rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

It has long been recognized that anticipation of a future event has an impact on immediate well-being (Bentham, 1970). Jevons (1905) was one of the first to apply the idea that anticipation is an important part of utility in the study of intertemporal trade-offs, denoting the anticipation of future events as ’anticipal pleasure’ when future consumption involved positive utility, e.g. a vacation, and as ’anticipal pain’ when it involved negative utility, e.g. a visit to the dentist for a root canal treatment.

For some positive events, e.g. the release of a blockbuster movie or receiving a government program (as in the case of this paper), the timing of the event is beyond the individual’s control, and because it may occur well after its anticipated date, individuals may feel disappointed while they wait (see e.g. Loomes and Sugden, 1986). We call this situation ’delay disappointment’.

Inaccurate expectations about the timing of future events may potentially impact subjective well-being (SWB) differently from life events, such as a birth, death, or illness in the family. Similarly, while delay disappointment may lower the _reference_ level of SWB, adaptation posits that some or all of the gains or losses in SWB after experiencing the event are offset over time (Frederick and Loewenstein, 1999; Di Tella, MacCulloch, and Oswald, 2003; Di Tella, Haisken-De New, and MacCulloch, 2010; Kimball, Nunny, and Silverman, 2015; Galiani, Gertler, and Undurraga, 2018).<sup>1</sup> Figure 1 considers the SWB of an individual before and after the announcement, at time _A_ , of a government program under three different implementation dates _T_ . In all three cases, SWB jumps after the announcement at _A_ , and then increases until _T_<sup>_e_</sup> . the expected program implementation date In Panel (a) of Figure 1, the program is implemented _before_ the expected date (i.e _T < T_<sup>_e_</sup> ) and so SWB jumps at _T_ by the sum of a permanent increase _βP_ and a transitory increase _βT_ . After _T_ , due to adaptation, SWB declines until the long-term level _βP_ is reached. In Panel (b), the program is implemented at the expected time (i.e. _T_ = _T_<sup>_e_</sup> ), and SWB first increases in anticipation of the program and then declines afterwards due to adaptation. In Panel (c) the program implementation is unexpectedly delayed (i.e. _T > T_<sup>_e_</sup> ). Due to delay disappointment, SWB declines after _T_<sup>_e_</sup> until _T_ , when SWB reaches a lower level than before the announcement at _A_ .

This paper uses a randomized control trial of a government program in the Dominican Republic and exploits differences in the timing of its implementation to assess the magnitude of delay disappointment and the degree of adaptation. The program upgrades the homes of low-income households by replacing dirt floors with

> 1See also Diener, Lucas, and Napa (2006) for a review of the evidence on the limits of adaptation.

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7 In a time series context for a country by country empirical analysis, Moura and Carvalho (2010) used forecasting techniques and found that Mexico and Brazil pursue a “tough” monetary policy, Chile and Peru pursue “mild” monetary policy against inflation, while Argentina, Colombia, and Republica Bolivariana de Venezuela do not change nominal interest rates in response to inflation, thus following “lax” policies. In their analysis, changes in the nominal exchange rate only appear to have an impact in Mexico during the period from January 1999 to January 2008. Civcir and Akcaglayan (2010) performed a VAR analysis for Turkey before and after the country adopted inflation targeting in early 2001. They found that nominal exchange rate deviations from its trend had a positive impact on interest rates after IT adoption, but not before. Another issue investigated in the literature is the extent to which “strict IT” adopters (implying a fully flexible exchange rate) perform better or worse than those that adopt a “flexible IT” regime (which results in foreign exchange interventions to moderate exchange rate volatility). Berganza and Broto (2012) examine 18 emerging markets that have adopted IT and a control group of 19 non-IT countries from 1995Q1 to 2010Q1 and find that foreign exchange (FX) interventions in some IT countries have been more effective to lower volatility than in non-IT countries. Cabral (2010) uses an asymmetric two-country model and shows that although dollarization is an effective device to achieve price stability and avoid credibility problems small open economies might be better off under a flexible regime than under dollarization following any symmetric or asymmetric shock. This suggests that the exchange rate regime is important and may incur a pass-through effect to interest rates that is not as straightforward as it may have been suggested.

Whether the central bank should target the exchange rate or not is also a matter of debate in the theoretical literature. In an overview of the early literature, Taylor (2001) concludes that monetary policy rules that, in addition to output and inflation, also react directly to the exchange rate do not
for these where _Yict_ is the outcome of interest for individual _i_ connected to a girl in club _c_ at time _t_ , Boys _c_ is an indicator that the boy is in a community that offers the _Boys_ intervention, Goal _i_ is an indicator that the female who is connected to the male was invited to the _Goal_ treatment, and Post _t_ is an indicator for the period after treatment is implemented. _Xict′_<sup>is</sup> a vector of controls that includes individual characteristics equivalent to the controls for the females’ models, except we control for whether the male speaks to his father about sexual reproductive health topics rather than his mother. Location fixed effects in _αc_ are at the region level to account for the level of stratification of treatment assignment to the _Boys_ arm.

At endline, males were asked their opinion on statements, such as “A woman should tolerate violence from her husband/partner,” in order to elicit their attitudes toward violence. We also ask males the same violence questions we ask females, but in terms of their perpetration of it, and construct equivalent IPV indices. Table 3, which is organized the same way as Table 2, presents estimates from equation 2. Table 3, column 1, shows that the _Boys_ arm has a strong impact in reshaping IPV attitudes, with males in this arm being 17.4 percentage points ( _p_ =.001) less likely than males in control communities to agree that women should tolerate violence from their husband or partner. The violence attitudes index suggests that males in the _Boys_ treatment arm have 0.290 standard deviations “better” attitudes regarding violence ( _p_ =.016). The magnitude of the attitudinal change is similar to RCT results from Dhar, Jain and Jayachandran (forthcoming), who engaged adolescents in classroom discussions about gender equality.<sup>17</sup> Table A6 further shows that improvements in attitudes were larger among boys who enrolled in Grassroot Soccer, with an improvement in the Violence Attitudes index of 0.442 standard devia17Table A5 shows that these shifts in attitudes are concentrated among males who were already sexually active at baseline, precisely the group of males who would be perpetrating IPV and consistent with female reports of greater reductions of IPV among females who were already sexually active at baseline.

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rural _Hukou_ and lives and works in an urban location, there is no consistent definition of “migrant” in the China literature. For example, when using the Population Census data to study migration trends, scholars often define a migrant as an individual who has crossed county boundaries and has stayed at a destination for no less than six months without a local _Hukou_ (Liang, 2016; Liang et al., 2014). The census data have clear definitions and offer a clean approach to estimating changes in the migrant population over long periods of time, but do not contain sufficient information, especially longitudinal data, that may be used to examine migrant impacts.

A number of household surveys offer promise for study of the impacts of migration. When using these surveys, interpretations may turn on how they define household membership, definition of a migrant, and approach to sampling. In contrast to common standards for household membership followed internationally, as in the World Bank’s Living Standards Measurement Survey (LSMS) and LSMS-type surveys, definition of household membership and migrant status are less clear in China’s household surveys. Categorization as a household member often follows the National Bureau of Statistics (NBS) standards for the NBS Rural and Urban Household Surveys that were implemented annually from the late 1980s through the early 2010s. Under longstanding NBS practice, household membership was based on whether a family member was co-resident for more than 6 months in a year _or_ was a family member with prior residence and who continued to have an interdependent economic relationship with the household. Most important for migration studies, non-resident children who still have a rural _Hukou_ and pool earnings with the household are counted as household members. Some surveys, like the China Health and Retirement Longitudinal Survey (CHARLS), and the NBS surveys starting in 2013, use residence in the household for six months of the year as the criterion for household membership.

When different criteria are applied to identify migrants, one finds different estimates related to migration. Even different waves of the same dataset cannot be used to establish migration trends if survey questions about migration, or available approaches to establishing migrant status or household membership, are not the same across survey waves (e.g., China Health and Nutrition Surveys and China Income Project Surveys). Using consistent definitions of migrant status is particularly important if the research objectives focus on estimating if and how the impacts of migration are changing over time.

Apart from residence in the household, sampling approaches for household surveys in China are also likely to have missed migrants in China’s cities until very recently. Most household surveys have used the sample frames developed by the rural and urban survey teams of the National Bureau of Statistics. A problem arises, particularly in urban areas, as sampling frames are constructed by residential structure 9
upazila population in 1991. The median share of urban population in upazila population was 11 percent in 1991. Our “rural sample” consists of 232 upazilas which had less than 11 percent of population classified as urban. The STE sample comprises of the other 232 upazilas with urban share higher than 11 percent. Table 1 provides the summary statistics for all upazilas in our sample over the years. The population censuses indicate strong employment growth in the overall non-farm sector.<sup>20</sup> Total employment in non-farm activities grew at an annual rate of 8.6 percent between 2000 and 2010. Total manufacturing employment posted an annual growth of 18.5 percent during this period. Services employment grew by 6.8 percent. Between 2000 and 2009, employment in firms with more than 10 employees grew at an annual rate of 5.7 percent, according to the economic censuses. Among the large firms, growth in manufacturing employment has been quite robust at 5.1 percent compared with a 2 percent growth in services employment. A comparison of employment composition shows that employment in small firms (10 or fewer employees) accounts for more than three-quarters of total employment in non-farm sector. Interestingly, the share of employment in larger firms in total non-farm employment declined from 0.22 in 2000 to 0.17 in 2010. While about 25 percent of all employment is in manufacturing in 2010, the share of manufacturing in total employment of large firms is much higher (43 percent in 2009). This is expected as few services enterprises are large in size. Larger services enterprises appear to be more concentrated in the provision of health (hospitals, clinics etc) and education (schools) (Table 1). A comparison of summary statistics between our rural and STE samples confirms that most larger firms are located in small towns. These firms tend to serve larger markets compared with the firms in rural sample which are smaller and serve mainly local markets.

The summary statistics in Table 1 indicate substantial growth in rice yield between 2000 and 2010. Average rice yield per acre has grown by an annual rate of 3.8 percent. This growth rate is consistent with about 3.7 percent growth in agricultural GDP during the same time.<sup>21</sup> There has been substantial

> 20According to the census data, the share of non-farm employment in total employment increased from 0.475 in 2000 to 0.53 percent in 2010. The total labor force during this period increased by an annual rate of 2.05 percent from 35 million to 42.5 million.

> 21Crop agriculture accounts for 56 percent of agricultural GDP and rice is the single most important crop in Bangladesh not only as a subsistence but also as a cash crop.

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marked improvements in service quality over the world (Figure 9). While Sub-Saharan Africa may have more than 10 days of service interruptions, the other regions have only several days on average. The ECA region has the lowest frequency of telecommunications unavailability.

Figure 7. Number of Days without Electricity by Region <!-- Start of picture text --> 200<br>180 2002/03<br>2005/06<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>East Asia & Europe & Middle East Latin South Asia Sub-Saharan<br>Pacific Central Asia & North America & Africa<br>Africa Caribbean<br>Source: BEEPS.<br>Figure 8. Number of Days without Water Supply by Region<br>70<br>2002/03<br>60 2005/06<br>50<br>40<br>30<br>20<br>10<br>0<br>East Asia & Europe & Middle East Latin South Asia Sub-Saharan<br>Pacific Central Asia & North America & Africa<br>Africa Caribbean<br>Source: BEEPS.<br>35<br>2002/03<br>30 2005/06<br>25<br>20<br>15<br>10<br>5<br>0<br>East Asia & Europe & Middle East Latin South Asia Sub-Saharan<br>Pacific Central Asia & North America & Africa<br>Africa Caribbean<br>Source: BEEPS.<br>Number of days without sufficient electricity per year<br>Number of days without sufficient water supply per year<br>Number of days without telecommunications services per year<br><!-- End of picture text --> Figure 8. Number of Days without Water Supply by Region Figure 9. Number of Days without Telecommunications Services by Region In the ECA region, Albania has the poorest quality of infrastructure services in all aspects (Figures 10 to 12). The number of days without sufficient electricity exceeds 200 days.
**Figure A1.** Comparing skills measures from Philippines STEP survey and O*Net database

<!-- Start of picture text --> Non-routine analytical Non-routine interpersonal Non-routine manual<br>2.0 2.0 1.5<br>1.5 1.5<br>s = 0.75 1.0<br>s = 0.57 s = 0.67<br>1.0 1.0<br>0.5<br>0.5 0.5<br>0.0 0.0<br>0.0<br>-2.0 -1.0 0.0 1.0 2.0 -2.0 -1.0 0.0 1.0 2.0<br>-2.0 -1.0 0.0 1.0 2.0<br>-0.5 -0.5<br>-0.5<br>-1.0 -1.0<br>-1.5 -1.5 -1.0<br>Philippines STEP survey Philippines STEP survey Philippines STEP survey<br>Routine manual<br>Routine cognitive<br>2.0 1.5<br>1.0<br>1.5<br>0.5<br>1.0 s = 0.49<br>0.0<br>0.5 -2.0 -1.0 0.0 1.0 2.0<br>-0.5<br>0.0<br>-1.0<br>-2.0 -1.0 0.0 1.0 2.0<br>s = -0.77<br>-0.5<br>-1.5<br>-1.0 -2.0<br>Philippines STEP survey Philippines STEP survey<br>Source: Authors’ elaboration based on 2015-2016 Philippines STEP survey and O*Net database.<br>Note: s indicates the Spearman’s rank correlation coefficient between skills measures.<br>U.S. O*Net database U.S. O*Net database U.S. O*Net database<br>U.S. O*Net database U.S. O*Net database<br><!-- End of picture text --> 58
NORTHERN AMERICA

# 1. **Estimates** and Projections

|_Yaw_|rPpulation|**Dep.**<br>**rwtt.**|**Pfriod**|**Growth**<br>**,ate**|<br>O|Cm|TF8|tO<br>**MElot**|Folo|**Io**|**X1000**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|I965|268039|50.4|1985-89|0.90|15.4|8.7|1.87|72.0|79.2|10|12|
|1990|280336|51.1|1990-94|0.80|14.1|8.3|1.86|73.2|80.1|9|1|
|1995|291730|51.2|1995-99|0.70|13.0|8.1|1.86|74.3|81.2|7|9|
|2000|302049|49.2|2000-04|0.64|12.8|8.0|1.90|75.4|82.1|6|8|
|2005|311898|47.3|2005-09|0.57|12.6|8.2|1.93|76.1|82.8|6|8|
|2010|320989|47.8|2010-14|0.53|12.6|8.3|1.96|76.8|83.5|5|7|
|2015|329551|51.6|2014-19|0.46|12.4|8.4|1.99|77.5|84.1|4|6|
|2020|337295|57.0|2020-24|0.37|12.1|8.7|2.03|78.3|84.8|4|5|
|2025|343547|63.4|2025-49|0.05|11.8|11.2|2.06|79.7|86.4|3|4|
|2050|348247|72.3|2050-74|-0.02|11.8|12.0|2.06|81.1|87.9|2|4|
|2075|346716|72.4|2075-99|0.03|11.8|11.5|2.06|81.9|88.7|2|3|
|2100|349012|73.4|2100-24|0.02|11.7|11.4|2.06|82.4|89.4|2|3|
|2125<br>2150|351157<br>352286|74.2<br>74.7|2125449|0.01|11.6|<br>11.5|2.06|<br>82.7|89.6|2|3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>ED 22 8~~~~5-89 4Xm<br>> gB 1 6~~~0-84 g S||<br>:gm |2 1 ~~55.59 IR% ||<br>50-54<br>45-49<br>URiZ 1 ~~~~40-44 1<br>bEZ^D g«g2 1 ~35-39 1>xwB<br>RRZZ2U UEZER 1 30.341 oA//M<br>25-29<br>20-24<br>gR22Z g2 1 1~~~~15-1 9<br>10-14<br>RSEGYY BUBgS $ ~5-9 1>50BB<br>55555 ZW 1 ~~0-4<br>10 8 6 4 2 0 2 4 6 810 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> NMOO CX 71)<br>/ ~ ~~~ Z 7AC c CAC < ~ ; /a^| 735S1<br>11~~~~~~~~~~~~~~~~ 6 X<br><!-- End of picture text -->
<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Export tax Import tax<br>Import subsidy Export subsidy<br>Percent<br><!-- End of picture text --> Figure 11. Contributions of various instruments to the border component of the welfare reduction index (WRI) for developing countries, 1960–2010 (%).

Source: Derived from estimates reported in Croser and Anderson 2011, updated using Anderson and Nelgen 2012b.

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through deforestation and forest degradation accounts for 11–20 percent of annual greenhouse gas emissions (Saatchi _et al._ 2011; van der Werf _et al._ 2009; UNEP 2012) and the total carbon stored in forests is estimated at 638 gigatons CO2 (UNFCCC 2011), with about 247 gigatons stored in Latin America, Sub-Saharan Africa and Southeast Asia. Community forestry management has generally been considered to be a successful means to not only to halt the deforestation and forest degradation but also craft institutional mechanisms for equitable benefit sharing in the communities. Therefore community managed forests will play an important role in the success of a REDD+ program since much of the forests in developing countries is actually owned or controlled to an important degree by the local communities (Agrawal _et al._ 2008). About 25% of developing country forests, or three times as much as is owned by the private sector, is under community ownership and/or administration and this percentage appears to be increasing over time. Decentralization reforms in developing countries in the past two decades have often promoted the community-based system of forest management (Agrawal et al, 2008). During the period 1997–2008, the area of collective ownership roughly doubled to 250 million hectares (World Bank 2009). Therefore it is difficult to envision a successful REDD+ initiative that does not incorporate community managed forests.

The prevention of deforestation and degradation may be an effective method used to reduce carbon in the atmosphere. For instance, Angelsen (2009) indicated REDD+ as a significant (can reduce 0.25<sup>0</sup> C temperature increment at no extra cost), cheap (much of the deforestation and forest degradation is marginally profitable), and quick (policy and institutional change- can reduce the emissions) strategy to mitigate climate change. Similarly, McKinsey and Company (2010) suggest that forest carbon sequestration can effectively compete with other mitigation approaches as reduced deforestation and forest degradation could reduce carbon at 5
graphic units. As Mayer and Mayer (2004) report, there is little evidence that the degree of returns to scale affects -p1ii . **irii.** In fact, Haaland et al. **(1999)** find that higher degrees of economies of scale are associated with lower relative and absolute concentration levels.

Third, plants will also choose location based on dynamic externalities **-** the Marshall **(1890),** Arrow **(1962),** Romer (1984) or MAR externalities. As plants tend to locate near one another, the extent of knowledge spillovers increases, leading to greater productivity of the industry. This results in a higher rate of growth of employment of that industry and that location. Other dynamic externalities include those modeled **by** Porter **(1991),** that relate the degree of competition in a location to higher innovation and hence to greater knowledge spillovers and greater concentration, as well as others modeled **by** Jacobs **(1969)** which relate variety and diversity in a location to greater knowledge spillovers, and hence to greater concentration in that location. Our specification will include controls for all these traditional determinants of :w1 i.ificpi. as described in Section **5.**

# **3 Policy Determinants of Agglomeration**

Before describing in detail the policy reforms in India, we discuss existing evidence regarding the explicit role of government policy for the geographic concentration of manufacturing. Lall and Chakravorty **(2005, 2007)** study the determinants of the location of new private and public sector industrial investments in India as structural reforms proceeded between **1992** and **1998.** Their results show that new private sector industrial investments were biased towards existing industrial clusters and coastal districts and depended on the size of the investments in the same location prior to the reforms and on the size of the investments in neighboring locations as reform proceeded. The authors infer that structural reform led to increased spatial inequality in terms of industrialization. These studies provide some interesting and **important** stylized facts about the location of manufacturing industry in India. However, due to the nature of the data used they are unable to analyze long-term patterns and determinants of '-g1iii n.-1i as we do in this paper. Bai et al. (2004) investigate the evolution of the Hoover index of :- :1 i constructed using **Cliij.** industrylevel data from **1985** to **1997.** They estimate the impact of local protectionism (i.e., the

**8**