rafmacalaba/datause-encoder-data huggingface dataset:
Our simulations are based on the following equilibrating mechanisms: (1) The aggregate consumer good is the _numéraire_ (thus the consumer price index is exogenously fixed to unity); (2) the domestic good market is brought to equilibrium through adjustment in the price of domestic sales; (3) factor markets are assumed to clear in the background through factor price adjustment, hence Xs is fixed exogenously; (4) the balance of trade is exogenous so that the market for foreign exchange is brought to equilibrium by adjustment in the exchange rate; (5) for the government account, revenues are determined by the tax system, spending is exogenous while savings are determined residually (they are the equilibrating variable); (6) investment is savings-driven.

# _Base Data_

Table 3.2. A Macroeconomic SAM for Indonesia (in 2002 Billion Rupiahs)

||**Activity**|**Commodity**|**Household**|**Government**|**Investment**|**World**|**Total**|
|---|---|---|---|---|---|---|---|
|**Activity**||1040070||||569942|1610012|
|**Commodity**|||1042148|132219|325334||1499701|
|**Household**|1538826|||||19246|1558072|
|**Government**|71186|12005|110845||||194036|
|**Savings**|||405079|61817||-141562|325334|
|**World**||447626|||||447626|
|**Total**|1610012|1499701|1558072|194036|325334|447626||

Data Source: LDB on Line (World Bank)

# _Implications of an Export Boom_

What might happen if the world price of exports increased by, say, 20 percent? Ceteris paribus, this would correspond to _an improvement in the terms of trade_ . There is an income and a substitution effect associated with this change and the final outcome depends on the dominant effect. As discussed above in the case of an increase in the world price of imports, the outcome hinges on the value of the elasticity of substitution between imports and domestic goods. The increase in income induced by the 25
countries, only 28 percent ever export their products. On average, exports represent 14 percent of total sales per firm in the sample. Among the seven countries, only Benin, Kenya, Madagascar, and Senegal have more than 30 percent of firms exporting products. In terms of average export intensity, Madagascar has an exceptionally high average rate relative to its exporter density.

Among manufacturing sectors, the textile, garment, and leather, and to some extent food and agro-industry sectors, have high export participation rates and average export intensity (Figure 2). Capital-intensive industries such as chemical, paints, and plastic have low export intensity levels relative to their export participation rates, implying that firms in these sectors are less concentrated in exporting. Labor-intensive industries such as textile, on the other hand, are more concentrated or “specialized” in producing products for export rather than domestic markets.

Among many investment climate conditions covered by ICS, efficiency of ports and customs is one of the major factors that directly affect firm-level efficiency in international trade. The ICS data include per-firm average number of days to clear customs for both exports and imports. As shown in Figure 3, the port/customs turnaround time is faster for exports than for imports in all seven countries in the dataset used in the study.

The ICS data also cover a wide range of factors related to the domestic business environment in the countries as experienced by the surveyed firms. This study examined efficiency in infrastructure such as inferior power services and customs delays. Among the seven African countries in the dataset, some cross-country variations are observed in terms of availability and quality of domestic infrastructure services. Figure 4 compares domestic infrastructure service quality across the seven countries in terms of average numbers of days required for a new land telephone line connection and a new electricity connection, and average numbers of days per year for which the surveyed firms experience disruptions in electricity (both power outage and current fluctuation) and in land telephone service.

# **4. Pattern of Market Diversification of African Manufacturing Sector: Bivariate Analysis**

# **4.1 Geographical Orientation of Exports**

Our dataset includes information on the volume of firm exports to specific countries, geographic regions, or country blocs such as “UEMOA countries” or “Asia”, organized in a way that allows us to break down the firms’ total exports to the following six geographically distinct groups: (1) exports within the same subregion; (2) exports to other African countries outside of the subregions; (3) 11
term risks accumulate substantial reserves which, despite the short-term nature of the risks covered, represent long-term resources for companies that experience a reasonably steady growth in their business. In fact, because the loss claims facing nonlife insurance companies suffer from various processing delays until they are settled and are thus exposed to the vagaries of inflation, nonlife insurance companies in OECD countries, especially in Anglo-American countries, tend to place a high proportion of their technical reserves in corporate equities that generally provide a better hedge against inflation than bonds. The reserves of life insurance and annuities business are of a long term nature and are generally invested in long term assets.

Since the life insurance sector is not well developed, most of the reserves of insurance companies represent reserves for nonlife general insurance business. Total reserves amounted in 1993 to 455 MD, up from 324.5 MD in 1990. They correspond to 3.1% of GDP. Admitted assets, i.e. assets that are recognized for reserving purposes, cover 78% of technical reserves<sup>8</sup> . There are two main reasons for the deficiency of admitted assets: the excessive level of claims on brokers and agents, which are not admitted if they are outstanding for more than three months and exceed 10% of premiums; and the accumulated losses of four out of the 13 companies. As discussed below, there is significant variation in the financial condition of different companies and, although no detailed data are published, it is likely that the coverage of reserves also varies significantly from company to company. The capital deficient companies in the public and mutual sectors are likely to have low to very low coverage of their reserves.

About 50% of admitted assets are invested in government bonds (Table 5). Another 17% is represented by equities, 11% by property securities, and 14% by liquid money market assets. In the past equipment bonds were earning a below market rate of interest, but in recent years government bonds are issued on close to market terms and conditions. Some of the equity holdings are, however, very low yielding shares in other state-owned enterprises. The big increase in property investments reflects the tendency of many of the private sector companies to build or acquire offices for own use as well for rental purposes.

**Table 5 Composition of Admitted Assets, 1990 and 1993**

||19|90|19|93|
|---|---|---|---|---|
||MD|%|MD|%|
|Government bonds|132.2|57.0|177.2|49.7|
|Shares|37.0|16.0|62.5|17.5|
|Property|24.6|10.6|40.3|11.3|
|Liquid assets|24.1|10.4|48.3|13.5|
|Other admitted assets|13.8|6.0|28.7|8.0|
|Total|231.7|100.0|357.0|100.0|

Source: Ministry of Finance, Direction Generale des Assurances 8 To ensure the solvency of insurance companies, insurance regulators do not admit for prescribed regulatorv ratios assets that do not meet certain criteria. For instance, excess claims on agents for premiums receivable, above a specified threshold, are not counted among admitted assets.

11
# **Section I Demographic Data**

# **A. Historical Data**

Historical demographic data presented in Chapter I illustrated the fact that population aging took place more slowly and at a later stage of development in the industrial countries of Europe and North America than will be the case in many developing countries. Historical demographic data for European countries shown in Figure 1.6 were calculated based on tables in Mitchell, B.R. _International Historical Statistics: Europe 1750-1988,_ Stockton Press, New York N.Y. 1992. For the United States, data were taken from Mitchell, B.R., Intemational Historical Statistics: The Americas and Australasia, Gale Research Company, Detroit, Michigan 1983.

Appendix Table I of this Annex present the calculations generated for each of 28 countries for 1) the percentage of the population over 60, 2) the ratio of the population aged 20-59 to the population over 60, 3) ratio of the population aged 15-64 to 65+, 4) the percentage of the population under 15 and 5) the ratio of women aged 60+ to men aged 60+. The first variable was used to generate the dates which corresponded to a doubling of the over 60 population from 9 to 18 percent. In the case of countries where the percent of the population over 60 was still below 18 percent in 1990, World Bank projections (see below) were used to compute the number of years which this doubling would require. Interpolation was necessary in order to estimate an exact year in which the population reached 9 or 18 percent, respectively. Historical demographic data for Mexico and Chile which appear in Figures 4.10 and Issues Brief Figure 6.1. are also generated from data in Mitchell 1983. The sources listed under Issue Brief Figure 6.1 should have included this reference.

# **B. Demographic Projections**

This study assumes that demographic aging will continue to take place around the world. This assumption is based on a consensus among demographers and corresponds broadly to projections made by the United Nations and others. Averting the Old Age Crisis relied exclusively on projections made by the 1
Rao, Vijayendra. “Mixed methods for poverty analysis”. World Bank, 2002. Available at <u>http://siteresources.worldbank.org/INTPAME/Resources/Training-Materials/Training_2002-0619_Rao_MixedMethods_pres.pdf</u> Ravallion, Martin. “The mystery of the vanishing benefits: An introduction to impact evaluation.” _World Bank Economic Review_ 15(1): 115-140, 2000.

Ravallion, Martin. “Evaluation in the practice of development.” _World Bank Research Observer_ 24(1): 29-53, 2009.

Ravallion, Martin. “The two poverty enlightenments: Historical insights from digitized books spanning three centuries.” _Poverty & Public Policy_ 3(2): 1-46, 2011.

Rogers, Everett. _The Diffusion of Innovations_ (4<sup>th</sup> ed.) New York: Simon and Schuster, 2010.

Rugh, Jim, Michael Bamberger, and Linda Mabry. _RealWorld Evaluation: Working Under Budget, Time, Data, and Political Constraints_ . Thousand Oaks, CA: Sage Publications, 2011.

Sana, Mariano; Gu Stecklov and Alexander A. Weinreb. “Local or outsider interviewer? An experimental evaluation.” 2012. Available at <u>http://paa2012.princeton.edu/papers/122313</u> Shaffer, Paul. _Q-Squared: Combining Qualitative & Quantitative Approaches in Poverty Analysis_ . New York: Oxford University Press, 2013.

Teddlie Charles and Fen Yu. “Mixed methods sampling: A typology with examples.” _Journal of Mixed Methods Research_ 1(1): 77-100, 2007.

Thelan, Kathleen and James Mahoney (eds.) _Explaining Institutional Change: Ambiguity, Agency, and Power_ New York: Cambridge University Press, 2010.

United States Agency for International Development’s Center for Development Information and Evaluation. (1996). Conducting Key Informant Interviews. (Performance Monitoring and Evaluation TIPS) Available at http://www.usaid.gov/pubs/usaid_eval/pdf_docs/pnabs541.pdf Weller, Nicholas and Jeb Barnes. _Finding Pathways: Mixed-Method Research for Studying Causal Mechanisms_ . New York: Cambridge University Press, 2014.

White Howard and Daniel Phillips. “Addressing attribution of cause and effect in small n impact evaluations: towards an integrated framework” International Initiative for Impact Evaluation 35
# 6. <u>REFERENCES</u>

Agosin, Alvarez, Bravo-Ortega. Determinants of Export Diversification Around the World: 1962-2000.” _The World Economy_ , 2012, pp. 295-315.

Al-Kawaz. “Economic Diversification: The Case of Kuwait with reference to Oil Producing Countries.” _Journal of Economic Cooperation_ , 2008, pp 23-48.

Artana, Auguste, Moya, Sookram, Watson. “Trinidad and Tobago: Economic Growth in a Dual Economy”. Inter-American Development Bank, 2007.

Balgobin and Omar. “Institutional Initiatives to Promote Private-Sector Business”. In: RojasSuarez and Elias, editors. _Policy perspectives in Trinidad and Tobago: From Growth to Prosperity_ . Inter-American Development Bank, 2006.

Benassy-Quere, Coupet and Mayer (2007). “Institutional Determinants of Foreign Direct investments”. _The World Economy_ , 2007, pp. 764-782.

Baum, C.F. An introduction to Modern econometrics using STATA, 2006.

Blonigen, B.A., and J. Piger. Determinants of Foreign Direct Investments. NBER Working Paper 16704. 2011.

Central Bank of Trinidad and Tobago, Economic Statistics, 2012.

ECLAC, 2003. The impact of FDI on Patterns of Specialization in the Caribbean.

Ebrahim-Zadeh, C. (2003). “Dutch Disease: Too much wealth managed unwisely”. _Finance and Development,_ IMF, 2003, Vol. 40, No.1.

Elías, Jaramillo, Rojas-Suárez. “Introduction” in From Growth to Prosperity. Policy Perspectives for Trinidad and Tobago, edited by Liliana Rojas-Suárez and Carlos Elías, Inter-American Development Bank, 2006.

Habib and Zurawicki. “Corruption and Foreign Direct Investment”. _Journal of International Business Studies_ , Vol. 33, No. 2 (2nd Qtr., 2002), pp. 291-307.

Hausmann, R., Rodrik, D. and Velasco, A., “Growth Diagnostics,” John F. Kennedy School of Government, Harvard University, March 2005.

Imbs, J. and Wacziarg (2003). “Stages of Diversification”. _The American Economic Review_ , 2003, Vol. 93, No. 1, pp. 63-86.

International Monetary Fund. _Article IV Staff Reports for Trinidad and Tobago_ , 2011 and 2012.

21
|Table 4: N|umber of missing values in productio|n function<br>**Small**|variables b<br>**Medium**|y size<br>**Large**|
|---|---|---|---|---|
||**a) INDIA**||||
|Totals bysize||3,621|2,271|957|
|Sales|Number of missing <sup>(a)</sup>|646|257|95|
||Perc over totals bysize<sup>(b)</sup>|17.8|11.3|9.9|
|Labor|Number of missing|0|0|0|
||Perc over totals bysize|0|0|0|
|Materials|Number of missing|688|278|101|
||Perc over totals bysize|19|12.2|10.6|
|Capital|Number of missing|1258|640|245|
||Perc over totals bysize|34.7|28.2|25.6|
||**b) TURKEY**||||
|Totals bysize||788|810|1048|
|Sales|Number of missing|335|365|449|
||Perc over totals bysize|42.5|45.1|42.8|
|Labor|Number of missing|34|37|46|
||Perc over totals bysize|4.3|4.6|4.4|
|Materials|Number of missing|346|396|521|
||Perc over totals bysize|43.9|48.9|49.7|
|Capital|Number of missing|462|388|507|
||Perc over totals bysize|58.6|47.9|48.4|
||**c) SOUTH AFRICA**||||
|Totals bysize||197|783|804|
|Sales|Number of missing|40|95|76|
||Perc over totals bysize|20.3|12.1|9.5|
|Labor|Number of missing|23|54|43|
||Perc over totals bysize|11.7|6.9|5.3|
|Materials|Number of missing|53|111|97|
||Perc over totals bysize|26.9|14.2|12.1|
|Capital|Number of missing|69|204|154|
||Perc over totals bysize|35|26.1|19.2|
||**d) TANZANIA**||||
|Totals bysize||361|302|127|
|Sales|Number of missing|129|121|40|
||Perc over totals bysize|35.7|40.1|31.5|
|Labor|Number of missing|28|21|11|
||Perc over totals bysize|7.8|7|8.7|
|Materials|Number of missing|114|87|38|
||Perc over totals bysize|31.6|28.8|29.9|
|Capital|Number of missing|53|111|97|
||Perc over totals bysize|14.7|36.8|76.4|

Small: less than 20 employees; medium: between 20 and 100 employees; large: more than 100 employees. (a) Number of missing includes both missing values and outliers in the corresponding variables. (b) Percentage over the total number of observations in each category of firms’ size. Source: Authors calculations with IC data.

28
which is about 10 percentage points lower than that of men (Table 2).<sup>3</sup> While the male workingage population is more likely to enter the labor market, male labor force participants are more likely to be unemployed than women. The unemployment rate among men (8.7 percent) is higher than among women (5.6 percent), implying the existence of discouraged women who do not seek employment. The unemployment rate is particularly high among male and young workers, reaching as high as 18.4 percent.

Poor male workers are more likely to be unemployed than non-poor male workers, while there is no substantial difference in labor status between poor and non-poor women. The unemployment rate among the poor male labor force is relatively high (12 percent). By contrast, poor women’s unemployment rate is only 5 percent. Poverty does not appear to have influenced labor force participation status, as the LFPR of poor women is not particularly low (54 percent) compared to other women.

**Table 2: Labor force status in Kinshasa by age, sex, and poverty (percentage)**

||**All**|**Male**|**Female**|
|---|---|---|---|
|**(A) Working-age population**||||
|Employed|56.1|59.8|52.7|
|Unemployed|4.4|5.7|3.1|
|Not active|39.5|34.5|44.1|
|Labor force participation rate|60.5|65.5|55.9|
|Unemployment rate|7.2|8.7|5.6|
|**(B) Only youth**||||
|Employed|23.8|23.0|24.4|
|Unemployed|3.7|5.2|2.4|
|Not active|72.5|71.8|73.2|
|Labor force participation rate|27.5|28.2|26.8|
|Unemployment rate|13.5|18.4|8.9|
|**(C) Poor**||||
|Employed|53.2|55.3|51.2|
|Unemployed|5.0|7.3|2.7|
|Not active|41.8|37.4|46.1|
|Labor force participation rate|58.2|62.6|53.9|
|Unemployment rate|8.5|11.8|5.0|

_Source_ : Kinshasa Survey 2018.

The female (particularly young) working-age population with higher education faces a particularly high rate of unemployment. A simple look at unemployment rates and workers’ educational attainment does not show any clear correlation (Panel A in Figure 3). The unemployment rate is 5–8 percent among those with primary education, secondary education, and higher education. Breaking down the relationship by gender, however, shows a contrasting pattern. The

> 3 Labor force is defined in this report as active if an individual either (a) worked during the last 7 days, (b) did not work during the last 7 days but had a job (that is, absent), or (c) did not work but looked for a job. The labor force participation rate is calculated as the share of active working-age population. Unemployment rate is calculated as the ratio of working-age population with no job to the total number of active working-age population.

7
the early 1990s. For example, life expectancy declined from 72.97 years in 1990 to 69.94 years in 1994, and the infant mortality rate increased from 20.7 per 1000 live births in 1990 to 28.6 in 1994.<sup>3</sup> In the mid-1990s, the government implemented an initial reform, intended to transform the health system from the National Health Service model to a Social Health Insurance (SHI) system and to shift the government’s primary responsibility from service provision to service regulation and financing (Both 2002). In 1995, entitlement to free health care was removed from the Constitution, and the SHI was created with contribution rates of 3% from the employer and 1% from the employee to finance a Universal Benefit Package (UBP) for the general population. However, due to limited funding, the actual provision of benefits is minimal and some require co-payment (European Observatory on Health Care Systems 2002). For services not covered by the UBP, patients have to pay directly out-of-pocket.

Since the early 1990s, out-of-pocket payment (OOP) has become the main source of health financing. The results from a Health Service Utilization and Expenditure Survey indicate that the most common reason (73%) for not seeking medical care is the high out-of-pocket expenditures (Gotsadze 2001). Figure1 illustrates the high OOP spending in Georgia compared to EU and CIS averages. The OOP expenditure in Georgia is about 70%–80%, compared to about 40% among CIS countries and 20% among EU countries. Almost half of the total OOP are informally paid (Belli, Gotsadze et al. 2004).

<!-- Start of picture text --> Figure1. Georgia has High Private Out-of-Pocket<br>Spending on Health, 1998-2004<br>80<br>70<br>60<br>50 Georgia<br>40 EU<br>30 CIS<br>20<br>10<br>0<br>1998 1999 2000 2001 2002 2003 2004<br>Year<br>Out of Pocket Spending as % Total Health Expenditure<br><!-- End of picture text -->

**Data source:** WHO European Health for All Database (HFA-DB)

Due to high out-of-pocket expenditures, lack of access to health services, and the poor quality of services, Georgia has almost the lowest health services utilization rate in the ECA Region **,** with less than 2 outpatient visits per person, less than 6 inpatient visits per 100 people, and less than 2 surgical procedures per 100 people per year. Figure 2 illustrates the surgical procedures per year per 100 people from 1998 to 2004. The average number of surgical procedures per year per 100 in 2004 is less than 2 in Georgia, compared to about 5 in the CIS countries and about 7 in the EU countries.

> 3 Data source: WHO European Health for All Database (HFA-DB)

3
-49-

The formal sector pays taxes at the border, at the level of firms' profits, and at the level of personal income. A significant part of the imports is undertaken by government agencies which do not pay border taxes. The effective average rate of the border taxes is therefore very high and has been estimated at about 65 percent<sup>8</sup> . A fraction of the border taxes falls on the informal sector. Making allowance for these two corrections, and using data on the taxes and the value added of the informal sector, the estimate for the average effective tax rate on activities in the formal sector is about 48 percent. The high rate of effective taxation may explain the slow but persistent reduction of the real level of activity in the formal non government sector since 1980. One branch of the formal sector has grown steadily however, the bottling of beverages. One may consider this production as an index of domestic production (and demand) that is more accurate than GDP.

The level of employment in the formal private sector which is already so small is not growing. In fact it has declined significantly since 1980 (Table below). The reduction of employment has reached 50,000 in six years, which corresponds to an average rate of 8 percent p.a. since 1982. It is remarkable that the number of African employees in managerial positions has increased, in balance with the decrease of non-Africans. All the reduction of the labor force in the formal sector has taken place in the employment of workers. This is not surprising: the minimum age restrictions must be especially effective for non qualified workers.

8This estimate is computed from data in Horton [1990b].