rafmacalaba/datause-encoder-data huggingface dataset:
transaction patterns. Our results are consistent with differential selection into the cards: those who have a higher demand for social status (although not, apparently, a higher demand for restaurants per se) might have been more likely to accept the platinum card offer. In either case, the results are consistent with customers using the platinum card to signal status.

## **4.2.3 The Value of Social Status**

Based on our calibration exercise in Section 3.3, the average consumer values the status aspect of the card by Rp. 218,000 ($15.5) per year. Assume that the utility customers derive from the social aspect of the card comes from the signal of higher income the card sends others when they use it in a social situation. The survey described in Section 2 confirms that people who observe the card correctly infer that platinum cardholders have higher income. Suppose their posterior upon observing a card is equal to the average annual income associated with the card type: Rp. 522 million for platinum cards, and Rp. 215 million for gold cards, a difference of approximately Rp. 300 million. The average platinum card customer uses the card 4.8 times per year in social or visible situations. Then, consumers on average must value being seen as having Rp. 300 million higher income by Rp. 218,000/4.8 (=$3.2) per interaction.

Of course, the above calculation relies on various assumptions. For example, the number of “views” of the card may be higher than we observe, if customers sometimes show the card to others without actually using it. This would cause us to overestimate the status value _per_ transaction. On the other hand, we are likely underestimating how much consumers value being seen as higher income, per dollar of higher income. One’s peers likely have somewhat informed priors about one’s income, and thus the extent of updating of beliefs from noticing the card is likely smaller than Rp 300 million. Customers would thus be willing to pay $3.2 for a much smaller boost in social image.

The platinum card provides limited opportunities to signal status, and is ultimately visible to a small if important set of peers. If consumers value income signaling similarly in other interactions, then products that convey status to a larger audience, such as expensive cars and clothing, might command a substantial premium. A car that signals a similar Rp. 300 million income difference to a thousand people might have a status value of $3,200. The economic importance of status considerations in consumption could thus be quite large.

# **5 Experiment 2:** **_Positional Externality_**

Intuitively, the signaling value of a status good depends on the type of customers who are expected to own it. To earn status, one wants to display goods that are known to be owned by ‘high types’, and inaccessible to ‘low types’.<sup>30</sup> This implies that when individuals with comparatively lower

> 30In our setting, ‘type’ is synonymous with income. However, there are of course also status goods that are not allocated based on income, such as membership in prestigious clubs or professional organizations.

17
Doms, M., Dunne, T. and Kenneth Troske. 1997. "Workers, Wages and Technology," _Quarterly Journal of Economics_ 112: 253-290.

Earle, J and Álmos Telegdy. 2008. “Ownership and Wages: Estimating Public-Private and Foreign-Domestic Differentials Using LEED from Hungary, 1986–2003”. In “Analysis of Firms and Employees - Qualitative and Quantitative Approaches”, F. Andersson, S. Bender, J. Lane, K. Shaw, and T. von Wachter, eds, NBER and University of Chicago.

Feenstra, Robert C. and Gordon H. Hanson (1997). "Foreign direct investment and relative wages: Evidence from Mexico's maquiladoras," Journal of International Economics 42, pp. 37 1-393.

Fajnzylber, P. and Ana Fernandes, 2004. “International Economic Activities and the Demand for Skilled Labor: Evidence from Brazil and China.” Policy Research Working Paper Series 3426, The World Bank.

Fernandes, A. and Isgut, Alberto E., 2005. "Learning-by-doing, learning-by-exporting, and productivity : evidence from Colombia," Policy Research Working Paper Series 3544, The World Bank.

Fernandes, A. and I. Sundaram. 2008. “Skill Demand and Openness in Indonesia.” Washington DC: World Bank, mimeo.

Gatti, R. and M. Honorati. 2008. _Informality among Formal Firms: Firm-Level, CrossCountry Evidence on Tax Compliance and Access to Credit_ . World Bank Policy Research Working Paper No. 4476. Washington DC: World Bank.

Hanson, G. and A. Harrison. 1995. "Trade, Technology and Wage Inequality." NBER Working Paper No. 5110.

22
59 Table 3

# **Labour Market Expenditure as Percentage of GDP** Poland

||1990|1991|1992|1993|
|---|---|---|---|---|
|Labour fund/GDP (%)|0.63|1.65|2.00|2.01|
|Labourfund/GDP (%)-<br>adjusted|0.54|1.50|1.84|1.93|
|PLMP/GDP (%)|0.32|1.35|1.72|1.68|
|ALMP/GDP**(%)**|0.29|0.26|0.25|0.31|
|ALMP/GDP (%)-adjusted|0.20|0.11|0.09|0.23|

_Source:_ G6ra (1995) and own calculations.

Table 4

**Expenditure on Labour Market Programmes** (Nominal; Billions PLZ) Poland

||1990|1991|1992|1993|
|---|---|---|---|---|
|Training/retraining|16.0|90.0|192.9|449.5|
|Intervention works|209.0|453.0|468.8|1366.3|
|Public works|||174.2|1196.9|
|Start-up loans|962.0|404.0|237.5|532.6|
|Apprenticeship|504.0|1213.0|1789.0|1321.7|

* Public Works started in 1992.

_Source:_ G6ra (1995).
migration flows in Vietnam. Whereas they find that provinces with a higher per capita income attract more migrants, the coefficient for income in the sending province in their regression turns out to be positively significant. As a potential explanation, they suggest that the “liquidity constraint effect”<sup>12</sup> in poorer regions outweighs the “push” effect of migration in Vietnam. Relating the migration rate between pairs of provinces in one period to the change in income differential between these provinces in the next period, they also find an inequality-reducing impact of migration for the flows going to HCMC and surrounding provinces.<sup>13</sup> Nguyen-Hoang and McPeak (2010) extend the macro gravity model by introducing urban unemployment rates at origin and destination provinces as a proxy for the probability of finding employment. Whereas they find that higher unemployment at places of origin generates more out-migrants, they also find, contrary to theoretical predictions, that higher unemployment at destination is associated with more in-migrants.

Nguyen, Tran, Nguyen and Oostendorp (2008) employ a micro approach to explore the determinants of out-migration using the data on panel households in the VHLSS (2002, 2004). Using a probit model, they specify the dichotomous outcome of migration (stay or move) as a function of household characteristics and the conditions in the sending areas. They find that larger households tend to have more out-migrants; households with members of age between 15 and 25 years are more likely to have out-migrants; and higher educational attainments of household members increase the probability of out-migration. Interestingly, for “economic” migrants, they find evidence of the existence of a “migration hump” with an inverted U-shape

> 12 According to Phan and Coxhead (2010), the “liquidity constraint effect” means that poorer provinces have low capacities to finance migration costs and that this in turn leads to poverty related labor immobility. The liquidity constraint hypothesis predicts that the out-migration rate first increases with respect to a sending province’s income and then decreases as income rises.

> 13 These results are consistent with Niimi, Pham and Reilly’s study (2009), which finds that a migrant who works in the foreign sector is more likely to remit money home, thus trickling-down the benefits of FDI to rural areas.
|Sample: All Children<br>**Table 9.**<br> **Childr**|9-15 years<br>OLS<br>**en Schooling O**|**utcomes**<br>Bo|ys 9-15|Gir|ls 9-15|
|---|---|---|---|---|---|
|Absent last|Work for|Absent last|Work for|Absent last|Work for|
|school day<br>(1=Yes)|Income<br>(1=Yes)|school day<br>(1=Yes)|Income<br>(1=Yes)|school day<br>(1=Yes)|Income<br>(1=Yes)|
|(2)|(3)|(5)|(6)|(8)|(9)|
|**Panel A: Intent to Treat Effect**||||||
|Business Training (1=Yes)<br>-0.003|0.025|0.009|0.013|-0.015|0.041*|
|(0.025)|(0.024)|(0.028)|(0.027)|(0.030)|(0.025)|
|Lottery Winner (1=Yes)<br>0.073*|0.010|0.077*|0.010|0.065|0.015|
|(0.038)|(0.033)|(0.041)|(0.038)|(0.050)|(0.034)|
|BT and LW<br>-0.024|0.029|-0.039|0.027|-0.002|0.039|
|(0.034)|(0.033)|(0.039)|(0.038)|(0.044)|(0.036)|
|R-Squared<br>0.04|0.06|0.04|0.06|0.05|0.07|
|P-value of t - test of ...||||||
|BT = BT and LW<br>0.52|0.88|0.22|0.71|0.74|0.96|
|LW = BT and LW<br>0.02|0.60|0.01|0.69|0.20|0.55|
|BT = LW<br>0.05|0.67|0.11|0.94|0.11|0.47|
|**Panel B: Intent to Treat Effects with Gender Interactions**||||||
|Business Training (1=Yes)<br>-0.027|-0.02|-0.022|-0.023|-0.035|-0.014|
|<br>(0.034)|(0.030)|(0.038)|(0.035)|(0.040)|(0.032)|
|BT x Female<br>0.051|0.096**|0.066|0.079|0.037|0.116**|
|(0.049)|(0.046)|(0.056)|(0.052)|(0.059)|(0.049)|
|Lottery Winner (1=Yes)<br>0.084*|-0.027|0.088*|-0.027|0.069|-0.028|
|<br>(0.047)|(0.041)|(0.051)|(0.047)|(0.065)|(0.044)|
|LW x Female<br>-0.027|0.085|-0.037|0.091|-0.006|0.094|
|(0.071)|(0.060)|(0.076)|(0.070)|(0.092)|(0.064)|
|BT and LW<br>-0.040|-0.015|-0.057|-0.013|-0.014|-0.012|
|(0.044)|(0.041)|(0.048)|(0.045)|(0.059)|(0.046)|
|BT and LW x Female<br>0.035|0.100|0.042|0.092|0.024|0.114*|
|(0.062)|(0.065)|(0.072)|(0.074)|(0.081)|(0.069)|
|R-Squared<br>0.04|0.06|0.04|0.06|0.05|0.07|
|P-value of t - test of ...||||||
|BT = BT and LW<br>0.77|0.88|0.47|0.81|0.71|0.95|
|LW = BT and LW<br>0.02|0.78|0.01|0.79|0.26|0.75|
|BT = LW<br>0.02|0.87|0.05|0.94|0.11|0.76|
|BT + BT x Female = 0<br>0.52|0.03|0.29|0.16|0.95|0.01|
|LW + LW x Female = 0<br>0.33|0.22|0.39|0.26|0.38|0.18|
|BT and LW + BT and LW x Female = 0<br>0.93|0.10|0.79|0.20|0.86|0.06|
|Mean of dependent variable among controls<br>0.35|0.21|0.35|0.23|0.34|0.19|
|N. Observations<br>5387|8572|3026|4524|2361|4048|

Note: The reported mean of the dependent variable is computed using the children of CO members not offered business training nor assigned to be lottery winners. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline, gender and branch dummies). Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.
**<u>Table 4. IFFs and tax revenues—System-GMM estimations</u>**

||**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|
|---|---|---|---|---|---|---|
|Lag (tax)<br>Illicit Financial Flows|<br>0.789***<br>(0.198)<br>-0.766**|<br>0.814***<br>(0.132)<br>-0.753**|<br>0.856***<br>(0.129)<br>-0.891***|<br>0.805***<br>(0.130)<br>-0.678**|<br>0.817***<br>(0.150)<br>-0.641**|<br>0.831***<br>(0.134)<br>-0.733**|
||(0.379)|(0.336)|(0.312)|(0.305)|(0.317)|(0.313)|
|Inflation|0.0120|0.00424|0.00752|0.00899|-0.000730|0.00912|
||(0.0323)|(0.0213)|(0.0230)|(0.0210)|(0.0197)|(0.0227)|
|Trade|<br>0.0527**|<br>0.0473***|<br>0.0508***|<br>0.0406***|<br>0.0474***|<br>0.0452***|
||(0.0215)|(0.0159)|(0.0171)|(0.0153)|(0.0136)|(0.0156)|
|Public debt|<br>-0.000796|<br>-0.00793|<br>-0.00629|<br>-0.00658|<br>-0.00883|<br>-0.00584|
|Debt service|(0.0109)<br>0.0523|(0.00707)<br>0.0670|(0.00747)<br>0.0563|(0.00686)<br>0.0548|(0.00771)<br>0.0629|(0.00750)<br>0.0582|
||(0.0694)|(0.0474)|(0.0564)|(0.0403)|(0.0404)|(0.0504)|
|GDP growth|<br>0.0781**|<br>0.0553**|<br>0.0677**|<br>0.0585**|<br>0.0546**|<br>0.0609**|
|<br>Impact|(0.0344)<br>2.070|(0.0268)<br>0.844|(0.0309)<br>0.935|(0.0283)<br>0.919|(0.0275)<br>0.858|(0.0293)<br>0.799|
|Effectiveness|(1.590)<br>0.410<br>(1.239)|(0.786)|(0.821)|(0.675)|(0.527)|(0.691)|
|Corruption||0.200<br>(0.886)|||||
|Law|||-0.0467<br>(0.849)||||
|Voice||||0.697<br>(0.597)|||
|Stability|||||-0.297<br>(0.577)||
|Regulatory||||||0.348<br>(0.826)|
|Constant|4.420|5.093*|4.909*|5.254**|4.020|<br>4.688|
||(5.966)|(2.619)|(2.784)|(2.430)|(2.694)|(2.902)|
|N|<br>485|<br>485|<br>485|<br>485|<br>485|<br>485|
|AR1 test p-value|0.050|0.063|0.060|0.064|0.067|0.061|
|<br>AR2 test p-value|0.746|0.740|0.829|0.721|0.670|0.766|
|<br>Hansen test p-value|0.645|0.708|0.732|0.726|0.804|0.666|
|<br>Number of groups|57|57|57|57|57|57|
|Number of instruments|34|43|43|43|43|43|

Note: Standard errors in parentheses. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

Finally, regarding the main result, column [1] of Table 4 shows that the effect of the variable illicit financial flows is negative and significant. Since this variable is computed as a dummy variable equal to 1 if a country is Non-Cooperative and to 0 otherwise, our estimations suggest that on average Non-Cooperative countries present significantly lower tax revenues compared with Cooperative countries. Given the dynamic setup, the loss of tax revenues is estimated at (in absolute value) around 0.77 percentage point in the short run, and at around 3.35 percentage points in the long run—comparable with the magnitude of our matching estimations. Consequently, System-GMM estimations support the causal effect of being on the FATF list on tax revenues outlined by our treatment analysis.

14
and completeness of the school’s name across the two databases; hence, matching on school name frequently required matching on key words and word patterns. In cases where we suspected that the combination of district name and school name (even with keyword and word pattern matching) did not yield a unique school record in a database, where possible, the set of matching variables was extended to include school address. This extension helped resolved a number of cases where we were tentative. On the basis of this exercise, 94% and 97% of school records in the SLQAT databases were linked with school records in the application databases for phases 3 and 4, respectively. Only the linked school records are used in our analysis, which yield sample sizes of 747 and 830 schools in phases 3 and 4, respectively. We refer to these samples as the SLQAT samples.

_Follow-up telephone interview data_ : We collected follow-up outcome data via telephone in October 2008 from phase-3 and phase-4 schools with SLQAT pass rates between  /  15 percentage points of the cutoff. This information was gathered by a small team of independent interviewers. We were able to collect the data using this mode as schools were required to provide the telephone number of the school owner or administrator when they applied to the program, and the information was included in the school application electronic databases for both phases.

We obtained data on the following outcomes: the number of students, teachers, classrooms, blackboards, and toilets (the data on the last two outcomes were collected only for the phase-4 schools).<sup>24</sup> We also obtained information on the program participation status of the schools, and crosschecked this information against PEF’s records of program schools as of September 2008. The school sample sizes within the selected pass rate range from the cutoff are

> 24 We are unable to speculate on the accuracy of these data vis-à-vis the same data collected through, for example, face-to-face interviews in the field, as we are unclear what context factors might lead to differences in accuracy across survey modes.

25
that included second order effects in their analysis. As mentioned in the review of literature section, the inclusion of second order terms generally resulted in only a marginal impact on welfare across a number of African countries (Minot & Dewina (2015) for Ghana; Kane et al (2015) in Mali; and Me-Nsope & Staatz (2016) in Cameroon). In this paper we also assume that the consumption and producer behavioral responses are likely to have a relatively small effect on net welfare in the short term. However, the effects on welfare are likely more sensitive to relaxing the assumption that proportional changes in prices are the same for both producers and consumers.

As is the case in most previous studies, producer price data are not consistently available for Mozambique and we cannot directly estimate the change in the marketing margins (difference between consumer and producer prices) during the period of the price rise. Therefore, following Minot & Dewina (2015) and Wodon et al. (2008), we examine the sensitivity of our base case estimates (Table 10 and Table 11), where we assumed proportional producer and consumer prices increases, by simulating the welfare changes under a range of marketing margins. More specifically, we simulate the four following scenarios: (i) zero percent increase in producer prices – an upper-bound on the negative welfare impacts of the food price increase; (ii) an increase in producer prices equal to 50% of the percent increase in consumer prices; (iii) 1.5 times the percent increase in consumer prices and (iv) twice the percent increase in consumer prices, the most optimistic scenario, proxying a lower bound.

Table 12 presents the net welfare impacts under each of the four scenarios. As one would expect, the highest negative effect is observed under the assumption of no producer price increase – 12.65% compared to 8.94% in the base scenario. The size of welfare reduction drops as the assumed percentage increase in producer prices becomes larger. However, we find that the welfare effects remain negative even if the percent increase in producer prices is twice that of the increase in consumer prices. Under this scenario the household welfare loss for the whole country is still around 5%. Only in the provinces of Nampula and Zambezia, which have high PR of maize, we see a positive impact under the optimistic scenario.

A similar pattern in seen in Table 13, which presents the corresponding impact on poverty prevalence under each of these scenarios. The overall poverty headcount ratio increases to 54.6 % 24
- [66] Lall, S., H. Selod and Z. Shalizi (2006) Rural-Urban Migration in Developing Countries: a Survey of Theoretical Predictions and Empirical Findings, _World Bank Policy Research Working Paper 3915_

- [67] Lamsal, L., R. Martin, D. Parrish and N. Krotkov (2013) Scaling Relationship for NO2 Pollution and Urban Population Size: A Satellite Perspective, _Environmental Science and Technology_ , 47, 7855-7861.

- [68] Lanjouw, J. and P. Levy (2002) Untitled: A Study of Formal and Informal Property Rights in Urban Ecuador, _Economic Journal_ , 112 (482), 986–1019.

- [69] Leung, C. (2014) Error Correction Dynamics of House Prices: An Equilibrium Benchmark, Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute, _Working Paper No. 177_ .

- [70] Libecap, G. (1986) Property Rights in Economic History: Implications for Research, _Explorations in Economic History_ , 23, 227-52.

- [71] Libecap, G., D. Lueck and T. O’Grady (2011) Large-Scale Institutional Changes: Land Demarcation in the British Empire, _Journal of Law and Economics_ , 54, S295-S327.

- [72] Lucas, R. (1988) On the Mechanics of Economic Development, _Journal of Monetary Economics_ 22, 3–42.

- [73] Macours, K., A. de Janvry and E. Sadoulet (2010) Insecurity of property rights and social matching in the tenancy market, _European Economic Review_ , 54(7), 880-99.

- [74] Malpezzi, S. (1999) A Simple Error Correction Model of House Prices, _Journal of Housing Economics_ , 8, 27-62.

- [75] Maskin, E. and J. Tirole (1987) A Theory of Dynamic Oligopoly, III: Cournot Competition, _European Economic Review_ , 31, 947-968.

- [76] Maskin, E. and J. Tirole (1988a) A Theory of Dynamic Oligopoly, I: Overview and Quantity Competition with Large Fixed Costs, _Econometrica_ , 56, 549-569.

- [77] Maskin, E. and J. Tirole (1988b) A Theory of Dynamic Oligopoly, II: Price Competition, Kinked Demand Curves, and Edgeworth Cycles, _Econometrica_ , 56, 571-599.

- [78] Maskin, E. and J. Tirole (2001) Markov Perfect Equilibrium, I: Observale Actions, _Journal of Economic Theory_ , 100, 191-219.

- [79] Marx, B., T. Stoker and T. Suri (2013) The Economics of Slums in the Developing World, _Journal of Economic Perspectives_ , 27, 4, 187-210.

- [80] Michaels, G., F. Rauch and S. Redding (2012) Urbanization and Structural Transformation, _Quarterly Journal of Economics_ , 127(2), 535–586.

37
an analytical perspective of its determinants drawing on new survey data from Bangladesh.

We randomly selected and interviewed over 821 farmers across Bangladesh with structured questionnaires containing information on their input use & yield; farmers’ characteristics; knowledge of pesticides and sources of information, precautions and damage-averting behavior along with any associated health effects. From their responses, we developed a simultaneous three-equation model of farmers’ overuse, risk perception and health effects of pesticides. Our analysis indicates that the toxicity of pesticides, crop composition, location and other socio-demographic factors determine the incidence of overuse. To the best of our knowledge, such in-depth studies of overuse of pesticides are non-existent in the literature, especially in the context of developing countries. The current study did not permit us to investigate issues such as the use of faulty equipment leading to overuse, mis-labeling or content switching, nor the role of pesticide subsidies in pesticide overuse. These issues remain as important research issues that should be the focus of further studies into the farmers’ behavior of pesticide overuse.

The remainder of the paper is organized as follows. In Section 2 we describe the trend of pesticide use and the associated problems in Bangladesh. In Section 3 we present the survey, and in Section 4 we describe the dataset. The models, along with our econometric findings, are documented in Section 5. In Section 6 we conclude and offer some implications of our findings.

# **2.  Pesticide Use in Bangladesh at a Glance**

As in many developing countries, Bangladesh<sup>2</sup> has promoted the use of pesticides to expand agricultural land and increase output per acre. Promotional activities have included extension services and significant subsidies (Rasul and Thapa, 2003; Hossain 1988). As a consequence of this expansive policy, pesticide use has more than doubled since 1992, rising from 7,350 metric tons to 16,200 metric tons in 2001 (See Figure 1). A

> 2 Approximately 84% of Bangladesh’s people are directly or indirectly dependent on agriculture for their livelihood, and agriculture contributes about 24% of gross domestic product (Bangladesh Bureau of Statistics, 2001).

3
Table V(a)

|Effect of Elections on<br>it-statistics in|Capit<br>paren|al Spen<br>thesis]|dinga||
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Independent Variables:|||||
|Scheduled election year|2.45|2.54|||
||[1.58]|[1.63]|||
|Midterm election year|||-2.47|-2.68|
||||[-1.96|] [-2.07]|
|State domestic product [SDP]||0.001||0.002|
|||[0.41]||10.44]|
|Share of agriculture in SDP||5.91||3.79|
|||[0.65]||[0.44]|
|Proportion of rural population||-98.29||-100.39|
|||[-2.05]||[-2.08]|
|Number of Observations|469|407|469|407|
|R-sq|0.64|0.66|0.64|0.66|

a. Capital outlays and SDP are in 1973 rupees per capita. Regressions include state and year effects, and are reported with robust standard errors.

43