rafmacalaba/datause-encoder-data huggingface dataset:
expectations to formulate their decision. Since subjects do not know the nationality of their counterparts, their expectations of others is dependent upon the information they have about their current institution. Individuals from corrupt countries may assign a higher subjective probability to the issuance/acceptance of a bribe by their counterpart. In Banuri and Eckel (2012b) this probability becomes known (through repeating the game), the only factor which can differ is the values (or what Rose-Ackerman terms the “moral costs” of bribing). Hence, if values differ across cultures, then we can expect differences in behavior when a game is repeated in different settings.

In an experiment related to the one carried out by Cameron et al. (2009), Alatas et al. (2009) use the data from their four-nation study to investigate the effect of gender and culture on corruption. Their results are striking in that they find the expected result of females being less corrupt to hold only in Australia. Behavior of both males and females in India, Indonesia, and Singapore was statistically indistinguishable. This is a particularly worrisome result, since it points to the difficulty of generalizing lab studies carried out in western cultures on corruption. It casts doubt on the policy recommendation of female participation in order to reduce corruption: of course one can still argue that participation should increase to reduce discrimination rather than an anti-corruption tool. This suggests that the importance of gender for corruption also varies by culture. They further extend the analysis by implementing a neutral frame with Australian subjects, and find that the gender differences hold when instructions are loaded, but not when instructions are neutral.<sup>17</sup> The one thing we do note is that of the four countries studied, Australia ranks the highest on Hofstede‟s (1980) masculinity index, suggesting that differences between genders would be starker in those cultures. Indeed, Gneezy et al. (2009) show that gender differences are subject to cultural forces.

Banuri and Eckel (2012b) implement an ABA design using the same game as above in the US and Pakistan. The study the long-term impacts of a short term policy shock on bribing behavior by conducting 10 rounds with no punishment, 10 rounds with punishment, and then 10 rounds without punishment. As in their previous experiment, subjects are matched once at the beginning of the session, and then keep their roles throughout the session. Their punishment between-subjects results are largely replicated. They show, however, that bribing behavior

> 17 This is not true for bribe acceptances, however.  They find that a significantly lower percentage of women accept bribes in the neutral frame, but not in the loaded frame.

17
Firm Performance, Participation in Global Value Chains and Service Inputs: Evidence from India* Ruchita Manghnani<sup>a+</sup> Birgit Meyer<sup>b+</sup> Sebastian Saez<sup>a</sup> Erik van der Marel<sup>c</sup> _Keywords_ : GVC, service inputs, firm productivity, complex services, FDI, exports, imports _JEL Codes_ : D24, F14, F23, F61, L80 _____________________________________________________________________________________

> * The paper is part of a larger report on GVCs and Services in India. The research was partially supported by the Umbrella Facility for Trade. Michael Ferrantino and Robert Carl Michael Beyer reviewed a first draft of the report and provided valuable comments and suggestions.

a World Bank

> b Austrian Institute of Economic Research, Vienna University of Economics and Business and Kiel Center for Globalization c Université Libre de Bruxelles and the European Center for International Political Economy

> + Corresponding email: rmanghnani@worldbank.org; <u>birgit.meyer@wu.ac.at</u>
# **1. Introduction**

Understanding the implications of large-scale forced displacement events such as refugee influxes or population movements due to natural disasters requires representative and comparable information about the demographic and socio-economic characteristics of the displaced and host populations. This is particularly important when trying to shed light on aspects related to the selectivity of those who are displaced compared to host communities, or the impacts of displacement. Representativeness is central to minimize drawing biased conclusions as decisions to relocate and where to locate in host areas are likely not random. In addition, comparability is key as contrasts between displaced and hosts are a core element of these types of analysis. National household surveys typically exclude displaced populations, particularly at the onset of these events. Humanitarian data collects information on those displaced but these are not designed to answer questions related to selectivity or impacts as they are developed to inform the humanitarian response. One drawback of these data is that they typically exclude representative information on the host populations.

Using the same representative survey to cover both hosts and displaced arises as a desirable strategy, however it is not a straightforward effort. One key challenge is having complete or updated sampling frames to draw the samples of both hosts and arriving migrants. Sampling frames of host populations are typically available but, in many cases, exclude forcibly displaced populations. This is certainly the case when the focus is collecting information right after a mass displacement event as it is unlikely that the sampling frame for that area will be updated to reflect the most recent population. On the other hand, population counts on the recently displaced usually collected by humanitarian agencies may face challenges in providing complete or accurate counts, particularly when there is significant movement of populations within the affected area or when the displaced spread across geographic areas. Additional challenges arise in identifying and counting the displaced in cases where they may not be willing to self-identify.

This paper describes a sampling approach that triangulates different sources of data to design a representative survey in the context of the recent Rohingya displacement from Myanmar to the district of Cox’s Bazar in Bangladesh. The Cox’s Bazar Panel Survey (CBPS) aims to produce representative information of both the Bangladeshi host population living in the district and the Rohingya displaced living in the district. This paper highlights the challenges and solutions in conducting the CBPS sampling strategy. The approach included combining information from the Bangladesh housing and population census, humanitarian data on Rohingya displaced living in camps, geospatial data and listing exercises. The next section explains the context and the core objectives of the survey. Section 3 describes the sampling exercise. Section 4 summarizes the lessons learned and concludes.

2
# Table 12: General hygiene

|VARIABLES|Caregiver hand<br>cleanliness index|Child cleanliness<br>index|Feces observed<br>in house|Smell of feces<br>in dwelling|Garbage is visible<br>in dwelling|Food is<br>covered|HH has ever<br>cleaned latrine|Flies visible<br>around latrine|Feces are visible<br>outside the latrine|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|HWWS (β1)|0.403**|0.077**|-0.000|-0.001|-0.037|0.111***|0.077***|-0.031|-0.007|
||(0.187)|(0.030)|(0.017)|(0.020)|(0.031)|(0.026)|(0.022)|(0.036)|(0.022)|
|TSSM ( β2)|0.037|0.025|0.007|0.002|0.035|0.089***|0.015|-0.018|0.014|
||(0.181)|(0.028)|(0.016)|(0.019)|(0.032)|(0.027)|(0.023)|(0.035)|(0.021)|
|HWWS + TSSM (β3)|0.455**|0.069**|0.008|0.011|-0.037|0.068**|0.072***|0.019|-0.043**|
||(0.179)|(0.030)|(0.018)|(0.021)|(0.031)|(0.029)|(0.024)|(0.037)|(0.021)|
|p-values β1= β2|0.020|0.061|0.602|0.826|0.013|0.398|0.005|0.673|0.341|
|for F-test β1= β3|0.740|0.790|0.617|0.504|0.993|0.124|0.831|0.162|0.115|
|β2=β3|0.008|0.118|0.951|0.618|0.015|0.473|0.017|0.280|0.009|
|_Observations_|3,606|5,585|3,583|3,601|3,524|2,780|2,921|2,974|2,896|
|_R-squared_|0.091|0.210|0.063|0.061|0.045|0.049|0.094|0.087|0.055|
|_Control Mean_|6.765|0.566|0.089|0.124|0.383|0.283|0.800|0.537|0.222|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects. Column (2) also includes child age (month) and gender dummies.

48
_works affect agricultural wages? Evidence from India_ . CSAE Working Paper 2012-05, Oxford: Center for the Study of African Economies.

- Bhargava, A.K. 2014. _The impact of India’s rural employment guarantee on demand for agricultural technology_ . IFPRI Discussion Paper 1381, Washington, DC: International Food Policy Research Institute.

- Binswanger-Mkhize, H.P. 2012. “Is there too much hype about index-based agricultural insurance?” _The Journal of Development Studies_ 48:187–200.

- Cameron, A.C., J.B. Gelbach, and D.L. Miller. 2008. “Bootstrap-Based Improvements for Inference with Clustered Errors.” _Review of Economics and Statistics_ 90:414– 427.

- Cameron, A.C., and D.L. Miller. 2015. “A Practitioner’s Guide to Cluster-Robust Inference.” _Journal of Human Resources_ 50:317–372.

- Carter, M.R., A. de Janvry, E. Sadoulet, and A. Sarris. 2014. _Index-based weather insurance for developing countries: A review of evidence and a set of propositions for upscaling_ . FERDI Working Paper P111, Clermont-Ferrand: Fondation pour les Etudes et Recherches sur le Developpement International.

- Chay, K.Y., P.J. McEwan, and M. Urquiola. 2005. “The Central Role of Noise in Evaluating Interventions That Use Test Scores to Rank Schools.” _American Economic Review_ 95:1237–1258.

- Cole, S., X. Gine, J. Tobacman, R. Townsend, P. Topalova, and J. Vickery. 2013. “Barriers to household risk management: Evidence from India.” _American Economic Journal: Applied Economics_ 5:104–135.

- Cole, S., X. Gine, and J. Vickery. 2013. _How does risk management influence production decisions? Evidence from a field experiment_ . World Bank Policy Research Working Paper 6546, Washington, DC: World Bank.

- Deininger, K., and Y. Liu. 2013. _Welfare and Poverty Impacts of India’s National Rural_

29
half of cane reference prices when these hit bottom in 1985. On average during the 10 years, they amounted to 14 percent of the domestic price of sugarcane in the three states.

It is important to note that there is some upward bias in the incentive indicators discussed above. First, as noted in the Goldar and Gulati study and in our discussion of the NPC estimates, no allowance has been made for arrears in payments by the mills for farmers' cane, which in difficult years for the mills are reported to have amounted to as much as twenty percent or more of the value of the cane purchased. These payment arrears obviously discourage farmers from growin sugarcane, but quite detailed research would be required to to quantify them and their effects.,5 Secondly, the methodology used assumes that the observed sugar mill processing margin in a given year would not change if reference prices for sugar and molasses were to replace the actual domestic prices of that year. If this would mean that domestic sugar prices would be much lower than actual prices, it is likely that the incomes ( i.e. wages and returns to capital ) of the primary factors in sugar milling would be depressed , thus depressing the processing margin and absorbing some of the price reduction, all of which in our estimates is assumed to be absorbed by the cane fanners. This in turn would mean that the cane reference price would be higher than in our estimates, thus giving lower NPC and other incentive indicator estimates for cane growing.On the other hand, as noted in our discussion of the NPC estimates, we have followed the somewhat arbitrary procedure adopted by Goldar and Gulati in assuming minimum cane reference prices in a few years of very low world sugar prices in which the cane reference prices would otherwise be extremely low or negative, which in turn implies absorption of much or most of the low world sugar prices by the mills. To quantify these effects more systematically would require research on actual milling margins over time in response to fluctuations in domestic prices and an attempt to project the observed responses to the counterfactual reference scenarios.

Finally, it is worth noting that the the averages of the sugarcane incentive indicators for the 1981-90 period are heavily influenced by the results for the outlier year 1985, when following Goldar and Gulati, we assumed a very low cane reference price in the largest producing state, UP, which covered only the paid out cost of intermediate inputs. Excluding 1985 brings down the three-state period averages of the NPC, EPC and ESC from 1.69, 2.04 and 2.38 respectively to 1.47, 1.65 and 1.92.

> 35 This would require looking at how the arrears are distributed among farmers and when and how they are eventually settled, as well as the farmers' sources of credit. It would also be necessary to consider differences between farmers in UP and Tamil Nadu and farmers predominantly working with cooperative mills in Maharashtra.

55
42 Table 6 (Dependent Variable **is** Return on Assets, ROA) OLS Results

|Variable|Coefficient|T-Statistics|
|---|---|---|
|C|-0.00454|1.26|
|MSHARE|0.037221|1.56|
|CA|0.123172|6.17|
|TA|-6.318E-10|_0.54_|
|LA|-0.02046|5.44|
|DT|0.007065|2.52|
|OEA|-0.177827|-3.19|
|MDGR|0.001463|0.59|
|CR5|||
|DI|0.89376|2.29|
|R-Squared||0.51|
|F-Statistic||20.98|
revenues, employment and exports. For our main specification, we measure TFPR using the Levinsohn and Petrin (2003) methodology, in which total expenditure on material inputs is used as a proxy for unobservable productivity shocks. We also provide results estimating TFPR as in Ackerberg et al. (2015). In order to allow for differences in technologies across sectors, different production functions were estimated for each 2-digit sector. As emphasized by De Loecker and Goldberg (2014), this is a commonly used measure of revenue-based TFP, which also reflects changes in markups, the product mix and product quality.<sup>4</sup> While we cannot exclude that changes in prices are at play, this measure of productivity should be strongly correlated with size, which we can also observe in our data (Kugler and Verhoogen (2011)).

We further conduct an event-study in which our measure of Germany specialization is interacted with time dummies: This approach has two advantages. First, it allows us to inspect for the existence of pre-trends or anticipatory effects on performance. Second, it allows us to investigate the persistence of the effects. We estimate all specifications using ordinary least squares and cluster the standard errors at the sector level.

The causal interpretation of these specifications relies on the assumption that, in the absence of the accession to the EU, Polish firms across different sectors would have experienced similar trends in performance (known as the parallel trends assumption). This assumption cannot be directly tested. Yet, our event study in equation (5) allows us to test for pre-trends in the pre-accession period. We also provide a set of robustness checks to deal with potential confounding effects. In particular, we are concerned that Germany’s specialization might be correlated with other relevant influential factors such as Poland’s specialization or with the specialization of other relevant commercial partners. To deal with this latter concern, we present a set of placebo tests where similar measures of specialization for Poland and other neighbouring non-EU countries are regressed on ownership and performance.

> 4Applications of this method in an international trade context include Pavcnik (2002), Amiti and Konings (2007), Fernandes (2007) and Topalova and Khandelwal (2011), among many others.

8
Table A(2) in Annexure A ranks countries by their tax effort (legal). Serbia, Bosnia Herzegovina, and Montenegro have the highest tax effort (legal) while Colombia, Peru, and Uruguay have the lowest tax effort over the period 2000-2010.

_Table 2._ **Tax Effort Indices: Region-wise**

||**Shadow**|**Actual**|**Tax**<br>**Revenue/**|**Pote**|**ntial**|**Tax**|**Effort**|**Tax**<br>**Effort**|
|---|---|---|---|---|---|---|---|---|
||**as % of**|**Tax/GDP**|**GDP**|||||**(Legal)**|
|**Country**|<br>**GDP**|<br>**ratio**|<br>**with**<br>**Shadow**|**_Econ_**|**_Legal_**|**_Econ_**|**_Legal_**|<br>**with**<br>**Shadow**|
|**ECA**|||||||||
|Albania|34.14|22.17|19.92|19.97|24.79|1.11|0.89|0.80|
|Armenia|43.70|18.52|16.18|17.10|21.96|1.08|0.84|0.74|
|Azerbaijan|57.66|18.00|15.12|19.43|21.73|0.93|0.83|0.70|
|Belarus|46.18|42.21|36.63|25.62|30.27|1.65|1.39|1.21|
|Bosnia&Herz|33.50|37.57|33.83|26.09|26.91|1.44|1.40|1.26|
|Bulgaria|35.05|30.64|27.46|26.73|32.05|1.15|0.96|0.86|
|Croatia|31.94|34.67|31.37|26.58|34.93|1.30|0.99|0.90|
|Czech Rep|18.25|35.07|33.08|31.06|35.72|1.13|0.98|0.93|
|Georgia|65.49|19.99|16.44|20.82|22.81|0.96|0.88|0.72|
|Kazakhstan|40.81|23.76|20.94|22.69|22.93|1.05|1.04|0.91|
|Kosovo|39.00|20.59|18.24|21.20|20.93|0.97|0.98|0.87|
|Kyrgyz Rep|40.30|19.61|17.31|13.90|19.59|1.41|1.00|0.88|
|Macedonia|37.48|28.78|25.61|25.53|27.78|1.13|1.04|0.92|
|Moldova|44.29|31.16|27.19|23.36|33.19|1.33|0.94|0.82|
|Montenegro|30.54|38.60|35.06|26.63|28.55|1.45|1.35|1.23|
|Poland|27.14|32.91|30.21|28.23|36.73|1.17|0.90|0.82|
|Romania|32.38|28.59|25.83|24.74|28.81|1.16|0.99|0.90|
|Russian Fed|43.40|31.62|27.66|25.75|29.12|1.23|1.09|0.95|
|Serbia|31.43|36.64|33.20|22.72|24.87|1.61|1.47|1.33|
|Slovak Rep|18.04|31.13|29.38|31.81|36.35|0.98|0.86|0.81|
|Tajikistan|42.04|16.40|14.40|6.76|12.44|2.43|1.32|1.16|
|Turkey|31.09|24.76|22.45|22.39|25.79|1.11|0.96|0.87|
|Ukraine|49.35|33.97|29.22|23.53|29.72|1.44|1.14|0.98|
|**OECD**|||||||||
|Austria|9.73|42.79|41.46|31.86|38.83|1.34|1.10|1.07|
|Belgium|21.83|44.12|41.16|32.23|44.00|1.37|1.00|0.94|
|Canada|15.64|33.29|31.66|31.65|31.55|1.05|1.06|1.00|
|Denmark|17.66|48.65|45.97|31.13|46.53|1.56|1.05|0.99|
|Estonia|31.18|31.63|28.68|30.18|32.96|1.05|0.96|0.87|
|Finland|17.58|43.92|41.51|29.85|39.56|1.47|1.11|1.05|
|France|14.94|43.63|41.58|27.82|36.55|1.57|1.19|1.14|
|Germany|15.91|36.08|34.28|30.04|37.31|1.20|0.97|0.92|
|Greece|27.40|32.26|29.59|28.15|34.83|1.15|0.93|0.85|
|Hungary|24.29|38.55|35.69|30.32|38.27|1.27|1.01|0.93|
|Ireland|15.75|29.27|27.82|33.43|39.81|0.88|0.74|0.70|
|Italy|26.93|41.99|38.57|27.00|37.80|1.56|1.11|1.02|
|Japan|10.90|27.06|26.12|26.87|28.82|1.01|0.94|0.91|
|<br>Latvia|29.01|28.72|26.21|28.00|30.69|1.03|0.94|0.85|
|Lithuania|31.83|28.87|26.13|27.73|30.43|1.04|0.95|0.86|
|Netherlands|13.16|38.34|36.74|33.66|39.80|1.14|0.96|0.92|
|Norway|18.66|42.82|40.34|29.00|43.06|1.48|0.99|0.94|
|Portugal|22.95|31.28|29.08|28.37|35.52|1.10|0.88|0.82|

18
<!-- Start of picture text --> 700000<br>Crop Livestock Agr. Wage Non‐agr. wage Self‐emp<br>600000<br>500000<br>400000<br>300000<br>200000<br>100000<br>0<br>2005/06 2009/10 2010/11 2011/12<br>Ugandan Schillings (2011 prices)<br><!-- End of picture text --> Source: UNPS 2006-2012 12
# **Is Optimization an Opportunity? An Assessment of the Impact of Class Size and School Size on the Performance of Ukrainian Secondary Schools**

**_Tom Coupé Anna Olefir Juan Diego Alonso_**<sup>**_1_**</sup>

Human Development Sector Unit Europe and Central Asia Regions JEL classification: I28, I29 Key words: Secondary Education, Inputs, Performance, Optimization, Class Size, School Size, Quality of Education

> 1 Tom Coupé, Associate Professor, Kyiv School of Economics (KSE); Senior Economist, Kyiv Economics Institute (KEI), World Bank Consultant; Anna Olefir, Economist, World Bank Consultant, ECA Human Development Sector Unit, Juan Diego Alonso, Education Economist ECA Human Development Sector Unit.
Table 8: Analysis of Relationship Between Job Quality Dimensions and Poverty

||Dep. Var|= Poverty|Indicator|
|---|---|---|---|
||(1)<br>b/se|(2)<br>b/se|(3)<br>b/se|
|Ins|1.489***|0.614***|0.610***|
||(0.126)|(0.153)|(0.191)|
|Ret|1.242***|0.590***|0.378*|
||(0.216)|(0.140)|(0.219)|
|Ten|-0.062|0.078**|0.200***|
||(0.094)|(0.031)|(0.049)|
|Cont|0.940***|0.442**|0.337***|
||(0.318)|(0.195)|(0.061)|
|Perm|0.628***|0.322**|0.351***|
||(0.224)|(0.141)|(0.075)|
|Sing|0.320***|-0.078*|0.095|
||(0.052)|(0.044)|(0.150)|
|Controls|No|Yes|Yes|
|Year Efects|No|Yes|Yes|
|Country Efects|No|Yes|Yes|
|Sector Efects|No|Yes|Yes|
|Pseudo-_R_<sup>2</sup>|0.101|0.239|0.203|
|Number|8,475,013|6,375,804|790,361|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors in parentheses. Significance: *** 1%, ** 5%, * 10%.

33
We see that the participation rate declines rather slowly until one reaches about the 50<sup>th</sup> percentile of the rural distribution. In fact there is no perceptible decline in participation in a neighborhood of the national headcount index of poverty of 36%; households just below the official poverty line are no more likely to participate in MGNREGS than those just above the line. The marked decline in participation rates does not emerge until we get to the upper half of the rural consumption distribution. By the 90<sup>th</sup> percentile, the participation rate reaches about 10%. Although far fewer ―rich‖ rural households participate, there are still some. This could reflect recent shocks, or poor individuals within generally well-off households.

The rationing rate follows a U-shape, declining initially as consumption increases until one reaches about the median, but then rising. However, this clearly stems from the high rationing rates in poor states that we have demonstrated above. Thus we see that, when we add state fixed effects, a steady increase in the rationing rate emerges as consumption increases across the whole distribution (Figure 4(b)).

The fact that the rationing rate tends to be lower for the poor does not, however, imply that more rationing would improve targeting. What the numbers in Table 3 and Figure 4(b) reflect is the rationing process at a given level of participation. When the participation rate rises through a reduction in rationing the self-targeting mechanism will start to play a bigger role. We will see evidence of this when we compare targeting performance across states with very different participation rates.

Also notice that, amongst participants, the days of work received shows a slightly positive gradient with consumption per person. The pro-poor targeting is achieved through both demand for work and the rationing of work, not by the amount of work actually received.

It is of interest to compare targeting performance across states. There are many measures of ―targeting performance‖ in the literature that might be used for this purpose. Ravallion (2009) surveys the various measures and tests their performance in predicting the impacts on poverty of a large antipoverty program in China, called the _Di Bao_ program. (This provides cash transfers targeted to those with income below the locally-determined _Di Bao_ poverty lines.) Amongst all standard targeting measures, the one that performed the best (and by a wide margin) in predicting the program‘s impact on poverty was the ―targeting differential‖ ( _TD_ ), originally proposed by 10