rafmacalaba/datause-encoder-data huggingface dataset:
estimation. Nonetheless, while employing ATET reduces the risk of selection bias, it does not fully eliminate the risk that results reflect some level of selection into zones and not the impact of zones.

For matching the estimated propensity score, the nearest-neighbor indices for observation _i_ , _i_ = 1 _, ..., n_ is ˆ where _pi_ = _p_ ( _zi, t,_ ˆ _γ_ ).

After estimating the effect of zone status on firm performance, we explore the role of public support services on the same set of firm performance measures. Support services are measured as the share of services used by each firm under the four categories presented in Table 3 of governance facilities, trade and connectivity support, infrastructure, and fiscal incentives. Specifically, we estimate: _yid_ = _α_ + _βigovernance_ + _βitrade_ + _βiinfrastructure_ + _βifiscal_ + _βizonestatus_ + _πi_ + _ε_ (6) Finally, to explore whether firms inside zones grow faster than those outside, we estimate the effects of zone status on firm age and employment growth. Employment growth is measured as the net change in employees over the previous six months. In particular, we estimate:

# **3 Results**

Table 4 presents the results on the effect of zone status on exports, FDI, employment, and output per worker relative to being outside a zone, before correcting for selection bias. Overall, these results confirm expectations that being inside a zone can have positive effects on firm performance. In particular, the results from our first set of regressions show a positive and significant effect of zone status on exports, FDI and employment.

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system to changes in CPI appears to be relatively limited, and much of the variance is attributed to price shocks that are exogenous to our system. However, as fluctuations decline over time, it is apparent from Table 1 that the role of our macro-variables is increasing towards the end of the period: over the sample period, the role of genuine price shocks steadily decreases from 55% in 1996-99 to 34% in 2012-14. A few factors have a notable impact in certain periods. This is the case for the fluctuations in output, especially in periods before 2002. Contractionary monetary shocks contributed to slowdowns in inflation in 2001 and in 2006. While copper prices do not play a prominent role in determining inflation in the first half of the sample period, it becomes more important in the late 2000s. This is plausible, as both copper production as well as prices were significantly higher in later periods (see Section 2).

<!-- Start of picture text --> 5<br>0<br>Copper Fed Money Output gap Exchange rate CPI<br>-5<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Figure 6: Historical decomposition of changes in Zambian consumer prices<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6: Historical decomposition of changes in Zambian consumer prices<br><!-- End of picture text -->

# _5.3 Differential exchange rate pass-through_

We will now examine the extent to which different shocks that affect the exchange rate entail different reactions of consumer prices. Our approach is similar to Forbes et al. (2015), in that we use the ratio of the IRF of CPI over the IRF of the exchange rate for a shock to any given variable. These estimates therefore indicate the change in _consumer prices induced by a shock that leads to a 1% appreciation in the exchange rate_ in any period. The interpretation is then slightly different to the IRFs reported in section 5.1. These traced the effect of a shock that causes a 1% appreciation _at impact._ The measures reported here are computed for a shock that causes a 1% appreciation in _the period itself._ For instance, in order for a shock to the copper price to lead to an exchange rate appreciation of 1% _at impact_ , it needs to have an amplitude of 1%/0.2 = 5% (see Figure 3). But the shock takes some time to fully deploy its effect on the exchange rate; a copper price shock that leads to an appreciation of 1% _after four quarters_ therefore only needs to have an amplitude of 1%/0.4 = 2.5%.

Note also that the direction of the original shocks we consider in this section need not be identical to that discussed in section 5.1 when describing the IRFs. Specifically, Figure 7 reports the result of a _contractionary_ monetary shock (reducing inflation), while our previous discussion as well as Figure 4 were based on _expansionary_ shocks (increasing inflation). This is because we consider shocks that lead to an exchange rate _appreciation_ , which for money needs to be a contractionary one.

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# **1. Introduction**

In this paper, we examine the question of whether road investments that cause an urban area to expand peripherally create a lock-in effect so that transit investments that favor the core are not as effective as they would be in the absence of the road investments. Such a lock-in effect is relevant to climate change policy because it results in high CO2 emissions which become difficult to reverse. The issue is especially important in rapidly growing megacities of the developing countries because these megacities already have high Greenhouse Gas emission levels and high auto ownership growth rates. Therefore, they may be in danger of entering a lock-in pattern.

According to the International Energy Agency (IEA) the world’s share of China’s and India’s combined CO2 emissions grew from 9.4% in 1990 to 24.4% in 2006 (IEA, 2008). These two populous countries together accounted for 51.8% of the world’s total growth of CO2 emissions over the period. China accounted for almost as much CO2 emissions as the United States in 2006 (5,607Mt vs. 5,697Mt), and informal reports of the acceleration of this trend suggest that China may have since surpassed the U.S. to become the world’s largest emitter of greenhouse gases (GHG).

Local air pollution is an important environmental concern in China. Beijing, China’s second largest city, is one of the world’s most polluted cities in terms of air quality, although the air quality has improved somewhat after the 2008 Olympics. In Gurjar et al.’s (2008) ranking of ambient air quality in 13 of the world’s megacities, Beijing places second for sulfur dioxide (SO2), second for nitrogen dioxide (NO2), and fifth for total suspended particulates (TSP). Similarly, the Millennium Cities Database (MCD, 2001) identifies Beijing as the world’s most congested city as measured by average road speed. According to the World Bank (2007), the estimated cost of health damages associated with urban air pollution (i.e., from sickness and premature death) ranges from 1.2 to 3.8% of GDP, making air pollution the costliest pollution problem in China.

The relationship between income growth and suburbanization is well understood theoretically and empirically (e.g. Margo (1992)).<sup>3</sup> The link between income growth and car ownership has been captured by studies spanning many countries (Ingram (1998)). As incomes grow, the demand for discretionary mobility (trips) also increases (Nelson and Niles, 2000), and this causes an increased demand for automobiles. An even higher demand for driving and one that is more than socially optimal arises, because the time delay and fuel consumption externalities of congestion remain un-priced, as congestion

> 3 One may see this also in the exposition of the standard theoretical model of the monocentric city of urban economics, applied repeatedly since the 1960s (e.g. Wheaton (1974), Brueckner (2000)). This model, however, has become an anachronism because it assumes that all jobs are located in the center of an urban area.

2
The qualitative results from the GIR for the Eurozone core and periphery are in line with our previous findings (Figures 9 and 10). In the two groups, the external balance responds negatively to _GR, YGAP_ and _REER_ shocks and positively to _R_ shocks. However, results from the forecast error GVD reveal interesting differences (Tables 2 and 3). For the Eurozone core, the current account balance is explained almost entirely by its own shocks and to a lesser extent by shocks in _GR_ and _YGAP_ (3.6 and 3.5 percent respectively); while _REER_ and _R_ only play a minor role (around 1 percent for each variable—see Table 2). On the other hand, the external balance in the periphery is also explained mainly by its own shocks, but a higher proportion of the variance is explained by the other endogenous variables (Table 3). Growth and relative per capita income explain together more than 12 percent of _CA_ fluctuations, whereas _REER_ and _R_ explain together less than 8 percent of the variance (around 3.5 and 4 percent respectively). Thus, as in our baseline results, the real exchange rate appears to have played a minor role in current account fluctuations either in the core or in the periphery. However, economic growth has played a more important role in external balance fluctuations in the periphery than in the core. The results obtained using the alternative VAR are overall in line with the results from the baseline VAR. The main difference is that in the Eurozone core the _ULC_ variable now explains around the same part of the variance of the current account balance as _GR_ or _YGAP_ (Table 2). However, these contributions remain low and are not statistically different. By contrast, for the periphery countries the contribution of _ULC_ changes to external imbalances appears negligible (Table 3). Finally, it is worth noting that these results remain unchanged when the composition of the two country groups is modified, with Italy included in the core country group (see Annex 2).

**Figure 9:** Responses of the _CA_ to one standard deviation shocks for **Eurozone core [1975-2011]**

<!-- Start of picture text --> R shock REER shock GR shock YGAP shock<br>0.3<br>0 0 .1 0<br>-0.2 -0.2 -0.4 -0.5<br>1 5 1 5 1 5 1 5<br><!-- End of picture text --> _Note._ The y-axis measures deviations from the baseline, while the x-axis represents years after shock.

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Informal businesses or establishments ( ), as defined earlier, are those that are _legally_ informal, that is they lack full or partial formal registration. The definition of legal informality jj ∈ UU must be uniquely tailored to each country based on relevant legislation and regulation for formal registration. In the case of the ISBS, an interview is administered to eligible informal business activities randomly selected from the list of informal businesses discovered as part of the enumeration exercise ( ). Combining ACS and a random selection of businesses within the enumerated blocks is not only an appropriate method of providing unbiased population estimates, jj with comparatively low variance and fieldwork efficiency, but also a means—as in the case of the ISBS—of providing a framework for more detailed information, while preserving representativeness.

Implementers will also want to establish monitoring and quality control procedures for both data and fieldwork effort. Since the latter concern is the main focus of this paper, a few key details merit attention. Specifically, the integration of GPS-linked software is often important to ensure enumerators sufficiently apply the listing exercise to all potential units in selected BAs. Sufficiently doing so requires full coverage of paths without enumeration outside of a delineated BA; path tracking software can be used for this purpose.<sup>28</sup> Implementers may also want to integrate fieldwork quality controls to minimize known potential pitfalls, such as undercounting of household-based activities.

Implementing this methodology also requires close monitoring of fieldwork efforts, since under ACS, the total number of BAs to be enumerated is not known _a priori_ . Practically, it may be recommended to enumerate the initial random sample of BAs to calibrate the value selected for the expansion threshold ( ). Based on simulation exercises such as those presented in Section 4, it is recommended to initially select a somewhat high value of , as a more conservative approach. CC That section showed that along the two dimensions of fieldwork effort and variance of population CC estimates, ACS is widely more efficient on both dimensions, holding the other constant: i.e., if one holds the level of variance constant, ACS is almost always more efficient in terms of fieldwork effort. However, practically, a survey implementer may want to have control over the expansions that occur through ACS, as they trigger additional fieldwork—which can be costly. That is, an implementer can start with a higher expansion threshold and then evaluate if the triggered expansions are within the needs and limits of the survey.

> 28 For example, the ISBS is integrated with the open source OruxMaps.

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This paper is organized as follows. In section 2 there is a description of rural electrification in Vietnam, and in particular the role of EVN (Electricity of Vietnam). In section 3 we describe the sample used for this analysis. It also addresses the attrition issue that may have affected the sample and analysis. Attrition results from not being able to retrace or match first round households with the follow-up sample. After a rigorous analysis, it is found that attrition is not a major issue for this analysis. Section 4 covers the electricity use pattern of rural residents of Vietnam during both survey years. In this section three important aspects are highlighted. First, it is documented that there has been a significant and unexpected growth in grid electrification by 2005 to all unconnected 2002 communes of the survey area by 2005. Second, many rural households in areas that did not have grid electricity in 2002 used a variety of sources for their electricity needs, including batteries and picohydro electricity systems. Third, the demand for electricity is estimated using a fixed-effects regression model. Section 5 discusses alternative impact assessment methodologies used in this paper to quantify electrification benefits, including their advantages and disadvantages. The results of electrification impact assessment are reported in Section 6. In Section 7, we examine whether longer term exposure to electricity use (measured by duration of electricity use) affects the impacts of electricity on development outcomes. Finally, section 8 concludes the paper with lessons learned and policy recommendations.

# **2. Rural electrification in Vietnam: What do we know?**

Historically, electrification among the poor households in rural Vietnam was very low – only 2.5 percent at the beginning of post-Vietnam war era (1975) according to the estimates of regional electricity supply companies (EVN 2001). From mid-1980s, however, electrification started to rise – thanks mostly to major extensions of grid network with the construction of 35 kV distribution lines.

4
2003), three major composite indicators of corruption are used extensively in this analysis: (1) Transparency International’s ‘Corruption Perceptions Index’ (CPI), which focuses on overall political corruption; (2) the World Bank’s ‘Control of Corruption’ (CC) dimension of governance, which is a broader measure of public sector corruption; and (3) the World Bank’s ‘Government Effectiveness’, a dimension of governance much more focused on the non-elected public sector.

# **(i) The Corruptions Perception Index**

The first major, freely available, and cross-national measure of corruption is Transparency International’s annual ‘Corruption Perceptions Index’. The aim of this index is to measure the abuse of ‘entrusted power for private gain’ (Transparency International, 2011). Each country receives a score, which can range from 0 (extremely corrupt) to 10 (no corruption) and individual country scores are developed by aggregating and averaging normalized scores of ‘corruption related data’ emanating from a variety of sources.<sup>6</sup> The aim of the index is to provide a measure of the extent to which public sector bureaucrats and politicians engage in corruption (Transparency International, 2011). It is thus a potentially noisy but valid measure of the level of corruption in a given country: _“The Corruption Perceptions Index...captures information about the administrative and political aspects of corruption.”_ Consistent with this definition, as Table 1 shows, most of the representative components of the CPI are concerned with capturing: both the abuse of public office by politicians (potentially elected) and unelected officials (e.g. ‘bribing and corruption that exists in the public sphere’); see Table 1. Specifically, seven of the eight representative sources of the CPI are concerned with overall corruption (e.g. ‘assessment of the pervasiveness of corruption among politicians and civil servants’; see Table 1), while one source is exclusively focused on the activity of politicians (‘assessment of corruption in government’; see Table 1).

> 6 The 2010 scores were calculated using data from 13 different surveys or assessments produced by 10 independent organizations (Transparency International, 2011).

9
<!-- Start of picture text --> geographical diversification, rather than product variety, might be the more important channel for<br>trade growth in this case. It is to be hoped that future work will investigate this possibility.<br>It would of course also be desirable to increase the sectoral reach of standards data. In particular,<br>it would be useful to include products of interest to middle income developing countries, such as<br>electronic goods. Building on previous efforts to exploit the Perinorm bibliographic database (e.g.,<br>Swann et al., 1996; Moenius, 2004) would likely prove very beneficial to empirical work in this<br>area.<br>TSLS estimates suggest that the size of a sector and its geographical distribution across EU<br>member states are important determinants of the total number of standards and the degree of in-<br>ternational harmonization. This constitutes important preliminary support for a political economy<br>view of trade-related standards and harmonization, in which these measures are influenced by the<br>kinds of factors more commonly associated with endogenous tariffs. However, that argument is<br>only briefly sketched out here, and results are not uniformly consistent with it. To my knowledge,<br>only Essaji (2005) addresses the political economy of product standards and trade from an em-<br>pirical perspective. A future expansion of his approach to a multi-country framework, along with<br>incorporation of Perinorm data, would provide important insights into the mechanisms at work.<br>Such research would have important policy implications since it could help lay the foundations for<br>a rigorous identification of those standards which are unduly tainted by protectionist pressures.<br>References<br>Balaoing, Annette, and Joseph Francois, 2006, "The Political Economy of Protection in a Cus-<br>toms Union: What Drives the Tariff Structure of the EU?", Mimeo, www.intereconomics.com/<br>francois/doc/EU_May06.pdf.<br>Baldwin, Richard E., 2000, "Regulatory Protectionism, Developing Nations, and a Two-Tier<br>World Trade System" in Susan M. Collins and Dani Rodrik (eds.), Brookings Trade Forum 2000,<br>Washington, D.C.: Brookings Institution Press.<br>25<br><!-- End of picture text -->
# **3. Design and Methods**

## **3.1 Sampling**

### **3.1.1 Full Sample**

We worked in four districts (Bombali, Tonkolili, Bo, and Kenema) that bisect Sierra Leone from north to south (see Figure A.1(a)). These districts include 319 government-run clinics (PHUs) that provide primary and maternal care. We sampled 254 of these clinics, such that all sampled clinics were separated by at least 3 kilometers to minimize spillovers. As a result, the average distance to the next nearest clinic is 10 kilometers. Moreover, in Sierra Leone, residents are “assigned” to clinics (i.e., told by government what clinics to use), which discourages use of more distant clinics. At baseline, the average clinic in our sample had just over 2 staff members present, reported being open 6 days per week, and saw roughly 450 outpatients in the month prior to the survey. Over 80 percent of clinics had walls and roofs in good condition, accessed piped or protected water, and had stocks of basic medications (e.g., oral re-hydration salts and antibiotics); yet, only 10 percent had functional electrical lighting.

We then randomly sample two communities from the catchment area of each clinic (defined as a 3.2 kilometer buffer around the clinic). This generates a sample of 508 communities. At baseline, we randomly sampled 5 households in each of these communities for an extensive household survey (2,540 households). We also randomly sampled an additional 15 individuals in each village and administered a shorter userfeedback survey focused on recent health episodes, service provision, and satisfaction. At endline, we re-surveyed the 5 households that took the baseline household survey. We also randomly selected 5 of the 15 individuals who took the user-feedback survey at baseline and administered the endline household survey, which was revised to incorporate modules from the user-feedback survey. This generates a sample of 10 households per community at endline (5,080 households). The households in our sample are poor: at baseline, 74 percent live in homes with mud floors and wooden walls; 24 percent have no toilet facility and another 58 percent use a pit latrine; only 20 percent own a mobile phone; and 62 percent have no formal education.

### **3.1.2 Ebola Sub-Sample**

Our data on Ebola cases are aggregated to the section-week. Sections are the smallest administrative units in Sierra Leone: the median section is under 40 square kilometers and had fewer than 2,500 residents in the 10
available to the child for work. However, further research is needed to better understand how cash transfer and supply-side interventions interact. Is their combined effect simply equal to the sum of the effect of the individual interventions? Or is there a synergy, such that the effect of the cash transfers and supply-side interventions is mutually reinforcing? This is a key topic for future research, not only for child labor but also for outcomes such as school participation.

# **<<A>> 4. Conclusion**

Cash transfer schemes are not often designed and implemented with the aim of reducing child labor, although social protection is recognized as one of the main instruments to address child labor (International Labor Office 2013b; Understanding Children’s Work 2010). However, as this review shows, cash transfers have a strong potential to address child labor. We have not identified a single program that increased child labor (although indications of negative spillover effects warrant further research). On the contrary, there is broad evidence that cash transfers, conditional and unconditional, lower both the extensive and intensive margin of child labor. Moreover, cash transfers appear to cushion the effect of economic shocks that may lead households to use child labor as a coping strategy.

The effects of the cash transfer programs on child labor are heterogeneous. There are differences by the child’s gender. Boys tend to experience a larger reduction in participation in economic activities, whereas girls experience relatively larger reductions in involvement in household chores. Moreover, reductions in child labor are particularly pronounced when beneficiaries are poor, signaling that the mitigation of credit constraints, which force households to use child labor as a consumption smoothing mechanism, are a key channel through which cash transfers lower child labor. This interpretation is confirmed by studies of old-age pension schemes that allow for a more direct test of the role of credit constraints.

Preliminary evidence suggests that the impact of conditional cash transfers depends partly on their integration with other interventions. Combining conditional cash transfers with supply-side 23