rafmacalaba/data-use-mentions-extended huggingface dataset:
development.<sup>4</sup> A few recent studies have used improved scientific approaches for data collection to estimate the impact of roads. Lokshin and Yemtsov (2005) demonstrate the economic benefits of roads and other infrastructural projects, using a propensity score-matched double difference method. van de Walle and Cratty (2005) also use the same method to estimate the impact of road development.<sup>5</sup> Finally, BIDS (2004) employs a panel household survey data of a quasi-experimental nature to assess the impacts of road improvement projects. However, the BIDS (2004) study employs bivariate analysis (e.g., a difference-in-difference technique), which is not ideal for this dataset given that the data are not of random experimentation.

Examining the distributional consequences of road development across different income levels has been an additional challenge, particularly on top of the need for a methodology that estimates effects over time and is able to control for unobserved heterogeneity. Aside from Lokshin and Yemtsov (2005), who also conduct their analysis on “poor” and “non-poor” samples to find that each group benefits differently from road development,<sup>6</sup> distributional effects of rural road investments have not been addressed extensively in the rural road literature, hampering assessments of the true breakdown of poverty reduction from infrastructural improvements.

Our paper, using the BIDS panel data, estimates the income-consumption benefits of road investment by controlling for both household- and community-level heterogeneity. Because poverty reduction is an overarching goal of policymakers and donors, we also assess the poverty reduction effect of road investment projects. We offer an empirical assessment on the impact and role of roads on economic behavior across different income groups by using a fixed-effects quantile estimation approach. Finally, we estimate the distribution of road benefits by gender, examining whether road investment benefits men and women as well as boys and girls differently.

The paper is organized as follows. Section 2 presents an econometric framework to estimate the impact of road development
then this is counted as two legal disparities as it applies to both married and unmarried women. Thus the value of a disparity is either 1 if it only applies to married, and 2 if it applies to both married and unmarried women. There is only one specific situation where the value of the disparity is not assigned a score of 1 or 2 – differences in paternity and maternity leave. In this case the disparity is measured as (1-(P/M)), where P is length of paternity leave and M is maternity leave. A value of 0 would indicate parity in paternity and maternity leave. A value of 1 would indicate the highest degree of disparity as length of paternity leave would essentially be zero. Thus the greater the length of maternity leave relative to paternity leave, the greater the gender disparity (paternity leave does not tend to be greater than maternity leave). Since the disparity applies to both married men and women, it is multiplied by 2. The rationale behind having a continuous measure for the disparity in paternity and maternity leave is to capture the heterogeneity in the amount of leave allowed across economies. A full listing of legal gender disparities and the respective score assignment are presented in table A1in the appendix by subcategory. Data on legal disparities are available for 167 economies based on data collected for the 2016 _Women, Business and Law_ report.

# **3. Economy Rankings and Regional Patterns**

The ordering of economies from the lowest to the highest number of legal gender disparities is presented in the appendix in table A2. Overall the top 5 in decreasing order are as follows, with the number of legal gender disparities indicated in brackets: Slovak Republic (2), Portugal (3), Australia (4) , Spain (5.77) and Mexico (5.88); all are high income OECD economies with the exception of Mexico.<sup>2</sup> At the other end, the bottom five (in order of lowest
macro approach that is not well grounded in the local realities to ensure inter-jurisdictional equity. These manna from heaven transfers also create an incentive and accountability structure that is not conducive to responsible, responsive, fair and accountable local governance. Simpler alternatives as outlined in the following paragraphs have the potential to enhance efficiency and equity of such transfers mechanisms.

# **DAU Simplification Alternatives**

In the following three alternative options for the reform of DAU are presented. Common elements of these three alternatives are :

- One size does not fit all. Requires grouping (clustering) of local governments by population size, area and class of local governments.

- The adopted formula must have a sunset clause of five years but interim changes in the formula should not be allowed.

- The formula must have ceilings and floors to keep yearly entitlements stable and predictable.

- National average (by size and class) gap filling or equalization standard to replace Williamson‘s index. Pool can be adjusted by affordability and allocation determined by standard.

- Gap filling or equalization by size and class of local governments and possibly equal per capita grants to villages.

- Fiscal capacity measurement based upon potential revenues plus tax shares and other transfers and 50% of resource revenues.

- Fiscal needs measurement to discontinue wages compensation (basic allocation), discontinue use of HDI and the Williamson‘s index and other indexes. Instead consider need meesaurement based upon service/client population for each service category.

19
to the public eye perform better under PMT. For example, livestock and durables ownership are negative and significant for both methodologies, but the difference in means is greater under RMG-eligible selection. Conversely, nutrition and income, the status of which might be known only to household members, perform better under PMT; the difference in the nutrition composite z-score t-test is negative and significant only for PMT, and the difference in means for income is significantly larger under PMT.

The village-level results suggest that when organizing beneficiary selection to meet the needs of the local context, the village heads selection was effective through leveraging the heads’ familiarity with the households. This familiarity, however, was limited when it came to assessing more obscure or private household characteristics. In such cases, the rigorous PMT methodology based on detailed survey data performed better.

We must also note that less-poor households (ranked ‘Middle-income’ or ‘Better-off’) were included in the RMG-eligible list in some villages in order to satisfy village quotas for the program. We would expect the inclusion of these non-poor households to worsen the average performance of the village heads selection. In a setting where the quotas for road maintenance were satisfied by the households from the

> 14 Comparison of RMG eligible observations and random sample using this restricted selection of variables provides results similar to those shown in Table 1. See Table A5 in the Appendix for all comparisons.

> 15 Please note, income per household member is shown as z-scores in Figure 1 in order to display the measure along with the other indicators in the graph, but the actual difference for the RMG-eligible vs. RS is -0.1426 (p<0.01) and, for PMT poor vs. nonpoor, -0.8213 (p<0.01).

> 16 Income per household member is again shown as a z-score to aid the graph. Actual difference between RMGeligible and RS is -0.2160 (p<0.01). Between PMT poor and nonpoor, the difference is -
purported advantage is significantly reduced when MBIs (in particular pollution fees or taxes) are used in combination with, and not instead of regulatory standards. In such cases, regulated facilities still have to comply with regulatory standards thus undermining the possibility for pollution control efforts to be distributed across industrial facilities in a cost-effective manner.

8 This approach is also gaining increasing popularity in developing countries. The Laguna Lake Development Authority (LLDA) in the Philippines has adopted a system of environmental user fees since 1997. This (amended) system has been implemented throughout the Philippines with the adoption of the Philippines _Clean Water Act_ in 2004. Vietnam has adopted a system of wastewater discharge fees, effective January 1, 2004. Sri Lanka is nearing the adoption of a similar wastewater discharge fee system. China has been using pollution discharges fees for more than two decades.

9 This is mostly because the traditional enforcement tools such as company shut-down and financial penalty cannot be easily established or enforced, especially in developing countries where economic development and employment are still of the first priorities.

10 A number of countries have set-up web-accessible pollution release transfer registries. While not necessarily being exhaustive, this list includes Austria, Australia, Canada, Chile, the Czech Republic, Denmark, England, France, Germany, Hungary, Italy, Japan, Mexico, the Netherlands, Norway, Scotland, South Korea, Spain, and Sweden. One challenge with this approach is how information on volumes of releases of different substances can be translated into degrees of absolute or relative risks resulting from these releases.

11 Among the concerns about public disclosure, terrorism has been one in some developed countries. But as an EPRD program only discloses the performance ratings, terrorism should not be a significant concern. 12 The Guangzhou Green Watch Program in China uses three categories while Indonesia’s PROPER now cover seven categories.

13 For example, it has empirically been demonstrated that market valuation of enterprises (listed on stock markets) is significantly reduced following the disclosure of inferior environmental performance. 14 The
The first two criteria require relatively long windows on each side of a turning point and for the full cycle in order to capture changes in interest rates that are not reversed soon afterward; this allows us to identify and study periods of “premature adjustment”, i.e., when a central bank shifts from raising to lowering interest rates (or vice versa) and then needs to reverse course. These longer windows also avoid classifying changes in interest rates that largely reflect market-driven movements as turning points, an issue earlier in the sample when interest rate data is more volatile and policy rates may not be directly set by central banks. In contrast, the third criterion allows for individual phases in a cycle to be short-lived; brief periods of rate hikes or cuts can potentially qualify as an easing or tightening phase—albeit within the longer parameters for a full cycle. This is useful in several cases when a central bank adjusts rates quickly by a large amount, and then does not adjust rates again (such as lowering rates to zero in one meeting in response to a negative shock).

After applying the algorithm, we make several adjustments to the dates identified in order for a month t to qualify as a turning point. These adjustments are mainly to address issues when interest rates are constant for an extended period around the lower bound. First, a month can qualify as the start of an easing (tightening) phase if there is no change in the policy rate but the central bank starts a new QE (QT) program (defined below). Second, if there is not a new balance sheet program, there must be an increase (decrease) in the policy interest rate to qualify as the start of a tightening (easing) phase. Finally, any such increase (decrease) in the policy rate must be meaningful and lasting, defined as |∆it| ≥0.50 percentage point over one month, or at least two rate changes (of any size) occurring over a year
students’ knowledge of various subjects (critical reading/language, mathematics, social sciences, natural sciences, philosophy, and English). The measure of individual-level academic readiness preSCP is the average of all the student’s SABER 11 subject scores, standardized by semester-year.

_Age_ is a variable whose meaning differs across estimation samples. Age is the student’s age when entering the program for the graduation sample; the age when she took the SABER PRO exam in the learning sample; and the age when she was employed in the labor market samples.

_Relocated to pursue a SCP_ is a variable that equals to 1 if the student moved away from the student’s high school city to participate in the SCP. We construct this variable based on the municipality codes of the student’s high school city and the city where she enrolls in her SCP.

_Entry year_ indicates the year the student started the program in the graduation sample. This year is estimated for the other samples as follows: entry year = student graduation year – average time to degree at the student’s specific program.

_Graduation year_ consists of the year when the student graduated in the graduation, employment, and wage samples; and the year when she took the SABER PRO test in the learning sample.

# **Measures of institution and program practices and other supply and local market conditions**

_Institution and program practices._ Using data from SPADIES, we define three practices that are relevant to our context:

- _Selective institution._ To proxy for institution selectivity, we estimate the average 2008 SABER 11 exam score across all programs taught by each institution providing SCPs, and rank institutions based on it. Institutions in the upper half of the ranking are classified as “selective.”

- _Institution field specialization._ For a particular field, this is the share of programs that an institution offers in that field relative to all the programs it offers. The index ranges between 0 to 1; an index close 1 indicates that the institution is
**Figure 3. Microfinance Products offered by MENA MFIs**

<!-- Start of picture text --> Region Total<br>Egypt<br>Morocco<br>Lebanon Microenterprise<br>Jordan Islamic finance<br>Iraq Consumer<br>Palestine<br>Housing<br>Syria<br>Mortgage<br>Tunisia<br>Yemen<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: Sanabel 2010 (2008 data)

**Microsavings are offered on a very limited scale in the region** , and are only found in West Bank & Gaza, Syria, and Yemen and only offered by a few institutions.  Al Amal Microfinance „Bank‟ in Yemen, for example, offers sight deposits, child savings accounts, and also time deposits, as well as Shariah-compliant insurance on loans (Takaful). Micro insurance for health and education (beyond standard death and disability protection offered by many MFIs on microloans) is being introduced by leading microfinance NGOs in Egypt and service companies, as service agents for insurance companies (Allianz and Alico).

# **Trade credit (credit provided by other firms) is used by many micro and small enterprises**

**in the MENA region.** A significant proportion of micro and small enterprises interviewed for IFC microfinance country reports in 2006-2008 reported accessing supplier credit, and most paid little or no interest charges. Half of micro and small enterprises surveyed for the IFC in Yemen (205 out of 405), 39.5 percent of those surveyed by Syria, 40.7 percent of those interviewed in Algeria, and 40.7 percent of those in Lebanon, used some form of supplier credit to acquire supplies, whether as an advance with balance upon receipt, or making a delayed cash payment after delivery.<sup>10</sup>

**Islamic financing instruments comprise only a
- 32 -

results in extremely high nominal tax rates. (In England, the tax rate is based on prices of 1972, and exceeds 200 percent in most jurisdictions.) Countries have found it more politically expedient to instead increase valuations and hold nominal tax rates more or less constant. The traditional approach to increasing valuations entails a physical reinspection of each property, generally at fixed intervals of five years. This approach is not appropriate for countries with significant inflation, however. Five year intervals between revaluations would result in four years of declining real revenues, followed by extremely abrupt (and politically hazardous) incretases in nominal valuations in the fifth year. More frequent general revaluatioas would be unacceptably costly, however. Countries affected by inflation have evolved a more cost effective means of adjusting values. Rather than physically reinspecting all properties, the valuation authorities adjust valuations according to a price index. In Brazil, for example, municipal governments are authorized to adjust property valuations up to the level of the inflation index for government bonds, without physically reinspecting properties. In Colombia, values may be automatically increased by 50-90 percent of the increase in the consumer price index in the preceeding year.-3/ Some local governments in the U.S. automatically adjust land values based on year-to-year comparisons of the average sales price of vacant land in their jurisdictions, and adjust building valuations using regional construction cost indexes.

13/ In principle, this adjustment is to be based on the results of a special property market survey carried out by the national statistical agency.
-2 -1 0 1 2 3 4 5 6<br>Year Year<br><!-- End of picture text --> Sources: Haver Analytics, International Monetary Fund, Oxford Economics, World Bank. Notes: Unweighted average investment growth. The horizontal axis denotes years. Zero refers to the year of the start of global downturns, which include global recessions and slowdowns. Average refers to unweighted average investment growth during global recessions and slowdowns of 1975, 1982, 1991, 1998, and 2001.

29
|0.4|0.0|
|Annual percentage change in food production<br>(% per year)|0.9|3.5|4.2|

_Source_ : Author’s estimates from World Bank (2013) poverty data and ILO (2013) food price data.

# _Methods_

For systematically testing the relationship between poverty and food prices, we use both least squares regressions and instrumental variables (IV) approach. In the least squares approach the first difference in the poverty headcount ( _P_ )<sup>7</sup> is regressed against the first difference in the log of domestic food prices ( _p_<sup>_d_</sup> ) and series of time dummies ( _T_ ), with _v_ denoting the error term: One concern with equation (1) is that changes in food prices might be correlated with the error term because of omitted variables. The 1998 Indonesian financial crisis, for example, increased 7. First differences is preferable to percentage differences for two reasons. First, poverty headcounts are already measured in percentage terms, and taking percentage changes of very small values produces results that are arguably counterintuitive. For example, an increase in the poverty rate from 1% to 3% in one country and 30% to 32% in another country should arguably be regarded as equivalent. In contrast, the two changes are very different in percentage terms: 200% in the first case, and 6.67% in the second case. See Deaton (2006) for a discussion of this problem in the context of mortality rates. A second problem is that taking percentage changes can sometimes produce outliers.

7
likelihood or the two-step method proposed by Heckman (1979). We report results from the former method only, but have veri...ed that the latter yields very similar estimates. As before, the results point to a positive and statistically signi...cant relationship between export revenue and migrant networks. Results from additional estimations (not reported but available upon request) con...rm that these estimates remain very similar when log employment is used as a proxy of ...rm e¢ ciency.

Overall, thus, these results suggest that, conditional on a ...rm serving a market, the presence of emigration stocks appears to be an important determinant of how much it sells there. This ...nding is in line with the theory presented in section 2, which suggests that migrant networks are a source of ...rm-destination demand shocks.

# 7 Concluding remarks

We have used detailed ...rm-level data from Portugal to examine the e¤ect of migrant networks on export participation and intensity. In doing so, we have exploited unique features from Portugal –notably stable, historically-determined stocks of emigrants with whom some ...rms are more likely to have close ties.

While accounting for the role of ...rm productivity heterogeneity and the customary gravity-type regressors, we have found that larger stocks of Portuguese migrants in a given destination increase the likelihood of export participation. This e¤ect tends to be signi...cantly larger for ...rms that are more likely to have a larger number of contacts among the emigrants –i.e., ...rms currently located in the North of the country; and ...rms that were already born when the bulk of emigration ‡ows took place. Conditional on a ...rm serving a market, the presence of migrant networks appears to be an important determinant of how much it sells there. Taken together, these ...ndings suggest that migrant networks are
|Enrollment trends|0.50|0.50|0.47|0.50|0.48|
|---|---|---|---|---|---|
|Student feedback|0.85|0.36|0.83|0.38|0.53|
|More than once per year:||||||
|Analyze student performance to solve problems|0.85|0.36|0.89|0.31|0.13|
|Collect student satisfaction data|0.71|0.45|0.67|0.47|0.30|
|Time assigned to practical training (%)|44.79|16.38|46.13|15.86|0.34|
|Internships outside institution are mandatory|0.25|0.44|0.30|0.46|0.20|
|Mandatory internships at the end of the program|0.10|0.30|0.11|0.31|0.77|
|_Costs_||||||
|Funding options used by some students||||||
|HEI scholarships|0.78|0.42|0.69|0.46|**0.02**|
|Government scholarships|0.80|0.40|0.74|0.44|**0.09**|
|Loans by HEI|0.26|0.44|0.27|0.45|0.70|
|Loans by banks|0.18|0.38|0.19|0.39|0.75|
|Other loans|0.24|0.43|0.31|0.46|**0.08**|
|HEI
.1%|-3.1%|1.0|
|richest|-2.4%|-2.7%|1.1|
|Total|-2.8%|-3.4%|1.2|

Source: Authors’ calculation using forecasted HEIS 2018/2019.

Table 7 shows pre-reform and post-reform estimates for poverty and inequality. The combined impact is modeled sequentially. First, we estimate the direct impact, construct new welfare aggregates and then

> 4 Detailed explanation of the model can be found in Araar and Verme (2015).

> 5 The Input-Output table is prepared by the Statistical Center of Iran (SCI) and is available at: <u>https://www.amar.org.ir/</u>

9
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