rafmacalaba/datause-encoder-data huggingface dataset:
_5_ their power needs from their own generators, from a mere 0.1 % to a high of 99.9%. Of these 171 firms that blended public and private electricity, 30 firms generated 1000 kilowatt hours per year or less, too little to be much more than "warming up" their generators from time to time. Our empirical analysis is restricted to 118 firms that produced and bought non-negligible amounts of electricity and for which we had clean data.

We supplement the econometric analyses of Nigeria and Indonesia with data from Thailand, a country where hardly any firms produce their own electricity. We rely on a survey of 300 Thai manufacturing firms, only 18 of whom had their own electricity generators; we estimate scale and price elasticities for the 250 Thai firms that did not produce their own electricity and for which we had clean data.

Nigeria, Indonesia, and Thailand offer a spread of power infrastructure climates. In Nigeria, the govemment has enforced a strict govemment power monopoly despite severe inadequacies in the quality of the public supply of electricity, forbidding sales of electricity across firms and making costly firms' investments in their own generating capacity. In Indonesia, govemment has a monopoly on power transmission, but the inadequacies in the quality of public provision are less severe than in Nigeria. Moreover, Indonesian firms<sup>have</sup> recently been allowed to sell electricity to one another, and the cost of generators to firms have been reduced of late by reductions in import duties and tariffs. In Thailand, the govemment monopoly on electricity sales has not led to such poor quality that firms seek to produce electricity for themselves.

Nigeria, Indonesia, and Thailand also offer contrasting economic climates. The longstanding economic boom in Thailand, more recent boom times in Indonesia,<sup>and the</sup>
|**Table 6:**|**Maximum**|**likelihood esti**|**mation of**|**condtion**|**al logit s**|
|---|---|---|---|---|---|
||**pecifiatio **|**nof location ch**|**oice**|||
||(1)|(2) (3)|(4)|(5)|(6)|
|_Interaction of medium to_<br>_high tech industries**** with:_||||||
|High FDI States||-0.903|-1.232|-1.005|-0.923|
|||(5.00)**|(7.08)**|(5.71)**|(5.02)**|
|Low FDI States||-1.056|-1.231|-1.109|-0.999|
|||(5.72)**|(6.94)**|(6.17)**|(5.27)**|
|_Interaction of "entrepreneurial_||||||
|_roots located" businesses with_||||||
|High FDI States||-0.109|-0.369|-0.178|-0.012|
|||(0.33)|(1.14)|(0.54)|(0.04)|
|Low FDI States||-1.874|-1.797|-1.850|-1.699|
|||(6.05)**|(5.93)**|(6.06)**|(5.21)**|
|_Interaction of startup size with_||||||
|High FDI States||-0.161|-0.240|-0.184|-0.159|
|||(2.46)*|(3.86)**|(2.91)**|(2.39)*|
|Low FDI States||-0.351|-0.367|-0.355|-0.326|
|||(4.98)**|(5.46)**|(5.19)**|(4.51)**|
|Interaction of business age with:||||||
|High FDI States|-0.362|-0.269|-0.330|-0.289|-0.282|
||(3.56)**|<br>(2.38)*|(3.10)**|(2.65)**|(2.45)*|
|Low FDI States|-0.217|-0.015|-0.082|-0.035|-0.017|
||(2.20)*|(0.13)|(0.74)|(0.31)|(0.15)|
|Constrained by labor regulation (%)||-0.344|-0.645|-0.913||
|||(4.09)**|(8.13)**|(10.41)**||
|Output lost to power outage (%)||-2.058|||-3.995|
|||(10.31)**|||(14.44)**|
|Domestic market access|-|2.913|5.000|||
||(|0.74)|(1.59)|||
|Own industry local size|-|3.650||8.902|-16.983|
||(|2.85)**||(6.44)**|(8.60)**|
|High FDI States|1.613|2.746|3.206|2.644|3.033|
||(5.92)**|<br>(5.73)**|(6.85)**|(5.60)**|(6.12)**|
|Low FDI States|1.373|4.151|4.186|4.035|4.068|
||(5.12)**|<br>(8.69)**|(9.05)**|(8.64)**|(8.24)**|
|Log likelihood|-1541|-1619<br>-129|1<br>-134|8<br>-132|8<br>-1256|
|Pseudo R2|0.05|0.01<br>0.1|8<br>0.1|4<br>0.1|7<br>0.2|
|LR chisq(k)|166|10.4<br>56|6<br>45|3<br>49|3<br>637|
|Observations|4299<br>|4299<br>4299|4299|4299|4299|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1%

*** medium to high tech industries include auto parts production, drugs and pharmaceuticals, and

45
Table A1: Weather Variables & Transformations

|**_Panel A_**_: Rainfall_||
|---|---|
|Daily rainfall|In mm|
|Mean|The frst moment of the daily rainfall distribution for the growing<br>season<sup>_†_</sup>|
|Median|The median daily rainfall for the growing season<sup>_†_</sup>|
|Variance|The second moment of the daily rainfall distribution for the grow-<br>ing season<sup>_†_</sup>|
|Skew|The third moment of the daily rainfall distribution for the growing<br>season<sup>_†_</sup>|
|Total|Cumulative daily rainfall for the growing season<sup>_†_</sup>|
|Deviations in total rainfall|The z-score for cumulative daily rainfall for the growing season<sup>_†_</sup>|
|Scaled deviations in total rainfall|The z-score for cumulative daily rainfall for the growing season<sup>_†_</sup>|
|Rainfall days|The number of days with at least 1 mm of rain for the growing<br>season<sup>_†_</sup>|
|Deviation in rainfall days|The number of days with rain for the growing season minus the<br>long run average<sup>_∗_</sup>|
|No rain days|The number of days with less than 1 mm of rain for the growing<br>season<sup>_†_</sup>|
|Deviation in no rain days|The number of days without rain for the growing season minus<br>the long run average<sup>_∗_</sup>|
|Share of rainy days|The percent of growing season days with rain<sup>_†_</sup>|
|Deviation in share of rainy days|The percent of growing season days with rain minus the long run<br>average<sup>_† ∗_</sup>|
|Intra-season dry spells|The maximum length of time (measured in days) without rain<br>during the growing season<sup>_†_</sup>|
|**_Panel B_**_: Temperature_||
|Daily average temperature|In <sup>_◦_</sup>Celsius|
|Daily maximum temperature|In <sup>_◦_</sup>Celsius|
|Mean|The frst moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Median|The median daily temperature for the growing season<sup>_†_</sup>|
|Variance|The second moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Skew|The third moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Growing degree days (GDD)|The number of days within bound temperature for the growing<br>season, following Ritchie and NeSmith (1991)<sup>_†_</sup>|
|Deviation in GDD|GDD for the growing season minus the long run average<sup>_† ∗_</sup>|
|Scaled deviation in GDD|The z-score for GDD|
|Maximum temperature|The average maximum daily temperature|

_Note_ : The table presents definitions for included weather variables and transformations from weather sources defined in Table 1.<sup>_†_</sup> Growing season determined for each country following FAO crop calendar (see Table A2).<sup>_∗_</sup> For variables when “long run” is referenced, long run is defined as the entire length of the weather dataset. While each weather source has a different start date, to ensure blinding all datasets were shortened to 1983, which is the latest start date of the data sources.

46
<!-- Start of picture text --> 40<br>£30<br>g<br>3<br>3S19 /A\—/N\ | ~ \\ 7 \<br>F —/ V \<br>y/<br>—<br>0 wae as<br>1990 2000 2010 2020<br>Year<br>——— saDbc ——— COMESA ——— EAC ———— ECOWAS<br>——— AMU ——— ECCAS CENSAD IGAD<br><!-- End of picture text -->

<!-- Start of picture text --> 30<br>sg<br>a2}<br>S<br>=2<br>3 .20<br>ro)a£10 ~\ VYaan/ \ ~~(Ny~ Y<br>o . 1 \ / » \V/<br>3S \ / Vv<br>ie} DSS aE<br>1990 2000 2010 2020<br>Year<br>—— sapbc ——— COMESA -——— EAC ——— ECOWAS<br>—— amu ——— ECCAS ———— _CENSAD IGAD<br><!-- End of picture text -->
# **3. Education sector funding and the Jakarta school grants program**

In Indonesia, decentralization in the early 2000s devolved responsibility for primary and secondary schools to provincial and district governments. Local governments account for over 60 percent of public education sector spending. The central government supplements local government funding through a range of national programs including a large school grant scheme introduced in 2005 – the _Bantuan Operasional Sekolah_ (BOS) program. The BOS program provides funding to all primary and junior secondary schools on the basis of a fixed amount per student. Schools have strict limits on the amounts they are allowed to collect from parents which makes schools heavily reliant on BOS funds. In 2010, BOS funds accounted for 83 percent of all discretionary funding that primary schools received (World Bank 2012). School funding from the BOS program has increased significantly since its introduction and in 2014 the average primary school received approximately US$ 10,000 a year (World Bank 2015).<sup>3</sup> In 2005, the Jakarta government introduced a school grants scheme modeled on the national BOS program. Initially, the program only covered government and non-government primary schools. However, the per-student amount of IDR 240,000 (US$ 25) received by primary schools was about 50 percent more than junior secondary schools received from the national program. As the program evolved, per-student funding levels increased and in 2007, the program expanded to cover all government junior and senior secondary schools. In 2014, per-student funding for junior secondary schools was IDR 1.3 million (US$ 111) compared to only IDR 710,000 (US$ 59.8) from the national BOS program. However, by this time the Jakarta government had withdrawn grant support for non-government schools.<sup>4</sup>

**Figure 1: National Examination Scores for Junior Secondary Schools (SMP) by Province (%), 2010-13**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>BANGKA BELITUNG NUSA TENGGARA... KALIMANTAN TIMUR KALIMANTAN BARAT KEPULAUAN RIAU JAWA TENGAH KALIMANTAN TENGAH SULAWESI TENGAH SUMATERA BARAT DI YOGYAKARTA PAPUA BENGKULU GORONTALO JAWA BARAT SULAWESI BARAT PAPUA BARAT MALUKU UTARA SULAWESI TENGGARA INDONESIA BANTEN LAMPUNG MALUKU RIAU DKI JAKARTA KALIMANTAN SELATAN NUSA TENGGARA... SULAWESI UTARA SULAWESI SELATAN ACEH JAMBI JAWA TIMUR SUMATERA SELATAN BALI SUMATERA UTARA<br><!-- End of picture text --> Source: World Bank (2014).

> 3 Roughly equivalent to the salary of two certified civil service teachers.

> 4 Between 2005 and 2013 school grants were provided to all schools except in 2011 where only government schools were beneficiaries. Since 2014, non-government schools are only included in government-financed scholarship programs for poor students.

6
which have an effect on the price which these graduates can command in the labor market. Second, the log-linear specification assumes that the rate of return to an additional year of schooling is no higher for the last year of schooling in a particular level-primary, secondary, university-than in any other intervening year. This may not hold if credentials themselves are rewarded, a hypothesis known as the "sheepskin effect" in the literature.

The assumption of linearity in the relationship between log wages and schooling has received much attention in the United States (see, for example, Card 1999; Deschenes 2000; Heckman, Layne-Farrar, and Todd 1996; Hungerford and Solon 1987; Katz and Murphy 1993; Katz and Autor 1999; Park 1994). There are also literally hundreds of empirical studies on the relationship between wages and education in Less Developed Countries (LDCs) (for reviews see Psacharopoulos 1973, 1981, 1985, 1989 and 1994). Many of these studies have attempted to identify differences in the rate of return across levels, but the data and methods used are often poor at best (see, for example, Bennell 1996 for a critique).<sup>3</sup> In this paper, I use data from a recent, high-quality household survey conducted in the Philippines to test the fit of the log-linear relationship between wages and schooling. Like many middle-income countries, the Philippines has a large nurnber of people in the labor force who have received very different amounts of schooling--ranging from no formal education whatsoever, to completed university education. This allows me to inspect the relationship between log wages and education over a broader range than 3Two studies which parametrize the earnings function in a similar way to the analysis conducted in this paper focus on Brazil (Lam and Schoeni 1993; Strauss and Thomas 1996). Both find convexity in the relationship between market wages and education, and significant, large sheepskin effects associated with the completion of particular years of schooling.

3
<!-- Start of picture text --> Export Quota Allocations,<br>Export Earnings,<br>and<br>Market Diversifications<br>by<br>Taeho Bark<br>and<br>Jaime de Melo<br><!-- End of picture text --> TABLE OF CONTENTS

|I.<br>Introduction .......|1|
|---|---|
|lI. Two-Tier Quota Allocation Systems ............<br>,|3|
|11X. TheModel .........<br>,<br>..|5|
|IV. Export Earnings and Efficiency Under<br>a Two-Tier Quota Allocation Policy.|9|
|V. Politcal Economy Justification and<br>Alternative Diversification Schemes .12||
|VI. goncluding<br>Remarks ......................|14|
|Footnotes.<br>......................................................|16|
|Appendix||
|1. TheModel ....................<br>......<br>Firm Behavior .<br>2. Welfare Estimates ...|17<br>18<br>22|
|References ...............<br>.<br>.<br>...<br>.<br>.|25|

# Mustrations

Table I ..l ....................... **4** Figure 1 . .7...... 7 **FigureA** .21.... ...... 24 TableAl ... . ... 24

**4** ...........,,,,
construction/development department is responsible for revitalizing the existing _waqf_ properties (Kahf 2000: 299-304).

A large percentage of _waqf_ assets have huge potential for revenue generation but remain undeveloped. For example, a study on a sample of 32 _waqf_ properties from four states in India done by Islamic Research and Training Institute showed that only two _waqf_ properties were developed, and the remaining 30 were either undeveloped or underdeveloped (IRTI 2000). With an average investment of USD 660,896, the expected income from each _waqf_ property could be increased by USD 126,547 annually, giving an average rate of return of more than 19%.

# **5. Infrastructure Development**

Infrastructure facilities provide the basic foundations for commerce and trade, enhance competitiveness, and are important determinants of long-term growth. While the total required global investments in infrastructure is estimated to be US$ 100 trillion over the next two decades, only US$ 24 trillion is expected to be spent before 2030, resulting in a shortage of around US$ 60 trillion (WEF 2013: 12-13). Sustainable infrastructure development would require using approaches that are friendly towards the environment and society. Factoring in the need to slow climate change adds an extra cost of US$ 0.7 trillion per year related to green investments (WEF 2013: 7).

While traditionally governments have been responsible for providing infrastructure facilities, in most countries they are burdened with large deficits and debt and find it difficult to generate resources. The huge demand for infrastructure investments calls for partnerships between private and public sectors, with the latter playing a facilitating role. The contribution of the private sector in providing infrastructure financing, however, is still small. World Bank (2008) estimates that 70-75% of total infrastructure financing in developing countries is provided by the public sector, 10% came from official development assistance and the remaining 15-20% from the private sector. There is a need to come up with innovative ways in which new players and financiers can fill the gap and contribute to the development of sustainable infrastructure.

Given the focus on the real economy and preferences for risk-sharing financing and social investments, the infrastructure sector provides an ideal business opportunity for Islamic finance. Moreover, financing infrastructure projects would be consistent with the ideology of Islamic financing, as these projects benefit the community at large (Miller and Morris 2008). The Islamic financial industry, however, has not been forthcoming in financing the infrastructure sector. For example, out of a total USD 40 billion _Shari’ah_ -compatible financing in the GCC, only USD 9 billion went into infrastructure financing (Ernst and Young 2008).

28
(not shown here). It would appear that even for extra-curricular activities, the prevalence of activities that require greater effort or competition are more prevalent in Vietnam compared to Dev7.

**Table 9: School resources and Management**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Resource quantity **|**and quality**|||||
|RATCMP15|Available computers<br>for 15-year-olds|0.3909<br>(0.5476)|39490|0.2216<br>(0.3411)|4875|
|COMPWEB|Ratio of computers<br>connected to Internet|0.7556<br>(0.3578)|37446|0.7795<br>(0.3109)|3634|
|SCMATEDU|Quality of school|-0.8145|41373|-0.4941|4882|
||educational resources|(1.1538)||(0.9718)||
|SCMATBUI|Quality of|-0.6322|41221|-0.3988|4882|
||physical infrastructure|(1.1113)||(1.0161)||
|SCL<br>~~E~~XTR<br>~~C~~L|School ofers|0.6538|40869|0.9584|4882|
||additional math classes|(0.4757)||(0.1997)||
|**Extra-curriculars**||||||
|EXC1<br>~~B~~AND|School ofers|0.4710|40044|0.1678|4882|
||Band, orchestra or choir|(0.4992)||(0.3737)||
|EXC2<br>~~P~~LAY|School ofers|0.5928|40122|0.8509|4882|
||school play/musical|(0.4913)||(0.3562)||
|EXC5<br>~~M~~CLUB|School ofers<br>mathematics club|0.453<br>(0.4978)|40154|0.2687<br>(0.4434)|4882|
|EXC6<br>~~M~~ATHCOMP|School ofers<br>Mathematics competition|0.6268<br>(0.4837)|40215|0.8032<br>(0.3977)|4882|
|EXC10<br>~~S~~PORT|School ofers|0.9321|40581|0.992|4882|
||sporting activities|(0.2516)||(0.089)||
|**Leadership account**|**ability and autonomy**|||||
|SCORE<br>~~P~~UBLIC|Achievement data<br>posted publicly|0.345<br>(0.4754)|40965|0.7567<br>(0.4291)|4882|
|SCORE<br>~~A~~UTHRITS|Achievement data<br>tracked by authority|0.8003<br>(0.3998)|41139|0.8282<br>(0.3773)|4778|
|SCHAUTON|School Autonomy|-0.2542|41394|-1.0419|4882|
||in admin. decisions|(1.1328)||(0.9378)||
|TCHPARTI|Teacher participation<br>in admin. decisions|-0.2169<br>(1.4457)|41394|-1.6445<br>(0.5188)|4882|
|LEADCOM|Communicating and acting<br>on defned school goals|0.2387<br>(1.1105)|41252|0.0894<br>(0.6744)|4882|
|STUDCLIM|Student-related aspects<br>of school climate|0.0485<br>(1.1642)|40973|0.0418<br>(0.6849)|4874|
|TEACCLIM|Teacher-related aspects|-0.1997|40973|-0.0873|4874|
||of school climate|(1.1474)||(0.7125)||

Notes: The variables relate to the questionnaires administered to schools. For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically different at the 95% significance level, except STUDCLIM. Figures in parenthesis represent standard deviations.

With regard to school leadership and autonomy, there appears to be less autonomy and more accountability in Vietnam. The index variable SCHAUTON indicates a Dev7 mean value of -0.2542, higher than the Vietnam mean value of -1.0419 (recall that indices are set to OECD mean of zero). Teachers in Vietnam have lower chances to participate 17
34 Table 4. Dispersion of Personal Income: Spatial Income Inequality in China and the United States, Selected Years

||Year|Percentag<br>Highest|e of mean<br>Lowest|Coefficient of<br>variation**(%)a**|
|---|---|---|---|---|
|Provinces**and**states|||||
|China urbanb|1983|137|75|13|
|China rural'|1980|194|70|28|
|China rural'|1985|195|62|31|
|United Statesd|1981|134|72|14|
|United Statesd|1986|140|70|17|
|Chinese counties|||||
|Anhui urban|1984|128|87|8|
|Jiangsu urban|1985|127|85|10|
|<br>Anhui ruralt|1984|163|49|23|
|Jiangsu rural'|1985|164|62|23|
|U.S.**states**|||||
|Michigan|1981|164|60|17|
|<br>Mississippi|1981|163|65|16|
|Oregon|1981|137|81|12|
|<br>South Dakota|1981|183|39|21|

- a. Standard deviation divided by mean.

- b. Average per capita income of urban residents in the capital cities of each of the twenty-nine provinces, based on household surveys.

- c. Average per capita income of rural residents in each province from household surveys.

- d. Average per capita income by state.

- e.

- Average wage per member of the urban work force.

- f. Average per capita income for the rural population.

- g. Average gross value of agricultural output (GVAO) per member of the rural population.

Source: State Statistical Bureau of China (1985, p. 62); (1986, p. 202); (1987, p. 204); U.S. Government (1983); Washington Post, August 21,1987, p. C2. Anhui Jingji Nianjian 1985; Jiangsu Jingli Nianlian 1986.
financing, either obtained (e.g., the sources of financing for fixed investment) or perceived (e.g., cost of or access to finance as an obstacle to business) are positively correlated with deep restructuring, however measured. We then use the same methodology as above – regression analysis with controls for firm characteristics – but now we include a measure of external financing. We use two different measures: whether or not the firm has a bank loan, and whether or not the firm has any external financing. The results are presented in Table VI.4 for specifications that pool the pre2001 EU members and the TEs, using our index of the two main restructuring measures (new product and upgrade); the results are similar when we use different measures of deep restructuring. The absence of external financing, either partial (no bank borrowing) or complete (not external financing at all) is associated with less restructuring. The impact of access to external financing is, interesting, quantitatively as qualitatively very similar in both the developed market economies of the pre-2001 EU members and in the TEs.

# **VI. Conclusion**

The move from plan to market in Central and Eastern Europe and the former USSR was to a large extent driven by the expectation that the return to the market would put these countries onto a growth path that would lead eventually to convergence with the developed market economies operating at the world technological frontier. Fifteen years after the start of the transition process, it is the right time to ask if such convergence has indeed been taking place. Most studies of convergence focus either on the macro aspect (convergence in terms of per capita GDP) or the micro aspect (convergence in firm productivity). In contrast, this paper looks at the function of, and convergence in, markets and firm behavior of the transition countries with those in the mature market economies. Our focus is on competition and market structure, finance and the structure of lending to firms, and how firms respond to the economic environment by restructuring.

We use data from several waves of the BEEPS exercise; due to a number of unique features, these data are particularly appropriate for studying the process of convergence 42
<!-- Start of picture text --> 7<br>6 a —— Benin<br>_ o— Burkina Faso<br>ra / seeee* Cape Verde<br>4:“.i=== Ghana<br>/<br>3 . — Lowincome<br>¢ ,<br>2 / of 7 —- Nigeria+.<br>e oTod<br>- ae. —— senegal<br>1 oe an<br>= s. . ° © —==Toz0<br>° —=P Oo<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> <!-- Start of picture text -->
|<br>rses ee|<br>A<br>ES es es<br>ESES<br>TS<br>ESES ES<br>ss<br>Esee es es<br>es ee<br>—_————<br><!-- End of picture text -->