rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix I.A.27 Historical Demographic Data, United Kingdom 1841-1981

|Year|1841|1861|1881|1901|1921|1931|1951|1961|1981|
|---|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>populationover65yearsold|13.41|12.86|12.87|13.49|10.95|9.27|6.06|5.46|4.33|
|Population aged 20 to 59/<br>population over 60 years old|6.49|6.38|6.28|6.78|5.68|4.84|3.50|3.06|2.54|
|Percentage of population<br>over 60 years old|7.20|7.42|7.38|7.40|9.43|11.57|15.93|17.25|20.13|
|Percentage of population<br>under 15 years old|36.09|35.64|36.45|32.42|27.72|23.83|22.15|22.96|20.45|
|Women over 60 years old/<br>Menover60yearsold|_1.15_|1.16|1.19|1.25|1.26|1.25|1.39|1.49|1.40|

Ratios calculated using data found in Mitchell, B. R. 1990 'Intenational Historical Statistics: Europe 1750-1988'.

# Appendix I.A.28 Historical Demographic Data, United States of America 1860-1985

|Year|1860|**1U80**|1900|1920|1940|1960|**1985**|
|---|---|---|---|---|---|---|---|
|Population aged IS to 64/<br>population over 65 years old|-|17.01|15.12|13.58|9.94|6.47|5.57|
|Population aged 20 to 59/<br>population over 60 years old|10.34|8.20|7.64|6.89|5.28|3.66|3.27|
|Percentage of population<br>over 60 years old|4.29|5.64|6.41|7.49|10.44|13.20|16.52|
|Percentage of population<br>under IS years old|40.49|38.10|34.38|31.80|25.04|31.11|21.72|
|Women over 60 years old/<br>Men over 60 years old|0.99|0.95|0.97|0.95|1.02|1.18|1.37|

Ratios calculated using data found in "Historical Statistics of the United States .Colonial Times to 1970". 1960 is the first year for which figures include Alaska and Hawaii.
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distributed. The **ZA,** E and I, are not linear combinations of the intercept term because some provinces are classified as being neither an exporter nor an importer, and some cities are not in any of the 3 zones. The results of fitting this equation to the February-August price data for each of the 19 commodities are shown in Table _5._ Generally, only a small part of the cities' state-store price deviations from the all-Russia price can be explained by these regressions. The R<sup>2</sup> (adjusted for degrees of freedom) range from .051 (cigarettes) to .320 (milk).

Of the variables intended to explain the price differences, income is most in accord with expectations. Higher income in a city causes higher prices, i.e., increased demand, for every commodity except potatoes and milk. The largest percentage effect is in vegetable oil where a 100 ruble (2.5 percent) income increase is associated with a 0.32 ruble (.7 percent) price increase. The next biggest effect is in butter, where a 100 ruble income increase raises the butter price .53 rubles (.3 percent).

The other variables have only sporadic and quantitatively small effects with only a few exceptions. The ruble effects are easy to interpret since the variables other than income are all 0-1 dummies. Thus, the estimated effect on beef prices of being in a regulated province is a 3.9 ruble (20 percent) reduction in excess of price over average price. Being in a net exporting region reduces the prices of vegetable oil, milk, sugar, vermicelli, potatoes, cabbage, and cigarettes significantly.

Table 6 shows regression results for collective farm markets. There are fewer commodities because several had few observations (ess than 200) with sporadic and extreme prices. The results are quite similar to the state-store regressions, but with higher explained variation of prices. The R<sup>2</sup> ranged from .12 (sour cream) to .51 (vegetable oil).

Since the variables of these regressions explain relatively little, the regressions cannot go far in explaining the rate of market integration in Russia. Even after accounting for differences in income, reported local regulation and other important economic variables, about 80 percent of the price deviation among cities remains unexplained. The residuals from these regressions may throw light on the process 17
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# Table 5.6: Base Simulation with Fixed Central Bank Reserves and Historical Government Spending

||19|83|1|984|1|985|
|---|---|---|---|---|---|---|
|Change in real GNP: AGNP a/|||2.5|<br>(3.7)|0.3|(2.7)|
|Government spending: G b/ c/|17.1|(18.9)|<br>14.7|(16.3)|<br>11.2|(18.7)|
|Tax revenues: T b/ c/|8.6|(12.9)|<br>9.3|(11.9)|<br>9.2|(11.9)|
|Government budget deficit: D b/|-8.5|(-6.0)|<br>-5.4|(-4.4)|<br>-2.0|(-6.8)|
|Private investment: I b/|10.2|<br>(9.7)|9.7|<br>(9.2)|12.2|(10.1)|
|Exports: X b/|13.5|(15.4)|<br>10.3|(14.2)|<br>9.3|(12.5)|
|Imports: M b/|5.9|<br>(5.4)|6.4|<br>(6.7)|<br>6.6|<br>(7.6)|
|Trade balance: TB b/|7.6|(10.0)|<br>3.9|<br>(7.5)|<br>2.7|<br>(4.9)|
|Inflation rate:wdj|a/||62.3|(63.6)|<br>35.6|(55.2)|
|Interest rate: i d/|70.0|(59.2)|<br>72.1|(49.5)|<br>72.7|(63.4)|
|Change in exchange rate: e|a/||36.7|(39.2)|<br>22.7|(52.3)|
|Reserves of the Central|||||||
|Bank: R e/|3.43||3.43||3.43||
|Change in the real exchange<br>rate:if/|||18.7|(17.5)|<br>10.5|<br>(1.9)|

- _a/_ We cannot calculate the percentage change in the first year.

- _hi_ In percent of GNP.

- _g/_ We have re-calculated the actual values of G and T so that their components correspond to those included in our model.

- A/ percent.

- _~/_ In billions of US dollars. This simulation assumes a fixed stock of reserves, so there is no point in making comparison with actual reserves.

- j/ The change in the real exchange rate is calculated as the change in the US dollar price of Mexican goods, hence as lr/e. Since, in reality, the Mexican government was actively devaluing during the time period, rather than floating as in our simulation, the real rate actually appreciated less than our prediction.
We conducted our experiment in Sorsogon, a rural province 10-12 hours by bus from the capital, Manila, where most recruitment activities take place. Reflecting its relative poverty and isolation, the Bicol region (where Sorsogon is located) has relatively low participation in international migration. The region accounts for 5.8% of the Philippine population, but only 3.3% of the country’s overseas worker deployments in 2011 (NSO, 2011).

# **3. Methods**

Early in 2010, we randomly selected 42 barangays from six municipalities in Sorsogon Province in which to conduct the baseline survey.<sup>3</sup> We collected a household roster from each barangay that included a list of households, and we used these to set barangayspecific target sample sizes proportional to population. We targeted approximately 5% of the total population from each barangay, or roughly 26% of households. We sorted households randomly and selected the first listed households to be our target. When a household could not be located or had no eligible members, we replaced it with the next household on the list.

From each household, interviewers screened the first member they met who had never worked abroad and was aged 20-45. Subsequent to the baseline survey, we learned from recruitment agencies that most individuals over age 40 would not be eligible for overseas work, so we restrict our baseline sample to the 4,153 individuals ages 20-40 we interviewed.<sup>4</sup> Houses selected were typically far enough apart from each other that concerns about information spillovers are second order; to the extent that there were spillovers, our treatment estimates are lower bounds on the differential impact of more information. The passport assistance was only offered to the respondents themselves, and 7
Similarly, interventions relying on individuals and businesses facing initial up-front costs with long-term benefits (such as those typical of energy efficiency investments) should consider the provision of immediate payoffs in the form of fiscal rebates or subsidies. Giving private actors a sense of long term policy direction is also useful. A 2007 international survey of business leaders found that 81 percent of those polled believed that government needs to provide clear long-term policy signals to help companies find the incentives to change and plan investments.<sup>32</sup> (How government can ensure long-term direction is explored below.) Climate policy should also heed individuals’ tendency to favor local, visible, and privately securable outcomes. Mitigation actions produce global and diffuse benefits. To most, the benefits of adaptation are distant, abstract, and uncertain, while its actual and associated costs are usually immediate, concrete, and certain. Interventions can be devised to better highlight upfront the ‘ancillary’ benefits. For instance, altering costbenefit tools can encourage public and private decisionmakers to act more decisively.

The estimation of costs and benefits of energy-efficiency projects often do not include nonenergy co-benefits. These include public health benefits from cleaner air and water, improved comfort of building occupants, increased labor productivity<sup>33</sup> and increased jobs through the multiplier effects of switching energy expenditure.<sup>34</sup> Evidence based on case studies in manufacturing concludes that these benefits can be considerable, and sometimes equivalent to the value of the energy savings alone.<sup>35</sup> The result is that the timeframe for investment paybacks can fall substantially, providing a better incentive to invest. Similarly, earmarking revenues from carbon or energy taxes should be considered as a way to increase the visibility of benefits of mitigation. Although fiscal earmarking is deemed to be economically inefficient, it helps achieve political acceptance of new taxes, because the public sees clearly where the money goes.

_Social norms_ . Social norms are established patterns of behavior that most people approve of—or the yardstick individuals use to assess the appropriateness of their own behavior. As they shape human action, social norms can achieve socially desirable outcomes, generally at a fairly low cost. The basic idea is that individuals want to act in a socially acceptable way. We tend to follow the lead of others, particularly when the others are numerous and appear similar to them.

Social norms have a particularly strong impact on recipients under conditions of uncertainty.<sup>36</sup> Rather than look for clues about how to behave when unsure about conduct and outcomes, people rely on what others do. Evidence from different environmental contexts shows that social norm-based appeals for pro-environmental

> 32 Clifford Chance 2007. 33 Romm and Ervin 1996. 34 Roland-Holst 2008. 35 Laitner and Finman 2000.

> 36

> Cialdini and Goldstein 2004; Schultz and others 2007.

8
Table 12: IV regression IT use on firm-level productivity considering China’s competition and initial IT

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Computers-per-worker|-0.963|-0.603|-0.180|0.256|0.077|-0.368|
||(1.584)|(1.460)|(1.567)|(1.514)|(1.508)|(1.980)|
|Competition*computers-per-worker|14.468***|12.841**|11.535**|10.810**|10.615**|12.318*|
||(5.545)|(5.473)|(5.316)|(5.224)|(5.175)|(7.102)|
|Competition|-0.946|-0.943|-0.581|-0.818|-0.615|-0.880|
||(1.204)|(1.168)|(1.061)|(1.030)|(1.012)|(1.272)|
|Initial IT*Competition|-10.888**|-9.408**|-9.278**|-8.312*|-8.412*|-9.777*|
||(4.923)|(4.627)|(4.536)|(4.430)|(4.395)|(5.884)|
|F-frst stage IT|7.0|9.7|9.1|9.9|10.0|9.9|
|F-frst stage IT*competition|12.5|14.9|16.8|17.8|17.8|16.2|
|Sargan|0.90|0.72|0.58|0.68|0.60|0.64|
|Share of labor with Internet|-2.135*|-2.296*|-1.494|-1.132|-1.051|-2.470|
||(1.174)|(1.378)|(1.420)|(1.321)|(1.316)|(3.044)|
|Competition*Share of labor with Internet|22.785***|22.959***|19.460***|17.441***|15.514***|20.178*|
||(6.056)|(6.542)|(6.169)|(5.925)|(5.780)|(11.821)|
|Competition|-1.742|-1.862|-1.521|-1.359|-1.008|-1.471|
||(1.115)|(1.134)|(1.029)|(0.989)|(0.964)|(1.352)|
|Initial IT*Competition|-13.430***|-13.859**|-12.133**|-10.802**|-9.779*|-14.529|
||(4.971)|(5.663)|(5.408)|(5.118)|(5.054)|(10.937)|
|F-frst stage IT|14.5|12.9|11.9|12.7|12.2|9.4|
|F-frst stage IT*competition|13.1|14.0|15.4|15.3|15.3|15.1|
|Sargan|0.28|0.32|0.40|0.44|0.45|0.30|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

32
interactions with border ruggedness to see if they affected famines either independently or through border ruggedness. Because our key prefecture-level indicators are not time-varying, we cannot include in these regressions the prefecture fixed effects. We thus include year and warlord fixed effects to control for the time trend and warlord-specific factors.<sup>13</sup> The warlordfixed effect reflects a fixed region with a unique warlord-ruling history: In the case of rule by one warlord, it captures his ability and policy benevolence; in the case of rule by multiple warlords, their ability and policy benevolence.

We cluster standard errors by warlord to allow for correlations among the error terms from prefectures controlled by the same warlord. In columns 1 and 2 of Table 7, both variables and their interactions with border ruggedness are insignificant, indicating that differential population composition is unlikely to be a channel for the link between famine and border ruggedness.

# **5.3.  Fiscal Capacity Is Not a Mechanism**

Apart from affecting the ruler’s incentives, border ruggedness could increase the warlord’s resources and thus the incidence of famines. For instance, a warlord surrounded by rugged terrain might need to spend less resources in military affairs, and would therefore have more resources at his disposal, and he would be less constrained in his ability to provide for his people. We should point out that, in the long run, differences in fiscal resources could be part of the stationary bandit effects, so our concern here is that the difference in fiscal resources merely reflects pre-existing endowment before the warlord fragmentation period. If so, our previous findings on the negative relation between border ruggedness and famine incidence may reflect not his calculation on his ruling horizon but his endowment.

To address this possibility, we consider two proxies for local resource constraints. The first is the amount of local taxes. We have data on prefectural business taxes _(Likin_ ) and land taxes, the two major sources of local fiscal income, in the year 1925. The second proxy for resource constraints is total warlord resources, measured as the sum of fiscal revenue of all the

> 13 An illustration might be useful here. Suppose a province had 5 prefectures, A, B, C, D, and E. Initially A, B, and C were under Warlord 1, D and E were under Warlord 2. Then in the middle of the warlord period, C changed hands to Warlord-2. Then the set of unique warlord history regions are: A and B; C; D and E.

23
# **3 Model properties**

We can define two operators that respectively filter the (spatial) autoregressive effects and produce the moving averages, and summarize the SVARMA as by defining _L_ as a lag operator that has the effect that _L_ **w** _t_ = **w** _t−_ 1, and where **H** ( _L_ ) = **H** 0 + **H** 1 _L_ + _..._ + **H** _pL_<sup>_p_</sup> and **M** ( _L_ ) = **M** 0 + **M** 1 _L_ + _..._ + **M** _qL_<sup>_q_</sup> are full rank matrix-valued polynomials.

Equation (13) is convenient notation for the SVARMA because it allows us to condition theory directly on components similar to the standard case of eq. (4), and understand standard results for invertibility, stability, and Granger-causality simply as high-level conditions on the spatially multiplied autoregressive and moving average components. In the general case of misspecification, model invertibility and process invertibility are not the same.<sup>6</sup> Though non-stationary processes may be invertible, they are generally not causal in the control theoretical sense (Boudjellaba et al., 1992). Analysis should therefore focus on invertible stationary processes under an axiom of correct specification. This complicates matters with respect to the more commonly excepted axiom of misspecification that provides descriptions in terms of pseudo- _true_ correlations in the data. When the model is correct, fading memory properties and process invertibility cannot simply be assumed to be properties of the data. Instead, these properties are directly related to the properties of the model itself and the range of parameter values considered.<sup>7</sup> Below, we will highlight relevant parameter regions and discuss invertibility, and stability results for SVARMA models following theory for standard VARMA models found in L ̈utkepohl (2005) or Brockwell and Davis (2002). The results will also show how multiple representations may equally well describe the data, which is why we shall discuss a penalized estimation criterion.

> 6See for example Blasques et al. (2018) for results on the relation between filters and _DGP_ s.

> 7Proofs for Stationarity and Ergodicity of data generated by VARMA models are widespread and can be found for example in (Nsiri and Roy, 1993). Stelzer (2008) treat multivariate Generalized ARMA models including nonidentity links, (Zheng et al., 2015) treat nonlinear theory for Multivariate Markov-switching ARMA processes, finally Andree et al. (2017) show that multivariate ARMA structures can generate geometrically Ergodic data even when a nonlinear observation-driven spatial dependence process is considered.

13
Table 17. R Sharing Equilibrium

||0|1|2|3|
|---|---|---|---|---|
|0|(0,0)|(0,1) D|(0,2) D|(1,2) D|
|1|(1,0) R|(1,1) D,R|(1,2) D|(2,2) D,R|
|2|(2,0) R|(2,1) R|(2,2) D,R|(3,2) R|
|3|(3,0)*|(3,1) R|(3,2) R|(4,2) R|
|Tab|le 18. Sy|mmetric Sha|ring Equilib|rium|
||0|1|2|3|
|0|(0,0)|(0,1) D|(0,2) D|(1,2) D|
|1|(1,0) R|(1,1) D,R|(1,2) D|(2,2) D,R|
|2|(2,0) R|(2,1) R|(2,2) D,R|(3,2) R|
|3|(2,1) R|(2,2) D,R|(2,3) D|(3,3) D,R|

With the opposite preferences there are at most two cases of coordination failure. In an equilibrium where both agents share coordination failure is eliminated completely.

Although coordination failure occurs in very few cases allocations do change from the complete information case. The agent who shares does that not only when the other agent’s budget is 0 but for the other budget sizes too. Therefore he shares too much (from complete information perspective) and turns some allocations unfavorable to himself.

## 4.2.3 Summary

When the agents have the opposite preferences, they each specialize in their priority sector and invest maximally one project in the lower priority sector. Some coordination failure can occur in equilibrium but its extent is minimal.

# 5 Conclusions

This paper has analyzed three versions of a foreign aid allocation game with increasingly different preferences between the agents. It fi rst analyzed an allocation game with two identical donors who prioritize sector 1, and found that incomplete information can lead to coordination failure. There are two types of coordination failure. First, the lower priority sector does not get enough funds as both donors concentrate too much on the priority sector 31
hardworking, obedient, healthy, spoke English, and was not an alcoholic. Agents were believed to use similar criteria, and also require a recommendation from a chief or pastor in many cases. Community leaders gave similar criteria when asked how people were chosen in their community: they said the community sought strong, healthy, wellbehaved people who could speak English in cases where the community chose, but that in many cases there was no selection at the community level. These cases of community selection are consistent with the idea that communities were trying to send very good workers in their initial recruiting, to signal the quality of their members and to build reputation for future recruitment.

# **5.3 The pre-departure orientation**

Seventy-three percent of RSE migrant workers had attended at the time of interview. They report sessions lasting between one and three hours. When asked what the most useful piece of information they learned was, the most frequent specific responses were learning that you can not drink alcohol during working hours, learning about the weather in New Zealand and the clothing required, and learning that you must wash hands after going to the toilet. When asked how the presentation could improve, many expressed appreciation for the video clips shown, and asked for more information to be presented in this format.

# **6. REASONS FOR APPLYING OR NOT APPLYING AND EXPECTATIONS**

The survey only provides baseline data, and as such, can not inform us of the development impact of the RSE program. Nevertheless, using the survey and supporting

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<!-- Start of picture text --> 100.00<br>90.00<br>80.00<br>70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>1980- 2000- 1980- 2000- 1980- 2000- 1980- 2000- 1980- 2000- 1980- 2000-<br>85 07 85 07 85 07 85 07 85 07 85 07<br>India Sri Lanka Bangladesh Pakistan China Korea<br>Service Industry Agriculture<br><!-- End of picture text --> <!-- Start of picture text --> Bangladesh<br>Industry<br><!-- End of picture text --> _Source_ : World Development Indicators, 2008, World Bank.

Apart from being the largest contributor to GDP levels, services are also the largest contributor to GDP growth in South Asia. Figure 2 compares the sectoral contribution of service, industry, and agriculture, to GDP growth for South Asian countries with China and Korea for the period 1980-85 and 2000-2007. Contribution of service sector to GDP growth in South Asia is nearly double than the contribution of industry to GDP growth. On the contrary, in China and Korea, the contribution of industry to GDP growth is much higher as compared to the contribution of service sector to GDP growth. Contribution of services in GDP growth is increasing over time in all South Asian countries, except Bangladesh.

# **_Growth in South Asia is driven by structural changes_**

Unlike other economies, the miraculous growth story of South Asia is scripted by the service sector growth. South Asia, it seems has consciously skipped the manufacturing sector, and has leaped straight from agriculture into services. This massive structural transformation from primary good producer region into the region of services has been a key driver of growth in the region.

9