rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6 Policy and Program Implications**

Because the extortion of migrants occurs in a distinct location from the environment in which victims are participating politically as deportees, we can distinguish the impact of extortion on civic engagement from potentially confounding factors related to the context in which victimization occurs. We explore extortion’s potentially oppositional psychological and economic effects. On one hand, extortion is a form of victimization which increases fear, and fear is known to depress political engagement. On the other hand, extortion serves as a negative economic shock which increases political participation. We show that extortion is correlated with both a greater likelihood of fear-induced avoidance behaviors in Guatemala and negative assessments of personal economic conditions. Here, higher levels of fear are correlated with lower civic interest, whereas negative economic assessments are correlated with higher interest in civic engagement. Although we find suggestive mediation effects for both pathways, the economic pathway in this case is larger and more robust, leading to an overall positive relationship between extortion and civic action. The results from our analysis of deported migrant data provides insights for policymakers and programming staff working to support displaced persons. We discuss takeaways from our analysis first related to the migration experience itself and then to the reintegration experience after deportation.

It is well-known that migration routes to the United States through Mexico are dangerous; however, our survey work enables us to quantify some of the negative experiences of the migration journey. Of the 1123 Guatemalan deported migrants who agreed to take our baseline survey, 17% experienced extortion during their last journey to the United States (extortion either in Mexico or after crossing the border into the United States). Furthermore, 8% of respondents were held against their will or without their identification documents, and 7% experienced assault. With limited economic opportunity and high violence experienced by would-be migrants living in Northern Triangle countries, large numbers of Guatemalans and other Central Americans will continue to attempt this migration journey despite its dangers. These statistics about migrants’ adverse experiences underscore a need for more sustainable
calculate the effects of tariff changes on domestic activity levels, tariff revenue, and welfare. This model is designed to be sufficiently simple that it can be calculated on an EXCEL spreadsheet, and to be flexible enough to allow application under differing degrees of availability of domestic data. The spreadsheet model yields an index of the effect of the preferential tariff reductions which is dependent on the assumptions made regarding the domestic production structure.

The structure of this model reflects the desire to combine information on tariffs and trade volumes, which is available at a very fine levels of product classification, with data on domestic input usage and consumption, which is available only at much more highly aggregated levels. By assuming separability between domestically produced goods (traded and non-traded) and imported goods in the production functions and utility function, we are able to combine data from these two different sources and obtain simple measures of the welfare effects of preferential tariff cuts. Chile presents an interesting example application of the model for several reasons. First, the initial tariff structure in Chile is essentially a uniform external tariff. The literature on tariff reform (Hatta (1977), Corden (1984)) has suggested that under certain conditions, welfare improvements can be obtained by either (i) a proportional reduction in all tariffs or (ii) tariff changes that move in the direction of a uniform external tariff (e.g. a reduction in the highest tariff rate and an increase in the lower tariff rate). This raises questions about the benefit of entry into a preferential trading arrangement for Chile, since it results in a movement away from a uniform external tariff. Second, the availability of two alternative preferential trading arrangements has the potential to provide insights about which types of preferential trading arrangements would be most desirable.

# I. Aggregation of Trade Data

In this section, we present a procedure for aggregating tariff line level data using nested CES functions that is compatible with domestic data on consumption and input use. The trade data consists of import values and base tariff
**Table 7: Observed mean hourly wages (2011 PPP USD)**

||Observed|Simulated|
|---|---|---|
|FT Males|1.5|1.3|
|PT Males|0.6|0.7|
|FT Females|1.2|1.2|
|PT Females|0.5|0.6|

_Source:_ Authors’ calculations.

_Note:_ Abbreviations: FT= employed full-time; PT= employed part-time. This table presents the converted values in 2011 USD PPP dollars.

# **6. Policy Experiments**

**In the region, some national action plans**<sup>**30**</sup> **exist for Roma inclusion, and some countries in the Western Balkans have funding for activities that promote employment specifically for Roma.  On the other hand, current monitoring and evaluation of policies are not adequate mostly because of the lack of ethnic disaggregated data (Robayo-Abril and Millan 2019** ). In this context, it is critical to evaluate ex-ante the impact of some policy measures that can have on Roma labor market participation and time allocation decisions. In this section, we perform seven counterfactual policy experiments designed to (i) improve Roma labor market conditions, and (ii) change the home production productivity distributions or the arrival of the fertility shock.

**The intervention policies affecting the labor demand include antidiscrimination measures and employers’ economic incentives to employ Roma population.** <mark>Examples of these policies are quota requirements in job positions and laws against labor discrimination and any type of violence.</mark> Positive action measures are being implemented in the region, including quotas in public administration in Albania, North Macedonia, and Serbia, and quotas for active labor market policies in Kosovo. Serbia spends a considerable amount on employment, as referenced in the 2016 National Progress Report. Most of the regional budget in
maximum is lower than this (see Table 9). In a few cases (3% of the total) the maximum is greater than 50,000 baht, which is in excess of the upper limit established by the guidelines produced by the VF headquarters.

A modest amount of interest-rate flexibility may sometimes be possible. If every household in a village could borrow at some market interest rate i0, and the VF lends at iV0<i0, then the benefit accruing to a borrower is (i0-iV0)L, where L is the value of the loan. Now suppose that from time to time households are hit by (unpredictable) shocks – a death, illness, crop failure – and need cash at these moments. If the cost of borrowing at such moments is i1>>i0, then an altruistic lender may want to keep some of its funds available to lend in such times of difficulty. Given that such loans have a shorter duration than standard loans, we might expect the VF to extend emergency loans at an interest rate iV1>iV0.

**<mark>Table 8.  Breakdown of VF loans by size</mark>**

|Loan amount(baht)in 2009|% breakdown|
|---|---|
|< 10,000|12.8|
|10,000|24.3|
|> 10,000 but < 15,000|8.5|
|15,000|12.1|
|> 15,000 but < 20,000|5.2|
|20,000|27.8|
|> 20,000 but < 30,000|2.6|
|30,000|3.2|
|> 30,000|3.6|
|Memo: Mean loan size (baht)|15,790|
|Memo: Standard deviation of loan size(baht)|8,335|
|Source: Thailand Socio-Economic Survey 2009||

*
ECCAS imports of meat and edible offal<br>from Africa (in thousands of US dollars) from the rest of the world (in thousands of US<br>dollars)<br> 40 000 450 000<br> 35 000 400 000<br> 350 000<br> 30 000<br> 300 000<br> 25 000<br> 250 000<br> 20 000<br> 200 000<br> 15 000<br> 150 000<br> 10 000 100 000<br> 5 000 50 000<br> - -<br>Source: FAOStat, estimates Source: Trademap<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Imports by the Economic Community of Central African States (ECCAS) of edible meat and offal have generally been on an upward trend for the past 20 years. Imports from Africa ranged from US$ 650,000 to US$ 34 million between 2001 and 2019, an average annual growth of 25 percent. Imports from the rest of the world are even more important (ranging from US$50 million to US$400 million between 2001 and 2019).

# **2.2. Market Potential in Nigeria**

Nigeria’s livestock imports over the past two decades has not varied much, following a steady trend (Figure 7). Cattle have the largest share in Nigeria's demand for livestock. FAO estimates indicate that the demand is relatively stable and is around US$ 100 million. As for sheep, demand is less significant and 6
**Table 1: Baseline summary statistics by experimental group**

||Control|No motiv,<br>no enroll|No motiv,<br>pre-enroll|Motiv,<br>no enroll|Motiv,<br>pre-enroll|
|---|---|---|---|---|---|
||Mean/SD<br>(1)|Mean/SD<br>(2)|Mean/SD<br>(3)|Mean/SD<br>(4)|Mean/SD<br>(5)|
|_Panel A: SEBRAE data (2017/2018)_||||||
|Used SEBRAE service during the year b|efore the interve|ntion||||
|Any type of service|0.202|0.145|0.212|0.192|0.197|
||[0.403]|[0.353]|[0.410]|[0.395]|[0.399]|
|Material|0.155|0.139|0.165|0.158|0.174|
||[0.363]|[0.347]|[0.372]|[0.366]|[0.380]|
|Training|0.089|0.023***|0.124|0.079|0.045*|
||[0.286]|[0.151]|[0.330]|[0.271]|[0.208]|
|Business practices used (%)|0.493|0.450|0.505|0.499|0.518|
||[0.255]|[0.273]|[0.272]|[0
We supplement our entry density data with a panel of explanatory country-level variables. First, we include macroeconomic measures (World Bank-World Development Indicators, 2010): GDP per capita, constant US$ 2000 and domestic credit provided to the private sector as a percentage of GDP. We find a strong correlation between entrepreneurship and economic development (Figure 4). As predicted by earlier studies on the relationship between financial development and economic growth (Demirguc-Kunt and Maksimovic, 2004; Rajan and Zinagles, 1998), we also see that firm creation is higher in countries with greater financial sector development, as measured by bank credit to GDP.

<!-- Start of picture text --> 10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>0 10,000 20,000 30,000 40,000 0 50 100 150 200 250<br>GDP per capita Domestic Credit (% GDP)<br>Entry Density<br>Entry Density<br><!-- End of picture text -->

**Figure 4: Entry Density and Economic and Financial Development**

Next, we include measures of the strength of the business environment and barriers to entry. We employ four Doing Business (World Bank, 2010) indicators.<sup>8</sup> The first indicator, Starting Costs, captures all official fees and additional fees for legal and professional services involved in incorporating a business, and is measured as a percentage of the economy’s income

> 8 The complete Doing Business database and additional details on its methodology is available at: www.doingbusiness.org.

10
Traffic growth has slowed down with the international crisis but it is expected to increase at a fast pace in the upcoming years together with the economy and containerization levels.

According to customs data<sup>11</sup> , the distribution of dwell times is indicated in graph 2.<sup>12</sup> <!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0-5 days 6-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 > 90<br>days days days days days days days days days days<br><!-- End of picture text -->

**Graph 2 - Dwell time distribution in the Port of Douala in 2009 (time between vessel discharge and gate exit)** Source: Cameroonian Customs data

An important observation is that dwell time variance is quite significant, with a standard deviation equal to 160% of the mean value. A sequential analysis of delays shows this variance is mainly the consequence of variance between vessel arrival and customs declaration lodging (referred to as “Arrival to Lodging” delay). Delay between payment of customs dues and gate exit (“Payment to Gate”) does also largely vary according shipment. These two intermediary delays account for about 75% of the total dwell time (“Arrival to gate”) in average. In contrast delay between lodging and payment of customs dues (“Lodging to payment”) is quite low and homogeneous in the whole sample.

||**Arrival to gate**|**Arrival to lodging**|**Lodging to payment**|**Payment to gate**|
|---|---|---|---|---|
|**Min**|1|1|1
and gap measures, which suggests that the gap measures are capturing important differences in malnutrition status that the headcount measure does not, which points to the importance of these new measures.

Figure 2 Comparison of stunting head count ratios and stunting gaps <!-- Start of picture text --> 50%<br>Pakistan 2010<br>45%<br>40%<br>Timor‐Leste 2009<br>35%<br>Burundi 2010<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Headcount (%)<br>1993‐2000 2001‐2005 2006‐2010 2011‐2014<br>Gap (%)<br><!-- End of picture text --> _Source: Authors’ calculations using DHS and MICS._ 14
> POLICY RESEARCH WORKING PAPER 1710

# Summary findings

Formal regulation of industrial pollution control standards has been hampered in some developing countries by the absence of a clear regulatory framework, by limited institutional capacity, and by limited information on emissions. For many manufacturing facilities in developing economies, the governmentimposed "price of pollution" is zero.

Yet Hartman, Huq, and Wheeler find strong evidence that despite weak or nonexistent formal regulation and enforcement of environmental standards, many plants in South and Southeast Asia are clean. Of course, many plants are also among the world's worst polluters. To account for the extreme variation among plants, the authors review evidence from a survey of pollution abatement by 26 pulp and paper plants in four countries: Bangladesh, India, Indonesia, and Thailand.

They find that the level of pollution abatement is significantly affected by three factors. Abatement is:

* Positively associated with scale and competitiveness.

* Negatively associated with public ownership.

• Unaffected by foreign links (in ownership or financing).

communities successfully pressure plants to abate pollution even if they have little or no support from formal regulation. High local income is a powerful predictor of effective informal regulation.

The authors also find that policy matters. Privatization, to the extent that it increases plant efficiency, can significantly improve environmental performance. As private ownership, competitiveness, and per capital incomes rise, communities may be in a better position to exert strong local pressure on polluting facilities to clean up production.

The government may need to intervene to prevent environmental injustice in communities whose citizens are mostly poor, poorly educated, or members of marginalized minority groups (and hence less capable of applying local pressure on firms and less likely to be knowledgeable about pollution). To compensate, national regulators may want to consider strategies for improving participation in pollution control in those communities, and for targeting regulation to the problems of poor communities.

A clear source of interplant differences is the level of community pressure, or informal regulation. Some This paper - a product of
, with estimates of up to 60% (Honig & Mulenga 2015). This would imply a transfer of more than 30 million ha of state land to large farms since 1995 -a more than six-fold increase with potentially far-reaching consequences if woodlands were affected (Wilson & Scholes 2020). The fact that according to FAO statistics (FAOStat) Zambia’s agricultural output at most doubled over the same period suggests that either land thus transferred is not used productively or the amount of land transferred is lower.

Zambia’s long-standing policy stance in favor of large farms is also evident from it having adopted one of Africa’s most dualistic land policies. It is one of few countries that maintains a clear dichotomy between customary and statutory land rights based on an implicit assumption of a linear progression from customary to statutory tenure (Alden Wily 2018).<sup>14</sup> The only way to have land rights formally documented is to convert customary to ‘state land’. Effecting such a conversion is complex (Adams 2003) and costly (Larson 2014). The fact that it involves high levels of administrative discretion (German _et al._ 2013b) and weak procedural

> 11 The 2006 Zambia Development Agency (ZDA) Act as amended establishes ZDA to consolidate different agencies that had historically been involved in administering such schemes (see Roth (1995), p. 173-99 for a concise summary of the historical evolution). 12 Before 2000, retired or retrenched civil servants accounted for a significant share of land acquisition while after 2000, speculative land acquisition by those who accumulated resources in non-agricultural ventures seem to predominate (Sitko & Jayne 2014). “Land titling may be implicated in process of elite capture of land at the possible expense of future small-scale farm growth and farm consolidation” (Sitko and Jayne 2014, p. 199).

> 13 The estimate by Jayne _et al._ (2014a) is based on binned DHS data. Sitko _et al._ (2015) noted that “
25

- We use the same WHO 2006 standards for childhood stunting and underweight in all surveys, whereas the DHS and MICS reports use whatever standard was in force at the time the survey was done. The change of standards apparently makes a difference (Ergo et al. 2009).

# <u>Indicator computation</u>

With the exception of the infant and under-5 mortality rates, which we calculate using the same life-table synthetic-cohort probability method employed in DHS reports and programmed in the Stata module SYNCMRATES (Masset 2016), and the childhood anthropometric z-score indicators from the MICS, which are computed throughout using 2006 WHO growth standards and programmed in WHO’s package IGROWUP,<sup>25</sup> all indicators are based on simple population-weighted means of variables constructed from the questions in the survey.<sup>26</sup> Where an indicator is available in more than one survey for a given year, we average over all data points.<sup>27</sup> Sometimes the way the data were collected in the surveys prevented us from achieving 100% consistency of definition across surveys. For a number of indicators such as pap smear and mammogram rates, and blood pressure and cholesterol levels, the sampled age-ranges differ (see notes in Tables 1-3). For other indicators, we could not fully eliminate _conceptual_ inconsistencies. For example, the MICS only asks about care-seeking for children with coughing _and_ rapid breathing if the caretaker reports that the respiratory problems originate from the chest. For MICS data, our

> 25 Program and documentation can be downloaded from http://www.who.int/childgrowth/software/en/. For the DHS, we use the 2006 WHO growth standard z-scores provided in the datasets to compute rates of childhood stunting and underweight. 26 Generally, construction of both the health equity and financial protection sides of the data set follows the methods summarized in O’Donnell et al. (2008).

> 27 We apply a different aggregation rule
appropriate we present alternative techniques using the same time span and sample for the sake of comparability.

# **III. Results**

# _Static Estimates_

We begin following the existing firm level literature by estimating static specifications. Because we want to be broadly comparable with the dynamic estimates that follow, we introduce a static version of the PSM estimator as well.

Table 3 presents estimates of equations 1a and 1b using yearly data from 19762000 under both the Negative Binomial and Poisson distributions, using the naïve levels, the Within Group (WG), and the Pre Sample Mean (PSM) specifications.<sup>17</sup> For comparison, we begin in the first panel with a linear estimator under the assumption that the aggregate data does not suffer “too much” from zeros and integer issues: the dependent variable is log of patents and a dummy variable is included for those observations with 0 patents.

The first point to highlight is that regardless of technique, strong evidence emerges for an aggregate innovation production function mapping the inputs to outputs of innovation. Further, the controls for US exports are generally of expected (positive)sign but the control for natural resources is highly unstable in sign and intermittently significant.

The test for over dispersion- that the conditional variance of patent counts in fact exceeds the mean- is significant in all of the Negbin regressions suggesting that of the two distributions, it is preferred over the Poisson model. Nonetheless, with a few notable exceptions, the results tell a broadly consistent story. The levels specification yields estimates of the elasticity of patents with respect to R&D ranging from 1.00 to 1.14 across distributional assumptions. Controlling for heterogeneity through either the WG

> 17 We use data on patents between 1963 and 1975 to construct the pre sample mean.

12
provided by the Ministry of Petroleum and data from World Bank (2017).

# **Assumptions and allocation overview**

When and where possible, the study allocates fiscal policy elements to individuals or households based on direct observation. For example, when an individual queried in a socioeconomic survey is asked to recall how much she has paid in sales taxes on all her purchases in the past seven days, or is asked to provide receipts detailing sales tax payments, we directly observe the total sales tax collection from her. These sales tax payments recorded by individuals then are assumed to be the same sales tax revenues listed in the executive, administrative, and other budget reporting for the same year.

In the HIECS 2015, however, very few fiscal policy elements could be allocated via direct observation.<sup>24</sup> Instead, we use imputation and simulation (sometimes in combination with direct observation). _Imputation_ is used when a survey unit’s benefit recipient (taxpayer) status must be inferred (rather than directly identified), or the amount received (paid) is retrieved from administrative records or program (tax) rules (rather than directly recorded in the survey), or both. _Simulation_ is available when neither 2015) was not available while the HEICS (2015) survey data did not allow us to identify public contributory pension recipients.

23 The allocations––including the assumptions and choices implicit in them––are described in the following section. 24 Access to publicly delivered health and education services is observed directly, as is the purchase of subsidized fuels and electricity. However, the subsidy received for transactions in these goods must be imputed.

21
sample’s average, the effect is equivalent to an approximately 20 percent reduction in the number of children born per woman.

That the IV estimates are larger than the OLS estimates is in line with the results of earlier studies using longitudinal data (Rosenzweig and Wolpin 1986; Pitt et al. 1993). These studies found that fixed effects estimates were larger than OLS estimates indicating a downward bias in OLS estimates. The larger IV effect can be taken as an indication of a compensatory approach to allocation of programs, where resources are provided to less-endowed areas with higher fertility.

The results for family planning access interacted with age groups suggest that access to family plan-

> 19Table A-1 shows the full results.

> 20 Choosing a different cut-off year does not substantially change the results for years immediately around 1990. The results for other years are available on request.

14