rafmacalaba/datause-encoder-data huggingface dataset:
<mark>freely available, World Bank approved microdata cataloging tool available at</mark> <u><mark>http://nada.ihsn.org/</mark></u> <mark>that can serve as a guideline for NSOs.</mark> Suggested practice 3: Provide links to other websites with country’s data Earlier we reported that not all microdata sets are hosted on the NSOs websites and about half of the publicly available microdata sets are accessible through international repositories. Where this is the case, providing a link to the websites with the relevant country data is best practice. Microdata available in other Microdata Library of the WB, IHSN, Eurostat, MICS and IPUMS data can be easily linked on the NSO’s website whether the NSO owns all the data available on these websites. Djibouti sets a good example for providing external links to its country’s data. At the time of the study, on the landing page of the website of the National Institute of Statistics of Djibouti, there is a tab named “database” with 3 dropdown tabs as follows (1) survey data. (2) Open data (3) key indicators. The survey data tab links to the World Bank's microdata library.

Suggested practice 4: Provide clarity for requesting restricted data In the classification of microdata accessibility in section 3.2, we differentiate between two classes of microdata accessibility - “satisfactory openness” and “excellent openness” where the former involves a situation where authorization and/or registration is required before a data user can access available data and the later a situation where microdata is available for immediate download on the website. As discussed earlier, “excellent openness” is ideal from the perspective of a data user, however requiring registration, authorization, and clearance before data is released by data guardians is acceptable. When microdata has “satisfactory openness”, it is important that NSO’s provide clarity regarding the steps that need to be followed to gain access, that access is granted within a reasonable period of time and that granting permission is ‘rule based’ and not dependent on ad-hoc criteria. However, for some MENA NSOs for which satisfactory openness is reported in table 3, the website indicates that the data is available upon request without clear instructions about the steps needed to obtain the data. The best scenario for “satisfactory openness” where following satisfactory registration, access to the data is granted automatically is standard practice for international organizations such as the WB, MICS, DHS, IPUMS reported in table 4.

Apart from these best practices that could be implemented by any NSO at a negligible expense, we also strongly advocate to close the microdata gap by investing in regular microdata collection.

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10 by the respondent at the plot-level, averaged across plots is greater than 50 percent.<sup>16</sup> The satisfaction of the latter condition is deemed equivalent to achieving a knowledgeable respondent profile in a given household while soliciting information on all labor provided on the plot by the respondent and other household and/or non-household members. The core specification for this purpose is estimated as: f (1) ff kk ff ff ff where f is the female labor share in crop production in household LL = αα+ RR + RR + ββXX + γγDD + εε _i_ , is a dummy variable that ff is equal to 1 if the respondent is female for the majority of the household plots, LL RR is a dummy kk variable which is equal to 1 if the household respondent profile is deemed knowledgeable in RR accordance with the definition above, is a vector of other household-level covariates affecting ff the outcome of interest, are location fixed effects, and XX ɛ is the stochastic error term, randomly ff ff distributed across households. Equation (1) is estimated by country using OLS.DD<sup>17</sup> Inclusion of the household level covariates _X_ further helps control against potential selection bias of the respondent. For example, it is important to control for the demographic composition of the household’s labor pool, i.e. the age and gender composition of all adults and in particular whether the household is female headed without male adults. Failure to control for the latter could significantly upwardly bias the effect of the gender of the respondent on the estimated

> 16 <mark>The respondents provide 100 percent of the agricultural labor on all plots in approximately 20 and 12 percent of the sample households in Nigeria and Malawi, respectively. Overall, the within-household (i.e. within-household, acrossplot) average share of agricultural labor provided by the respondents is above 50 percent in 54 and 53 percent of the sample households in Nigeria and Malawi, respectively. Thus, each respondent provide information on his/her own labor input in the majority of the cases. The findings are robust to using an alternative measure to gauge whether the household respondent profile could be deemed knowledgeable, specifically whether the respondent was the manager in the majority of the household plots. The results from these alternative estimations are available upon request.</mark>

> 17 The household-level database that underlies each estimation is based on the aggregation of individual-level agricultural labor data that are also the basis for estimating the female share of aggregate agricultural labor at the country-level. The findings are robust to the use of the (double-sided censored) Tobit estimator (available upon request).
tructure improvement, provision of school materials, teacher training, and school-based management (Garcia-Moreno, Gertler, & Patrinos, 2020; Murnane, Willet, & Cardenas, 2006; Santibanez, Abreu-Lastra, & O’Donoghue, 2014; Skoufias & Shapiro, 2006). Our study focuses specifically on the parental involvement component (Apoyo a la Gestión Escolar). A previous study of AGE used quasiexperimental methods to estimate the effect of the program (Gertler, Patrinos, & Rubio-Codina, 2012). This paper uses data from two randomized control trials conducted by the government of Mexico to understand the effectiveness of different program components – information, grants at the extensive margin, and grants at the intensive margin – in improving parental involvement in schools.

The rest of the paper proceeds as follows. In Section 2, we present relevant background details of the study setting and the experimental design. We describe our data sources in Section 3, introduce our conceptual framework in Section 4, and present our empirical strategy in Section 5. In Section 6, we present results on the effect of each intervention on parental involvement in school, parenting behavior at home, teacher behavior in school, and children’s educational outcomes. In Section 7, we explore trust between parents and teachers as a mechanism for understanding the effectiveness of parental involvement programs. Section 8 concludes.

# **2 Background and Study Design**

In 1996, the Government of Mexico established _Apoyo a la Gestión Escolar_ (AGE), a parental involvement program targeting parents of children enrolled in primary schools. The program has two key components: (1) financial grants to parent associations and (2) information provision to parents through parent associations. The grant is provided annually, ranging from USD 500 to 700 depending on the size of the student population. Parent associations can decide how to use these funds for school infrastructure, supplies, and activities. These funds are not permitted to be used towards increasing teacher or principal salaries.<sup>2</sup> The information component of AGE provides parents with guidance on how to become more involved in their children’s schools and ways to support their children’s education. Each school appoints a community advisor, who is responsible for disseminating information to parents and

> 2The grants were to be used for non-wage expenditure given that over 97 percent of school spending in Mexico is allocated towards teacher and principal salaries (Santibanez et al., 2014).

5
**_Figure 1 ‐ Innovation rates in the main ES and innovation follow‐up survey by type of innovation_**

<!-- Start of picture text --> 80%<br>68%<br>70%<br>59%<br>60%<br>51%<br>47%<br>50% 45% 43%<br>39% 41% 41%<br>40% 35%<br>27% 27%<br>30%<br>22%21%<br>20%<br>10%<br>0%<br>Product Process Marketing R&D Methods Logistics Auxiliary<br>ES Innovation follow‐up<br>% of firms reporting innovation<br><!-- End of picture text --> _Source: Enterprise Surveys and Innovation Enterprise Surveys_

**_Figure 2 ‐ Differences in innovation rates between short and long questionnaire_**

<!-- Start of picture text --> Product Innovation Process Innovation<br>KEN UGA<br>KEN UGA NPL GHA MWI NAM<br>NPL TZA GHANGASDNMWINAM SSDZMBIND PAK SDNDRCTZANGASSD ZMBIND BGD<br>DRC<br>PAK BGD<br>0 20 40 60 80 0 20 40 60 80<br>Long questionnaire Long questionnaire<br>Marketing Innovation R&D<br>KEN<br>NAM UGA<br>SDN NGATZA MWISSDGHA INDNPL ZMB NAM<br>DRC<br>BGD UGA IND<br>PAK PAKSDNSSDGHABGDZMBKENDRC MWI<br>NGA TZA<br>NPL<br>0 20 40 60 80 100 0 20 40 60 80<br>Long questionnaire Long questionnaire<br>80 80<br>60 60<br>40 40<br>20 20<br>Short questionnaire Short questionnaire<br>0 0<br>80 80<br>60 60<br>40 40<br>20 20<br>Short questionnaire Short questionnaire<br>0 0<br><!-- End of picture text --> _Source: Enterprise Surveys and Innovation Enterprise Surveys_ 26
expenditures when filling the questionnaire. Finally and for the purpose of poverty modeling, the welfare aggregate was transformed into per capita basis.

Table 1 provides the summary statistics for income and expenditure. It is clearly visible that income per capita is underreported when compared to expenditure. It is also evident that the UNHCR cash program contributes, on average and per capita, with about 7 JD per month, which is in line with what we should expect given the coverage of the program in 2014. Figure 1 also shows how the shape of the welfare aggregate of choice (expenditure per capita net of UNHCR cash) exhibits the expected suitable characteristics of a bell-shaped distribution.

The poverty line adopted for the study is 50 JD per capita per month. This threshold was selected because it was the poverty line adopted by the UNHCR for providing cash assistance. At the end of 2013, this line was equivalent to 71 USD and 160 USD at Purchasing Power Parity (PPP) per capita per month. This latter amount corresponded to 5.25 USD PPP per day, a significantly higher amount than the international poverty line of 1.9 USD PPP recommended for poor countries. Considering that Jordan is a middle-income country and that most refugees live in urban areas, this poverty line is not farfetched. Based on the sample used for this study, a poverty line of 50 JD/capita/month results in a poverty rate of 52.5 percent for cases and 69.2 percent for the population. In other words, 7 in 10 refugees were estimated to be poor based on a 50 JD per person poverty line.

**Table 1 - Summary statistics of the main case welfare aggregates (in JD)**

|Variable|Mean|Std. Dev.|Mi|Max|
|---|---|---|---|---|
|Income per capita|34.95|64.41|0|3000|
|Income per capita with no zeroes|65.50|75.99|0.5|3000|
|Expenditure per capita|77.92|74.72|1|1675|
|Expenditure per capita net of UNHCR cash|70.60|76.29|0|1675|

Source: Authors’ estimations based on Jordan UNHCR data.

7
about 15%. Hence, the difference between inequality in Russia and Poland becomes smaller when we average incomes or expenditure over 4 periods rather than looking at a single period. Whether the Gini in Russia drops more because Russians are more mobile or because incomes and expenditures are measured with more noise is explored below.

The OECD (1997) examined by how much the Gini of earnings inequality dropped in 6 OECD countries when weekly or monthly earnings of full-time workers are averaged over a 4 year period.<sup>7</sup> While earnings of full-time workers are not strictly comparable to equivalent income or expenditure, it is nevertheless remarkable that in these OECD countries the Gini coefficient only dropped by 3 to 5 percent. Rutkowski (1999) performed a similar calculation for Hungary and found that the Gini dropped by 8.2% when monthly earnings are averaged over a 5 year period. In contrast, Galasi (1998) finds the Gini of annual per capita household income in Hungary dropped by 11.7% when incomes are averaged over a _5_ year period.<sup>8</sup> This suggests that the relatively low reductions in the Ginis in the calculations using earnings may in part be related to the focus on a sample of continuously employed full-time workers. Gibson, Huang and Rozelle (1999) calculated Gini coefficients for a sample of 232 Chinese urban households using both monthly and annual household expenditure. They find the Gini based on monthly expenditure is about 50% to 80% higher than the one based on annual expenditure, indicating that monthly expenditures are subject to many shocks that get averaged out over the year.

The second approach removes any transitory component (including measurement error) in income using the methodology described formally in the previous section. T he main assumption of this methodology is that the income or expenditure distribution can be approximated well by a log normal distribution. The intuition, however, can be explained easily with the fashion metaphor. We measure the variety in each person's wardrobe by examining how much the quality of this person's clothes vary from day to day. When this day-to-day variation is higher, the quality of the outfit worn on any particular day more likely reflects an idiosyncratic choice rather than the underlying 7The countries examined are Denmark, France, Germany, Italy, the United Kingdom and the United States, and the data covers the period 1986-1989.

17
asset poor above the income poverty line, but weather anomalies can potentially prevent this from happening.

We look at rainfall only since the inter-annual variation is more pronounced than for temperature and therefore can be expected to have a larger impact on, for example, crop income. A deviation from normal rainfall will have different effects, depending on whether it is above or below the historical mean, and the level of the historical mean. In Table 5 we therefore distinguish between positive and negative deviations, and wet and dry areas. For reference, we also include the historical (30 year) rainfall (mean and SD).

For the dry areas, 17 % of the households (762 of 4,473) experienced rainfall that was more than 1 SD higher than the 30-year mean during the survey year. Close to 35% of this subset of asset poor have an income above the poverty line, as compared to close to 20% for the full sample. Also, in this subset (>1SD rainfall) we find a slightly lower share among the asset rich that are income poor (16% as compared to 21% for the two groups of asset rich households). While such simple comparisons are not a test of causality, these results are consistent with the possible effects discussed at the beginning of this sub-section: favorable weather conditions – more rainfall in dry areas – can help some asset poor households climb above the income poverty line. Also noteworthy, among those that had rainfall of more than 1SD above normal, the share of _income & asset rich_ (71.5%) is almost twice the share in the full sample of dry area households (38.7%).<sup>15</sup> Among those experiencing lower than normal rainfall (<-1SD), there is no distinct pattern.

**Table 5: Exposure to rainfall anomalies among households in dry and wet areas**

||_Income &_<br>_assetpoor_|_Income rich_<br>_& assetpoor_|_Income poor_<br>_& asset rich_|_Income &_<br>_asset rich_|_Total_|
|---|---|---|---|---|---|
|_Dry areas_||||||
|_Means for poverty categories and total_||||||
|Mean 1981-2010 precipitation (mm)|1088|1097|1152|1176|1130|
|SD 1981-2010 precipitation (mm)|201|183|181|180|189|
|Precipitation anomaly in survey year<br>(deviation/SD)|-0.04|-0.04|0.32|0.54|0.22|
|_Distribution and # of household_||||||
|All households (HH)|41.0%|10.2%|10.1%|38.7%|4473|
|HH with rain > (mean + 1SD)|9.6%|5.1%|13.8%|71.5%|762|
|HH with rain <(mean - 1SD)|38.5%|13.2%|10.5%|37.8%|886|
|_Wet areas_||||||
|_Means for poverty categories_||||||
|Mean 1981-2010 precipitation (mm)|2315|2244|2175|2092|2201|
|SD 1981-2010 precipitation (mm)|339|305|283|268|300|
|Precipitation anomaly in survey year|0.09|0.15|0.23|0.34|0.21|
|_Distribution and # of households_||||||
|All households (HH)|36.0%|12.1%|12.2%|39.6%|2856|
|HH with rain > (mean +1 SD)|33.3%|2.0%|5.1%|59.6%|198|
|HH with rain <(mean - 1SD)|62.4%|13.6%|4.7%|19.6%|213|

For the wet areas, the picture is less pronounced. Only 14.4% had a deviation in rainfall of more than 1SD, as compared to 36.8% for the dry areas. Among asset rich households that

> 15 We also did the analysis for more extreme deviations (>2SD), and the same patterns hold.

17
continuing fiscal and investment incentives provided by public and private institutions on R&D and human capital.

According to Bjorvata and Eckel (2006), with many countries competing for FDI and trade, particularly in the presence of an asymmetric competition among countries, there are undesirable welfare effects for developing countries.

Authors like Suyanto et al. (2009) suggest that policies for strengthening the absorptive capacity of domestic firms through investments in knowledge and human capital formation may be superior to policies that provide concessions for FDI and trade.

In this context more general policies should be pursued, that not only attract FDI but also benefit domestic firms, for example, building modern infrastructure, increasing and strengthening the institutions for accelerating and sustaining economic growth.

# **References**

Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977): ―Formulation and Estimation of Stochastic Frontier Production Function Models‖, _Journal of Econometrics,_ (6). 21-37.

Aitken, B. J., and Harrison, A. E. (1999). Do domestic firms benefit from direct foreign investment? Evidence from Venezuela, _The American Economic Review_ , 89(3), 605–618.

Battese, G. E., and Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. _Empirical Economics_ , 20(2), 325–332.

Battese, G.E. and Coelli, T.J. (1988), Prediction of Firm-Level Technical Efficiencies with a Generalised Frontier Production Function and Panel Data. _Journal of Econometrics_ , (38). 387-399.

Battese, G.E. and Coelli, T.J. (1993): ―A Stochastic Frontier Production Function Incorporating a Model for Technical Inefficiency Effects‖, Working Papers in Econometrics and Applied Statistics No. 69.

Bjorvata, K., & Eckel, C. (2006): ―Policy competition for foreign direct investment between asymmetric countries‖, _European Economic Review_ , 50 (7), 1891–1907.

Blomström, M., Sjöholm, F. (1999). Technology transfer and spillovers: does local participation with multinationals matter?, _Eur. Econ. Rev_ . 43, 915–923.

Blomstrom, M., Kokko, A., (1996): ―Multinational Corporations and Spillovers‖. CEPR Discussion Paper, No. 1365.

Borensztein , E, J. De Gregorio, J., Lee, J. W., (1998): ―How does foreign direct investment affect economic growth?‖, _Journal of International Economics_ , (45) 115–135.

Brainard, L.S., (1993): ―A Simple Theory of Multinational Corporations and Trade with a TradeOff Between Proximity and Concentration‖, NBER Working Paper, No. 4269.

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the same financing terms as above, except that the corresponding reinsurance premium at that level would be 4% of the loss coverage layer, given the lower probability of use.

Testing Alternative Risk Financing Instruments: IBRD, IFC and Commercial Loan Terms **Using 1%** Catastrophe Event Probability

||Reinsurance|**IBRD**Terms|IFC Terms|Commercial|
|---|---|---|---|---|
|Simulation period|400 years|400 years|400 years|400 years|
|Event probability|1%|1%|1%|1%|
|Premium Rate|4%||||
|Interest Rate|na|6.5%|10.5%|11.0%|
|Commitment fee|na|0.75%|0.5%|1%|
|Front end fee|na|1%|1%|1%|
|Maturity/grace|na|15/5|10/2|6/ 1|
|Guarantee fee|na|||0.7%|
|**Financial Results**|||||
|PV Cost Ratio:|||||
|Financing / Reins.|1.0|0.2|0.3|0.4|
|Ratio of: avg. debt<br>service / Premium|1.0|3.5|4.8|6.5|

The second scenario above provides a much more favorable cost/benefit outcome in terms of relative PV costs of using financing versus reinsurance. An additional benefit would be the absence of volatility in the 'reinsurance' rate due to pre-set financing terms. For this purpose, the IBRD loan facility has the additional option of being converted to a fixed rate loan following disbursement through a currency swap/conversion agreement which, under current IBRD policy would only charge a 0.125% fee. At a LIBOR rate of 5.6%, for example, and IBRD's loan spread of 0.55%, the total cost before fixing the rate, would amount to 6.15%. The additional fixed rate cost plus the transaction fee would raise this rate higher, therefore, the 6.5% rate used in the simulation is reasonable and within expected bounds. In addition, the commitment fee assumed is 0.75% while present waivers only require a 0.25% fee payment currently, and in the foreseeable future.

As can be observed, when the lending proceeds are utilized (1% probability), the relative debt service ratio compared to reinsurance premiums is much higher than in the first scenario with 5% probability, and if the loan repayment were annuitized, it would be equivalent to a 13% rate on line. The main reason for this is that, at the 5% probability level and leaving the loan termns unchanged, the premium itself is substantially higher (7% vs. 4%) and therefore the relative debt service ratio in the first case appears more favorable. However, even if this were not the case, there exists another key problem with engaging risk financing at the 5% probability level: At 5% event probability, the excess of loss reinsurance cover represents a low to middle layer cover among the possible layers of cover. At this level, this constitutes a key segment of the XL reinsurance market and therefore using credit facilities, particularly at favorable terms, could constitute market interference. It is therefore not recommended, unless private commercial parties are willing to provide financing at terms similar to IBRD, to engage credit financing for catastrophe risk at the 5% probability layer.

However, the same argument does not apply to the 1% probability layer. This is because at the highest (least likely) but potentially more destructive event probabilities, it