rafmacalaba/datause-encoder-data huggingface dataset:
**_Energy demand models for policy_** **_<u>formulation</u>_**

- Trujillo, L., E. Quinet and A. Estache, 2000, Forecasting the demand for privatised transport: What economic regulators should know and why? Policy Research Working paper 2446, World Bank, Washington D.C.

- Tuan, A. and T. Lefevre, 1996, Analysis of household demand in Vietnam, Energy Policy, 24(12), pp. 1089-99.

- UN 1991, Sectoral energy demand studies: Application of the end-use approach to Asian countries, Report 33, Energy Resources Development Series, United Nations, New York.

- Urban, F., R. J. M. Benders and H. C. Moll, 2007, Modelling energy systems for developing countries, Energy Policy, 35, pp. 3473-82.

- Urga, G. and C. Walters, 2003, Dynamic translog and linear logit models: a factor demand analysis of interfuel substitution in US industrial energy demand, Energy Economics, 25, pp. 1-21.

- Uri, N. D., 1982, The demand for energy in the transport sector in the United States, Journal of Transport Economics and Policy, 16(1), pp. 65-84.

- Uri, N.D., 1979a, Energy demand and interfuel substitution in India, European Economic Review, 12, pp. 181-90.

- Uri, N.D., 1979b, Energy substitution in the U.K.- 1948-1964, Energy Economics, 1(4), pp. 241-44.

- Vaillantcourt, K. M. Labriet, R. Loulou, and Jean-Philippe Waaub, 2008, The role of nuclear energy in the long-term energy scenarios: an analysis with the World TIMES model, Energy Policy, 36(7), pp. 2296-2307.

120
# Table A6. Distribution of Manufactruring Establishments by Industry (SIC code) and Employment Size, Indonesia

## **SIC** Cods **frm. mneja product Ffrm Size (I of EapLoyles**

<!-- Start of picture text --> lowi Pet<br>CoL ftt 11-19 120-49 5S0-99 1100-199 1200-499 1500-999 11000 & 1<br>.I I I I I I lover ITot&L<br>31Fesd~3wrg I 0.69 7.23 I3.101p 2.411 069 0.01094<br>4 3S o 00 19 S 15.22 14.3S 4.35 I2.17<br>32.Textil./LeethI I 211 12 81 9 101 61 67<br>0 34 7 2 4: 14 2:76 1 310 3:45 2:07 123.10<br>I20.00 I22.34 I21.43 I12.90 I20.45 152.63 I60.00I<br>33.Uoo 0.0 ~ a 31 6 1 6j I 0.00,<br>0.0 2.76 11.03 12.07 1 2.07 0.34 3 . 0 28<br>0 00 33.33112.50125.00 25.001 417' 0.00<br>0.001 8.51 I5.36 I9.68 I 1.64 5:261 0.00w<br>34.Paper I 0 71 51 61 2 01 01 20<br>I 0001 2441' 1721 2.07 0 0691 0.001 0.00 6.90<br>10.001 35:001 25.001 30.00 110.00 10.00 0.001<br>3S.Chmical/Rubb ......... 1 ...... .4..... 201 ..... 131 .4..... 7 4---.-4........... 6 1 20.34<br>0.0 6.90 4.481 5.186 2.07 0.34 06 03<br>0.00 I33.90 I22.03 I28.311 10.171 1.69 3 391<br>0.00: 21.28 123.21 127.42 1 1.64 5.26 20.001<br>--------- ......................... .... ...........<br>10.00 1 1.721 1.03 1.03 1 1.72' 0.691 0.001 6.21<br>I0.00 I 27.78 116.67 16.67 1 27.78 I I 11.11 0.001<br>I0.00 1 5.321 5.361 4.U'4 11.361 10.531 0.001<br>............. .................. .........-. 4. ....<br>37.Baaic-NotaL 1 0 . II0 01 ( 01 3<br>10.341 0.001 0341 0.00 0.341 0.00 0 .001 1.03<br>33 331 0 00 33.331 0 00133 33 0.00 I 0001<br>120.00 0.001 1.79 0.00 2.271 0.001 0.00<br>311.Fab-Netal.EIcl 0 1 91I 101 14 1 111 2 1 .0I<br>0.001 3.101 3 45 4 83 3.79 0.69 0.00115.86<br>0.00 19.57 121.74 I30.43 123.91 14-351 0.00<br>10.00 I9.57 117.861 22.581 25.00 1¶0.53 10.001<br>.... .4....4 .. .......................4. 4..- . 4...<br>39.Other I I I11 01 II 2 i I1 I 7<br>I0.34 0341 0.00, 0.34 0691 0.341 0341 2.41<br>I/ 1429 J 14.29 I 0.00 i 14.29 1 28.57 i 14.29 J 14.29<br>120.00 1.061 0.00 1.611 45S 5.261 10.001<br>-------.... 4.... 4. ............. 4 4 ..- ..... . 4...<br>Total ~ ~~~~5 94 56 62 44 19 10 29<br>1.72 32.41 19.31 21.38 15.17 6.55 3.45 100.00<br><!-- End of picture text -->
35 complicate the analysis. For example, when banking systems grow too quickly, booms are inevitably followed by busts, in which case size and depth may actually reflect policy distortions rather than development. Hence, it is not surprising that the econometric link between banking depth and aggregate economic growth has weakened in recent years, in particular when the data set includes the 1997-98 East Asia crisis (Rousseau and Wachtel, 2005) or is limited to turbulent Latin American countries (Loayza and Ranciere, 2005). Designing empirical proxies that capture more closely what is meant by financial development is an important area of further research. For example, recently there have been efforts to define and measure the access dimension of financial development, which is widely stressed in theory but mostly overlooked in empirical applications due to lack of availability of data (World Bank, 2008).

There are also issues related to non-linearities and threshold effects. For example, below a certain level of development, small differences in financial development do not seem to help growth (Rioja and Valev, 2004a and b). Distinguishing between short-run and long-run effects of financial development is also important. Loayza and Ranciere (2005) estimate both effects using a pooled mean group estimator. While they confirm a positive long -run effect, they also identify a negative short-run effect, where short-term surges in bank lending can actually signal the onset of financial crisis as discussed above. Also, financial development may boost income and allow developing countries catch up, but not lead to an increase in the long run growth rate. Aghion, Howitt, and Mayer-Foulkes (2005) develop a model that predicts that low-income countries with low financial development will continue to fall behind the rest, whereas those reaching the higher level of financial development will converge. Their empirical results confirm that financial development helps an economy converge faster, but that there is no effect on steady-state growth.
# Table 18 – Probit Model – Internal Migration for Work Only

We ran the same model for those who migrated to work. The dependent variable is a binary which takes ‘1’ if the person migrated to work and ‘0’ otherwise. The results concur with the previous model.

|VARIABLES|migwrk|
|---|---|
|Male|1.458***|
||(0.0642)<br>|
|Urban area|0.421***<br>|
||(0.0361)|
|Lower Egypt|-0.205**<br>|
||(0.0915)|
|Upper Egypt|-0.357***|
||(0.0783)|
|frontier|0.469***|
||(0.0922)|
|married|0.601***|
||(0.0443)|
|Read & write|-0.00687|
||(0.0490)|
|Below Intermediate|-0.115**|
||(0.0519)|
|Gen. Secondary|-0.0353<br>|
||(0.0926)|
|Tech. Secondary|0.174***|
||(0.0416)<br>|
|Above Intermediate|0.0662|
||(0.0758)|
|University|0.0264|
||(0.0475)|
|Above University|0.396**<br>|
||(0.164)|
|age|0.0188***|
||(0.000975)|
|agrisec|-0.0794|
||(0.0518)|
|unpaidfw|-0.381*|
||(0.205)|
|hhcpc|-0.956**|
||(0.437)|
|logrgdp|-0.0941|
||(0.180)|
|Constant|-3.592**|
||<br>(1.601)|
|Observations|87,998|
|Standard errors in|parentheses|
|*** p<0.01, ** p<|0.05, * p<0.1|

21
emigration have tended to be through family reunification categories as destination countries imposed points‐based systems for work migration that favored highly skilled migrants. In 2002, New Zealand introduced a new migration pathway, the Pacific Access Category (PAC). This allows a quota of 250 Tongans to permanently migrate each year. Any Tongan citizen aged 18 to 45 who meets certain English, health, and character requirements can register for this program and a lottery (random ballot) is used to select among the applicants. The odds of success were approximately 8 percent during the 2002‐05 years that our sample is drawn from. If their ballot is drawn, applicants have six months to find a full‐time job offer in New Zealand, and then may immigrate with their spouse and any unmarried children aged under 24.<sup>2</sup> In contrast, any other family members, such as their parents and siblings, are ineligible to accompany them under the PAC rules and are difficult to subsequently sponsor under family reunification categories which primarily provide for marriage migration or which require an income or wealth threshold to be exceeded which excludes most Tongans.

# **2.2 Survey**

Our initial work using this lottery was based on a survey that interviewed a subsample of applicants to the 2002‐2005 ballots approximately one year after they had moved to New Zealand, which showed immediate large gains in income to the principal applicants who migrated (McKenzie et al, 2010). This sample including just over one‐third of the migrants who moved to New Zealand, with many of the others reserved for a sample frame for an official government survey of different immigrant streams taking place at the same time. In late 2012 the research team received permission from the immigration authorities to view the names of all PAC ballot winners from the 2002 to 2005 ballots. There were a total of 4,696 applicants, of whom 367 were randomly selected as ballot winners. Official records based on passport scans showed that, by late 2012, 307 of these winners (84%) had approved residency applications and had ever migrated to New Zealand, while the remaining 60 winners did not migrate and are thus non‐ compliers to the treatment of migration.

Only the names and address details from seven to ten years earlier were available for these migrants. Using this information and an extensive, long, and expensive tracking procedure (described in more detail in Gibson et al, 2015) in both New Zealand and Tonga, between late 2013 and early 2015, we attempted to locate the ballot winners and interview them. Even less information was available for those who did not migrate and so we used the home villages in Tonga of the migrants to sample the non‐migrants and

> 2 See McKenzie et al. (2010) for more details of the PAC program.

5
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The second step was a four month _implementation_ phase given only to the treatment plants. In this phase, the consulting firm followed up on the diagnostic report to help introduce as many of the 38 key management practices as the firms could be persuaded to adopt. The consultant assigned to each plant worked with the plant management to put the procedures into place, fine-tune them, and stabilize them so that they could readily be carried out by employees. For example, one of the practices was daily meetings for management to review production and quality data. The consultant attended these meetings for the first few weeks to help the managers run them, provided feedback on how to run future meetings, and adjusted their design. This phase also involved about 15 days a month of consulting time per plant.

The third phase was a _measurement_ phase which lasted until August 2010. This phase involved only three consultants (and a part-time manager) who collected performance and management data from all treatment and control plants. In return for the firms’ continuing to provide this data, the consultants provided some light consulting advice to both the treatment and control plants. This phase involved about 1.5 days a month of consulting time per plant.

So, in summary, the control plants were provided with the diagnostic phase and then the measurement phase (totaling 225 consultant hours on average), while the treatment plants were provided with the diagnostic, implementation and then measurement phases (totaling 733 consultant hours on average).

# **_III.C. The experimental design_**

We wanted to work with large firms because their complexity means management practices are likely to be important. However, providing consulting to large firms is expensive, which necessitated a number of trade-offs detailed below.

<u>Cross-sectional sample size: We worked with 17 firms. We considered hiring cheaper</u> local consultants and providing more limited consulting to a sample of several hundred plants in more locations. But two factors pushed against this. First, many large firms in India are reluctant to let outsiders into their plants because of their lack of compliance with tax, labor and safety regulations. To minimize selection bias we offered a high quality intensive consulting intervention that firms would value enough to take the risk of allowing outsiders into their plants. This helped maximize initial take-up (26% as noted in section II.B) and retention (100%, as no firms dropped out). Second, the consensus from discussions with Indian business people was that achieving a measurable impact in large firms would require an extended engagement with high9
# **Introduction**

Countries that invest in telecommunications services benefit in several ways. Firms in industries that produce telecom equipment and services benefit directly. And firms in other industries can benefit indirectly if telecom services—especially broadband Internet services—are generalpurpose technologies (Basu and Fernald, 2008), which benefit a large segment of the whole economy and have long-lasting benefits.

Telecom services can be considered general-purpose technologies for several reasons. First, well-developed telecom services allow firms to spend less on communications and to obtain inputs more efficiently (Hardy, 1980; Roller and Waverman, 2001). Second, strong telecom services help firms expand into new regions and countries, allowing them to achieve economies of scale. Finally, good telecommunications can cut the costs of collecting information— encouraging firms to create and share knowledge and to organize more efficiently.

Many studies have found that countries with well-developed telecom services grow faster (Hardy, 1980; Roller and Waverman, 2001; Lee and others, 2011). But most of those studies have focused on fixed-line and cellular services. Because Internet and broadband services have expanded only in recent years, fewer studies have looked at the links between Internet services and economic growth (Qiang, 2009). That is unsurprising: general-purpose technologies take time to affect economic performance (Basu and Fernald, 2008).

In this paper we use comprehensive firm-level data from around the world to analyze the effect of access to telecom services—with particular attention to the Internet—on firm productivity and growth. We find evidence that the Internet acts as a general-purpose technology: firm growth and productivity are much higher when Internet access is greater and when firms use the Internet more intensively; and Internet access benefits firms in both high- and low-tech industries, firms of all sizes, and exporter and non-exporter firms. Small firms appear to benefit more from the Internet than large firms do. In contrast, fixed-line and cellular services are not robustly linked to firm performance.

2
# **3 District Expenditure Data**

The financial data used are the District budget data for the years from 2001 to 2012 (Fiscal Year of January-December every year). They were derived from the Regional Financial Information System (Sistem Informasi Keuangan Daerah, SIKD) of the Ministry of Finance. The district expenditures are available for 12 different sectors/functions (such as agriculture, health, education, etc.) and for four economic classifications (personnel, goods and services, capital, and other). The data are provided for 511 districts.<sup>3</sup> The 12 sectors are presented in the top panel of Table 2, where nominal numbers are converted into yearly ratios, which eliminate inflationary issues as well as spending differences due to district size and wealth.<sup>4</sup> The three largest sectors are General Administration (GA), Education and Infrastructure. General Administration is - as the name suggests - a very general budget sector, which includes posts regarding planning, civil administration, governance, statistics and communication.<sup>5</sup> Most of the sectors have between 4,000 and 4,500 observations, but public law and order, housing and - in particular - religious affairs have much fewer observations, implying that the overall ratios might be skewed a bit by districts that report these sectors compared to districts that do not. However, on average the under-reported sectors constitute less than 5 percent of the total expenditures, making their overall impact small. The reason for the fewer observations<sup>6</sup> and the lack of completeness are unknown, but we will be treating missing data as missing and make ratios based on a

> 3Of these 511, we will be using 299 in our final dataset. Details on these are outlined in Section 4.

> 4Nominal data are shown in appendix B.

> 5Specifically the posts included are: Development Planning, Population and Civil Administration, General Governance and Public Administration, Apparatus, Village and Community Empowerment, Statistics, Archive, Communication and Information and finally Transmigration.

> 6Religious affairs are only reported for Aceh, implying that it is a province specific category.

8
Guiso, L., P. Sapienza, and L. Zingales. 2003. "People's opium? Religion and Economic Attitudes." _Journal of Monetary Economics_ 50 (1): 225-82.

Hassan, N., M. Pitt, and M. R Rosenzweig. 2010. “Human Capital Investment and the Gender Division of Labor in a Brawn-Based Economy.” Economic Growth Center Working Paper 989, Yale University.

Hill, A., and E. King. 1993. “Women’s Education in Developing Countries: an Overview” in _Women’s Education in Developing Countries_ . Ed. Elizabeth M. King and M. Anne Hill, 1-50. Baltimore, MD: The John Hopkins University Press.

Jayachandran, S., A. Lleras-Muney, and K. V. Smith. 2010. "Modern Medicine and the Twentieth Century Decline in Mortality: Evidence on the Impact of Sulfa Drugs." _American Economic Journal: Applied Economics_ 2 (2): 118–46.

Kaufmann, D., A. Kraay, and M. Mastruzzi. 2008. “Governance matters VII: Aggregate and individual governance indicators, 1996-2007.” World Bank Policy Research Working Paper 4654, World Bank, Washington D.C.

Klasen, S. 1999. “Does Gender Inequality Reduce Growth and Development? Evidence From Cross-Country Regressions.” Background paper for Engendering Development, World Bank, Washington D.C.

Klasen ,S. 2002. "Low Schooling for Girls, Slower Growth for All? Cross-Country Evidence on the Effect of Gender Inequality in Education on Economic Development." _World Bank Economic Review_ 16 (3): 345-73.

Klasen, S., and F. Lamanna. 2009. "The Impact of Gender Inequality in Education and Employment on Economic Growth: New Evidence for a Panel of Countries." _Feminist Economics_ 15 (3): 91-132.

Knowles, S., P.K Lorgelly, and P. D. Owen. 2002. “Are educational gender gaps a brake on economic development? Some cross-country empirical evidence.” _Oxford Economic Papers_ 54 (1): 118-49.

21
<!-- Start of picture text --> Burkina Faso Ethiopia Malawi<br>100 100 100<br>80 80 80<br>222<br>560 FA s 60 3 5 60<br>8 40 a 8 40 + 8 40 a<br>s s s<br>20 20 20<br>0<br>000<br>aa rs rs | ars<br>jan-21 oct-21 jul-22 apr-23 jan-21 oct-21 jul-22 apr-23 jan-21 oct-21 jul-22 apr-23<br>Date Date Date<br>Nigeria Uganda<br>100 100<br>0<br>80 80<br>55 * 8a s5 * — Admin Data<br>>g40 g 4 a o Survey Estimate<br>a ><br>20 20<br>0 0<br>rs rr ss |<br>jan-21 oct-21 jul-22 apr-23 jan-21 oct-21 jul-22 apr-23<br>Date Date<br><!-- End of picture text -->

Note: Share of people with at least one COVID-19 vaccine dose assuming no one below 15 was vaccinated. Admin data source: Mathieu et al. (2021)
these aims, we adapt the vignette methodology that has been used in a number of recent studies of subjective data on ( _inter alia_ ) health status, political efficacy and job satisfaction. King et al. (2004) and King and Wand (2007) designed vignettes to establish common points on the heterogeneous reference scales regarding political efficacy in China and Mexico. Kristensen and Johansson (2008) used vignettes in anchoring subjective scales for job satisfaction. Bago d’Uva et al. (2008) used them for correcting self-assessed health data for reporting bias. Vignettes have also been used in testing the competence of doctors (Das et al., 2008). To our knowledge, this is the first work to use vignettes for anchoring self-reported economic status, although Kapteyn et al. (2008) use vignettes to compare life satisfaction between respondents in the U.S. and the Netherlands.

The paper provides three tests for FORB where our subjective welfare measure is selfreported economic status. In the first we simply test whether vignette responses are correlated with the regressors typically used in subjective welfare analysis, including objective welfare measures. If everyone has essentially the same idea of what it means to be “poor” or “rich” then we would not expect to find significant correlations between the vignette responses and the covariates used to explain subjective welfare. Under certain forms of FORB, we may find a negative economic gradient in the vignette responses. Consider again the three people in the example above and now suppose that these three people are the vignettes. A plausible set of responses to the vignette questions is given in Table 1, indicating that the wealthier the respondent, the lower (or at least not higher) the rating of each vignette.

So Test 1 provides a very direct test for FORB in subjective welfare regressions, under which we would look for a negative wealth gradient in each set of vignette responses as well as correlation with other household characteristics.

We then provide two further tests that can help quantify the extent of any bias due to heterogeneity in scales by providing a method of correcting subjective welfare regressions for the presence of individual-specific scales or standards. In Test 2, the vignette responses enter as control variables in standard regressions, to purge the error term of the heterogeneity in scales used, under the assumption that differences in vignette responses are solely attributable to differences in the personal scales used. In the third test, an alternative to Test 2, we use the rescaling method proposed by King et al. (2004) in which subjective welfare measures are recalibrated for consistently across respondents based on the vignette responses.

5
# XXIV INTRODUCTION

Figure 8 Percentage Distribution of World Population by Continent, 1985 <!-- Start of picture text --> Eircpe arn U.S.S.R. (1S.9.) Africa (il 5%)<br>icoanl (C0.5%)<br>ASIG (se 3)<br><!-- End of picture text --> Figure 9 Percentage Distribution of World Population by Continent, 2000 <!-- Start of picture text --> EWrope non U.S.S.R. (13.1i) AfriCa (14 0t<br>AaW Ica (`13 3X)<br>Oceania (0.5%)<br>AsiB (59.<br><!-- End of picture text --> Figure 10 Percentage Distribution of World Population by Continent, 2025 <!-- Start of picture text --> Europe ancd U.S.S.R. C10.1%0<br>AawIca C12 4%)<br>OMeanis (0.5%)<br>Asla (59 2 / /<br><!-- End of picture text -->