any additional studies satisfying our inclusion criteria.<sup>14</sup>

> 13 Our pre-analysis plan is available here.

> 14These additional searches were conducted in February 2022, following the comments received by colleagues and conference participants. Naturally, we only considered a study as eligible if it was released before we registered our project.

7
The use of an annuity instead of individual values for depreciation and return on capital serves two purposes. First, it simplifies the calculations. Second, the use of a constant annuity implies adopting an increasing pattern of depreciation. Given that we evaluate capital over a 35-year period, an increasing depreciation rate provides the correct allocative signal, since the system is bound to have excess capacity at the beginning of the period.

# **3.5 Estimating operation and maintenance costs**

In general, during tariff revision processes, and in particular when estimating long-run marginal costs, a generalized percentage of total assets is used to estimate annual operation and maintenance (O&M) costs when no detailed information is available. The reference values generally fall between 2.5 percent and 3 percent of invested assets. For the purposes of this study, the upper limit of 3 percent is assumed as a reference value.<sup>20</sup> When this methodology is applied to all utilities represented in the IBNET database, O&M costs represent an average 24.7 percent of total efficient costs, which is similar to the average 28 percent figure from our sample of Chilean model firms.

# **3.6 Estimating efficient tariffs for utilities listed in IBNET**

Having estimated the operating costs, depreciation, taxes, and capital remuneration for utilities listed in IBNET, we can now compute an efficient revenue requirement for water and sanitation services separately for each utility using Equation 1. We then estimate an efficient average tariff by dividing the efficient

# **3.7 Estimating full tariffs for utilities listed in IBNET**

The tariffs computed in the previous section are intended to recuperate the capital and O&M costs of an efficient model firm. But this assumption of efficiency is unrealistic: most utilities present larger inefficiencies mostly having to do with excessive operations and maintenance costs due to overstaffing and water production losses. However, capital expenditures can also be inflated since extra assets are required to increase production to make up for incurred water losses. The objective of this section is to compute a tariff that reflects the effective level of efficiency of each utility in the IBNET database in terms of two variables: losses and labor costs. We call this a _full tariff,_ since it allows a return compatible with a utility’s opportunity cost of capital (i.e., to be economically sustainable) with costs including a certain level of inefficiency.

## **3.7.1 Water Losses**

SISS in Chile assumes overall water losses of 15 percent in their model firms. But the losses of most utilities, particularly in developing countries, are substantially higher. Assuming that losses of 15 percent

> 20 The reported operations costs from IBNET in 2017 U.S. dollars were not sufficiently reliable to be used in the estimate, as random cross-checks against original sources (that is, the balance sheets of various utilities) showed significant differences for several observations.

10
estimator when the lagged dependent variable is included on the right hand side, specifications including the lagged dependent variable and fixed effects are estimated via IV, using the Anderson-Hsiao estimator. In the regression for the world, the test for a Latin American specific effect is an F-test that the coefficients on the right hand side variables for the Latin American countries are jointly different from the coefficients for the rest of the world.

# 3.3 **The determinants of growth in internet hosts**

Table 4 summarizes the results of two regressions testing the determinants of internet hosts in the world. The first column ignores the possible diffusion and test for the relevance of time and country fixed effects. The second column summarizes the test of the diffusion model and thus include a lagged dependent variable. The models are estimated including additive continent and year specific dummies (to allow for allow for unobserved differences across continents plus worldwide macro economic shocks), their interaction (to allow for continent specific macroeconomic shocks) and additional country specific fixed effects, controlling for time invarying country specific characteristics. Similarly, when we run the regression for Latin America only we allow for additive year and country dummies. P-values are reported alongside the estimated coefficients.

A first important result emerging from the table is that the coefficient on the lagged dependent variable is not significantly different from zero. This suggests that the exogenous diffusion process found by Kiiski and Pohjola (2001) tends to disappear once fixed country effects are properly accounted for. On the other hand, the lagged dependent variable would be positive and significant if one allowed only for additive continent or year dummies (results are not reported here). For these reasons, specification I is our preferred one.

**_Table 4. Dependent variable: Ln hosts_** _pc_ **_in the world_**

|(firstcolu|mniscoefficient,|secondisp-v|alue)<br>||
|---|---|---|---|---|
||||2||
|Laggeddependent<br>variable|||-0.464|0.38|
|LnGdp pc|0.713|0.033|0.935|0.122|
|LnCosts|-0.345|0.109|-0.505|0.213|
|LnLinespc|0.573|0.06|0.758|0.068|
|LnPC'spc|0.87|0.00|0.642|0.17|
|R2|0.996||0.993||
|Obs.|353||353||
|F-testforLatinAmerica|<br>1.41|0.211|1.01|0.403|
|Allspecifications controlforadditiveyearand|continenteffe|cts,interac|tionsofthe|twoandcountryeffects.|

12
I 11 These mechanisms involve various mixes<sup>of control and assistance. Control</sup> linkages enable one organization to<sup>determine some aspect of another's</sup> performance, while assistance linkages<sup>facilitate program implementation by</sup> compensating for gaps in the performance<sup>of implementing organizations. The</sup> ID strategy should stress assistance<sup>linkages (that is, representation and</sup> technical assistance) over control linkages<sup>(that is, regulation or input</sup> delivery) as the former create incentives<sup>for increased local commitment and</sup> capacity to implement development programs<sup>(Marshall 1982:59-66).</sup> The value of institutional linkages and<sup>central-local</sup> communications flow is exemplified in<sup>the area of agricultural research and</sup> extension. In their in-depth study of<sup>small farmer agriculture in Africa,</sup> MADIA<sup>4</sup> researchers found that in some countries,<sup>despite massive investments</sup> in extension and research, relatively<sup>little of this knowledge was adopted by</sup> small farmers. The technologies that<sup>were developed (for example, high-</sup> yielding varieties, HIYVs, that require<sup>significant inputs of fertilizer and</sup> water) did not reflect the realities of<sup>small farmer resources. For example,</sup> less than five percent of farmers in Malawi<sup>have adopted hybrid maize despite</sup> thirty plus years of maize-breeding<sup>work and twenty years of agricultural</sup> development projects (Lele, Kinsey,<sup>and Obeya 1989:26). This results</sup> partially from the fact that information<sup>has flowed only one way: from the</sup> researcher to the farmer (Lele, Kinsey,<sup>and Obeya 1989:27). The important</sup> 4 The Managing Agricultural Development<sup>in Africa (MADIA) study involved</sup> a detailed analysis of six African countries'<sup>policies and performance over</sup> the last 20-25 years. The countries<sup>included: Kenya, Malawi, Tanzania,</sup> Cameroon, Nigeria, and Senegal.
climate therefore appears likely to limit business (and hence employment) growth. Some key findings in this respect are as follows:

- The time and particularly costs involved in starting a business in Timor-Leste are high relative not only to developed countries but also to many neighboring countries.  This can be seen in Figure 1.  In Timor-Leste, the average number of days required to start a business is 92 days.  This is less than a number of developing countries such as Indonesia, Mozambique and Brazil, but substantially higher than many Pacific and East Asian countries.  In terms of the cost of staring a business (relative to income per capita), the costs in Timor-Leste are even higher, at 125 percent of GDP per capita.  This compares to Indonesia at 102 percent of GDP, Mozambique at 95 percent, and East Asian countries much lower, at 30 percent in PNG, 21 percent in Malaysia, 6 percent in Thailand, and only 3 percent in Hon Kong.

<!-- Start of picture text --> Figure 1: Time and costs of starting a business,<br>2004<br>140 180<br>160<br>120<br>140<br>100<br>120<br>80 100<br>60 80<br>60<br>40<br>40<br>20<br>20<br>0 0<br>Days<br>% of PC GDP cost<br>Timor-L PNG Indon Malay Thail Mozam Brazil Australia H.Kong<br><!-- End of picture text --> Source: World Bank, Doing Business, 2006.

- The flexibility of labor regulations is another factor in incentives to formality and willingness to hire new workers.  Relative to most East Asian and Pacific countries, Timor-Leste’s labor regulations are reasonably rigid, though less so than both Indonesia and the bulk of Lucaphone countries.  The comparative flexibility of labor regulations is shown in Figure 2, where lower scores indicate greater flexibility and higher scores less flexibility.  The index is composed of sub-indicators on difficulty of hiring and firing, and rigidity of work hours.  On the sub-items, Timor-Leste stands out as having a particularly difficult environment for hiring of workers.

<!-- Start of picture text --> Figure 2: Flexibility of employment<br>regulations, 2004<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Timor-L PNG Fiji Thai Malay Sing Brazil Mozam Indon Austral<br><!-- End of picture text --> 6
|Own color television|26.1|24.5|
|---|---|---|
|Father’s schooling|8.4|7.4*|
|Mother’s schooling|9.1|8.1*|

*Mean is significantly different between program and non-program samples at the .05 level.

# **Varying implementation lags**

Discussions with the ECD project management office and field administrative data indicated that there was substantial variance in the timing of the implementation of the project across municipalities and barangays, where implementation means procurement and receipt of material inputs and provider training related to the program. This variance implies differences in the duration of exposure to the program across program areas, and thus differences in the amount of time that the interventions could have had an impact. If program effects are estimated as if the program began before its actual effectiveness at the barangay level, then those effects might be underestimated. Many evaluations do not take into account this variation in the duration of program implementation, often because the program start is presumed to be well-defined (though in reality official starting dates often do not reflect start-up delays on the ground) and also because data on timing are not available. One exception is the analysis of the Bolivian PIDI pre-school program mentioned above (Behrman, Cheng and Todd 2004), which explicitly takes into account the dates of enrollment of individual children into the program and find that impact is most clearly observed among children who have been exposed to the program for more than a year, compared with those exposed for less time. Another is the evaluation of PROGRESA by Gertler (2004) who found no program impact after only six months of program exposure, but with 24 months of program exposure the illness rate of the treatment group was 39.5 percent lower than the control group, a difference that is significant at the 1-percent level.

With the help of the central project implementation office, we are able to use administrative data to add another measure of the availability of the program. The mean length of exposure is about 14 months (with a substantial standard deviation of six months). (Appendix Figure B.1 shows the distribution of the length of exposure to the program.) Table 7 shows the distribution of children, by age in years at Round 3, across four exposure categories. We restrict our estimates to children who lived in barangays that had at least four months of exposure at Round 3 because children with less than four months of exposure are unlikely to have had enough exposure to the program to show any measurable impact, particularly given initial start-up problems in modifying effectively existing programs or in introducing new ones. As shown in Table 7, this involves dropping 252 children from the analysis. Two barangays received the program inputs about a year and a half before Round 1, so we exclude these two barangays (N = 33 children); in these cases the matching variables, measured at baseline, are not likely to be exogenous to the 17
Table of Content

|I.|Inroduction<br>.............................................|.....<br>I|
|---|---|---|
|II.|Data, Methodology and Empirical Procedures ..............................|2|
|Im.|Principal Features of Trade Regime as Envisaged in the<br>iAs<br>.4||
|IV.|The Imporrnce of Industrial Products in CEE-5 Imports and Access to EC Markets ...|... 9|
|V.|The Scope of Preferential Treatm<br>of Industrial Products in the ITAs .............|11|
||A-. le<br>"One-Year-Delayed" Duty-Free Group .......<br>.....................|12|
||B. The "Four-Year-Delayed" Duty-Free Group ...........................|14|
||C. The "QuotatFive-Year-Delayed" Duty-Free Group .......................|15|
||E. The MFA Group ..........................................|20|
||D. TheECSC Group .........................................|23|
||E. The "Residual" Free Trade Group .<br>................................|26|
|VI.|Time Profile of Attining Free Trade in<br>dustrial Products ........<br>............|29|
||A. Copenhagen Concessions .......................................|31|
||B. Reliability of free trade component estimates: How "Free' is a Free Trade Regime? . .|.<br>32|
|VII.|Conclusion..................................................|<br>34|
|Refer|ences .......................................................|36|

i
<!-- Start of picture text --> Terms of Trade Loss in South Asia (Jan 2003 - May 2008)<br>S _30.0ee<br>°<br>20.0<br>o<br>6<br>© 10.0<br>o<br>; cl cl)<br>o 0.0<br>i<br>-10.0<br>Bangladesh India Sri Lanka Maldives Nepal Pakistan<br>Source: DECPG, The World Bank © All CommoditiesIJ Food§) Energy<br><!-- End of picture text -->
# **Glass Barriers: Constraints to Women’s Small-Scale Cross-Border Trade in Cambodia and Lao PDR**<sup>**1**</sup>

# **Marlon Seror, Richard Record, Julian Clarke**

_Keywords:_ Economics of Gender, Informal Sector, Corruption, Tax Evasion, Trade _JEL codes:_ J1, O17, D73, H26, L81

> 1 The research undertaken through this was supported by the Trade Development Support Program in Cambodia, the Second Trade Development Facility Multi Donor Trust Fund in Lao PDR, and the Umbrella Facility for Gender Equality.

Cambodia and Lao PDR
and Madagascar. The implications for total consumption are in line with the changes in GDP, but slightly more pronounced. By 2025 regional total consumption would be almost 3 percent lower than in the baseline.

Figure 7 illustrates the changes in real investment, which are only due to the collapse of financial flows. The magnitude of the impact depends on the economic characteristics of various countries – such as the structure of the economy, composition of consumption, evolution of relative prices, demographic development like aging etc. The larger the existing current account deficit, the larger the reduction of capital during the shock and the larger the adverse impact of the shock. For example, with demographic changes very similar to a developed country and an elderly dependency ratio increasing significantly, Mauritius will already experience a significant shortage of domestic saving as a share of GDP and more reliance on capital inflows in the baseline. When coupled with a lack of financial inflows, the further effects on savings results in sharply declining investment as well as income and ultimately lower consumption in this counterfactual scenario (see Figure **8** ).

**Figure 7 Deviations of investment share of GDP in the recession scenario with a collapse of financial flows as compared to the baseline (percentage points).**

**Investment share of GDP (deviations from the baseline)**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>2015 2020 2025<br>-12<br>Cameroon Côte d’Ivoire Ghana Nigeria Senegal Ethiopia Kenya Madagascar Malawi Mauritius Mozambique Tanzania Uganda Zambia Zimbabwe Botswana Namibia South Africa Rest of SSA<br><!-- End of picture text --> 10