rafmacalaba/datause-encoder-data huggingface dataset:
how the current levels of expenditure and particular patterns of allocation are driven by the mix of incentives created by the structure of the healthcare financing system, such as the provider payment mechanisms, the pharmaceutical procurement and pricing systems, the nature of the benefits package, and the human resource policy, among other factors.

# **<u>D1 A large share of national resources are allocated to healthcare expenditure</u>**

An analysis of healthcare expenditures in the Western Balkans reveals that total healthcare expenditure (as a share of GDP) in the sub-region is similar to that in EU countries. However, the proportion of this expenditure that can be attributed to private spending is much larger than in the EU. Still, these expenditure levels translate into only small per capita levels of expenditure, even when the figures are adjusted for purchasing power parity. Also, while aggregate public expenditure is in line with EU and EU-15 levels and per capita spending levels are low, there is no fiscal space to increase public expenditures since healthcare already absorbs a large share of total government expenditure.

# _<u>D1.1 Current expenditure levels as share of GDP are aligned with EU countries</u>_

The share of GDP allocated to healthcare by countries of the Western Balkans tends to exceed that of most other lower middle income countries and, instead, is on par with that of many of the EU members (see Figure 3). The total share of GDP spent on healthcare is lowest in Montenegro (6.6%) and Albania (6.7%), but health shares in the FYR of Macedonia, Kosovo and Bosnia and Herzegovina exceed the average expenditures of the EU-12. Total health expenditures, as a percentage of GDP, are highest of all in Serbia (10.6%), exceeding the EU and EU-15 averages of 9.2% and 9.3% respectively.

While total healthcare expenditure in the sub-region is comparable to EU countries, the share of public expenditure is lower (with the exception of Serbia), meaning that private out-of-pocket expenditures account for an unusually high share of total expenditure – much more than in the European Union and comparator countries. Moreover, there is reason to believe that, at least in some cases, private healthcare expenditure in the sub-region tends to be under-estimated by official data. In the FYR of Macedonia, for example, the use of Household Budget Survey data increases the private expenditure estimate by a whole percentage point. This may be due, at least in part, to the incidence of informal payments, as much as to limitations of official data sources.

Across the sub-region there is a fair amount of variation in the mix of public and private expenditure. While in the FYR of Macedonia, Serbia and Montenegro, health expenditures are financed mainly from public sources, as in most other EU countries, in Bosnia and Herzegovina public and private shares are very similar and in Albania private expenditures slightly exceed public expenditures.

15
Policy Research Working Paper 6268

# **Abstract**

Recent research shows that employment in Mexico’s offshoring _maquiladora_ industries is twice as volatile as employment in their U.S. industry counterparts. The analyses in this paper use data from Mexico’s social security records and U.S. customs between the first quarter of 2007 and the last quarter of 2009 to identify four channels through which economic shocks emanating from the United States were amplified when transmitted into Mexico’s offshoring labor market of Northern Mexico. First, employment and imports within industries are complements, which is consistent with imports being used as inputs for the assembly of exportable goods within industries. That is, when imports fell during the crisis, employment in Mexico was reduced rather than protected by the fall of imports. Second, contrary to other studies, employment is more responsive than wages to trade shocks. Third, fluctuations in Mexico-U.S. trade were associated with changes in the composition of employment, with the skill level of workers rising during downturns and falling during upswings. This implies that the correlation between average wages and trade shocks is partly driven by labor-force compositional effects, which may obscure individual-worker wage flexibility. Fourth, trade shocks affecting related industries (industries linked by employment flows affect employment at least as much as own-industry trade shocks, thus amplifying employment volatility through the propagation of shocks across industries within Northern Mexico. Furthermore, the data suggest that the observed fluctuations in U.S.Mexico trade at the onset of the Great Recession in the U.S. were not associated with pre-existing employment trends in Northern Mexico.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at dlederman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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Post-2030 Spending
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**Table 2. Instrumental variable** **<u>estimation of firms’ inventory behavior</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|ln_CONN_1|-1.091 *|-0.205|||||
||(0.572)|(0.190)|||||
|ln_CONN_1ln_CONN_1||0.289<br>(0.226)|||||
|ln_CONN_2|||-0.166 **<br>(0.084)|-1.709 ***<br>(0.557)|||
|ln_CONN_2ln_CONN_2||||0.187 ***<br>(0.058)|||
|ln_CONN_3|||||6.109<br>(4.436)|-20.457 *<br>(12.413)|
|ln_CONN_3ln_CONN_3||||||2.161 *<br>(1.262)|
|ln_POP_|0.393 **|0.243 ***|-0.080|0.102 **|0.708|0.149 ***|
||(0.181)|(0.067)|(0.074)|(0.043)|(0.481)|(0.037)|
|ln_SALE_|0.184 ***|0.139 ***|0.102 ***|0.142 ***|0.264 **|0.130 ***|
||(0.042)|(0.018)|(0.018)|(0.019)|(0.111)|(0.017)|
|_DFO_|-0.056|0.057|0.181 *|0.080|-0.337|0.077|
||(0.138)|(0.094)|(0.098)|(0.097)|(0.357)|(0.094)|
|ln_AGE_|0.259 ***|0.214 ***|0.167 ***|0.194 ***|0.322 **|0.196 ***|
||(0.072)|(0.052)|(0.049)|(0.052)|(0.128)|(0.051)|
|_DIM_|0.670 ***|0.707 ***|0.735 ***|0.747 ***|0.746 ***|<br>0.740 ***|
||(0.116)|(0.097)|(0.094)|(0.098)|(0.148)|(0.095)|
|_DIM_|0.224|0.100|0.018|0.084|0.289|0.049|
||(0.152)|(0.098)|(0.093)|(0.100)|(0.248)|(0.095)|
|cons|-6.594 **|-3.575 ***|2.774 *|1.573|-41.545|46.366|
||(3.377)|(1.093)|(1.597)|(1.311)|(30.048)|(30.427)|
|Obs|1754|1754|1754|1754|1754|1754|
|Wald chi2|406.80|499.59|488.56|461.09|217.20|500.27|
|R squared|0.051|0.180|0.195|0.154||0.187|
|<br>No of industry dummy|||||||
|.<br>variables|4|4|4|4|4|4|
|Exogeneity test statistics|4.237 ***|21.751 ***|1.458|43.939 ***|4.654 **|32.178 ***|
|Elasticity at means:|||||||
|∂ln_S_/∂ln_CONN_|-1.091 *|-0.388 ***|-0.166 **|0.788 ***|6.109|0.979 ***|
||(0.572)|(0.112)|(0.084)|(0.225)|(4.436)|(0.258)|
-51-

GRAPH 5b: ACTUAL COMMODITY PRICE CHANGES a LICPNF FORECASTS AND ERROR BARS <!-- Start of picture text --> &LICPNF =_ FORECAST=<br>* 7GB<br>.5,,<br>197t 199'Lt8t 198t ls82 s8 198'4 1985 ;98t 1987 1988 1989<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988
 Matching businesses in the GUFE database with businesses in the program database involved several steps. In order to match two businesses, we used the variables business owner names, business address, gender of the owner, sector of activity and phone number; however, in some cases name spellings were different in both databases, and most of the times addresses were not precise. For this reason, we used a conservative definition of a match between the business names in the two databases as “two businesses with a close surname, and at least one close first name, and either the same phone number, or the same sector of activity and an address in the same neighborhood.” Using this definition, the likelihood that a business in the program database was considered as formal, whereas the business was in reality not formal, was very low (see Annex 1 for additional details of the matching process). The opposite case (i.e. a business was considered as informal, whereas it is in reality formal) is however more likely, so this measure of formalization most probably underestimates the actual number of businesses which formalized in all groups. This does not imply that the measure of program impact is biased, but just that formalization rates in both treatment and control groups are underestimated by the same percentage.

Other main outcomes on business performances (profits and turnover) and intermediate outcomes like business knowledge and practices, taxes and banking were measured through inperson interviews with business owners. The first survey of the selected sample of businesses was conducted in March-April 2014 prior to program implementation and was also used for the sampling. A follow-up survey was conducted in April-May 2015 to measure short-term effects one year after the program started. Attrition at this survey was 11.9 percent and was not correlated with treatment status. One year after the baseline survey, 6.1 percent of the businesses had closed their operations. Business closure is also not correlated with treatment status. Table 4 presents survey rate, closure rate and reason for attrition by groups.

Program implementation data were also collected to better understand the quality of services delivered. These included detailed monitoring data from CGA and qualitative surveys with implementing agencies and program participants. 30 semi-structured qualitative interviews were conducted with program participants at different stage of the program, a qualitative surveyor was also regularly sent with the CGA advisors to assess if the study design was respected (29 surveys). In addition, 61 qualitative interviews were conducted with business owners not selected for the program to monitor potential externalities of the program. Finally, 16
-4-

# 1.1 General economic situation

Mali is one of .,e world's poorest countries. According to the national accounts kept by the international organizations, Mali's GNP per capita, estimated at $180 in 1986, is one-hundredth that of Switzerland or the United States and one-tenth that of Brazil or Mexico ((71 p. 256-257). According to studies based on the concept of purchasing power parity--which, by correcting the error due to the use of exchange rates, are known to reduce the level of living differences between countries In relation to the current measurements ((81-in 1985 GDP per capita in Mali was 3S of that of the United States ([91, table 23).

Mali is also a country that is growing poorer as the years go by: the assessment- of purchasing power parity indicate that Mali's GDP per capita has fallen co!;.-nuously since 1964 from what its 1960-1963 level, and its ratio to per capita GDP in the United States has fallen by half (from 5.77X in 1960 to only 2.81S in 1983; as we shall see later, the 3.21% attcained in 1984 was due to external aid (cf. (9], table 23). The following graph depicts this trend and compares it with that experienced in C8te d'Ivoire.

<!-- Start of picture text --> 03 C3th d'(u .<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>4<br><!-- End of picture text --> Real GDP Per Capita as Percentage of That of the United States Source: Based on (91, tables 17 and 23.

Mali is also a country that has for a long time received substantial aid: its current public resource management problems are of long standing and were known to every Malian official and international expert; however, as is recognized today in the documents drawn up in preparation for the inevitable structural adjustment, they have been masked, since the 1972-73 drought, by the rapid growth in external aid flows. Even though this inflow apparently leveled off at the beginning of the 1980s, official development aid alone accounted for 322 of Mali's GNP in 1984 ((101 p. 238), 34.92 in 1985 (Ell] p. 274)--the absolute world record for each of those two years--and 22.7S in 1986 ([71 p. 298).
If the exporter is credit constrained, then performance risk must be a serious issue, and the hedging strategy may not be available. Instead it may be more logical to consider how commodity bonds might be used for consumption smoothing even in the presence of default risk. Let us begin with a brief review of commodity bonds as they have been used in practice.

# 3. Commodity Bonds

Commodity bonds are bonds whose terminal value (and perhaps dividend payments) are denominated in units of physical commodity (or the terminal value of some appropriate futures contract). Thus, a country might issue a bond paying 10 ounces of gold in 10 years' time with a current face value of $3,000 or a bond paying one lot of 10 tonnes of December maturing U.S. futures in cocoa for 10 years with a terminal payment of 25 contracts for a current face value of $350,000. Typically, the buyer has an option to receive the face value or the commodity bundle. That is, the bond usually comes with a call option for the buyer.

Before the second (1979) oil shock awakened the corporate interest in commodity bonds, governmerts were already using these instruments for various purposes. In 1863 the Confederate States of America issued bonds payable in bales of cotton (O'Hara). The French government used an electricity-indexed bond to compensate for 1945 nationalization of its utilities; and in 1973 "Le Giscard," a $1.5 billion issue with an untimely gold-guaranteed redemption value, was designed to persuade French gold hoarders to deposit their hidden treasure with the government _(New York Times)._ The type of 21
<!-- Start of picture text --> Sleeping Working Household Tasks<br>- am ot<br>© | 3<br>c c oN<br>2°fo) £°oO fa \<br>a a a<br>ow. os [ 9<br>o- a= o- \ |<br>N g —<br>oO Oo oO<br>0 400 800 1200 1600 2000 2400 0 400 800 1200 1600 2000 2400 0 400 800 1200 1600 2000 2400<br>Time of Day Time of Day Time of Day<br>Education Leisure<br>© ©<br>\ -|<br>cw \ \ ———_ Lowest Quartile<br>A) ct | .<br>= | Q Quartile 2<br>gS =<br>8 gS \ ——— Quartile3<br>aon | 8<br>| aa | —— Quartile 4<br>°°<br>0400 800 1200 1600 2000 2400 0 400 800 1200 1600 2000 2400<br>Time of Day Time of Day<br><!-- End of picture text -->
16-C Figure 6.b <!-- Start of picture text --> Devaluation and Parallfi Premium<br>Sudan: 1970-89<br>-<br>100 400<br>90<br>-<br>80 ' < ' f \ { -: 300<br>70<br>60<br>50 19200<br>40<br>30<br>10~~~~~~~~~~~~~0<br>20<br>10~~~~~~~~~~~~~~<br>190 19i72 1974 1976 1978 1980 1982 198 1986 19 88<br>Devaluation ()--Premium (%O).<br><!-- End of picture text -->
The probabilities of advancing one additional year of schooling conditional on having completed prior years are interesting. Thirteen percent of the sample, 9 percent of boys and 16 percent of girls, never attended school, but for those who began school, most finished primary and junior secondary schooling. But only 60 percent of those who completed junior secondary schooling (64 percent of boys and 56 percent of girls) go on to senior secondary school, where the advancement probabilities again increase until the end of senior secondary schooling (12 years). Similarly, while only 48 percent of those who completed senior secondary schooling entered university, once there, the advancement probabilities are again high.

The costs of a year of school combine Ghana Ministry of Education data for public expenditure per student plus the average amount reported for tuition and fees, expenditure on books, uniforms and sports clothes, transport to school, and PTA dues in the 2016/17 GLSS for students in public schools, by level. These are quite similar across males and females except for senior secondary school where they are 18 percent higher for girls.

Note that some of the year-by-year net present value calculations actually show losses in labor earnings for an additional year of schooling. These correspond to years when the slope in figure 7 is flat or negative so that the loss of a year of work is greater than the additional labor income earned. However, the recursed estimates smooth this out as there is a benefit to, for example, the fourth year of schooling beyond the difference in labor earnings compared to those with only three years: the chance to advance to lucrative higher years of schooling. Note that the benefits to additional years of schooling are higher for women than men even though their labor earnings are lower, especially after completion of junior secondary school.

Figure 8a shows our estimate of the benefit-cost ratios for men and women by year of schooling completed. The benefit-cost ratios are always greater than one except for men who complete exactly 17 years of schooling, i.e., those who drop out after one year of graduate school. The ratios also spike at the completion of each level of schooling. Note also that the benefit-cost ratios for men are higher at lower levels of schooling than they are for higher levels, a function of the much higher costs of secondary and postsecondary schooling. For women, though, gains in labor earnings at higher levels are so great that the benefit-cost ratios are flatter through the years.

23
10

# **Natural Gas Markets**

The unbundling of gas supply and transportation and the opening of natural gas markets to competition have created new requirements relating to how natural gas is traded and how market participants interact. System operation rules guide the interactions between gas shippers and the monopolistic gas transporter. But the industry structure and trading mechanisms determine the efficiency of gas markets.

An efficient natural gas market performs three functions:

- It aggregates supply and demand to determine system demand and supply curves.

- It facilitates market clearing to determine the market price of natural gas.

- It signals the market value of gas.

Market aggregation is achieved by concentrating trading among producers, traders, and shippers at one or several trading points. These trading points can be market centers, typically located at the major pipeline interconnection or at the major entry point to the high-pressure transmission system. In the U.K. the onshore terminals where producers deliver their gas to BGT's pipeline network have become the trading points.

- Market clearing is facilitated by trading mechanisms bilateral trading, brokerage, spot markets, and auctions. Which of these mechanisms is used depends on the characteristics - of supply and demand as reflected in the dimensions of gas contracts typically time and location of delivery, pressure and calorific value of gas, security of supply, and, of course, the unit price of gas. Participants in the U.K. gas markets use all four trading mechanisms.

Price signaling is facilitated by the collection and dissemination of information about prices and trading volumes by reporting and consulting agencies, journals, and newsletters. The reporting of this information enables market participants to choose trading and consumption strategies according to market signals. In the U.K. several firms regularly report spot market prices and volumes, including PH Energy Analysis and Argus Petroleum. At present there is no statistical or panel reporting of spot market prices.

Natural gas markets allow the trading of long-, medium-, and short-term natural gas. While long- and medium-term gas contracts are more typical for the integrated gas industry, short-term gas contracts are important in the deintegrated gas industry because of market participants' need to achieve physical balance between demand and supply in a short time frame (typically between one day and one mronth). Thus system balancing affects how natural gas is traded. In the U.K. the balancing requirements of the Network
# **Table 5: The determinants of training provision (Marginal effects from the Bivariate Probit model)**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Perceptions (No obstacle)_||||||
|Minor obstacle|0.011<br>(0.018)|||||
|Moderate obstacle|0.061<br>(0.022)**|||||
|Major obstacle|0.058<br>|||||
||(0.038)|||||
|_Age (Less than 5 years old)_||||||
|5 to 15 years old|0.012<br>(0.019)|0.009<br>(0.017)|0.008<br>(0.018)|0.013<br>(0.018)|0.013<br>(0.018)|
|16 or more|-0.043|-0.026|-0.052|-0.025|-0.023|
||(0.032)|(0.024)|(0.030)+|(0.026)|(0.025)|
|Ownership (Public)|0.075|0.055|0.078|0.048|0.050|
||(0.026)**|(0.028)+|(0.024)**|(0.024)*|(0.026)+|
|Innovator|0.143<br>(0.034)**|0.168<br> (0.025)**|0.137<br>(0.037)**|0.176<br>(0.024)**|0.182<br>(0.025)**|
|_Size (Less than 20 employees)_||||||
|20 to 100 employees|0.181|0.165|0.192|0.160|0.160|
|More than 100 employees|(0.022)** <br>0.287|(0.020)**<br>0.263|(0.022)**<br>0.303|(0.020)**<br>0.260|(0.020)**<br>0.259|
||(0.021)**|(0.020)**|(0.020)**|(0.021)**|(0.021)**|
|Sector (Manufacturing)|-0.024|-0.011|-0.012|-0.028|-0.028|
||(0.036)|(0.039)|(0.035)|(0.036)|(0.036)|
|_Country income level (Low income)_||||||
|Lower Middle income<br>Upper Middle income or high income|0.138<br>(0.062)*<br>0.317|0.163<br>(0.060)**<br>0.351|0.137<br>(0.065)*<br>0.324|0.169<br>(0.057)**<br>0.370|0.163<br>(0.065)*<br>0.363|
||(0.056)**|(0.052)**|(0.054)**|(0.046)**|(0.049)**|
|_Labor regulations indexes_||||||
|Regular employment||0.614<br>(0.127)**||||
|Temporary employment|||0.095<br>(0.100)|||
|_Regulatory mix – median (Low TER and l_<br>High TER and High RER|_ow RER)_|||0.215<br>(0.053)**||
|Low TER and High RER||||<br>0.219<br>(0.050)**|<br>|
|High TER and Low RER||||<br>0.122+<br>(0.063)||
|_Regulatory mix – mean (Low TER and low_|_RER)_|||||
|High TER and High RER|||||0.185<br>(0.052)**|
|Low TER and High RER|||||0.210<br>(0.049)**|
|High TER and Low RER|||||0.014<br>(0.079)|
|Observations (number of countries)|9676 (38)|||||
|Predictedprobabilityat the mean|0.568|0.570|0.569|0.570|0.570|

Robust standard errors in parentheses (and adjusted for clustering on country). + = significant at 10%; * = significant at 5%; ** = significant at 1%. Where relevant, base categories of dummies are indicated in parentheses in italics. Data source: ICS.

28