rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix I.A.23 Historical Demographic Data, Philippines 1939-1970

|Year|1939|1948|1960|1970|
|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|15.45|16.72|18.91|15.35|
|Population aged 20 to 59/<br>population over 60 years old|7.27|8.24|9.17|7.53|
|Percentage of population<br>over 60 years old|5.63|4.88|4.32|5.49|
|Pcrcentage of population<br>underl5yearsold|43.00|44.16|45.69|43.11|
|Women over 60 years old/<br>Men over 60 years old|0.97|0.98|0.94|1.04|

## Ratios calculated using data found in Mitchell, B. R 1983 "International Historical Statistics: The Americas and Australasia".

_a%_

# Appendix I.A.24 Historical Demographic Data, Portugal 1890-1981

|Year|**1890**|1911|1930|1950|1970|1981|
|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|9.87|10.07|9.93|8.65|6.40|5.51|
|Population aged 20 to 59/<br>population over 60 years old|4.68|4.84|5.04|4.66|3.37|3.15|
|Percentage of population<br>over 60 years old|10.16|9.59|9.60|10.80|14.43|15.84|
|Percentage of population<br>under 15 years old|33.12|34.41|31.97|29.35|28.48|25.52|
|Women over 60 years old/<br>Man over 60 years old|1.23|1.30|1.36|1.36|1.41|1.37|

Ratios calculated using data found in Mitchell, B. R. 1990 "International Historical Statistics: Europe 1750-1988".
Absolute income is almost invariably used in conjunction with relative income to test the relative income hypothesis and recall that this is the type of specification we focus on in this paper. A first observation is that empirical models that try to explain happiness with absolute and relative income can explain only a small part of the variation in happiness. Ferrer-i-Carbonnell (2005) noticed that the pseudo-R2’s for all regressions tested in the paper were between 0.07 and 0.08. This is in accordance with the general finding in the literature that only about 8% to 20% of individual SWB depends on observed variables that are typically used in these types of models (Kahneman et al., 1999). Therefore, testing the relative income hypothesis does not amount to testing whether this variable is the most relevant factor in explaining happiness but boils down to testing whether this variable is consistently significant in happiness equations.

Almost all studies find a positive association between **absolute income** and satisfaction. This evidence is consistent with studies that used cross-section surveys, panel data and cross-country studies. Cross-section studies on single countries have consistently found a positive and significant relation between income and life satisfaction or happiness with or without demographic controls. This is true for developed countries (Blanchflower and Oswald, 2004; Shields and Wheatley Price, 2005) as well as for developing countries (Graham and Pettinato, 2002; Lelkes, 2006). The main difference between developed and developing countries seems to be the slope of the relation, steeper for poorer economies as compared to richer economies. Panel data have been used to control for unobserved heterogeneity, such as personality traits. This section of the literature also concludes that changes in absolute incomes are correlated with changes in happiness (Winkelmann and Winkelmann, 1998; Ravallion and Lokshin, 2002; Ferreri-Carbonell and Frijters, 2004; Senik, 2004; Ferrer-i-Carbonell, 2005; Clark et al., 2005).

Some of the work with panel data has also gone further in an effort to establish a causality link between income and satisfaction by instrumenting income with exogenous shocks. Frijters et al. (2004) used the average increase in incomes in East Germany after reunification and the average decrease in incomes in Russia after the collapse of the Soviet Union as exogenous shocks and find a greater effect of income on happiness than in much of the previous literature. Gardner and Oswald (2007) use information on lottery winnings in the British Household Panel Survey (BHPS) as reflecting exogenous income movements. In both the level and panel equations, lottery winnings are found to significantly reduce mental stress scores. These studies are consistent in finding a positive and significant relation between income and happiness and concord in finding 19
20 TABLE 5

# TRADING DATA ON ULAANBAATAR HOTEL INC.'s SHARES as of June 7, 1992

|Total Issued||335,297 shares|||||
|---|---|---|---|---|---|---|
|Esop<br>Total of Tra|:<br>ded:|21,140 shares<br>314,157 shares|<br>(5.71 percen<br> <br>(84.85 perce|t)<br>nt)|||
|Trading<br>Day||Number of<br>Shares|Voucher<br>Value|Hi|Share<br> <br>Low|Price<br> <br>Average|
|February 2,|1992|9,582|2,395,500|250|<br>250|<br>250|
|February 11,|1992|22,640|5,345,244|252|<br>210|<br>231|
|February 18,|1992|12,115|3,150,351|350|<br>251|<br>270|
|February 25,|1992|40,000|10,200,000|261|250|<br>254|
|March<br>3,|1992|45,000|11,490,000|259|250|<br>254|
|March<br>10,|1992|75,000|18,970,000|259|250|<br>252|
|March<br>17,|1992|65,000|16,400,000|259|250|<br>252|
|March<br>24,|1992|45,000|11,445,000|255|252|<br>254|
||TOTAL|314,157|79,396,095|-|-||

# SHAREHOLDER DATA

Total Issued: 335,297 shares Total Number of Shareholders: 12,700 Esop: 302 people obtained shares through the ESOP scheme Number of Shares per Shareholder: 26 The Names of People With the Highest Number of Shares and the Province They Reside in:

1. G. Damdin, 269 shares, Dornogobi Aimak

2. J. Narmandah, 245 shares, Ulaanbaatar 3. S. BAyraa, 243 shares, Dornogobi Aimak 4. N. Ouin, 207 shares, Ulaanbaatar 5. J. Purev, 200 shares, Dornogobi Aimak Source: Mongolian Stock Exchange
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2493 Annuity Markets in Comparative<br>Perspective: Do Consumers Get<br>Their Money's Worth?|Estelle James<br>Dimitri Vittas|November 2000|A. Yaptenco<br>31823|
|WPS2494 The Relevance ot Index Funds for<br>Pension Investment in Equities|Ajay Shah<br>Kshama Fernandes|November 2000|A. Yaptenco<br>31823|
|WPS2495 The Australian Annuity Market|David M. Knox|November 2000|A. Yaptenco<br>31823|
|WPS2496 Perspectives on the Sources of<br>Heterogeneity in Indian Industry|Somik V. Lall<br>G. Chris Rodrigo|November 2000|R. Yazigi<br>37176|
|WPS2497 State Policies and Women's<br>Autonomy in China, India, and the<br>Republic of Korea, 1950-2000:<br>Lessons from Contrasting<br>Experiences|Monica Das Gupta<br>Sunhwa Lee<br>Patricia Uberoi<br>Danning Wang<br>Lihong Wang<br>Xiaodan Zhang|November 2000|M. Das Gupta<br>31983|
|WPS2498 Sustaining Economic Welfare:<br>Estimating Changes in Per Capita<br>Wealth|Kirk Hamilton|November 2000|L. Rivera<br>82819|
|WPS2499 The Treatment of Non-Essential<br>Inputs in a Cobb-Douglas Technolog<br>An Application to Mexican Rural<br>Household-Level<br>Data|Isidro Soloaga<br>y:|December 2000|L. Tabada<br>36896|
|WPS2500 Investigating Corruption|Canice Prendergast|December 2000|H. Sladovich<br>37698|
private sphere, most production is nevertheless for the domestic market (only 12.9 percent is exported) while most domestic demands are satisfied with domestic output (imports only amount to 16.7 percent).

**Table 5.4. Sectoral structure, low‐income archetype country in 2015 (percent)**

||Value<br>added|Production|Exports|Export‐<br>Output<br>ratio|Imports|Import‐<br>Demand<br>ratio|
|---|---|---|---|---|---|---|
|Private sector|96.4|92.9|100.0|12.9|100.0|16.7|
|Government sector|3.6|7.1|0.0|0.0|0.0|0.0|
|Total|100.0|100.0|100.0|12.0|100.0|15.6|

Source: Authors’ calculations based on Table 3.6.

Table 5.5 shows the factor shares in total sectoral value added. As indicated, the government is only recorded as paying for labor; it does not pay any rent for its use of government capital (created from government investment) The overall labor share in value added (56.7 percent) is within the expected range (Gollin, 2002).

The information found in Tables 5.1‐5.5 could be presented in a more disaggregated manner if it were based on a more disaggregated SAM. This would be important for many analytical purposes. For example, analyses of issues related to structural transformation and income distribution could benefit from more disaggregated sector information in Tables 5.4 and 5.5. When a SAM serves as data input to a CGE model, the model would explain the evolution of the payments in the different SAM cells, including that they often depend on changes in prices, quantities, and tax rates.

**Table 5.5. Sectoral factor intensity, low‐income archetype country in 2015 (percent)**

||Labor|Capital|Total|
|---|---|---|---|
|Private sector|55.1|44.9|100.0|
|Government sector|100.0|0.0|100.0|
|Total|56.7|43.3|100.0|

Source: Authors’ calculations based on Table 3.6.

‐23‐
Regarding the endogeneity of export promotion, we control for numerous determinants of exports that may be also correlated with export promotion budgets. The control variables are: GDP per capita, an index of trade restrictiveness imposed on imports, an index of trade restrictiveness faced by each country’s exports in the rest of the world, volatility of the exchange rate, an indicator of the regulatory burden that measures the average number of days it takes to comply with all necessary regulations to export goods, the geographydetermined trade to GDP ratio, and regional dummies for EEA, LAC, MENA, SSA, and the OECD. We also estimated specifications with infrastructure variables (share of paved roads, main telephone lines per capita) and indexes of institutional quality (ICRG indexes) as control variables. These are highly collinear with GDP per capita and were not statistically significant. Moreover, in some cases they significantly reduced our sample. Since this paper is about what works in terms of export promotion, and these variables did not affect qualitatively our results on export promotion, we do not report these specifications.

The basic export equation to be estimated is then: where the _β_ s are parameters to be estimated. _Exp/popc_ are exports per capita in country _c_ , and _Bud/popc_ is the budget of the EPA per capita in country _c_ . _GDP/popc_ is GDP per capita measured as the average for the period 2000-2004 in 2005 constant U.S. dollars from the World Bank’s World Development Indicators. _Tc_ is an index of trade restrictiveness imposed by country _c_ on its imports from the rest of the world, _MAc_ is an index of market access restrictions imposed by the rest of the world on exports of country _c_ , and both are borrowed from Kee, Nicita and Olarreaga (2009). _V olc_ is the volatility of the exchange rate in country _c_ , measured by the coefficient of variation of the dollar to local currency exchange rate during the period 2000-2004 obtained from the World Development Indicators. _Regc_ is the number 9
Furthermore, agglomeration leads to the concentration of jobs in a given location. This in turn makes it more appealing for people to move to an agglomeration in search of work. For this reason, we expect to find more self-declared ‘unemployed’ individuals near gold mines. If these individuals are looking for work primarily outside mining, we would also regard this as consistent with proto-urbanization. Another pattern we expect to find is the rise in the proportion of people (mostly women) who list themselves as homemakers. If they were located in a rural village where agriculture dominates activity, these homemakers would be spending part of their time in the fields, and would typically be counted as employed in agriculture. Finally, if gold mines trigger economies of agglomeration and returns from specialization, we also expect incomes to be higher – a feature that would manifest itself as an increase in night light.

# **5.1 Census data**

We start with the cross-sectional analysis of the census data. We report in Figures 5 and 6 the regression estimates of _γk_ from model (4) estimated using the 2010 data. The two figures correspond to a different set of dependent variables from the census data. Coefficient estimates _γk_ are reported graphically, together with their 95% confidence interval based on standard errors clustered at the district level. As explained earlier, we focus our attention on distances up to 100 km from a gold mine. Since distance is divided into intervals of 10 km each (see equation 3), we report ten _γk_ coefficients for each regression. Coefficients represent the effect on local employment shares (in percentage points) of a one ton increase in gold production at a certain distance interval, relative to localities further away than 100 km. Mining variables represent average annual mining output in the preceding decade. Table 4 provides an overview of the distribution of our regressor of interest _mikt_ , tons of gold produced at distance interval _j_ from location _i_ . By construction, the number of locations that have positive _mikt_ increases with distance from the mine. About 60 percent of all locations in 2010 are further than 100 km away from any active gold mine; these locations constitute the omitted category in our regressions.<sup>6</sup> Similarly, we express city population _cikt_ in thousands of inhabitants of a city. Our definition of city includes all EAs that are classified as urban by the Ghana Statistical Service, and that

> 6In 2000 about 45 percent of all locations are further away than 100 km from any active mine.

17
# **ANNEX A. REGRESSION TABLES**

**Table 11. Importance of legal and regulatory environment**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0368|-0.0206|||-0.0668|-0.0356|||
||(0.0450)|(0.0446)|||(0.112)|(0.117)|||
|Export % of sales|-0.00164***|-0.00151**|-0.00155**|-0.00145**|-0.00437***|-0.00364**|-0.00424***|-0.00361**|
||(0.000614)|(0.000607)|(0.000612)|(0.000606)|(0.00151)|(0.00155)|(0.00157)|(0.00161)|
|Share of sourcing via imports|0.000487|0.000541|0.000208|0.000165|0.00168|0.00172|0.00124|0.000970|
||(0.000680)|(0.000672)|(0.000692)|(0.000686)|(0.00171)|(0.00176)|(0.00177)|(0.00183)|
|High-income source country|0.0180|0.0947|0.0374|0.110*|0.0408|0.281|0.0808|0.319*|
||(0.0646)|(0.0662)|(0.0639)|(0.0648)|(0.164)|(0.182)|(0.167)|(0.182)|
|>250 employees|0.156***|0.147***|0.166***|0.156***|0.404***|0.400***|0.437***|0.431***|
||(0.0516)|(0.0516)|(0.0520)|(0.0516)|(0.132)|(0.139)|(0.138)|(0.144)|
|Cumulative inv. >US$10M|-0.0566|0.0157|-0.0690|0.00472|-0.112|0.0337|-0.146|0.00577|
||(0.0515)|(0.0511)|(0.0513)|(0.0507)|(0.127)|(0.131)|(0.130)|(0.135)|
|Years in country|-0.000308|-0.00163|-0.000248|-0.00174|-0.00144|-0.00554|-0.00136|-0.00586|
||(0.00174)|(0.00163)|(0.00174)|(0.00166)|(0.00444)|(0.00447)|(0.00462)|(0.00467)|
|Level of foreign ownership (%)|-0.00223**|-0.00102|-0.00199**|-0.000838|-0.00679***|-0.00386|-0.00624**|-0.00330|
||(0.000922)|(0.000986)|(0.000913)|(0.000966)|(0.00248)|(0.00269)|(0.00253)|(0.00273)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.069|0.041|0.091|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important were the following factors in your foreign owner company's decision to invest in this country? [Business-friendly legal and regulatory environment]". Higher numbers denote greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

28
evidence that disclosure categories react slightly more than the other categories, but the difference is not significant and small in magnitude. This supports the descriptive statistics that similarly do not show greater improvements in the disclosure categories than overall (either before/after program announcement or when looking at changes between first/last evaluation cycle, in Table 7 and Table A.5, respectively). In fact, the raw means suggest that there is less improvement for the disclosure questions than the other questions.

_Sixth, we explore whether transparency leads some firms to drop out of the program._ Table 15 tests for factors correlated with firm attrition, including firm size, firm age, baseline non‐compliance, the non‐ compliance rate of the last compliance assessment, as well as the Public Disclosure Program. There is some evidence that firms are more likely to drop out of ILO‐IFC Better Work Vietnam after the announcement of the Public Disclosure Program than before. There is a positive and statistically significant correlation between the variable POST and firm attrition, though the coefficient is relatively small in magnitude. This holds when also controlling for firm size, firm age, and baseline non‐compliance.

Non‐compliance rates are also a strong overall predictor of dropping out of the program; we find evidence that firms with higher non‐compliance rates in their last compliance cycle are more likely to drop out of ILO‐IFC Better Work Vietnam. This suggests that the average trends in non‐compliance rates over time may be influenced by firms dropping out of the program. Larger firms are also less likely to drop out, suggesting that they have more to gain from successfully participating in the program. Current evidence therefore shows previous compliance performance remains the strongest predictor of whether a firm drops out of the programme, but public disclosure seems to add to that effect.

**Table 15: Determinants of attrition**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Non‐compliance rate in t‐1|1.610***||1.527***||1.505***|
||(0.133)||(0.146)||(0.138)|
|POST||0.290***||0.289***|0.287***|
|||(0.023)||(0.023)|(0.022)|
|Firm size|||‐0.079***|‐0.142***|‐0.086***|
||||(0.024)|(0.022)|(0.022)|
|Firm age|||‐0.046**|‐0.03|‐0.017|
||||(0.022)|(0.022)|(0.021)|
|Baseline noncompliance|||‐0.023|0.060***|‐0.027|
||||(0.024)|(0.022)|(0.022)|
|Test: x * POST + x + POST=0|0.91|32.12|327.33|139.68||
||0.34|0.00|0.00|0.00||
|Year FE|No|No|No|No|No|
|Observations|1386|1386|1386|1386|1386|
|R‐squared|0.099|0.111|0.109|0.147|0.217|

Note: Firm drops out=1. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance.

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

# Conclusions

This research leveraged factory‐level data on compliance from the ILO‐IFC Better Work Vietnam program to assess the relationship between transparency on working conditions and firm compliance of labor standards in Vietnam. It exploited a change in the policies of Better Work Vietnam when, in 2015, the 29
Table 6: robustness tests: cumulative conditions

|robustness test|linear|quadratic|logarithmic|
|---|---|---|---|
|DPL_>_0|.002|.|.|
||(.001)<sup>_∗_</sup>|||
||.|.006<br>(.003)<sup>_∗_</sup>|.|
||.|-.00002<br>(.00002)|.076<br>(.060)|
|closing year|.004<br>(.001)<sup>_∗∗∗_</sup>|.|.|
||.|.008|.|
|||(.003)<sup>_∗∗_</sup>||
||.|-.00002|.119|
|||(.00002)|(.068)<sup>_∗_</sup>|
|controls|.005|.|.|
||(.002)<sup>_∗∗∗_</sup>|||
||.|.009|.|
|||(.004)<sup>_∗∗∗_</sup>||
||.|-.00002|.126|
|||(.00002)|(.078)|
|EFW|.007|.|.|
||(.002)<sup>_∗∗∗_</sup>|||
||.|.008|.|
|||(.004)<sup>_∗_</sup>||
||.|-.000006|.096|
|||(.00002)|(.057)<sup>_∗_</sup>|
|Heritage|.031|.|.|
||(.020)|||
||.|.127|.|
|||(.049)<sup>_∗∗∗_</sup>||
||.|-.0005|.757|
|||(.0002)<sup>_∗∗_</sup>|(.766)|

Note: Panel regression results from several robustness tests. Dependent variable: CPIA clusters A and B average. Variable of interest: number of cumulative market reform conditions. Only coefficient estimates and clustered standard errors of conditions variable are reported. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample. Taking into account the exclusion of Argentina, observations and number of countries are similar to table 5.

27
# Do Capital Incentives Distort Technology Diffusion? Evidence on Cloud, Big Data and AI

Timothy DeStefano<sup>∗</sup> Nick Johnstone<sup>†</sup> Richard Kneller<sup>‡</sup> Jonathan Timmis<sup>§¶</sup>

**Keywords:** Capital incentives, Firms, Cloud computing, Artificial Intelligence

**JEL Codes:** J21, J24, L20, O33

> ∗Timothy DeStefano, Georgetown University: timothy.destefano@georgetown.edu

> †Nick Johnstone, International Energy Agency: nick.johnstone@iea.org

> ‡Richard Kneller, University of Nottingham, GEP, CESIfo, CITP: richard.kneller@nottingham.ac.uk

> §Jonathan Timmis, World Bank: jtimmis@worldbank.org

> ¶We would like to thank Giuseppe Berlingieri, Chiara Criscuolo, Shane Greenstein, Marco Iansiti, Nan Jia, Aaditya Mattoo, John Mayo, Kristina McElheran, Carlo Menon, Giuseppe Nicoletti, Sam Ransbotham, Neil Thompson and participants at NBER Economics of Digitization Spring Meetings, Annual AI in Management Conference, CESIfo Economics of Digitization Conference and ZEW Conference on the Economics of Information and Communication Technologies for helpful comments and suggestions.
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