rafmacalaba/datause-encoder-data huggingface dataset:
employment. For a more detailed discussion of relationship between informality and economic cycle we refer to Perry et al. (2007), Bosch and Maloney (2010) and Nikolovova et al. (2010), who have analyzed workers‘ transitions between formal and informal jobs, and Bajada and Schneider (2009), Schneider et al. (2010) and Hazans (2011b) who have studied the effect of economic growth (among other macro factors) on the size of informal economy and prevalence of informal employment.

Inspection of Table 5 reveals that in countries where a significant change in informal dependent employment has occurred between 2006-2007 and 2008-2009, this change was negative: 6.9 points in Ireland, 5.4 points in Cyprus, 3.9 points in Denmark, 2.4 points in Bulgaria and Latvia, 1.7 points in Netherlands; the only exception was Slovenia with a significant increase by 2.7 points. On the other hand, informal self-employment increased significantly in Poland, Estonia, and Netherlands, whilst in Portugal, Ukraine, Slovenia, Czech R., Slovakia, the UK, and France a (statistically insignificant) increase by 1 to 2 percentage points has been registered; a substantial (by 2 points) decline in the prevalence of informal self-employment is found only in Norway and Switzerland.

Figure 3 which refers to 2004-2009 (and thus covers both growth and recession episodes) suggests a negative association between the change in dependent informality rate and the change in the rate of joblessness within extended labor force. First, the whole scatter diagram is consistent with a downward sloping curve (summarizing both within-countries and between-countries variation in the two indicators). Second, almost all segments connecting the points corresponding to the same country are downward sloping, suggesting that within countries unemployment and informality tend to move in opposite directions (the UK, Hungary, and the Netherlands seem to violate this pattern). Finally, 47 out of 48 observations lie outside the positive quadrant – in other words, there are virtually no cases when the rate of dependent informality and unemployment go up simultaneously. This does not necessarily contradict to the ̳safety net story‘ of displaced workers switching to self-employment (Harris and Todaro, 1970).

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Third, the high consumer need for essential goods and services accelerated human pressure on the environment and its resources, especially for fuelwood and bamboo extraction. Prominently, the fuelwood demand for domestic household energy use contributed to forest area loss (Bdnews24.com, 2017; Paul, 2017; Tani & Rahman, 2018). According to Uddin and Khan’s (2007) study, the average family in the area consumes 6kg of fuelwood per day (Tani & Rahman, 2018). The annual per capita fuelwood consumption in Teknaf was estimated at 1,168 kg (Tani & Rahman, 2018). Before the introduction of the pilot phase of the liquefied petroleum gas (LPG) project in August of 2018, an estimated 6,800 tons (6,800,000 kg) of fuelwood was collected monthly by the Rohingya refugees (UNDP & UN Women, 2018). Scaled up in February 2019 to include more refugee and host population households, the LPG project is anticipated to reduce the local demand for firewood use for cooking. The LPG program, according to UNHCR, has resulted in an “80% reduction of demand for firewood in the Rohingya households in the camps, reducing deforestation to well within sustainable forestry rates, while the overall demand for firewood in the area has dropped to well below pre-influx levels” (UNHCR, 2020c). Additional research is needed to use high-resolution satellite imagery to precisely measure and evaluate the environmental impact of the LPG project in the region.

Besides fuelwood, both Rohingya refugees and the local population rely on bamboo sticks from the forests for shelter construction and other uses. Data from a recent shelter assessment survey shows that 38 percent of all assessed shelters are in “bad condition,” 59 percent are in “moderate condition,” and only 3 percent are in “good condition.” As the primary building material, nearly 99 percent of all refugee shelters currently required both “muli” and “borak” bamboo types for shelter improvement (UNHCR, 2020a). Reports highlight that 63.54±11.99 bamboo culms are needed to construct a refugee family unit shelter (UNDP & UN Women, 2018). According to local reports, the bamboo supply to meet this increasing demand is currently sourced outside Cox’s Bazar.

There is anecdotal evidence of the in-migration of both Rohingya refugees (earlier settlers) and the host communities’ members (Bangladeshis) towards the camp areas. Current economic activities centered around tourism, including eco-tourism, agriculture (betel leaf farming), aquaculture (shrimp hatcheries), capture fishery, fish processing, accommodation, food services, and wholesale and retail trading, could attract more people to migrate towards the Cox’s Bazar 32
# **References**

- Anderson, M. L. (2008). Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. Journal of the American Statistical Association, 103(484), 1481–1495.

- Avdeenko, A. & Gilligan, M. J. 2015. International Interventions to Build Social Capital: Evidence from a Field Experiment in Sudan. American Political Science Review, 109(3): 427-449.

- Beath, A., Christia, F., & Enikolopov, R. (2013). Empowering Women through Development Aid: Evidence from a Field Experiment in Afghanistan. American Political <u>Science Review, 107(3), 540–557.</u>

- Beath, A., Christia, F., & Enikolopov, R. (2015). The National Solidarity Programme: Assessing the Effects of Community-Driven Development in Afghanistan. <u>International Peacekeeping, 22(4), 302–320.</u>

- Bouguen, A., Huang, Y., Kremer, M., & Miguel, E. (2018). Using RCTs to Estimate LongRun Impacts in Development Economics. NBER Working Paper, 25356, 1–65.

- Casey, K., Glennerster, R. & Miguel, E. 2012. Reshaping Institutions: Evidence on Aid Impacts using a Preanalysis Plan. Quarterly Journal of Economics, 127(4), 1755-1812.

- Casey, K. 2018. Radical Decentralization: Does Community-Driven Development Work? <u>Annual Review of Economics, 10, 139-163.</u>

- Casey, K., Glennerster, R., Miguel, E. & Voors, M. 2019. Skill versus Voice in Local Development. Working paper, Stanford GSB.

- Fearon, J. D., Humphreys, M. & Weinstein, J. M. 2009. Can Development Aid Contribute to Social Cohesion after Civil War? Evidence from a Field Experiment in Post-Conflict Liberia. American Economic Review, 99(2), 287-291.

- Fearon, J. D., Humphreys, M., & Weinstein, J. M. 2015. How Does Development Assistance Affect Collective Action Capacity? Results from a Field Experiment in PostConflict Liberia. American Political Science Review, 109(3), 450–469.

- Humphreys, M., Sanchez de la Sierra, R., & Van der Windt, P. (2012). Social and Economic Impacts of Tuungane: Final Report on the Effects of a Community Driven Reconstruction Program in Eastern Democratic Republic of Congo.

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Kormendi and Meguire 1985, Landau 1983, Marsden 1983, Ram 1986, Venieris and Cupta 1983). This implies a homogeneity hypothesis whose significance can be 'ariously interpreted, ecg. according to the nature of the independent variables concerned. For example, the impact of initial per capita product on growth rate can vary with level of development: whereas the catch-up hypothesis, implying a negative impact, may be more apt when a certain level of development is attained; below that levei a lower per capita income can mean a handicap, associated tor example with a lower human capital, and hence a positive impact. The literature in fact contains both positive and negative results, depending on the composition of the sample: a negative impact when the developed countries are included (Kormendi and Meguire 1985), positive with only the developing countries (Singh 1985) but also negative with only the developing countries (Landau 1986). An econometric solution to this problem can be sought in the estimation of ..onlinear relationships, following Chenery and Syrquin. This alternative makes it possible to measure the respective weight of the two but not to define the way in which each influences the results.

The same problem arises, but to a lesser degree, within the set of developing countries. A number of cross-sectional studies relate to only some of the developing countries, either the middle-income or semiindustrialized countries (Feder 1983, 1986) or the low-income countries (Helleiner 1984), while some estimate the relationship for different incomes-(Demeocq and Guillaumont 1985, Kavoussi 1984). The distinction between subsets of countries at different incorme levels can also be used to test the hypothesis that the transmission mechanisms differ accordin3 to income (Peder 1986, Kavoussi 1984).

The distinction between sets of countries at different income level partially overlaps the distinction between geographical subsets (Africa, Latin America, Asia). Some studies relate only to the countries of a given continent (e.g. Africa, Wheeler 1984 or Helleiner 1984; Asia, Rana 1987). Others estimate the relationship for several geographical subsets (Paini, Annez and Taylor 1984, Demeocq and Guillaumont 1985). In still others the African countries are de facto eliminated (e.g. Kormendi and Meguire 1985).

Examining the factors affecting growth separately for a regional subset comes down to making the hypothesis that these factors operate differently in different regions of the world. These hypotheses may be based on historical or geographical considerations but the latter do not always emerge clearly. It is true that the results obtained can vary appreciably from one regional subset to another, as is evident from our illustrative model (table 3, equations 18 through 21). Some studies have sought to take account of regional specificity by introducing a dummy variable corresponding to each regional subset (Singh 1985); however, this method does not capture a possible regional specificity of the impact of the various independent variables affecting the regression coefficients.
inclusion, financing, and transparency. The evaluation and performance data used in this study, as well as by the PVP management team for GP presidents’ evaluations, were based on a subset of this monitoring data, with an additional layer of verification conducted by the research team. For this additional verification, an independent survey firm was deployed to collect data from VPRC records, GP office records, focus group discussions. Section 2.2.1 and Appendix B provide more details on the collection and verification of project monitoring data.

In addition, at the end of the evaluation period the survey team also collected hamlet-level data for the four types of public infrastructure investments most commonly delivered through GPs, and the IAY housing scheme. This data was similar to the baseline data, except they included information on new investments and IAY transfers that were implemented after the intervention was announced (during the evaluation period). As in the baseline, this data was also verified through a village focus group meeting, and on-site verification in three randomly selected hamlets per GP.

We supplement this survey and monitoring data with three rounds of the Indian Census, and GP election data from the Tamil Nadu State Election Commission that we compiled and digitized. These data sets allow us to identify reservation status of the contested seat for all GP elections, including the reservation status for the next GP election scheduled for 2016.

# **5.2 Key outcomes and multiple hypothesis testing**

We first examine GP presidents’ response to our incentives by estimating the average treatment effect (ATE) on their overall GP-level performance metrics. For this purpose, we use the evaluation score (and the letter grades) received by GP presidents, based on which their eligibility for rewards was determined, as the primary outcome of interest.

In addition, we also estimate ATEs on two sets of secondary outcomes, each related to one of the two key functions of a GP president: (1) expanding access to private transfer schemes for eligible beneficiaries in the GP; and (2) targeting new and maintaining existing local public infrastructure goods. These secondary outcomes are directly related to the PVP project’s main goals related to inclusion and public service delivery and the analysis helps shed more light on the role of our incentives in improving these core GP functions.

On private transfer schemes, we focus on new beneficiary enrollment on various PVP as well as non-PVP schemes. These include two PVP-specific schemes—youth skill development and women’s 14
Table 3: Dry Bulk Average Spot Rate

|**ISO Code**|**Economy Name**|**TC rate**|**DWT**|**TC rate/DWT**|**Count**|
|---|---|---|---|---|---|
|BR|Brazil|15107.65|70584.86|0.23|605|
|BN|Brunei Darussalam|,<br>10,500.00|,<br>52,223.00|0.20|1|
|CA|Canada|13,144.44|59,302.11|0.23|9|
|CL|Chile|14,250.00|60,656.00|0.24|2|
|CN|China|11,733.57|85,474.21|0.15|2,495|
|CI|Côte d’Ivoire|21,750.00|59,589.50|0.36|2|
|CM|Cameroon|19,375.00|50,629.50|0.41|4|
|CD|Democratic Republic of the Congo|15,750.00|53,413.00|0.29|1|
|CG|Republic of Congo|15,000.00|56,925.00|0.26|1|
|CO|Colombia|16,094.29|62,087.26|0.29|35|
|CV|Cabo Verde|11,583.33|48,251.00|0.24|3|
|CR|Costa Rica|31,000.00|61,384.00|0.51|1|
|CU|Cuba|19,333.33|31,686.67|0.63|3|
|DE|Germany|15,684.12|77,911.42|0.21|149|
|DK|Denmark|11,882.56|51,600.42|0.26|43|
|DO|Dominican Republic|14,750.00|42,179.00|0.36|4|
|DZ|<br>Algeria|14,667.50|56,995.81|0.27|21|
|EC|Ecuador|14,915.69|78,756.74|0.19|838|
|EG|Egypt, Arab Rep.|17,569.03|73,046.89|0.25|88|
|ES|<br>Spain|15,806.75|74,979.96|0.23|126|
|FI|Finland|14,100.00|37,985.00|0.37|1|
|FR|France|14,880.95|68,593.94|0.24|85|
|GA|Gabon|14,020.00|59,442.40|0.24|5|
|GB|United Kingdom|15,205.85|77,411.61|0.21|124|
|GE|<br>Georgia|13,416.67|34,405.33|0.39|3|
|GH|Ghana|18,285.71|58,057.57|0.32|7|
|GI|Gibraltar|15,983.66|88,155.99|0.19|231|
|GN|Guinea|10,501.32|69,028.05|0.16|19|
|GR|Greece|14,387.50|51,635.89|0.31|28|
|GT|Guatemala|16,500.00|38,246.50|0.43|2|
|HK|Hong Kong, SAR China|10,770.85|76,276.70|0.15|293|
|HR|Croatia|14,350.00|81,895.00|0.18|1|
|HT|Haiti|14,000.00|56,982.00|0.28|2|
|ID|Indonesia|11,270.00|64,128.69|0.18|270|
|IN|India|13,881.78|70,522.49|0.20|782|
|IE|Ireland|15,898.53|80,095.97|0.20|34|
|IR|Iran, Islamic Republic of|16,150.00|56,655.00|0.28|2|
|IQ|Iraq|17,958.33|60,391.17|0.30|6|
|IS|Iceland|13,250.00|75,200.00|0.18|1|
|IL|Israel|17,500.00|89,092.67|0.28|3|
|IT|Italy|16,444.26|84,897.43|0.21|122|
|JM|Jamaica|13183.33|79954.83|0.20|12|
|JO|Jordan|,<br>16,325.00|,<br>62,847.25|0.26|4|
|JP|Japan|12,267.62|80,874.57|0.16|421|
|KE|Kenya|12,150.00|60,729.80|0.20|5|
|KH|Cambodia|12,750.00|55,564.10|0.23|10|
|KR|Korea, Rep.|11,234.89|84,478.56|0.14|427|
|KW|Kuwait|13,930.77|55,481.38|0.25|13|
|LY|Libya|45,000.00|174,316.00|0.26|1|
|LK|Sri Lanka|12,237.93|74,459.17|0.17|29|

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# **5 Results**

The estimation sample is constrained by the spatial and temporal coverage of the price and poaching data. Overall we do not have continuous coverage for both sites and countries, with gaps occurring in both the poaching data and the price data. Appendix Tables A1 and A2 summarize the patterns of availability for both data sources. In the poaching data, only 9 out of 77 sites have complete coverage between 2002 and 2015, and the median site has 9 out of 14 years of data available. In the ivory price data, no country has complete coverage, but major elephant range countries such as Botswana, Kenya, and Tanzania have only one or two gap years. Overall, the sparsity in data coverage is unlikely to bias our results as the main ivory-producing countries are indeed covered continuously in both ivory and price data, especially in our estimation sample after the year 2002. Moreover, the composition of the price data stayed stable over our estimation sample as well. Around 90 percent of the price data in our Sub-Saharan African sample after year 2002 are sourced from government valuations and are local middleman prices, and the composition is stable over time.

## **5.1 Poaching elasticity with respect to domestic prices**

The primary goal of this paper is to estimate the price elasticity of elephant ivory poaching as specified in equation (4). Our main outcome variable, _ysct_ , is measured by (the logarithm of) PIKE in site _s_ , country _c_ , and year _t_ . The main right-hand side variable _pct_ is on the other hand measured at the country-year level, as the price data are not recorded for individual poaching sites. In addition, the regressions include a vector of controls, such as site fixed effects and characteristics of the poaching site such as the total area and the number of carcasses found at the site in a given year _t_ (see Appendix Table A4 for summary statistics of the variables used in our estimations).

Finally, given that in our main specifications the instruments are global variables that only vary over time, we cluster our standard errors at the year level. In all the tables discussed in the main text, given the small number of clusters we adopt the parametric Moulton (1986) correction factor. Below we also implement alternative clustering approaches – without Moulton correction and two-way (year and country) clustering following Cameron et al. (2011)– to assess the robustness of the results.

The estimation results of (4) are shown in Table 1. The first column reports the bivariate regression, which implies an elasticity estimate of 0.12. Adding site fixed-effects increases the magnitude of the coefficient to 0.19. With supply shift controls such as a conflict variable 16
levels of income, urban concentration would be high, which would help conserve expenditure on infrastructure and enhance information spillovers at a point when the economy suffers from a severe scarcity of infrastructure and information. With higher incomes, it becomes possible to spread the infrastructure and information into the hinterland while rising costs in congested urban areas push producers and consumers out of these erstwhile centers. This pattern of income growth, resulting initially in higher and later in lower urban concentration, is supported by a number of empirical studies (ElShakhs 1972; Rosen and Resnick 1980; Wheaton and Shishido 1981; Mutlu 1989; Ades and Glaeser 1995; Junius 1999; Davis and Henderson 2003; Moomaw and Alwosabi 2004).

The question of whether trade liberalization intensifies the forces of urban agglomeration or dispersion becomes an empirical one. The empirical literature on the subject may be divided into two groups.<sup>9</sup> The first group relies on cross-country regressions, whereas the second group studies the heterogeneous responses of different regions within a country. One remarkably consistent finding that emerges from the first group is that trade openness has no statistically significant effect on urban concentration. The results obtained by the studies in the second group are mixed, with half of the 14 papers surveyed by Bruelhart (2011) finding support for the hypothesis that trade openness is associated with spatial divergence and three papers suggesting the opposite. A more careful recent study in this second group by Redding and Sturm (2008), which examines the effects of the loss of trading partners triggered by the division of Germany on urban concentration, finds that trade reduces urban concentration.

> 9Here, we follow the recent survey of this literature by Bruelhart (2011).
with respect to the endogeneity concern discussed in Section 3, since import flows above 5,000 USD are more permanent.<sup>24</sup> In the third experiment reported in column (8), we find our results to be qualitatively unchanged for an alternative measure based only on the freight rates for countryindustry relationships lasting the entire sample period.<sup>25</sup> The evidence in columns (6) to (8) suggests that our decision to use information on freight costs for all import flows in our main specification is, if anything, underestimating the effect of import competition on product quality.

# **4.3. Unit Values and Quality**

Increases in unit values seem to correspond well to the definition of incremental innovation in the OECD Oslo manual (1997) which covers “existing product[s] whose performance has been significantly enhanced or upgraded”. For certain consumer products such as automobiles or washing machines, it is clear that higher prices are directly correlated with higher quality. This explains why various studies in the trade literature have taken for granted the idea that increases in export unit values represent improvements in quality (Fontagné and Freudenberg, 1997). The summary statistics on the heterogeneity in unit values presented in Table 1 support this argument. Industries with little scope for quality differences show low relative variation in unit values while industries where quality is expected to play an important role such as professional equipment (which includes information technology products) exhibit a much higher variability in unit values.

An extensive industrial organization literature has examined the role of product pricing as a signal for quality. The market for ‘lemons’ of Akerlof (1970) illustrates this clearly: in the presence of imperfect information, firms with high quality products need to introduce signals -

> 24 About 63 percent of those country-industry relationships last the entire sample period compared to 49 percent of the country-industry relationships corresponding to all import flows.

> 25 Note that while the specifications in columns (6) to (8) provide a relevant robustness test, they could introduce a sample selection bias due to the omission of some country-year relationships.

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