rafmacalaba/data-use-mentions-extended huggingface dataset:
Public spending on primary and secondary education is in general progressive, with the poorer sections of the population benefitting proportionately more. The degree of progressivity is greatest in Costa Rica and Panama, while it is smallest in Honduras. Nevertheless, what is important is not just the quantity but also the quality of the spending that goes to the poor. As was clear from the analysis of transition, many poor students simply fail to complete primary education and enter and complete secondary education.

Another way to measure the redistributive effect of the public expenditure on education is the Reynolds and Smolensky (RS) index, which measures the redistributive effect as the reduction in the Gini coefficient due to public expenditure.<sup>27</sup> This is done by assuming that each individual using the education service receives the same amount of benefit which is equal to the mean public expenditure on that service. For example, if the person attends a public primary school then the per student public expenditure on primary education is assigned to him as extra income. This index is presented in Table 8.

These estimations show that while public spending on primary and secondary education has some redistributive capacity in all the countries, expenditure on tertiary education has virtually no effect. For countries with relatively high Gini coefficients - Panama, Guatemala and Honduras - public spending on primary and secondary education is largely equitable: RS indexes are above 2 points implying a large subsidy to families in lower quintiles. In tertiary education, RS indexes are much closer to zero so public spending does not alter income distribution.<sup>28</sup>

|**Table 8: R**|**edistributiv**<br>|**e capacity**<br>**C**<br>|**of Public Ex**<br>**irca 2008**<br>|**penditure in**<br>|**Education (**<br>|**PEE)
travel<br>for work outside<br>the village/city|0.74<br>(0.02)|0.47<br>(0.02)|0.63<br>(0.02)|0.41<br>(0.02)|-0.12***|-0.06**|
|Her job is|0.67|0.50|0.63|0.47|-0.04|-0.03|
|physically<br>demanding|(0.02)|(0.02)|(0.02)|(0.02)|||
|She works in a|0.56|0.31|0.45|0.25|-0.11***|-0.06**|
|hotel/restaurant|(0.02)|(0.02)|(0.02)|(0.02)|||
|Observations|5|15|6|35|1,|150|

**Table 18.2 Approval of women’s work under different contexts, non-migrant vs. migrant households**

||Females w/o<br>Mea<br>|migrant spouse<br>n/(SE)<br>|Females with<br>Mea<br>|migrant spouse<br>n/(SE)<br>|Pairwi<br>differen<br>|se t-test Mean<br>ce<br>|
|---|---|---|---|---|---|---|
||**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>
from the Bureau of Labor Statistics (BLS), which are measured at the individual good level for manufacturing sectors (NAICS31-33). I calculate changes in log prices over the life of the good in the sample (conditional on a price change) which Gopinath and Itskhoki (2010) argue is able to uncover the long-run structural pass-through coefficients in a menu cost model.<sup>8</sup> The sample is 1992-2007 for import goods from the top 10 US trade partners (which represent around 2/3 of US imports) and 1994-2006 for US producer prices. All prices are real (deflated by the US CPI). I drop the top and bottom 1% outliers. For US producer prices regressions, I control for materials costs (domestic and imported) using NBER-CES materials costs index in the same NAICS 6 digit sector. Unfortunately, I have no good data on source imported materials prices for import prices regressions, though I tried a few unsatisfactory proxies (see Section 3.5 for a discussion).

Competitors’ RERs are the import-weighted average of bilateral RERs of all countries importing to the US in the same NAICS 6d sector (excluding the bilateral RER for import price regressions). The index of foreign competitors’ prices is constructed from import price microdata from the same NAICS 6d sector using a similar method as GI2011 (though I remove prices of other goods from the same exporting country). Compatriots’ prices are constructed from BLS microdata in a similar manner and aggregate US producer price indices by NAICS 6d sector are taken from the BLS website (both introduced in Section 6). See the appendix for more detail on data sources and the construction of key variables.

3.2. **Pass-through of competitors’ RER to import prices.** I estimate Equation 8, with the results shown in Table 1 in columns 1-4 (change in log prices over the life of the good, conditional on a price change). pR,ijk is the log real import price from country
polio does not even rank among the top 25 contributors to life-days-lost (Ghana Health Assessment Teram, 1981, Table 2). As a rough approximation, assume that the Ghanaian figure of 1.2 days of life lost per person per year applies to Java as well. Then adding polio vaccination would save an additional 164 life years in the population of 50,000 Javanese (1.2 x 50,000 / 365.25), while requiring an a-iditional 18,300 vaccinations on the assuaption that the children getting DPT get polio vaccinations at the same time. Thus the net effect of these three adjustments to the Javanese - immunization program would be a loss of 697 life-years (449 + 164 1310) and an increase in the number of vaccinations by 17,700 (1500 + 18,300 - 2100). To arrive at an estimate of b for Java, subtract 697 from 1790, multiply the result by the number of days in a year and add 22,500 days of averted disability (from column 9 of Table 2) for a total of 421,700 LDS. Then divide this total by the 44,700 (27,000 + 17,700) vaccinations that would be required to achieve it. The resulting estimate is 9.4, a substantial reduction in average impact from the program defined by the Michigan study.[15] 2. Estimates of Life-Days-Saved Per Encounter. The impact of an immunization program is inherently easier to estimate than that of a VHW program, because effective immunization produces a measureable change in blood chemistry which accurately predicts whether an individual will ever contract the disease in question. In some cases sero-conversion correlates highly with an even more visible sign, a scar at the vaccination site. In contrast, the impact of VHWs on health can only be measured by observing a change in health status associated with their activites. Nevertheless the absence of information on the impact of VI W services on health status is surprising in light of the available experience with VHW projects
Gardiner, B. A., G. R. Stacey, R. E. Belcher, and Wood, C. J. (1997). Field and wind-tunnel assessment of the implications of respacing and thinning on tree stability. Forestry 70:233-252.

Gardiner, B.A., Berry, P., Moulia, B. (2016). Review: Wind impacts on plant growth, mechanics and damage. Plant Science 245.

Gijón Mancheño, A., Herman, P.M.J., Jonkman, S.N., Kazi, S., Urrutia, I., van Ledden, M. (2021). Mapping mangrove opportunities with open access data: A case study for Bangladesh. Sustainability 13.

Global Forest Watch (2020). https://www.globalforestwatch.org Government of Bangladesh (2012). Detailed design of 5 polders. Volume I. Design report.

Herrera-Silveira, J. A., Teutli-Hernandez, C., Secaira-Fajardo, F., Braun, R., Bowman, J., Geselbracht, L., Musgrove, M., Rogers, M., Schmidt, J., Robles-Toral, P. J., Canul-Cabrera, J. A., & Guerra-Cano, L. (2022). Hurricane Damages to Mangrove Forests and Post-Storm Restoration Techniques and Costs. The Nature Conservancy, Arlington, VA.

Horstman, E., Dohmen-Janssen, M., Narra, P., Van den Berg, N.J., Siemerink, Martijn., Hulscher, S. (2014). Wave attenuation in mangroves: A quantitative approach to field observations. Coastal Engineering. 94. 47–62. 10.1016/j.coastaleng.2014.08.005.

Iftekhar, M.S., Islam, M.R. (2004) Degeneration of Bangladesh Sundarbans mangroves: a management issue. Int Forest Rev 6:123–135 Iftekhar, M.S., Saenger,
Figure 3. Discontinuity in the Probability of Treatment on June 8, 2018

- (a) Panel A: Probability of Treatment by Week of Arrival

<!-- Start of picture text --> Jan 1 2017 Jan 1 2018 RAMV closes Dec 31, 2018<br>Date of arrival<br>Families by week of arrival<br>Probability of treatment (left)<br>[McCrary test p-value: 0.969]<br>* Observations grouped by week of arrival<br>1<br>150<br>.75<br>100<br>.5<br>50<br>Number of families<br>Probability of treatment .25<br>0 0<br><!-- End of picture text -->

- (b) Panel B: RD Plot with Fitted Local-linear Polynomial

<!-- Start of picture text -->
-200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1<br>1<br>.75<br>.5<br>PEP [=1]<br>.25<br>0<br><!-- End of picture text -->

_Notes:_ Panel A illustrates the weekly probability of treatment for all of the sample on a weekly basis (blue line) and the number of observations in each week in the survey (gray bars). Panel B illustrates the discontinuity in treatment probability for 200 days around June 8, 2018, when the RAMV census closed. The bars illustrate 40
# Appendices

## A. Survey data collection and sampling

|**Table A.1**<br>**Region**|**: Total and sampled number of firm**<br>**Stratum (sector)**|**s in Tanzania, by**<br>**Number of firms**|**stratum and reg**<br>**Percentage in**<br>**region total**|**ion**<br>**Number of**<br>**firms surveyed**|**Percentage in**<br>**region sample**|
|---|---|---|---|---|---|
|**Dar es**<br>**Salaam**|Accommodation and food service<br>activities, Construction|5,105|12.8|84|13.5|
||Communication and other services|6,385|16.1|120|19.3|
||Manufacturing, Mining, Water,<br>Energy, Agriculture|10,458|26.2|170|27.3|
||Transportation and storage|1,115|2.7|48|7.7|
||Wholesale and retail trade, Repair<br>of motor vehicles and motorcycles|16,844|42.2|201|32.3|
||**_Subtotal_**|**_39,907_**|**_100_**|**_623_**|**_100_**|
|**Dodoma**|Accommodation and food service<br>activities, Construction|941|10.6|11|10.9|
||Communication and other Services|3,469|39.2|
<u>Food Insecurity: The analysis estimates that the net PAF impact on incidence of food</u> insecurity (as defined as self-reported food sufficiency for six months or less) is a decline of 19 percentage points when using a combined IV with difference-and-difference method, and a decline of 22 percentage points when using IV on post-treatment data only. The effect is stronger for disadvantaged households, such as those from _dalit_ and _janajati_ communities<sup>8</sup> . The results are significant since chronic food insecurity is a particularly important concern in Nepal and substantial amounts are spent per year on public works programs aimed at alleviating hardship for food insecure households. Sustained food price inflation remains a concern and an estimated 3.7 million people are currently food insecure. The impact of high prices and food insecurity is most severe on economically, geographically and socially marginalized communities. Since Nepal’s poorest households spend more than 75% of their income on food, high prices will continue to affect poverty alleviation efforts. The strong impact demonstrated by this evaluation suggests that the PAF is an effective program to address food insecurity.

<u>School Enrollment:</u> School participation among 6-15 year old children also appears to have been significantly affected by PAF interventions. The impact estimate is an increase of 14 percentage points in school enrollment for _all_ children in the given age-group but girls appear to have benefited even more, with 21 percentage points net increase. The effects are as strong among children from disadvantaged caste/ethnic groups. These are all notable and statistically significant impacts. While child education is not a direct outcome associated with PAF intervention, one can think of at least two ways by which a treated household would change its behavior in relation to this outcome. First, actual or perceived positive change in income/consumption for a household is more likely to reduce potential constraints of sending a child to school. Second, community mobilization and social-networking, as part of the PAF community organization (CO), may
Walker, J. Gernay, and S. Grantham-McGregor **.** 2004. **“** Feasibility of Integrating Early Stimulation into Primary Care for Undernourished Jamaican Children: Cluster Randomised Controlled Trial.” _British Medical Journal_ 329(7457)89: 1-4.

- Reynolds, A.J., J.A. Temple. 1998. “Extended Early Childhood Intervention and School Achievement: Age Thirteen Findings from the Chicago Longitudinal Study.” _Child Development_ 69(1):231-46.

26
prices are significantly smaller than earlier estimates based on different stylization of complete or partial liberalization The results in scenarios I and II are based on the assumption that restraints on domestic support will be ineffective. In contrast, scenario IV (column 5) assumes that domestic support restraints will be effective. The benchmark simulation used 1989/93 protection levels for the period 1994/2002. Where protection is negative (in the case of import subsidies or export tax), no changes in protection rates were assumed. As expected, the price impacts from scenario IV show larger prices increases than in Scenario I and II.

The last column are from the FAO study (1995) based on a partial equilibrium model and excludes the impact of manufacturing liberalization. The analysis is based on the detailed data on agreed bound tariffs rather on stylized reductions used in other studies. However, the FAO study does not consider the extent of "slippage" due to the excessive tariffication and ineffective disciplines in domestic support. That is, the FAO study measures liberalization by the reduction in the bound tariff rate.

Overall, the price changes from several simulations shown in Table 2 suggest that world agricultural prices relative to the price of OECD manufacturing exports are unlikely to rise significantly as a result of the actual outcome of the Round. The estimates are significantly smaller compared with the price boom in food prices in 1995 following the completion of the Round. In utilizing these results, it is important to keep in mind the inherent difficulty in defining the true counterfactual rate of protection. Since protection rates are measured based on world prices, there is a question of establishing the representative world market prices at the end of the implementation period. Further, as shown by Anderson and Hayami (1986), protection rates in agriculture are determined by economic variables and influenced by the level of development of a country. If the empirical evidence of the upward trend in protection in some countries (e.g. East Asia and EU) are true and assumed to continue in the future
page 15 First, consider the numbet of addicional life years that would be saved if mea.sles vaccination were added to the Javanese program. The Michigan study estimated the incidence rate at zero for infants less than one and only 200 per chousand among children aged one to four. In the latter group the study assumed the case fatality rate to be 4.8% (0.5Z among the 20X treated and 5% among the 80% untreated). If measles vaccine is 60% effective as assumed in Table 1, then it will save 5.76 lives per thousand vaccinated (200 x .048 x .6). Assuming that 1,500 children are vaccinated per year just as they are entering the 1-4 age bracket where their life-expectancy is 52 years (from Table 2, column 3), the addition of measles vaccination would save an additional 449 life years per year in this Javanese rural population of 50,000 (5.76 x 1.5 x 52).

However, the incidence of neona.al tetanus in Java was estimated at 21.3 per thousand with a case fatality rate of 90%. Thus, removing the 2100 doses of t,.tanus toxoid given to the pregnant mothers (assumed 95% effective by Grosse et al, Appendix A, V.3) would increase deaths in the zero to one age group by 18.2 per thousand. For the 1,500 in this age group whose life expectancy is 48 years, the life-years lost would be 1,310 (18.2 x 1.5 x 48). The Michigan study did not include polio among the 31 diseases analyzed, possibly because its impact on mortality and morbidity was deemed small. Indeed in Ghana polio does not even rank among the top 25 contributors to life-days-lost (Ghana Health Assessment Teram, 1981, Table 2). As a rough approximation, assume that the Ghanaian figure of 1.2 days of life lost per person per year applies to Java as
30 one to the sample maximum of seven reduces the fiscal costs of bank crisis as a fraction of GDP by approximately 31 percentage points when M,/GDP is at its lowest value (0.17), but increases the fiscal costs of bank crisis by 57 percentage points at the highest (1.87).2?

<!-- Start of picture text --> Table 1: Checks and balances and the magnitude of banking crises<br>hite-adjusted standard errors in parentheses<br>Dependent (1) (2) Q)<br>variable: Fiscal Base Controlling for Controlling for<br>costs of crises specification income/capita terms of trade<br>constant 29.3 29.1 18.3<br>checks 7.6 -8.0 4.6<br>(3.3) (3.8) (3.0)<br>checks * M,/GDP | 10.7 11.1 7.0<br>(4.3) (5.0) 3.4<br>M,/GDP 28.3 24.6 -16.2<br>(12.8) (14.2) (11.7)<br>No. of years from | 1.6 15 27<br>First year ofcrisis to | (1.5) (14 (1.2)<br>year of next<br>election<br>GDP/capita -0.12<br>(.28)<br>Terms of trade -18.7<br>shock 13.2<br>R 0.21 0.22 0,34<br>N 40 39 31<br><!-- End of picture text -->

Note: Regression 2 excludes Taiwan because income per capita not available for Taiwan from International Financial Statistics, IMF. Observations are “clustered”
with approximately $0.4 trillion required across the low and high adoption scenarios to deliver universal broadband across Africa using the same technology (4G with a wireless backhaul). Such an approach equated to a saving of roughly 38%-41% compared to using 3G with a wireless backhaul. A key caveat to all the supply-side cost estimates in this analysis is that the user device cost has been excluded.

# 6.1. Limitations

There are several limitations of this analysis which are worth discussing and identifying as areas of future research. Firstly, data limitations are a key issue, even for the six detailed country assessments. For example, a commitment was made to use the GSMA adoption data for unique mobile subscribers because the data set covers most countries globally, but there can be differences in these estimates compared to those released by the telecommunication regulators in various countries. This highlights the fact that it is challenging to undertake both detailed country modeling, while also aiming to provide breadth of analysis across an entire continent. Ultimately this leads to a trade-off in local data quality, to provide systematic insight at the continental scale. Further research should explore the implications of different distributional assumptions in the statistical generalization method applied here.

Secondly, the assessment was based on scenario analysis, and included forecasts for future demand based on a variety of different potential futures. Hopefully by using variation in these forecasts, the model was able to capture uncertainty around this key set of parameters, but it is also important to recognize these comparative scenarios are not necessarily predictions. Moreover, the exogenous treatment of cell phone adoption is a simplification and overlooks the fact that adoption may be dependent on the quantity of infrastructure deployed. In general, future research needs to focus more on demand-side aspects, particularly the impact of device affordability.

37
origin and destination (push and pull factors).** The most representative and common economic variable used is the per capita gross domestic product (GDP).<sup>9</sup> In terms of labor market variables, both employment rate and informality rate in the origin and destination are included as regressors. Average wages are also included to capture income perspectives in the respective states. Labor market indicators at the state level were calculated using the National Labor Force Survey (ENOE).

**Environmental variables are those that affect the quality of life.** These variables are included to account for the fact that people may decide to move from one state to another not only due to economic reasons. As a proxy of the quality of life in each state, the Deprivation Index, _Índice de Rezago Social_ ,<sup>_10_</sup> which accounts for deprivations in non-monetary dimensions such as education, health care, and basic services (electricity, sewerage, etc.); the incidence of crimes,<sup>11</sup> following Anjomani (2002); and a variable capturing perceived levels of public sector corruption<sup>12</sup> in the states of origin and destination are added as regressors. Variables indicating preschool educational offer in the origin and destination are also added as they might be important push and pull factors, especially for women with children.

**Additionally, the stock of internal immigrants in the destination state at the time of migration is included as a proxy for the availability of social networks.** This variable is expected to have a positive

> 8http://www.conabio.gob.mx/informacion/metadata/gis/muni_2018gw.xml?_httpcache%20=%20yes&_xsl=/db/metadata/xsl/f <u>gdc_html.xsl&_indent%20=%20no</u>

> 9 GDP data come from INEGI (National Accounts).

> 10 The _Índice de
- 11 -

We expect that the management training offered in survival clusters will decrease σ<sup>2</sup> . It is not clear _a priori_ whether innovative firms have higher or lower σ<sup>2</sup> in dynamic clusters. But as appropriate management practices are established, we expect to observe that large firms have high μ and low σ<sup>2</sup> .

# **Case Studies**

In this section, we confront the above hypotheses with enterprise data collected in two industrial clusters in China and one each in Vietnam, Ghana, Ethiopia, and Bangladesh. This empirical exercise is not intended to perform rigorous hypothesis testing, but rather to provide suggestive evidence. The six clusters that we take up here as examples have different degrees of success in multifaceted innovation, as will be explained shortly. Table 1 shows each cluster’s location, main product, population of final goods producing firms, the mean and median of employment size and labor productivity (which is defined as value added divided by the number of workers) in the year in which we gathered information.

Figure 2 consists of 12 panels of scatterplots showing the distributions of labor productivity and employment size in these six clusters for two years. The summary statistics of these distributions are reported in columns (5) and (6) of Table 1. The vertical axis of the scatterplots measures the monthly value added per worker in US dollars as labor productivity.<sup>5</sup> The horizontal axis measures the logarithm of the number of workers.<sup>6</sup> The scatterplots share the same scale on each axis to allow comparison among the clusters and are useful for seeing whether the data are consistent with our hypotheses. Later in this section, we will turn to Table 2, which presents the results of some randomized controlled experiments in which basic management training was provided for entrepreneurs.

Cluster 1 produces sweater and other knitwear items for an export market in Eastern
Figure A.3. Refugee Characteristics before and after moving to Tajikistan <!-- Start of picture text --> .5 1<br>0.46<br>0 .8 3<br>.4 0 .3 8 .8 0 .7 7<br>.3 .6<br>0.55<br>.2 .4<br>0 .1 6<br>0.29<br>0 .1 0<br>.1 Female Male .2 Female Male<br>Tajikistan Afghanistan Tajikistan Afghanistan<br>50 40000<br>4 6.0 7<br>30843.08<br>45 30000<br>40 4 0.0 2 20000<br>150 77 .52<br>35.83<br>35 10000<br>31.43<br>30 Female Male 0 79.70 Female 496.90 Male<br>Tajikistan Afghanistan Tajikistan Afghanistan<br>.45 7<br>6.86<br>0.41 6.77<br>.4<br>0.38<br>6.5<br>.35<br>.3 0.29 0.29<br>6.03<br>6<br>5.92<br>.25 Female Male Female Male<br>Tajikistan Afghanistan Tajikistan Afghanistan<br>Employed [=1]<br>Labor Contract in Afghanistan [=1]<br>Weekly Hours Work in Afghanistan<br>Monthly Wage in Afghanistan (Somoni)<br>Student [=1]<br>Household Size<br><!-- End of picture text --> _Notes:_ The data comes from the Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. The Figure includes the 5,592 individuals using in the main regressions. Red lines represent 95%