rafmacalaba/datause-encoder-data huggingface dataset:
Hindu and Muslim households live in rural areas. In the 2011 Indian Census, the Hindu rural share was 71% compared to the Muslim rural share of 60%. These demographics align with those in IHDS-sampled households, for which approximately 68% of Hindu households resided in rural areas compared to 56% of Muslim households. Figure 1 plots the share of Indians and Muslims living in urban areas over time, which shows longstanding differences in urbanization between Hindus and Muslims and the (predominantly Hindu) Indian population. In each decade, Muslims are more urbanized than the overall Indian population.<sup>12</sup> The differences between Hindus and Muslims in latrine ownership reported in the IHDS data (Table 1) are similar to the differences in latrine ownership reported in the DLHS3 data (Appendix Table 1) and differences in latrine use reported in the NFHS-3 data (Appendix Table 2). The analysis calculates large unconditional differences between Hindus and Muslims, between 18 and 23 percentage points, which are similar to the 25 percentage point difference reported by Geruso and Spears (2018) using NFHS data pooled across 199293, 1998-99, and 2005-06. This overall difference is similar to the rural difference between Hindus and Muslims, with smaller differences between Hindus and Muslims in urban areas.

Table 1 also includes other sanitation practices: whether household members wash their hands after defecating, and whether the surveyor did not observe fecal material near the home. Hindu households report slightly higher handwashing than Muslim households in urban areas (row 2, column 6) and slightly lower handwashing in rural areas (row 2, column 9). Surveyors are less likely to observe fecal material near the homes of Hindus, and this between Hindus and Muslims in urban areas and the difference between Hindus and Muslims in rural areas. The overall difference also reflects lower latrine ownership in rural areas than in urban areas, along with a greater share of Hindus living in rural areas than the share of Muslims living in rural areas. For example, there could be no difference between Hindus and Muslims in both rural areas and urban areas and there could still be an overall between Hindus and Muslims.

> 12According to Gayer and Jaffrelot (2012, p. 10), Muslims represent the most “urbanized religious community of any significant size in India” and more than “50% of Indian Muslims are living in towns and cities in seven states, which, otherwise are predominantly rural.” Muslims historically dominated Indian Ocean trade routes, and were more likely to settle along pilgrimage routes and in urban areas, which is a pattern that has persisted (Jha, 2013). In addition, according to the 2011 Indian Census, Muslims were less likely to own land than other religious groups, and hence may move toward urban centers and out of the agricultural sector. Traditionally, Muslims worked in industries such as silk, hand and power looms, the leather industry, automobile repairing, and garment making (Sachar et al., 2006).

7
for example, stabilize the KHR to USD exchange rate in response to a US policy rate increase by the Fed. Therefore, we can expect a decrease in international reserves when there is a positive shock in the US federal fund rate. International reserves are included in the estimation as a share of GDP ( _intresvgdp_ ).

# **4. Data and estimation results**

# **4.1 Data**

Quarterly series between the fourth quarter of 1994 and the fourth quarter of 2016 for the variables mentioned above are obtained from various sources.<sup>9</sup> The data for US federal fund rate, KHR to USD exchange rate, lending interest rate, and CPI are obtained from the International Financial Statistics (IFS) published by the International Monetary Fund (IMF). Information on exports and imports is obtained from the Direction of Trade Statistics (DOTS) published by the IMF. Since quarterly GDP data for Cambodia is not available, it is generated by interpolation from the annual GDP series obtained from the World Development Indicators (WDI) published by the World Bank (WB). Broad money (M2) is the sum of money in circulation, demand deposits, and time and saving deposits of both KHR and foreign currencies. Data is obtained from the Economic and Monetary Statistics published by the National Bank of Cambodia (NBC). Data on crude oil price is retrieved from the Primary Commodity Prices published by the IMF. International reserve levels are obtained from the NBC. The dummy variable is 1 for 1997Q3-1998Q4, 2003Q3-2003Q4, 2008Q3-2009Q4, and 2013Q3-2013Q4, and zero elsewhere. Potential GDP in real values required for the calculation of output gap data is generated applying the Hodrick-Prescott filter on the real GDP series.

Since our estimation uses quarterly data, adjustments are conducted to correct for seasonal fluctuations, except for the US federal fund rate and domestic lending interest rate. CPI is used to convert nominal GDP into real GDP.

# **4.2 Unit root tests**

As mentioned above, we need to apply unit root tests to the variables under consideration to identify their integration orders. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests are employed. The results are provided in Table 1. From the table, while _z_ , _gygap,_ and _infl_ are I(0) (i.e. variables having integration order of 0), _ffr_ , _bmgdp_ , _ttbgdp_ , _tbeugdp_ , _tbusagdp_ , _lendint_ , and _intresvgdp_ are I(1) (i.e. variables having integration order of 1).

> 9 The sample periods are different by specification, depending on data availability.

10
**Table 2 : External Debt and Growth: three-year average panel**

||1<br>[OLS]|2<br>[FE]|3<br>[GMM system]|
|---|---|---|---|
|Present Value of Debt [PVY]/Exports (avg)|-0.591|-0.93|-0.397|
||[2.46]*|[2.96]**|[0.46]|
|Present Value of Debt [PVY]/Exports (initial)|-0.445|-0.325|-0.735|
||[1.96]*|[1.12]|[0.89]|
|Present Value of Debt [PVE]/Exports (avg)|-0.454|-1.468|-1.21|
||[1.55]|[3.78]***|[1.33]|
|Present Value of Debt [PVE]/Exports (initial)|-0.175|-0.691|-0.873|
||[0.62]|[1.99]**|[0.89]|
|Total Outstanding Debt/Exports (avg)|-0.562|-1.207|-0.178|
||[1.88]*|[3.44]***|[0.17]|
|Total Outstanding Debt/Exports (initial)|-0.408|-0.593|-0.206|
||[1.45]|[1.92]*|[0.17]|
|Present Value of Debt[PVE]/GDP (avg)|-0.559|-0.4|-1.292|
||[1.95]*|[1.08]|[1.26]|
|Present Value of Debt [PVE]/GDP (initial)|-0.388|0.01|-0.606|
||[1.48]|[0.03]|[0.54]|
|Total Outstanding Debt/GDP (avg)|-0.607|-0.378|-0.364|
||[2.05]*|[1.11]|[0.30]|
|Total Outstanding Debt/GDP (initial)|-0.541|-0.024|-0.296|
||[2.04]**|[0.08]|[0.31]|

* significant at 10%; ** significant at 5%; *** significant at 1% Absolute value of z statistics in brackets Note: [GMM System]: Two System Estimator with Small Sample Windmejer  (2005) Robust Correction [PVE]= PV data from B.Easterly; [PVY]: PV data from Yuri Dikhanov Set of Control Variable identical to Table 1 All regressions include time effects

24
- 17 -

significantly positive. This looks consistent with the fact that the vast majority of farmers do not own their own transport means. The result indicates that not only transport infrastructure but also transport and trade services are important. Interestingly, the size of household also has a positive impact on the market participation. The coefficient of _SIZE_ is always positive and significant. It may be interpreted to mean that the market participation is timeconsuming, requiring extra labor force, for example, to bring their produce to the markets or the places where their produce is consolidated.

32. Since the exogeneity hypothesis cannot be rejected in the two cases, the tobit regression was also performed ( **Table 5** ). The results just confirm the above findings. The accessibility to the large city in the islands has a negative and significant coefficient.

**<u>Table 4. IVTOBIT estimation results for market sales</u>**

|ln_S_|ln_S_|ln_S_||ln_S_|
|---|---|---|---|---|
|ln_COSTMORONI_-1.950 (0.804) **||||-2.022 (1.062) *|
|ln_COSTCITY_|-0.952 (0.342) ***|||-0.623 (0.437)|
|ln_MAI_||2.501|(7.588)|-9.701 (5.997)|
|ln_L_<br>-0.286 (0.243)|-0.444 (0.249) *|-0.254|(0.255)|-0.314 (0.285)|
|_SEED_<br>-0.127 (0.263)|-0.148 (0.243)|<sup>-0.102</sup>|<sup>(0.288)</sup>|0.001 (0.283)|
|_FERT_<br>0.035 (0.319)|-0.031 (0.294)|0.160|(0.313)|0.039 (0.321)|
|_COOP_<br>3.515 (0.396) ***|3.651 (0.418) ***|3.397|(0.375) ***|3.693 (0.406) ***|
|_MKT_<br>3.582 (0.203) ***|3.485 (0.203) ***|3.587|(0.199) ***|3.471 (0.204) ***|
|_TRAD_<br>3.542 (0.400) ***|3.679 (0.356) ***|3.757|(0.348) ***|3.450 (0.463) ***|
|_MALE_<br>-0.069 (0.229)|-0.271 (0.237)|<sup>-0.147</sup>|<sup>(0.258)</sup>|-0.003 (0.305)|
|_SIZE_<br>0.599 (0.232) **|0.623 (0.241) ***|0.514|(0.243) **|0.577 (0.241) **|
|_AGE_<br>0.374 (0.295)|0.277 (0.292)|0.321|(0.296)|0.436 (0.318)|
|_CAR_<br>-0.930 (0.426) **|-0.958 (0.451) **|-0.807|(0.416) *|-1.013 (0.418) **|
|_MOTR_<br>0.289 (0.551)|0.203 (0.554)|0.193|(0.561)|0.354 (0.560)|
|_BICY_<br>-0.949 (0.816)|-0.631 (0.935)|<sup>-1.067</sup>|<sup>(0.941)</sup>|-0.092 (1.024)|
|Constant<br>0.664 (1.343)|1.597 (1.255)|2.597|(1.841)|-1.563 (2.057)|
|Obs.<br>2587|2587|2587||2587|
|Obs.<br>1247|1,265|1,265||1,265|
|Censored|||||
|Wald chi2<br>1174.4|1195.0|1192.7||1108.7|
|Dummy variables:|||||
|Crops<br>Yes|Yes|Yes||Yes|
|Prefectures Yes|Yes|Yes||Yes|
|Endogeneity test:|||||
|Wald chi2<br>11.90 ***|4.66|0.06||20.09 ***|
<!-- Start of picture text --> available data. We use cross-sectional not tion to keep the exposition simple. In practice,<br>methods to their cases, we discuss two ge eral estimation frameworks in the next subsec-<br>tion. We conclude with an example of asp _ific panel data model that can easily be adapted<br>y common empirical settings.<br>tinuing the example of the variance of treatment effects, we can point identify<br>we are willing to assume a particular value of the lation between the treated<br>untreated outcome. For example, if potentia tcomes are uncorrelated<br>~(y<br>Vv seen above, the true covariance in our example is itive, so assuming that it is<br>s to an overestimate of the variance of treatment e .<br>More generally, we need to be able to identify the dependence between the treated and<br>untreated outcomes to identify the distribution of treatment effects. So far, we have primari-<br>plicitly chooses values for the parameters that determine the dependence of potential out-<br>comes. While additional assumptions are always required, a much better case for them can<br>be made in practice for two reasons. Fi e esearcher may have a model that includes<br>the relevant dependence parameters a e able to estimate them from this model<br>under weaker conditions. For example,<br>vidual choice such as the (generalized) R |, which can be estimated under conditions<br>tion, we may be able to estimate the required parameters under plausible as umptions by<br>amending the RCT with a model of individual choice. Second, the assumptio s are usually<br>only required to hold conditional on observables, i.e., after controlling for<br>tion 4.3.2 describes.<br>Identification of the distribution of treatment effects comes from restrictions of the depend-<br>the size of the treatment effect. In this subsection, we discuss why these assumpti ns ar<br>necessary, how they solve the identification problem and why cova iates are importa t eve<br>interchangeably. We define parameters and estimators in terms of here, as the dist butio<br>of individual “intent-to-treat” parameters is unlikely to be interes ng and is not cle rly d<br>fined. If perfect compliance fails, one needs to adapt the re-weig ting or IV methods from<br>part 3. For simplicity, we assume that the effect of covariates is linear and additively separa-<br>one equation as:<br><!-- End of picture text -->
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- [12] Dobbie, W. and R. G. Fryer. 2013. “Getting beneath the Veil of Effective Schools: Evidence from New York City.” _American Economic Journal: Applied Economics,_ 5(4): 28-60.

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- [15] Ferreyra, M. M. 2007. “Estimating the Effects of Private School Vouchers in MultiDistrict Economies.” _American Economic Review_ , 97 (3): 789-817.

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- [17] Henig, J. R. and J. A. MacDonald. 2002. “Locational Decisions of Charter Schools: Probing the Market Metaphor.” _Social Science Quarterly,_ 83(4): 962–980.

- [18] Glomm, G., D. Harris, and T. Lo. 2005. “Charter school location.” _Economics of Education Review,_ 24(4): 451–457.

- [19] Imberman, S. A. 2011. “Achievement and Behavior of Charter Students: Drawing a More Complete Picture.” _Review of Economics and Statistics,_ 93 (2): 416 - 435.

- [20] Mehta, N. 2012. “Competition in Public School Districts: Charter School Entry, Student Sorting, and School Input Determination.” Working Paper, _Western Ontario University._

- [21] Nechyba, T. J. 2000. “Mobility, Targeting and Private School Vouchers.” _American Economic Review,_ 90 (1): 130–146.

- [22] Neilson, C. 2013. “Targeted vouchers, competition among schools, and the academic achievement of poor students. Working Paper, _Yale Univesity_ .

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39
The analysis of revenue performance, in conjunction with the indications provided by the expenditure analysis, gives pause for thought, particularly for the countries in group 2. There, government spending is high, but spending patterns might be difficult to alter as this would require a fundamental reform of social security expenditures. Some of these countries, particularly the Czech Republic, Hungary, Poland, Slovakia and Slovenia, where governments enjoy a relatively good access to credit, may be considered as surely converging towards the Western European expenditure levels. But, even in these countries, the transition towards a sustainable public finance system is not completed at all. As the regression data suggest, most of the group 2 countries are likely to face a pressure on revenues. This pressure may be increasing also with the rising share of the private sector share in GDP, when compliance with high taxation becomes more problematic. But beyond paying increasing attention to tax administration reform, the message for these countries is similar to the message for the rest of the transition world: A serious reform of social expenditure is inescapable.

30
was also reported around the same time among citizens of the EU (Eurobarometer 2009), Canada, and Australia (Puglise and Lyons 2010). <u>Franzen and Volg (2013)</u> analyze trends in 33 OECD and non-OECD countries, where they find a moderate decline in environmental concerns over the two decades before 2010.

More recent research, conducted from the late 2010s to today, indicates heterogeneous but mainly elevated climate change concerns. <u>Marlon et al. (2022)</u> find increases in the importance of climate change issues and perceived harm in every state in the US between 2008 and 2020. They show that policy support grew more in liberal states but remained unchanged elsewhere. According to <u>Lachapelle and Borick (2022), between 2011 and 2021, public opinions in Canada and the US</u> shifted toward acceptance of the anthropogenic nature of climate change. Both countries registered only moderate increases in support for policy interventions. However, the degree of politicization and polarization in the debate on nature and optimal policies toward climate change was much greater in the US. Combining data from Eurobarometer and Twitter, Crispino and Loberto (2024) show that in Italy in 2022, attention to climate change had increased but sentiments toward climate change deteriorated. Kenny (2024) reveals that prioritization of environmental issues in the UK rose throughout the 1980s, falling dramatically in the early 1990s and recovering by 2001 before falling in subsequent years. This decline intensified following the financial crisis of 2008. Support for environmental issues recovered during the last decade and increased between the Spring of 2018 and the Winter of 2019.

# **3. Data**

We use data from the 2016 and 2023 rounds of the Life in Transition Survey (LiTS) conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank (EBRD <u>2023). The survey covers the transition economies of Europe and Central Asia and several</u> comparator countries in Western Europe, the Middle East, and North Africa. It included 34 countries in 2016 and 39 countries in 2023. The survey is based on a nationally representative sample of around 1,500 households per country in the 2016 round and around 1,000 households 4
# **Figure 4: Density of daily casual wages, by BREGS participation status**

<!-- Start of picture text --> Round 1 Round 2<br>Casual work, all Casual work, all<br>.04 .05<br>.03 .04<br>.03<br>.02<br>.02<br>.01 .01<br>0 0<br>0 50 100 150 200 0 50 100 150 200<br>rupees per day rupees per day<br>Casual work, women Casual work, women<br>.015 .03<br>.01 .02<br>.005 .01<br>0 0<br>0 50 100 150 0 50 100 150<br>rupees per day rupees per day<br>Casual work, men Casual work, men<br>.03 .15<br>.02 .1<br>.01 .05<br>0 0<br>0 50 100 150 200 0 50 100 150 200<br>rupees per day rupees per day<br>public works wage<br>non-public works wage: BREGS participants<br>non-public works wage: BREGS excess demanders<br>non-public works wage: rest<br>density density<br>density density<br>density density<br><!-- End of picture text --> _Note_ : Based on questions about casual work done in the last week. Wage data are unweighted. _Source:_ Authors’ estimates based on BREGS Survey.

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fundamentals. Cognizant of such risks and their potentially destabilizing impact, central banks often intervene with the aim of building sufficient buffers of FX reserves, despite exchange rate floating, so as to be in a position to better resist the consequences of unforeseen shocks. Even in the case of inflation-targeting central banks, if large deviations of the exchange rate from medium-term fundamentals have to be contained, the use of sterilized FX intervention as a supplementary policy instrument would be justified, along with the use of the interest rate as a primary instrument geared toward the inflation target (Ostry, Ghosh, and Chamon, 2012).

Although, as mentioned, this paper is not concerned with the rationale behind the central bank’s interventions, it is useful to briefly summarize the several objectives often sought by central banks when they intervene under floating exchange rates (see also Moreno, 2005; Shogo et al, 2006; Basu and Morita, 2006):

- i. Containing volatility:  High exchange rate volatility can be costly to foreign trade and undermine inflation targets. There is evidence that exchange rate pass-through to inflation is relatively high in developing countries (Hausmann, Panizza and Stein, 2001). Moreover, because of imperfect competition in product markets, exchange rate fluctuations may affect prices asymmetrically, with depreciations reflected in price increases more than appreciations are reflected in price decreases. Among 22 emerging market central banks surveyed by the Bank for International Settlements (BIS) over 2000-04, 48 percent intervened to dampen volatility (BIS, 2005).

- ii. Preventing exchange rate misalignment: Persistent exchange rate overvaluation may undermine the economy’s external competitiveness, while undervaluation may cause overheating and inflation. Among the central banks surveyed by BIS, 28 percent intervened to prevent misalignment.

- iii. “Leaning against the wind”:  While central banks intervene with the aim of reversing exchange rate trends, they are also often concerned with slowing the pace of exchange rate fluctuations without reversing the underlying trend. Reduced market uncertainty is seen as a benefit of this practice.

5
# **<u>Table 8. Effects of Standards on Firm Export Entry:  Role of Firm Size</u>**

|||Dependen|t Variable:||
|---|---|---|---|---|
||Export Entry<br>Dummyc,i,k,d,t<br>(1)|Export Entry<br>Dummyc,i,k,d,t<br>(2)|Export Entry<br>Dummyc,i,k,d,t<br>(3)|Export Entry<br>Dummyc,i,k,d,t<br>(4)|
|Rel_restrictivenessc,k,d,t|‐0.190<br>[0.025]***|‐0.109<br>[0.034]***|‐0.253<br>[0.034]***|‐0.180<br>[0.072]**|
|Log Agro_exportsc,i,t‐1|0.051<br>[0.001]***|||0.030<br>[0.003]***|
|Rel_restrictivenessc,k,d,tX Log Agro_exportsc,i,t‐1|0.010<br>[0.002]***|||0.011<br>[0.005]**|
|Log Agro_exports_destc,i,d,t‐1||0.040<br>[0.002]***|||
|Rel_restrictivenessc,k,d,tX Log Agro_exports_destc,i,d,t‐1||0.000<br>[0.002]|||
|Log Exports_productc,i,k,t‐1|||0.044<br>[0.002]***||
|Rel_restrictivenessc,k,d,tX Log Exports_productc,i,k,t‐1|||0.021<br>[0.003]***||
|Log 1+Tariffc,k,d,t|‐0.020<br>[0.004]***|‐0.012<br>[0.006]**|‐0.017<br>[0.009]*|‐0.050<br>[0.012]***|
|Log GDP per capitad,t|0.030|‐0.336|0.296|0.087|
||[0.042]|[0.051]***|[0.061]***|[0.114]|
|Log Total Exportsc,k,t|0.041<br>[0.002]***|0.038<br>[0.002]***|0.047<br>[0.004]***|0.043<br>[0.007]***|
|Exporting Firm‐Importing Country‐Product Fixed Effects|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|
|Estimation method|Probability<br>Model|Probability<br>Model|Probability<br>Model|Probability<br>Model|
|Observations|297,409|168,151|132,626|40,436|
|R‐squared|0.379|0.465|0.406|0.635|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variable is defined as follows: entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ .

36