rafmacalaba/datause-encoder-data huggingface dataset:
With this background, discussion centered on what is known of a specific model of Safe **Motherhood programs.** Presentations by several field workers provided various insights on **factors affecting** maternal health, implementation issues, and indicators of successful interventions.

A model of maternal health factors and their impacts, developed at Columbia University, identifies the range of Safe Motherhood program options and traces their impact on mortality through intermediate factors, which include health and reproductive behavior, heaiih status, and access to health services. These intermediate factors will then have an impact on one or more of the model's three outcomes:

- Incidtnce of pregnancy

- Incidence of complications among pregnant women

- * The outcome of complications that arise.

An appropriate set of activities to achieve these outcomes was suggested by a **programmatic model** presented by the MotherCare Project. The major components of the MotherCare program include:

- **building community** awareness to promote recognition of danger signs and increased use of services;

- **enhancing services and training** through improved supervision in EOC, family planning, counselling, prenatal and postnatal care;

- X **upgrading/maintaining facilities;**

- and developing the referral and information system, through systems of meetings between levels of the health care system, communications, and transport.

The appropriate balance of the above activities would be based on the conditions in the country or region in which the program is to be implemented. Three general typologies can be derived from the MotherCare exoerience, which illustrate how existing infrastructure, as well as knowledge, attitudes, and practices, help determine the most appropriate program mix. These include:

1. Rural area; no doctor; no formal health care infrastructure; deliveries attended by untrained personnel, family members.

**Program focus:** community awareness building and training; hospitalization is not possible therefore program focuses on changing behavior surrounding pregnancy and delivery.

2. Limited rural health services and referral infrastructure; low utilization rates; most 8
industrial base is extremely diverse, but its sheer scale assures a large potential pollution problem. On the other hand, Shanghai has joined Guangdong as a center of China's economic modernization, with attendant benefits for pollution reduction. Guangdong has its own particular interest for the analysis, because its industrial development has featured the growth of light <!-- Start of picture text -->
|ive Induetrial Regiona<br>i<br><!-- End of picture text -->

**Figure 3: Featured Provinces**

manufacturing which operates at smaller scale than China's average. Finally, Sichuan is located in the Red Basin, in south-central China. It is considerably poorer than the other four provinces, and its industry is highly pollution-intensive.

Figure 4 displays income (consumption) per capita in 1988 and 1993: Shanghai had the highest level initially, and has bounded ahead in the period of economic reform. Guangdong and Liaoning grew more rapidly than Beijing during the five-year period, arriving at near-parity in 1993. From a much lower base, Sichuan has grown at approximately the same rate as Guangdong and Liaoning. It remains the poorest of the five provinces by a considerable margin.

It mnight seem logical for China's richest provinces to be the most highly polluted, but <!-- Start of picture text --> income Per Capita (SUS)<br>....... ..................<br>2500-<br>2000 l ¢ liSS<br>soo i | | | a .<br>} f 1 S X<br>Figure 4: Income Per Capita, 1988 and<br>1993<br><!-- End of picture text -->

Figure 5 strongly suggests the converse: There is an inverse relationship between industrial pollution and economic development, most strongly in the case of air pollution. Sichuan is the clear leader, with both sulfur dioxide (S02) and total suspended particulate (TSP) emissions exceeding 1,000,000 tons/year in both 1998 and 1993. Although it has substantially reduced organic water pollution (chemical oxygen demand - COD) in recent years, Chongqing remains, with Liaoning, the most highly polluted province in this category as well. Despite its huge industrial base, Shanghai has far lower pollution loads, while Beijing and Guangdong are also modest emitters by comparison.<sup>4</sup> _4_ Analysis of air emissions data for all twenty-nine provinces reveals the same pattern: The poorest provinces have significantly higher total emissions.

**_5_**
**_Energy demand models for policy_** **_<u>formulation</u>_**

use 1995 Commercial Buildings Energy Consumption Survey of 1995 to produce a detailed end-use and fuel-level analysis.

# **End-use method**

The approach taken by the end-use models for commercial sector analysis is similar to that of the residential sector. However, instead of population other activity variable is used as demand determinant because of the heterogeneity of the sector activities. Example include: floor space for commercial areas, sales or economic output for business activities, number of patients or students or room occupancy for hospitals, schools/ colleges, and hotels respectively, etc. Generally, disaggreation at an appropriate level is considered to capture different activities covered by the sector. The demand is forecast by the product of activity and unit consumption (or specific consumption).

Energy demand for various end-uses such as space heating, air conditioning, water heating, cooking and use of electrical appliances including lighting is taken into consideration.

The issues related to the commercial sector are same as in the case of residential energy demand and are not repeated here.

# **5. Features of Specific Energy Demand Forecasting Models**

This section considers specific models with either detailed country-level coverage or a regional/ global coverage. Clearly, the selection of models has been guided by the information available on the model and the usual constraints of time and resources. The section is organized as follows: the first sub-section provides a brief description of the models; this is followed by a comparison of the models in terms of a set of criteria such as their coverage, data requirements, complexity, skill requirement, etc.

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divided into three subsystems:

- ® the Geographic/Common Subsystem, a database of necessary codes;

- ® the Air Quality Subsystem, containing ambient air quality data;

# @e the Air Facility Subsystem (AFS).

The AFS contains the emissions and compliance data mandated by the Clean Air Act that are provided by individual facilities monitored by the EPA and state agencies. There is some overlap with the TRI, because the AFS data include emissions of some chemicals listed in TRI, but the AFS also includes a number of additional substances and parameters. The most important are the US EPA's six criteria air pollutants: sulphur dioxide (SO,), nitrogen dioxide (NO,), carbon monoxide (CO), particulate matter (TP), fine particulates (PM10), and volatile organic compounds (VOC). Although air emissions data have been collected since 1973, we have only used the data from 1984 onwards. Access to information from years prior to this is more difficult.

# 2.1.3 National Pollutant Discharge Elimination System (NPDES)

The US EPA's NPDES database contains the self-monitored reports of facilities with NPDES permits for discharges of waste water. Both the permits and the monitoring are mandated by the Clean Water Act. Some 60,000 facilities file reports on monitoring that they perform on a monthly basis. In the database 7
# **State Financial Institutions: Mandates, Governance, and Beyond**

By Heinz P. Rudolph<sup>1</sup> The World Bank

# **1. Introduction**

In the past three decades the presence of State Financial Institutions (SFIs) has been highly controversial. The evidence suggests that state-owned banks have underperformed and have negatively impacted economic growth. La Porta et al. (2002) find that government ownership of banks is associated with slower subsequent financial development, lower economic growth and lower growth of productivity. Beck and Levine (2002) fail to find any positive effect of government ownership of banks on growth. Caprio and Martinez Peria (2002) show that government ownership of banks is associated with a higher likelihood of banking crises. Dinc (2005) provides evidence that state financial institutions increase their lending during an election year and that in emerging markets state financial institutions finance the government to a greater degree than do private banks. Most of the poor performance of SFIs is related to the lack of a clear mandate, and a governance system that allows the presence of weak boards of directors and management, which are subject to political intervention. Evidence provided by Caprio et al. (2004), La Porta et al. (2002), and Dinc (2005) shows that most state financial institutions around the world are characterized by lack of managerial skills and subject to government intervention in credit decisions.

This paper discusses good practices for aligning the functions of SFIs with solving market failures. Mismanagement of resources is frequently a consequence of weak board of directors and management, political interference, and lack of a clear mission of the SFI. Based on four case studies, this paper analyzes the importance of having clear and sustainable mandates, and an institutional framework able to mitigate political interference. The paper analyzes good practices

> 1 1 The author is a Senior Financial Sector Specialist in the Financial and Private Sector Development VicePresidency of the World Bank (hrudolph@worldbank.org). The author would like to thank senior management at BDC, BancoEstado, Finnvera, and DBSA for providing insightful information for this note; David Scott, Jerome Nycz, Lily Chu, and Francisco Bernasconi for useful comments; and Denisa Mendelsohn for valuable research assistance.  The paper is part of a larger effort in the department to strengthen financial systems by improving the quality and effectiveness of state interventions in finance. The views presented in this paper do not necessarily represent the views of the World Bank.

2
15 energy price chmges on the fim will depend on both the interfiel substitution possibilities and the possibilites of substituton between energy and other priry factors of production"

# _EAw& Demad ofthe Hourehold Sector_

Households usenery for lgt cooldg healn cooling refrigan, washing etc Demand is depedent on the stock of enery-using appliances in of the household, their pefeences for the above sic mnd o nernn-ee **gwods, and** income In the short-run, it is reasonable to assume tht tho stock of appli.nces is fixed and dws the impact of an increase in energy price on the household demand for energy is detrmined by a change in the rate of use of tih exsting stock of appliances (depending on prences between aer and non-energy goods, and income). In the long-run, households can adjust heir stock of appliances. Tbe main implications of ftis infle7ibility in the adjustnt of applianc stoc and rigidity of preferec for household energy demand is that in the short-mn the impact of an energy price incre on household demand woud be lower as compared to the long-tem. when both now appliances can be acquired and preferences may change.' Ths implies that the wdfare cost of a pice ncreasewod be reduced infhe long-nm, when houseolds have a chance to fiuly adjust in respos to the cne in prices.

# _MAcroeconmic In pacf_

An increase in domestic energy prices can be expected to shift cost stuctres in h user sectors ad theby the conmer pr level. Depending on the response of nomina wages to an raem i coumer pnre leve, an energy price crease may spread throughout the economy and icrease th general price IekV3 An increase in energy piices chged by public enteprises, provided it leas to an iceae in he opeaig suues of tese finns, shoud also resut in lower fiscal subsidies To the tnt lowe subsdies resuilt lower levels of taation for other inputs, the inflationuy impact of an eneY price increse would benmodea Eney subsidies are ofen finaced through state borrowng firom th cent bak In this case, higher energy prices that reduce subsidies and fiscal deficits of th public enterpi "For **a** gmel f _imuafio the ne of **th** fim to an anew price _izmm, am_ **ax 1.5. F** or a gmw. _fonmlijof_ **debs** hun3o'l respa to an mrgy pace **incea** amam 1.6. "Fora ydir **wl** wokadaluuig - - -- ip inpof fdomealcnmrypri oI g s.eWoidB*k (1983): I i, Suatod dI. of 13.p Pi Polii, VL I, }lpatn No. 4285-DID, pp. 49-52, **W=ui,gtmu** D.C.
Fiscal consolidation began in 2010 and comprises several measures on both the expenditure and revenue sides of the budget. The number of employees on the public payroll has been reduced from 1.399 million at its peak in 2008 to 1.267 million in January 2011 (Romania Fiscal Council, 2011: 19). Salaries have been cut by about 10 percent on a net basis during 2010 and 2011 (there was a 25 percent cut in 2010 and a 15 percent rise in 2011) and various bonuses and the 13<sup>th</sup> month salary payment have been abolished. The cuts in the public payroll and salaries have reduced salary and wage expenditures in the general government budget from 9.4 percent of GDP in 2009 to a projected 7.5 percent of GDP in 2011. On the revenue side there have been increases in the VAT rate, the elimination of some minor VAT loopholes and increases in social security contributions.

# **Figure 1: Romania’s fiscal consolidation in comparison with that of other EU countries; percent of GDP**

<!-- Start of picture text --> 15<br>Structural budget deficit in 2009 Structural budget deficit in 2012<br>Fiscal adjustment Fiscal adjustment - primary balance<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>GR RO ES PT UK IE BG SK NL PL EU27 FR EA CZ BE LT LV IT SI CY FI DE MT AT SE LU HU DK EE<br><!-- End of picture text --> Source: European Commission (forecasts from Autumn 2010) 6
<!-- Start of picture text --> Tanzania: Quality by SITC1 Sector, 2014<br>8 1s<br>fo} 2 . $ 3 a r ; e 4 =<br>1] i i<br>c}8 ! :<br>iB 5 p "8<br>° s y “@<br><!-- End of picture text --> <!-- Start of picture text --> ° 3<br>aNeo ~ aat wh cawie<br>oon<br>MEE Percent of Total Exports © Quality Ladder<br>95 pct interval lower bound 95 pct interval upper bound<br>* Tanzania position<br>pwt 9.0<br><!-- End of picture text --> <!-- Start of picture text --> 8 Malaysia: Quality by SITC1 Sector, 2014 Feee<br>°<br>s rat eT Ee ; =<br>} | : | z i<br>is ©<br>BY eee<br>8° s a °88<br>i ° : § “|<br>8 e oo<br><!-- End of picture text -->

<!-- Start of picture text --> ° 3<br>pontecp Srenneeeseopti 6d pois 4 cad?”us «GOOouetinetB> AOK gre<br>yao<br>GEE Percent of Total Exports ® Quality Ladder<br>95 pct interval lower bound 95 pct interval upper bound<br>e Malaysia position<br>put 9.0<br><!-- End of picture text --> <!-- Start of picture text --> Vietnam: Quality by SITC1 Sector, 2014<br>8 = te<br>2 a . | 3 6 a * ' oe<br>i |r<br>a8 H<br>ssgS : i a o8<br>ff, u . 8 4 i as |<br><!-- End of picture text -->

<!-- Start of picture text --> ° $<br>Nee aea NN<br>ore<br>MRE Percent of Total Exports ® Quality Ladder<br>95 pet interval lower bound 95 pct interval upper bound<br>bd Vietnam position<br>pwt 9.0<br><!-- End of picture text --> <!-- Start of picture text --> a foChina:we Qualityff. by SITC1of Sector,a i 2014| aae<br>.S | | i<br>bei Ty | I tie<br>3gic=] u . : i i a os“a&<br>TM ] 8 . i a8<br><!-- End of picture text -->

<!-- Start of picture text --> ° 3<br>pintaoF pocottaa2 cient card?”voG00”eenet nde gineTM<br>ose<br>MR Percent of Total Exports ® Quality Ladder<br>95 pct interval lower bound 95 pet interval upper bound<br>* China position<br>put 9.0<br><!-- End of picture text --> <!-- Start of picture text --> All Sectors<br>3<br>©<br>1960 1980 ver 2000 2020<br>=—— Low income<br><!-- End of picture text --> <!-- Start of picture text --> Commodities<br>a<br>J<br>1960 1980 2000 2020<br>year<br>—— Low Income<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing<br>i SLOPE TSE EOE<br>i<br>©<br>1960 1980 eat 2000 2020<br>=|Low Income<br><!-- End of picture text -->

<!-- Start of picture text --> Agriculture<br>* —_—s~e~- aresnrnrnreereeerrr<br>: aes Saks<br>1960 1980 2000 2020<br>year<br>————— Low income<br><!-- End of picture text -->
# **1. Introduction**

Armenia and Georgia are small countries in the Caucasus region located between the Black Sea, Russia, Turkey, Azerbaijan and Iran thereby connecting Eastern Europe to Western Asia. Both countries are currently developing low carbon strategies<sup>3</sup> for their economic development while promoting economic growth and prosperity. The strategies aim to reduce the growth of greenhouse gas (GHG) emissions. The low carbon strategies are also important because both countries depend on imports for their oil and gas supply. Both countries submitted their Intended Nationally Determined Contributions (INDCs)<sup>4</sup> in response to the decision made by the UNFCCC in its 19th Conference of Parties in Warsaw, Poland in 2013 (UNFCCC, 2013). The Paris Agreement reached at the 19th Conference of Parties in Paris in December 2015 (UNFCCC, 2015) implies that these countries will take actions to implement their INDCs. In the past, both countries provided an indicative list of options to reduce GHG emissions in their National Communications (NCs) to UNFCCC, and also in preparation of nationally appropriate mitigation actions (also referred to NAMAs<sup>5</sup> ) in accordance with an agreement made in the 16th Conference of Parties (COP) of the United Nations Framework Convention on Climate Change (UNFCCC) in Cancun, Mexico in 2011 (UNFCCC, 2011).

The low carbon strategies, NAMAs, NC and INDC all present list of potential options that could be implemented to reduce GHG emissions. While various approaches could be adopted to prioritize GHG mitigation options, the tool often used in low carbon strategies<sup>6</sup> , NAMA and NC, is the marginal abatement cost (MAC) curves. Energy efficiency options are the most common GHG mitigation options identified in a MAC curve analysis. These options are often found as the cheapest ones with negative costs of GHG abatement in most existing literature on green growth and low carbon strategies (see e.g., World Bank, 2014; World Bank, 2013; Cervigni et al. 2013; de Gouvello et al. 2010; Johnson et al. 2009; Mckinsey and Company, 2009, 2010, 2011). However, the economies of energy efficiency vary across countries depending on the rate at which future energy consumption is

> 3 The low emission development (LED) strategy is being developed under the financial support of USAID through Winrock International; it is known as “Enhancing Capacity for Low Emissions Strategies (EC-LEDS)/ Clean Energy Program” in Georgia.

> 4 INDC refers to UNFCCC parties’ post 2020 action plan outline in response to the decision made at COP19. 5 NAMAs are GHG mitigation actions voluntarily offered by Non-Annex I countries. They include policies, programs and actions across the economic sectors with potential to reduce GHG emissions from the baseline in 2020 (UNFCCC, 2011). 6 Please see the series of low carbon studies produced by the World Bank over the last few years (World Bank, 2014; World Bank, 2013; Cervigni et al. 2013; de Gouvello et al. 2010; Johnson et al. 2009).

2
# **4 Data Sources and Descriptive Statistics**

## **4.1 Data**

**Trade data** : Data on exports and imports by municipality, year, destination and product come from the tax authority of Mexico (Servicio de Administracion Tributaria), and covers each municipality over the 2004-2014 period. Data are reported using the HS (Harmonized System) 2002 classification at the 4-digit level, and converted to the ISIC Rev. 4 classification using correspondence tables. These data are also used to construct the RCA weights _ωm,i,_ 2004 from equation 1.

**Automation data** : Data on the stock of robots by country, year and sector of economic activity comes from the International Federation of Robotics (IFR). The data are based on a survey of robot suppliers, and cover 50 countries which represent 90 percent of the world market of robots. The sector classification follows the ISIC classification closely (but not exactly), at roughly the two-digit level for the manufacturing sector, and two-digit level for non-manufacturing sectors. The stock of robots is estimated using the perpetual inventory method, where the initial stock is adjusted by the yearly delivery of robots and depreciation rate.

To express the number of robots in per worker terms for the group of European countries, the US, Mexico and Brazil, we use data from several sources. For Europe, we use data on employment from EUROSTAT for circa 2000; for the US, from the 2000 Current Population Survey (CPS); for Brazil, we estimate the number of employees by sector using microdata from the IPUMS sample of the 2000 Census of Population; for Mexico, we use data from the 1999 Economic Census.

**Labor market indicators** : We use tabulations from the 2000 and 2010 Census of Population and Housing and the 2015 Population Count to obtain labor market indicators, as well as demographic characteristics of the population at the municipal level.<sup>2</sup> Given that

> 2Even though the 2015 population count is a survey, its sample size is large enough to obtain statistics representative at the municipality level. See Enamorado et al. (2016).

11
Now consider an alternative model in which output depends on two factors of production, labor Lij and human capital Hij. Further assume that relative output prices are the same across locations. Factor prices are written _wj_ for labor and rj for human capital. Relative factor prices depend on their relative scarcities: locations with a lower _HjlLj_ have a higher rj/wj. Comparative advantage dictates that locations specialize in the activities using more intensively the factor they have in abundance. So, for example, if activity k uses more human capital and human capital is abundant in location j, labor used in activity k will be higher in locations with a higher human capital, i.e., _Lij_ is an increasing function of _Hj._ Further assume that _Hj_ varies systematically with proximity to town and market, say, because schools are located near towns and market centers. Regressing _Lij_ on proximity to town and market, as we have done so far, would show a relationship between Lij and distance even in the absence of differences in relative prices, i.e., in the absence of market effects.

The above discussion suggests an indirect way of testing whether patterns of production vary because of differences in factor endowments or because of differences in marginal returns possibly driven by proximity to markets. Say we have data on factor endowment _Hj._ First regress _Hj_ to ascertain whether it varies systematically with distance. If it does, differences in factor endowment might be responsible for the measured effect of distance on activity. To test this possibility, regress Lij on _Hj_ as well as distance: if including _Hj_ drives the effect of distance to zero, factor endowment differences might be responsible for the observed spatial division of labor. If distance remains significant, distance affects marginal returns through some other process, possibly relative prices, possibly other unobserved endowment. A similar reasoning can be extended to choices other than production. Consumption, for instance, may vary systematically across locations not because relative prices are different but because incomes vary with location.

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