rafmacalaba/datause-encoder-data huggingface dataset:
Equation (13) indicates that labor demand in the rural export sector is positively related to the level of net output, _VAT,_ and negatively related to the effective product wage, (1 + IL_,)WAT. Labor demand does not depend directly on the price of the exported agricultural good, _PXAT_ (which is proportional to the value added price, as shown below in equation (47)), because the nominal wage rate paid to workers, _WAT,_ varies proportionally to _PVAT_ in order to keep the real wage rate fixed at _WAT._ Again, the assumption of perfect real (product) wage rigidity is not necessarily appropriate for all countries; there are cases where partial indexation of nominal wages might be more appropriate. The assumption of full indexation can be easily relaxed in the IMMPA software for practical applications.

Note also that the product wage rate is multiplied by (1 + IL-1 ), where _IL-,_ is the one-period lagged bank lending rate, to account for the fact that firms in this sector rely on working capital to pay wages in advance of the sale of output. As a result, firms consider the _effective_ price of labor, which includes the cost of borrowing, when making labor hiring decisions.'<sup>0</sup> We assume that the cost of credit specified in loan contracts negotiated for the current period is based on the interest rate prevailing in the previous period.

The supply of labor to the nontraded agricultural sector is determined residually, as the number of workers who are unable to find a job in the better-paying sector seek employment in the nontraded sector: The wage rate in the nontraded agricultural sector is flexible and determined so as to equate labor demand (derived from standard profit maximization conditions), _UAdN,_ and labor supply, given by (14). The market-clearing equilibrium product wage, _WAN,_ is thus given by The size of the labor force in the rural sector, _UR,_ is predetermined at any given point in time. Over time, _UR_ grows at the exogenous population '<sup>0</sup> There is a large literature emphasizing the role of interest rates in a credit-in-advance economy on the supply side; New Structuralist economists (see e.g. Taylor (1983)) have been particularly strong advocates of the necessity to take this effect into account when specifying macroeconomic models. See also Izquierdo (2000) for a more recent discussion.

18
63 instance, Wong (1998, pp. 198 ff.).<sup>127</sup> Indeed, the Chinese government also encourages voluntary organizations to help the poor via donations (“charity”).<sup>128</sup> Let me finally turn to two country-specific problems connected with China’s emerging systems of social insurance. One is that risk pooling often only takes place across limited geographic areas, such as a city or a province, which creates a high degree of fragmentation of the systems across geographical areas and sectors. A second problem is the pronounced urban bias of the new emerging social insurance system – a bias which may to some extent be seen as an inheritance from the pre-1978 reform period.

At first glance, the limited geographical domain of risk pooling may look like a trivial problem, since these domains are often more populous than many European nations. However, the composition of industries often differs strongly across geographical areas, so that the rates of payroll taxes also vary considerably across such areas. In particular, firms in areas with many unemployed or pensioners are exposed to much higher social costs than firms in other areas. For instance, payroll taxes are relatively high in regions with old industries, such as mining and steel, whereas they are relatively low in regions with new industries, such as banking, electronics and civil aviation. Thus, in spite of attempts to unify the social insurance systems across geographical areas and production sectors, economic and demographic factors have led to contributions rates that vary greatly across localities and sectors of the national economy. As a result, the social insurance system in China is highly fragmented. One consequence is that the relative competitiveness of firms is influenced in a rather arbitrary way, since local wages may not fully adjust to differences in payroll taxes. Another consequence is that variations in benefits across firms and regions limit the portability of entitlements and hence impede the emergence of a national labor market.

The most obvious problem inherent in China’s emerging new arrangements for income security is, however, the bias in favor of urban insiders. First, within urban areas, individuals with only temporary residence permits, or no permits at all, are covered to a

> 127 By the end of 2003, about 54 million individuals were reported to have signed voluntary old-age insurance with individual accounts. The Chinese Government also experiments with economic rewards in the form of an additional pension benefit (amounting to 600 yuan per year above the age of 60) for households that effectively practice family planning. 128 China’s Social Security White Paper (September 7, 2004) reports that there were 28,000 such social donation centers, with aggregate donations amounting to 23 billion RMB during the period 1996-2003.
Table 3 Change in Total Income by Factor and Industry due to Mode1 Service Trade between 2001 and 2006 (2001 US$ Millions)

|||**Skilled Labor**|**Unskilled**<br>**Labor**|**Capital**|**Total**<br>**Industry**<br>**Income**|
|---|---|---|---|---|---|
|**Ariculture**|**Base**|1,533.72|45,333.52|67,104.73|113,971.97|
|**g**|**Change **|515.07|2,164.30|‐8,054.77|‐5,375.39|
|**Mnftrin**|**Base**|3,893.31|27,424.08|48,736.36|80,053.75|
|**auacug**|**Change **|1,215.15|706.53|‐14,042.86|‐12,121.17|
|**Services**|**Base**|42,555.43|78,157.48|123,703.90|244,416.81|
||**Change **|17,215.43|7,429.94|45,613.40|70,258.78|
|**Total Factor**|**Base**|47,982.46|150,915.08|239,544.99|438,442.53|
|**Income**|**Change **|18,945.66|10,300.78|23,515.78|52,762.21|

Source: Author’s Simulations

# **5.4 Decomposition by Direction of Trade**

In light of the changes in factor prices and total factor incomes by sector, the contribution of the Mode 1 exports must be separated from the contribution of imports, as proxies for the export demand effect and the Mode1 import supply effect. This decomposition is important for a deeper understanding of the mechanisms at work and the possible implications of restricting imports or exports of Mode 1 services.

Table 4 show the individual contributions of Mode 1 exports and imports to Indian factor price changes (columns I and II), and to the change in the total incomes of each factor (columns III and IV). The directions of the impacts (positive or negative) attributable to each trade flow direction are the same. However, the magnitudes of the export demand effect and import supply effect are quite different. The import effects of Mode 1 services were much smaller than the effects of Mode 1 services exports. This is consistent with the popular perception that Indian Mode 1 services exports are improving incomes, although the Mode 1 import supply effect is important for skilled labor wages.

The most significant differences are in the prices of capital in agriculture and manufacturing, which decline much more due to exports than due to imports. This can be explained in part by the pressures of the real appreciation caused by higher export demand for services. As more services are exported, exports of agriculture and manufacturing decline while imports of these commodities rise.

21
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 2. Mali’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-<br>income<br>countries|**Mali**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Classified road density|km/1000 km<sup>2</sup>of arable land|132.1|**38.3**|318.4|
|Classified road density|km/1000 km<sup>2</sup>of land|88.2|**27.9**|278.4|
|Rural accessibility Index-<br>HH Survey|% of rural population within 2km of all-season road|34.1|**14.0**|62.7|
|GIS rural accessibility|% of rural population within 2km of all-season road|23.1|**16.7**|31.5|
|Paved road traffic|Average annual daily traffic|1341.1|**547.5**|3797.7|
|Unpaved road traffic|Average annual daily traffic|38.5|**21.5**|74.7|
|Paved network condition|% in good or fair condition|86.2|**64.8**|82.0|
|Unpaved network condition|% in good or fair condition|55.8|**—**|57.6|
|Perceived transport quality|% firms identifying roads as major business<br>constraint|27.6|**20.1**|18.2|
|Overengineering|% of main road network paved relatively to low traffic|29.6|**47.7**|18.4|

Source: Gwillliam and others 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Mali’s main (primary and secondary) road network is adequate to achieve regional and national connectivity.<sup>1</sup> The share of business identifying road transportation as a constraint for doing business is 20 percent, which is much lower than in other low-income countries in Africa and comparable to middleincome countries. Strategically, Mali has primarily focused on maintaining and rehabilitating its portions of the regional road corridors. This has allowed the country to ensure that its major urban areas and areas of production are connected by road to the major deep water ports of Dakar, Abidjan, Takoradi, Tema, and Lomé (figure 5).

> 1 Regional connectivity refers to the road networks needed to connect the national capitals and cities with populations of 50,000 or more to the major ports. National connectivity refers to the road networks needed to connect provincial capitals and cities with populations of 25,000. This includes the network identified by the Trans African Highways.

11
|Of which due to state dummies<br>|0.055|0.018|0.017|0.022|
|---|---|---|---|---|
|Conditional R<sup>2</sup>|0.127|0.061|0.025|0.028|
|Skewness of state effect|N/A|N/A|N/A|N/A|
|Kurtosis of state effect|N/A|N/A|N/A|N/A|
|Estimated variance of state effect|N/A|N/A|N/A|N/A|
|Estimated household residual|N/A|N/A|N/A|N/A|
|Ratio of estimated state effect variance to total<br>|N/A|N/A|N/A|N/A|

variance

# **Municipality - Simple Random Sample within Selected AGEBs**

|Number of (non-state) variables selected<br>|41|22|19|10|
|---|---|---|---|---|
|Marginal R<sup>2</sup>|0.132|0.071|0.038|0.032|
|Of which due to state dummies<br>|0.055|0.018|0.017|0.022|
|Conditional R<sup>2</sup>|0.293|0.170|0.132|0.077|
|Skewness of municipal effect|-0.102|0.222|4.047|1.011|
|Kurtosis of municipal effect|4.840|6.168|36.395|5.679|
|Estimated variance of municipal effect|0.004|0.002|0.0013|0.0008|
|Estimated household residual|0.017|0.017|0.012|0.017|
|Ratio of estimated municipal effect variance to<br>total variance|0.186|0.107|0.098|0.046|

# **Municipality - Full Enumeration of Selected AGEBs**

|Number of (non-state) variables selected<br>|33|26|28|39|
|---|---|---|---|---|
|Marginal R<sup>2</sup>|0.277|0.224|0.164|0.158|
|Of which due to state dummies|0.113|0.060|0.082|0.100|
|Conditional R<sup>2</sup>|0.597|0.451|0.467|0.525|
|Skewness of municipal effect|-0.610|-0.394|2.460|1.613|
|Kurtosis of municipal effect|6.128|4.779|24.136|12.472|
|Estimated variance of municipal effect|0.004|0.001|0.0011|0.0015|
|Estimated variance of household residual|0.005|0.003|0.0020|0.0020|
|Ratio of estimated municipal effect variance to<br>total variance|0.442|0.292|0.363|0.436|
|Number of AGEBs|7,733|7,733|7,733|7,733|
|Number of municipalities|1,072|1,072|1,072|1,072|

Dependent variable was transformed using arcsine transformation.

We focus here on the municipality-level diagnostics. The marginal R<sup>2</sup> values are generally low for the simple random sample, especially for unemployment rates. The low R<sup>2</sup> does not necessarily mean that the small area estimates do not improve on the direct estimates. The smaller the sample and the greater the heterogeneity in outcomes across AGEBs, the more likely a small area 14
|Treaty; Major<br>strategic<br>alliance<br>(regional or<br>international)|||||||
|---|---|---|---|---|---|---|
|Total<br>cooperating<br>events<sup>b</sup>|143|37|156|22|3|3<br>56|

<u>aSource: Dinar et al (2011)</u> bIn multilateral treaties the number of events is a sum over complaints between all relevant riparians.

**Table 8 : Analysis of Mekong and Zambezi Treaties with regards to the water regulation mechanisms and existing institutions**

||Mekong|||Zambezi||||
|---|---|---|---|---|---|---|---|
|Type of BAR|Quantity|Hydropo<br>wer|Flood|Quantity|Quantity|Hydropow<br>er|Flood|
|Agreement year and<br>context|4/1995<br>Multilater<br>al|8/1965<br>Thailand<br>-Laos|4/1995<br>Multilat<br>eral|5/1987<br>Multilate<br>ral|7/1987<br>Zambia-<br>Zimbab<br>we|7/1987<br>Zambia-<br>Zimbabwe|5/1987<br>Multilat<br>eral|
|Water Regulation||||||||
|ALLOCATION<br>METHOD(0/1)|1|0|n/a|0|1|1|n/a|
|TYPE OF<br>ALLOCATION<br>METHOD|By<br>consultati<br>on|0|n/a|0|Percenta<br>ge|Percentage|n/a|
|ADDITIONAL<br>CODING FOR<br>ALLOCATION<br>METHOD|Percentag<br>e|0|n/a|0|Fixed,<br>recouped<br>in next<br>period|Fixed,<br>recouped<br>in next<br>period|n/a|
|ADDITIONAL<br>CODING FOR<br>ALLOCATION<br>METHOD|Fixed,<br>recouped<br>in next<br>period|0|n/a|0|Fixed,<br>varies by<br>water<br>availabil<br>ity|Fixed,<br>varies by<br>water<br>availabilit<br>y|n/a|
|SPECIFIC FLOOD<br>CONTROL<br>MECHANISM(0/1)|n/a|n/a|1|n/a|n/a|n/a|0|
|Institutions(0/1)||||||||
|SELF-<br>ENFORCEMENT<br>MECHANISMS|0|1|0|1|1|1|1|
|MONITORING|1|0|1|1|1|1|1|
|ENFORCEMENT|1|0|1|0|1|1|0|
|CONFLICT<br>RESOLUTION|1|0|1|0|1|1|0|
|COMMISSION<br>/COUNCIL|1|1|1|1|1|1|1|
|ADAPTABILITY<br>MECHANISM TO<br>DROUGHT(0/1)|1|0|n/a|1|0|0|n/a|

36
# **Table 9 Total Merchandise Exports, Manufacturing Exports and Services Exports, US$ millions and percentage of world exports, Armenia, Georgia, Kyrgyz Republic and Tajikistan: 2000-18**

|US Dollar millions annual averages|2000‐04|2005‐09|2010‐14|2015‐18*|
|---|---|---|---|---|
|Armenia|||||
|Total merchandise exports|510|976|1,345|1,984|
|Manufactured exports|301|534|382|452|
|Services exports||644|1,373|1,777|
|Total Merchandise exports/world exports(%)|0.0069|0.0076|0.0074|0.0113|
|Manufactured exports/world manufactured exports(%)|0.0056|0.0062|0.0033|0.0039|
|Services exports/world services exports(%)||0.0198|0.0298|0.0333|
|Georgia|||||
|Total Merchandise exports|419|1132.4|2,403|2,602|
|Manufactured exports|136|506|1387|1058|
|Services exports||1022|2449|3720|
|Total Merchandise exports/world exports(%)|0.0057|0.0086|0.0132|0.0148|
|Manufactured exports/world manufactured exports(%)|0.0025|0.0056|0.0120|0.0091|
|Services exports/world services exports(%)||0.0315|0.0528|0.0696|
|Kyrgyz Republic|||||
|Total Merchandise exports|554.8|1282.6|1,917|1,636|
|Manufactured exports|155|277|441|414|
|Services exports||490|881|838|
|Total Merchandise exports/world exports(%)|0.0077|0.0096|0.0107|0.0094|
|Manufactured exports/world manufactured exports(%)|0.0025|0.0031|0.0038|0.0035|
|Services exports/world services exports(%)||0.0148|0.0192|0.0158|
|Tajikistan|||||
|Total Merchandise exports|777|1239|1,190|1,049|
|Services exports||152|401|245|
|Total Merchandise exports/world exports(%)|0.0109|0.0095|0.0067|0.0060|
|Services exports/world services exports(%)||0.0048|0.0089|0.0046|

*2015-17 for manufactured exports

Services export data are only available from 2005 Source: WTO For services exports, a consistent data series using BOP Manual 6 definitions is only available from 2005 onwards. Armenia and Georgia have developed substantial services export sectors, with the value of the services exports of Georgia exceeding its merchandise exports during 2015-18 while the value of Armenian services exports was almost as great as that of its 15
## Table of Contents

# PUBLIC SECTOR PRICING IN A FISCAL CCNTEXT by

## Christopher Heady

|EXECUTIV|E SUMMARY<br>i|
|---|---|
|SECTION|1: INTRODUCTION....................................................1|
|SECTION|2: THE APPROPRIATE CONCEPT OF MARGINAL COSTS ....................... 3|
|A.|The Advantages of Short-Run Marginal Cost .......................... 3|
|B.|The Consequences of Price Variability .............................. 5|
|C.|The Relationship Between Pricing and Investment Decisions .... .....<br>9|
|SECTION|3: FISCAL AND FINANCIAL OBJECTIVES ................................ 13|
|A.|The Relationship Between Fiscal and Financial Objectives ........... 13|
|B.|Fiscal Gains from Pricing Above Marginal Cost ......................<br>19|
|C.|The Distortionary Cost of Pricing Above Marginal Cost .............. 21|
|D.|Distributional Effects of Pricing Above Marginal Cost .............. 26|
|E.|The Choice Between Taxes and Higher Prices for Enterprises ........ 27|
|F.|Data Requirements ................................................. 29|
|SECTION|4: METERING PROBLES ............................................... 31|
|A.|Charging for Different Uses ....................................... 32|
|B.|The Use of Indirect Charges ....................................... 34|
|C.|Data Requirements ..................................................<br>37|
|SECTION|5: SUNMARY AND CONCLUSIONS ......................................... 38|
|REFERENC|ES ..................................................................<br>41|
|#PPENDIX|: THE DESIGN OF ROAD USER CHARGES ................................. 43|

I am grateful to Esra Bennathan, Ian Heggie and Pradeep Mitra for help.7ul comments on an earlier draft.
financial audit gathered information on O&M cost, water tariff, connection charges, and revenue collection from the water tariff _Household Survey:_ Household surveys were conducted to find out the benefit and satisfaction from the service provided. The survey asked questions related to availability of water at the household level, adequacy of water, the quality of water provided, reliability of service etc. It also included questions related to satisfaction with service quality, and affordability of water tariff and capital contribution/connection charges.

_Institutional Assessment:_ The BG survey was conducted to assess the institutional strength of the community management schemes. The process included interviews of the key stakeholders from the Beneficiary groups as well as focus group discussions of members of the BGs. Institutional assessments collected information on composition of executive committee, frequency of meetings, regularity of election to the Executive Committee, maintenance of records, preparation of annual report, and financial and investment decisions of the BGs.

Field surveys were undertaken from March to June 2014. The survey team was unable to locate some of the schemes. A total of 2,583 households from 157 schemes were surveyed. Technical and financial audits were carried out in 172 schemes. Moreover, 135 BGs were surveyed for the institutional assessment. Table 2 provides a summary of the data collected by scheme type through various survey instruments.

**<u>Table 2: Data Collection by Survey Instrument and Scheme Type</u>**

|**Dataset**|**Household**<br>**Survey**|**Technical**<br>**Audit**|**Financial**<br>**Audit**|**Institutional**<br>**Assessment**|
|---|---|---|---|---|
|Jalanidhi|87|87|87|90|
|KWA BM|22|35|35||
|Other Community Managed<br>Schemes|48|50|50|45|
|Total No of Schemes|157|172|172|135|
|Number of Observations|2,582|172|172|135|

Since the analysis is at the scheme level, data collected through household surveys were aggregated at the scheme level. The technical and financial audits as well as the institutional surveys collected data at the scheme level only. We used imputation technique to handle missing data due to non-response and lack of information. Data imputation at the household level was done only if at least 20 percent of the surveyed households from a particular scheme responded to the question. The missing value was predicted based on the information collected from the 20 percent (or more) households such as education and proxies for income and other characteristics of the schemes. To impute data at the scheme 10
O.A.M.D.G.

**Demsetz, Harold** : “Towards a Theory of Property Rights”, AER Papers and Proceedings 57: 347-59, May 1967

**Deininger Klaus** : “ Causes and consequences of civil strife: micro-level evidence from Uganda”, _World Bank Working Paper 3045_ , 2003

**Disney Richard, Henley Andrew and David Jevons** : “House price shocks, negative equity and household consumption in the UK”, _mimeo_ , University of Nottingham, 2003

**Echandía Carlos** : “El Conflicto Armado Colombiano en los Años Noventa” in _Colombia Internacional, Special Edition No. 48-50_ , Centro de Estudios Internacionales, Uniandes, 2001

**Echandía Camilo:** “Expansión Territorial de la Guerrilla Colombiana: Geografía, Economía y Violencia”, _Documento de Trabajo No. 1, Paz Publica_ , CEDE Uniandes, 1997

**Echandía Camilo:** “Geografía del Conflicto Armado y las Manifestaciones de Violencia en Colombia”, Institute of Latin American Studies, University of London _, 5_<sup>_th_</sup> _Annual Conference on Colombia_ , 1999

**Echandía Camilo, Escobedo Rodolfo, Llorente Maria Victoria and Mauricio Rubio** : “Violencia Homicida y Estructuras Criminales en Bogotá”, _Documento de Trabajo No. 20, Paz Publica_ , CEDE Uniandes, 2001

**Echeverry JC, Salazar N and Navas V** : “Nos Parecemos al Resto del Mundo? El Conflicto Colombiano en el Contexto Internacional.”, _Archivos de Macroeconomica, DNP, Documento 143_ , February 2001

**Econometría S.A** .: “Evaluación de las Necesidades de Alimentos de la Población Desplazada en Colombia”; _World Food Program Colombia_ , May 2003

**Eymann Angelika and Axel Boersch-Supan:** “Household Portfolios in Germany”, in _Guiso L, Haliassos M and T Jappelli, ed., “Household Portfolios”_ , MIT 2002

**Fajnzylber P, Lederman D and N Loayza** : “The causes of crime and violence: a guide for empirical researchers.”, _Crime in Latin American Cities, LAC_ , World Bank, 1999a

**Fajnzylber P, Lederman D and N Loayza** : “Inequality and Violent Crime”, Working Paper for _Crime and Violence in Latin America, LAC_ , World Bank, 1999b

**Gaitán Fernando** : “Una Indagación sobre las Causas de la Violencia en Colombia., in _Deas and Gaitán: “Dos Ensayos Especulativos sobre la Violencia en Colombia”_ , FONADE and Departamento Nacional de Planeación, 1995

**Gaitán Fernando and Santiago Montenegro** : “Un Análisis Crítico de Estudios sobre la Violencia en Colombia”, _Conference “Crime and Violence: Causes and Prevention Policies”_ , Bogotá, 2000

**Gaviria Alejandro,** "Increasing Returns and the Evolution of Violent Crime: The Case of Colombia," _University of California at San Diego, Economics Working Paper Series 98-14_ , Department of Economics, UC San Diego, 1998

44