rafmacalaba/datause-encoder-data huggingface dataset:
equal groups. We then rolled out a subsidy scheme sequentially, each group receiving a 50 percent discount on the price of piped water for six months, followed by a 75 percent discount for another six months after that. At any point in time, therefore, at least one group did not receive a subsidy and could thus serve as a control. To make the changes in price throughout the experiment immediately salient, we undertook a personalized communication campaign, both at the home and at the time of monthly bill payment, which was always done in-person at a central location. Meanwhile, we collected data from the utility on monthly piped water usage and, at a quarterly frequency, from households on, among other things, the number and duration of stay of residents (including visitors) and on water-sharing practices.

In addition to a modest response to the current price, the experimental data reveal persistence: households that faced a lower water price during months in the recent past (and thus had higher usage in those months) also consumed more in the current month, _conditional_ on the current month’s price. We take this as evidence that households were building consumption capital, either in the form of habits or learning through experience.<sup>1</sup> Alternative explanations can be plausibly ruled out: lags in adjustment to new prices were obviated by our proactive information campaign; loss aversion or reference dependence, which could lead to apparent demand persistence, is inconsistent with the symmetric demand responses to upward and downward price changes observed in the data and cannot account for demand growth prior to the experiment; finally, household investments in piped water infrastructure (such as plumbing and washing machines) cannot be the source of the experimental findings given the size and duration of the price subsidies.

To quantify the welfare gains from utility pricing strategies that leverage such persistence, we next use our experimental data to structurally estimate the dynamics of piped water demand. Under our maintained assumption that, in deciding on their current piped water usage, consumers do not internalize its effect on future demand, consumption depends only on the current price and on past usage.<sup>2</sup> Our experiment, by generating random variation in price, not only allows us to identify the (short-run) price elasticity of piped water demand, but also delivers valid instruments, namely lagged prices, for causally estimating the effect of past con-

> 1Dupas (2014) interprets experimental evidence on demand persistence for bednets in Kenya through the lens of a structural model to demonstrate a large experience good effect. Habit formation is a less plausible source of persistence in her setting.

> 2We justify this assumption of consumer lack of awareness in detail below as, among other things, consistent with projection bias (Loewenstein et al., 2003).

2
# 3 Empirical Strategy

## 3.1 Econometric Speci fi cation and Identi fi cation

In order to assess gender differentials in the impact of migration on child nutritional status, we need to estimate a regression function of the form Where Zijv is child’s i’s weight( or height) for age z-score in household j and village v. Mjv is an indicator of whether the household has a migrant, Gijv is the child’s gender, and Cijv and Xjv are vectors of exogenous child and household characteristics. The mean zero error term ηjv captures the effects of unobserved factors common to a given village and household. Village level unobservables could range from locational advantages, availability or quality of health care and public health interventions to weather related shocks and access to credit or other markets. Household level unobservables could include, in particular, access to credit and insurance markets, and preferences that may in fl uence both the decision to migrate and child nutritional status, including gender differences in the latter. Included in this term would also be the effect of costs that affect child health status but are not observed in the data. The child-speci fi c error term εijv re fl ects measurement error in WAZ (HAZ) and, potentially, unobserved attributes of the child, including innate health, for example.

Since Mjv is likely to depend on at least one of the factors captured by ηjv, we need to contend with a potential endogeneity problem. We tackle the problem in two ways. Our fi rst strategy is to instrument for migration. To do this, we need instruments correlated with the migration decision but uncorrelated with unobserved attributes of the child, the household or the community.

The census based measure of migrant networks within each village (V M) provides us with just such an instrument.<sup>21</sup> It in fl uences the opportunity to migrate,<sup>22</sup> but household

> 21 A number of recent papers have used a measure of the migrant network to instrument for migration. Migrant networks are seen as reducing the costs of migration for potential migrants via two channels First, they constitute an information network which can educate potential migrants about conditions in speci fi c migration destinations as well as potential hazards and costs, both at home and in migration destinations (Massey 1988; Orrenious (1999)). Second, they serve to relax credit constraints (Genicot and Senesky (2004)). A number of studies have also shown that networks increase the economic returns to migration (Munshi (2003); McKenzie (2005)).

> 22 This is indeed borne out in several empirical studies. For example, Winters et. al. (2001) show that the

8
# **Appendix B: VAR-based impulse response results for Malaysia and Singapore**

# **B1. Estimation framework and data**

For comparison, the impact of a US policy interest rate shock on two non-dollarized economies in the Southeast Asian region, Malaysia and Singapore, is examined. This is done by investigating VAR-based impulse responses of the variables under consideration with respect to a change in the US policy interest rate.

Variables included in the VAR model are the same as the estimation for the Cambodian case. Specifically, they include the US federal fund rate ( _ffr_ ), the growth rate of domestic currency to USD exchange rate ( _z_ ), the trade balances with the EU ( _tbeugdp_ ) and with the US ( _tbusagdp_ ), money balance as a share of GDP ( _bmgdp_ ), domestic interest rate ( _domint_ ), output gap ( _gygap_ ), and consumer price index (CPI) inflation ( _infl_ ). Output gap is the difference between real GDP and real potential GDP (i.e. GDP under full employment), scaled by real potential GDP. Potential GDP in real values for the calculation of output gap data is generated by applying the Hodrick-Prescott filter on the real GDP data. Furthermore, the fluctuation of the crude oil price represented by its change rate ( _goilp_ ) and a dummy variable ( _dummy_ ) are exogenously included in the VAR system as a control for external shocks. The dummy variable is included to reflect effects resulting from the 1997-98 Asian financial crisis and the 2008-2009 global financial crisis. Like the Cambodian case, quarterly data are considered for the estimation. Due to the availability of data for domestic interest rate, the sample period for Malaysia is from 1994Q4 to 2015Q3 and for Singapore is from 1994Q4 to 2013Q2. The data for US federal fund rate, domestic currency to USD exchange rate, broad money (M2, a proxy for money balance), domestic interest rate,<sup>19</sup> GDP, and CPI are obtained from the International Financial Statistics (IFS) published by the International Monetary Fund (IMF). Data for exports and imports are obtained from the Direction of Trade Statistics (DOTS) published by the IMF. Data on crude oil price is taken from the Primary Commodity Prices published by the IMF. For the dummy variable, it is 1 for 1997Q3-1998Q4 and 2008Q32009Q4, and zero elsewhere. Seasonal adjustments are applied on our quarterly data, except for the US federal fund rate and domestic interest rate, and CPI is used in converting nominal GDP into real GDP.

> 19 Domestic interest rate for both Malaysia and Singapore is the interest rate of government securities

> (i.e. treasury bills). Even if lending interest rate is used for both countries, we obtain similar impulse response results.

24
# **Table A5: Panel A – Nested Logit Regressions, 2005**

Log Odds ratio between Renting and Owning

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.08|0.03|0.32 ***|-0.36 **|-0.48 **|-0.76 **|-21.9 ***||-3.61|-0.28|
|Estonia|-2.77|0.09|-0.37 ***|0.97|-1.86 **|-0.69|0.53||5.99 *|-0.74|
|Hungary|-19.53 ***|0.15|-1.72 ***|5.23 **|2.38 *|6.47 ***|0.30|13.27 *|42.05 ***|2.74|
|Latvia|-3.31|1.25|-0.86|0.26|1.09|3.10|-2.74 *|-22.09|-11.12|-1.48|
|Lithuania|-0.31|-0.36|-0.11|0.21|-1.22|-0.80|0.33||15.69|0.23|
|Poland|6.26 ***|1.14 **|0.83 ***|-1.04 *|1.55 *|-1.37|1.38|19.64 ***|-0.39|-2.49 **|
|Slovak Republic|-1.37 **|-0.46 **|0.63 ***|-0.57 *|-0.38|-0.74 *|17.07 ***|14.49 ***|14.85 ***|-0.42|
|_EU-15 and Cyprus_|||||||||||
|Austria|-2.60 ***|-0.55|-0.52 ***|0.18|-0.01|3.21 ***|-3.12 ***||19.03 ***|3.01 *|
|Belgium|-0.49|0.26 ***|-0.05|0.39|-0.23|0.04|-2.18 ***|22.59 ***|15.24 ***|-0.53 ***|
|Cyprus|-2.05|-0.12|-0.17|0.58|0.13|0.27|-0.98 ***|11.14 ***|13.46 ***|0.28|
|Denmark|0.39|0.44 ***|-0.27 ***|0.13|0.04|0.83 ***|-2.37 ***|11.10 ***|13.40 ***|-0.88 ***|
|Finland|-0.06|0.05|-0.06|0.04|-0.09|0.23|-1.55 ***|21.60 ***|21.16 ***|-0.81 *|
|France|-0.61|0.12|-0.13 ***|0.34|-0.43 ***|0.03|-2.19 ***|20.43 ***|20.30 ***|-0.14|
|Greece|-0.33|-0.08|-0.36|0.41 **|-0.43|-0.35|-0.51|19.50|14.06|-0.23|
|Ireland|1.20 *|0.33 **|0.15|0.12|0.16|-0.83 **|-2.93 ***|27.46 ***|17.85 ***|-2.30 ***|
|Italy|2.19|-0.01|0.17|-0.69|-0.37 ***|-0.74|-0.20|9.78 ***|7.16 ***|-0.17|
|Luxembourg|0.80 *|-0.40 **|-0.07|-0.17|-0.03|-0.02|-2.39 ***|14.43 ***|6.71 *|0.14|
|Netherlands|-2.25 ***|0.41 ***|-0.45 ***|0.90 ***|-1.25 ***|-0.79 ***|-0.17||14.02 ***|-1.25 ***|
|Portugal|1.75 ***|0.25 *|0.25 ***|-0.72 ***|-0.19|-1.36 ***|1.41 ***|11.81 ***|9.53 ***|-0.51|
|Spain|-0.37|0.03|-0.10|0.03|-0.26 **|-0.05|-0.32 ***|7.81 ***|8.77 ***|0.12|
|Sweden|0.86|0.16|0.05|-0.09|0.08|-0.25|-2.74 ***|14.88 ***|13.77 ***|-0.91 ***|
|United Kingdom|-0.67 ***|0.20 ***|-0.04|0.42 ***|-0.34 ***|0.37 ***|-1.74 ***||17.63 ***|-1.84 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

35
many as 32 more developing countries can get on track for the MDGs--an average increase of 44 percent in the number of on-track countries (figure 5). This forecast is based on a greater than 50 percent probability of each country getting on track. Statistically, the probability of lagging countries can only reach 100 percent as an upper (asymptotic) limit, but a 95 percent confidence interval of a 50 percent increase will generally cover that upper limit. The percentage increase in the number of countries getting on track generally rises most for the targets farthest behind-targets such as under-five mortality (89 percent), hunger (64 percent), access to sanitation (54 percent), maternal mortality (37 percent), and access to safe drinking water (36 percent). For the other MDGs (such as poverty, primary education completion, and gender equality in primary and secondary education), the increase in the number of countries is about 30 percent, still substantial. Individual countries that are good candidates to get on track are those currently very close--that is, within 10 percent of getting on track (table 6).

**_How achievable are these gains?_** Recent history suggests they may be. Achieving the growth assumption for developing countries appears possible. To put the one-standard-deviation growth increase in context, per capita GDP growth will need to double from its historical rate of 1.9 percent a year. Even so, the historical rate is an average covering all types of developing countries and the uneven subperiods during 1990–2009, including the recent global crisis years (2008–09). The increase, in fact, is very much within the realm of actual performance for SubSaharan African countries during periods of growth acceleration (3.9 percent), including the high-growth period 2000–07 (See, for example, Arbache, Go, and Page 2008).

**Table 9. A one-standard-deviation increase in growth is definitely achievable**

|||**Developin**<br>|**g countries(GDPper ca**<br>|**pitagrowth rate)**<br>|
|---|---|---|---|---|
|||**on target**|**close to the target**|**far from the target**|
|**Growthperiods**|**Years covered**||||
|**I. Reference period:**|**1990-2009**|2.42|1.77|1.22|
|**II. Recent growth accelerations**|||||
|**Modern trend-break:**|**1995-2007**|3.46|2.61|2.01|
|**New millennium:**|**2000-2007**|3.97|2.90|2.25|
|**Boom years:**|**2003-2007**|4.82|3.65|3.07|
|**III. Recent global economic crisis**|||||
|**Crisis years:**|**2008-2009**|1.48|1.79|1.48|
|**Peak crisis**|**2009**|-1.09|0.28|0.65|
|**IV. Growth prospects:**|**2010-1015**|3.58|3.33|3.22|
|_Source:_Authors’ calculations based on<br>**V. Growth assumption**|data from the World|Development In|dicators database. Growt<br>3.57|h prospects are from the<br>3.02|

_Source:_ Authors’ calculations based on data from the World Development Indicators database. Growth prospects are from the IMF’s World Economic Outlook (2010).

_Note:_ The growth assumption is growth during reference period plus one standard deviation, or 1.8. Growth rates are all simple averages, giving equal weight to each country GDP per capita, purchasing power parity constant 2005 international dollars.

21
-16-

**TABLE 2.1**

# **Brazilian "Valor de Transformac&o". Gross Value Added. Employment and Productivity in 1975**

|Industrial|Col.(1)|Ratio|Employment|<br>Present|Present|
|---|---|---|---|---|---|
|Census Concept<br>of Gross<br>Value Added<br>"Valor de<br>Transformacao|adjusted to<br>Present<br>National<br>Accounts<br>" Concept|Col.(2)<br>to<br>Col.(1)|<br>(average<br>for the<br> <br>year)|National<br>Accounts<br>Concept of<br>Gross<br>Value Added|National<br>Accounts<br>Concept of<br>Gross<br>Value Added|
||of Gross<br>Value Added<br>at factor cos|<br>t||per Person<br>Employed<br>(cruzeiros)|per Person<br>Employed<br>(US**$)a**|
|(million|cruzeiros)|||||
|(1)|(2)|(3)|(4)|(5)|(6)|
|Food & Kindred||||||
|Products<br>34,681|28,724|82.8|482,434|59,540|7,324|
|Beverages<br>5,494|4,647|84.6|52,080|89,230|10,975|
|Tobacco<br>3,212|3,018|94.0|23,965|125,925|15,489|
|Textiles<br>18,829|16,448|87.4|324,682|50,658|6.231|
|Clothing &||||||
|Footwear<br>11,606|10,261|88.4|278,269|36,875|4,536|
|Wood products<br>8,954|7,360|82.2|192,695|38,194|4,698|
|Furniture<br>6,099|5,104|83.7|127,176|40,133|4,936|
|Paper & Allied||||||
|<br>Products<br>7,750|6,394|82.5|82,972|77,061|9,479|
|Printing &||||||
|Publishing<br>11,283|9,715|86.1|121,559|79,923|9,831|
|Chemicals<br>48,552|43,276|89.1|177,920|243,231|29,918|
|Rubber Goods<br>5,119|4,490|87.7|45,700|98,247|12,085|
|Plastic Goods<br>6,909|6,o40|87.4|75,166|80,350|9,883|
|Leather & Leather||||||
|Products<br>1,609|1,375|85.4|33,873|40,596|4,993|
|Stone, Clay and||||||
|<br>Glass Products 19,161|15,678|81.8|311,361|50,350|6,193|
|Metal Products<br>38,781|32,050|82.6|429,539|74,615|9,178|
|Machinery (except||||||
|Electric)<br>31,692|27,715|87.5|377,555|73,407|9,029|
|Electric Machinery||||||
|& Equipment<br>17,655|15,757|89.2|170,425|92,455|11,372|
|Transport Equip-||||||
|ment<br>19,500|16,984|87.1|218,025|77,897|9,581|
|Miscellaneous||||||
|Manufactures<br>5,915|5,105|86.3|78,411|65,102|8,008|
|Supportive||||||
|Industries<br>4,090|3,131|76.6|67,849|46,150|5,676|
|Totalb<br>306,893|263,269|85.8|3,671,656|71,703|8,820|

a) converted at official exchange rate of 8.13 cruzeiros to one US S.

b) excludes head office and auxiliary units located outside establishments. At the end of 1975 these activities employed 152,682 persons, who earned 6,550 million cruzeiros.

Source: Figures derived from Censo Industrial (1981a), column (1) from table 1, column (2) derived from tables 1, 15 and 35, column (4) from table 2 (annual average of monthly figures).
# **ANNOTATED BIBLIOGRAPHY**

## **Citizen Knowledge and Attitudes Regarding Climate Change**

Bostrom, Ann, M. Granger Morgan, Baruch Fischoff and Daniel Read 1994. “What Do People Know About Global Climate Change? I Mental Models” Risk Analysis 14(6): 959-970.

The authors conduct three exploratory studies that employed “mental model interviews.” Results indicate widespread misinformation regarding climate change in the general public, including confusion between ozone hole and global climate change, and between weather and climate. Confusion regarding causes of climate change was also observed (“automobile use, heat and emissions from industrial processes, aerosol spray cans, and pollution in general were frequently perceived as primary causes of global warming”). When asked what could be done, respondents tended to focus on general pollution control rather than reductions of carbon dioxide emissions or energy consumption. The authors argue that this general public misunderstanding of climate science is part of the widespread failure of response.

Bord, Richard, Ann Fisher and Robert O’Connor 1998. “Public Perceptions of Global Warming: United States and International Perspectives.” Climate Research 11 (1):75-84.

The authors review international survey data on public knowledge, concern, perceived risk and willingness to pay regarding global warming. While the authors find “solid awareness of and support for general environmental goals” and some measure of awareness and concern regarding global warming, they also find a widespread poor understanding of climate change in the public around the world. Concern tends to be highest in Canada, most of Europe and South America. Although the public expresses considerable perceived threat from global warming, the threat is less so than for most other issues surveyed. In conclusion, they find that although global warming is an issue of concern worldwide, it is not a ‘front-burner’ issue. The also find a “limited willingness to sacrifice” in response to climate change. In conclusion, the authors note that “Our own data support but go beyond earlier data by implying that global warming is not a salient issue, and that people across the globe will support global climate change initiatives that do not levy unusual hardships; but they cannot be expected to voluntarily alter their lifestyles.” Bord, Richard, Robert E. O'Connor and Ann Fischer 2000. In what sense does the public need to understand global climate change? Public Understanding of Science, 9(3): 205-218.

In a survey of 1,218 Americans, the key determinant of behavioral intentions to address global warming is a correct understanding of the causes of global 48
**<u>Table 4: Infant and neonatal mortality and drinking water salinity, Categorical</u>**

||(1)<br>_model 1_|(2)<br>**Infant**<br>_model 2_|(3)<br>_model 3_|(4)<br>_model 1_|(5)<br>**Neonatal**<br>_model 2_|(6)<br>_model 3_|
|---|---|---|---|---|---|---|
|freshwater: gwec (0 mS/cm, 0.7|0.0219***|0.0176***|0.0163***|0.0176***|0.0132**|0.0124**|
|ms/cm]|[0.00470]|[0.00601]|[0.00606]|[0.00456]|[0.00571]|[0.00569]|
|moderate salinity: gwec (2 mS/cm,|0.00324|0.00749|0.00625|-0.00196|0.00285|0.00217|
|10 mS/cm]|[0.00531]|[0.00580]|[0.00586]|[0.00507]|[0.00534]|[0.00538]|
|severe salinity: gwec (10 mS/cm,|0.0295***|0.0346***|0.0312***|0.0188**|0.0239***|0.0220**|
|above]|[0.00942]|[0.00957]|[0.00973]|[0.00909]|[0.00916]|[0.00922]|
|child male||0.00477|0.00477||0.00545|0.00540|
|||[0.00396]|[0.00395]||[0.00363]|[0.00362]|
|child birth order||0.00195|-0.000835||-0.000113|-0.00207|
|||[0.00186]|[0.00197]||[0.00168]|[0.00178]|
|maternal age||-0.00145***|-0.00105*||-0.00120**|-0.000935*|
|||[0.000546]|[0.000553]||[0.000509]|[0.000518]|
|mother's years of education|||-0.00131*|||-0.00120*|
||||[0.000672]|||[0.000624]|
|partner's years of education|||-0.000787|||-0.000320|
||||[0.000543]|||[0.000515]|
|mother currently married or living|||-0.00956|||-0.0133|
|in union|||[0.0151]|||[0.0147]|
|urban residence|||-0.00142|||-0.00293|
||||[0.00446]|||[0.00406]|
|standardized asset index|||-0.0115|||-0.00858|
||||[0.0128]|||[0.0120]|
|arsenic|||-0.00000576|||1.44e-08|
||||[0.0000248]|||[0.0000236]|
|groundwater depth|||-0.000142|||0.000261|
||||[0.000654]|||[0.000634]|
|improved household sanitation|||-0.000749|||-0.000736|
||||[0.00449]|||[0.00427]|
|basin: Ganges-Padma|||-0.00428|||-0.000471|
||||[0.00922]|||[0.00853]|
|basin: Meghna|||-0.0127|||-0.0133*|
||||[0.00781]|||[0.00682]|
|Constant|0.0384***|0.0824***|<br>0.107***|0.0341***|0.0759***|<br>0.0961***|
||[0.00386]|[0.0144]|[0.0233]|[0.00382]|[0.0134]|[0.0221]|
|Birth Year Dummies|No|Yes|Yes|No|Yes|Yes|
|Division Fixed Effects|No|Yes|Yes|No|Yes|Yes|
|R-squared|0.00210|0.0104|0.0119|0.00189|0.00994|0.0110|
|Obs.|22158|22158|22158|22158|22158|22158|

Notes: omitted group of basins is basin: Brahmaputra-Jamuna; model 1: unadjusted (no controls); model 2: adjusted for maternal age, birth order and sex of the child; model 3: additionally adjusted for both parents years of education, rural or urban residence, household wealth score , improved sanitation, maternal marital status, groundwater arsenic level, depth of tubewell, riven basin children's birth year and geographical division fixed effects 26
... one does not argue about tastes for the same reason that one does not argue over the Rocky Mountains – both are there, will be there next year, too, and are the same to all men.― (Stigler and Becker 1977, p. 76) ―Almost all sociologists take it as obvious that individuals‘ preferences are formed by society and that society, so to speak, exists within persons.‖ (Peter Hedstrom, Oxford University, personal communication) There is a standard view in economics that to explain outcomes, one can begin by characterizing the preferences of individuals and then go on to describe the society that the individuals will create. The preferences remain the same regardless of the society that emerges. This view contrasts sharply with the conception of the individual in other social sciences, in which institutions and other socially defined variables influence preferences. Casual observation suggests that there are vast behavioural differences across societies and even across groups within societies, and cross-cultural research has documented striking differences in preferences across societies ( _e.g_ . Henrich _et al_ . 2005, Henrich _et al_ . 2010). However, despite the documentation of large cultural differences, conclusive scientific evidence that preferences are causally shaped by social institutions and cultural practices is very rare.

Take, for example, the study of Henrich _et al._ (2010), which shows that the preferences of individuals to be fair in anonymous transactions and to punish unfairness increase with the level of the society‘s market integration, measured as households‘ average percentage of calories that are purchased. It is a plausible hypothesis that market integration shapes preferences for fairness because in market exchanges the _quid pro quo_ is a salient characteristic of interactions that may support the development of such preferences. However, it is also possible that stronger fairness preferences, _e.g_ . preferences to honour informal contractual obligations, foster market integration.<sup>1</sup> Thus, one cannot unambiguously conclude from the observed correlations that the cultural and economic institutions prevailing in the different societies have causally shaped the preferences for fairness and punishment.

> 1 The positive correlation between altruistic punishment and community size can also be interpreted in two ways. It is a plausible hypothesis that larger communities develop more elaborate forms of institutional punishments that, in turn, increase the willingness of individuals to punish altruistically. However, it is also plausible that a higher willingness to engage in altruistic punishment enables small-scale societies to enforce the social norms that support the development of larger, more productive communities.

2