rafmacalaba/datause-encoder-data huggingface dataset:
- 33 -

where so is the percentage change in the quantity of fuel i used caused by a 1I change in the price of fuel j. To calculate theme values of the ahares (SI) are required - conventionally the man sample shares are used. Standard errors for these shares are more difficult to obtain. Possible solutions **_are_** discussed by Pindyck [19793 and Berndt 119913. The system of fuel share equations allows only for substitution between fuels but not for the additional effects that a change in the general energy price will have upon the total amount of energy used. Hence to obtain a tgtal price elasticity (eU*) (i.e. holding output constant) an adjustment is required as explained by Pindyck (19793 e=* 8 eV + eEBSJ (13) where el is the own price elasticity of aggregate energy in the total cost function.

Since the trannlog function is an approximation device it is not automatically consistent with all of the theoretical properties that an aggregate cost function would have. It has already been pointed out that the required properties of linear homogeneity and symmetry can easily be imposed on the estimation equations. However two other properties have given more problems for empirical work. It is necessary that the estimated cost function should be monotonically increasing and strictly quasi-concave in input prices. The former requires that tI'9 fitted shares should all be positive at every data point while the latter requires that the matrix of substitution elasticities be negative semi-definite at each observation. The former is checked by using the estimated coefficients in (10) and the actual prices to show that the estimated shares are all positive. The substitution elasticities (Hick-Allen form) are given by au - eg/SJ au - (14) For a three fuel model the negative semi-definite property required for strict quasi-concavity requires that (i) each an 0 at each observations (ii) each submatrix: ag _ag_ (i "J) au _a0_ has a positive determinant at each observations
<!-- Start of picture text --> 0 . 03 0 . 02<br>2. 0 RM1 5. 0 LMPI 0. 02 DLCPI RP<br>1. 5 0. 01 0. 01<br>4. 5<br>0.00<br>1. 0<br>1990 2000 1990 2000 1990 2000 1990 2000<br>- 3 .75 1 6<br>4 .00 LEXCH PPP LLENDING<br>-4 .00<br>3. 75 15<br>3. 50 -4 .25<br>3. 25 -4 .50 14<br>1990 2000 1990 2000 1990 2000<br>0 . 2 DRM1 0 .1 DLMPI 0 . 02 DDLCPI 0. 005 DRP<br>0. 1<br>0.0 0.00 0.000<br>0.0<br>- 0 .1<br>- 0 .1 -0 .02 - 0 .005<br>1990 2000 1990 2000 1990 2000 1990 2000<br>0. 2 DLEXCH 0. 2 DPPP 0. 05 DLLENDING<br>0.0 0.0 0.00<br>-0 .2 -0 .2 - 0 .05<br>1990 2000 1990 2000 1990 2000<br><!-- End of picture text --> Figure 1: Log levels and ...rst di¤erences of the variables, 1989:01- 2004:12 Thailand faces some data problems because often monthly macroeconomic variables are only available for a short sample period, usually starting at the end of the 1980s. For example, most studies on monetary policy and the monetary transmission mechanism in Thailand have to rely on quarterly data because measures of real GDP are not available on a monthly basis. For instance, Disyatat and Vongsinsirikul (2003) only employ 40 observations in their study which makes their results unreliable due to limited degrees of freedoms in their unrestricted VAR model estimation. To avoid or minimize these problems, we chose to use the Thai manufacturing production index (MPI) as a proxy for GDP, and we acknowledge the limitations in accuracy of this choice.<sup>7</sup> The 14-days repurchase rate (RP) is a measure of the short-term interest rate.<sup>8</sup> Figure 1 presents the (log) levels and ...rst di¤erences of the relevant system variables for the full sample period.

We assume that long-run price homogeneity holds, that is, (DLM 1 � DLCPI) � I(0) so that money growth and in‡ation are cointegrated and move together in the long-run. Given long-run homogeneity, we know that (LM 1 � LCPI) � I(1). Here, we follow the treatment of Juselius (2007) who also assumes long-run price homogeneity. Including nominal money growth and prices into the cointegrated VAR model could potentially complicate the analysis because of possible I(2)

> 7 As a consistency check, we calculated the correlation coe¢ cient between quarterly GDP from its ...rst available date and the analogue MPI. The correlation coe¢ cient from the ...rst quarter of 1993 to the fourth quarter of 2004 is around 0.94. Even though, the comparison does not cover the ...rst four years in our sample, it can serve as a indication that the MPI is a good proxy for GDP ‡uctuations.

> 8 To make further analysis comparable with logarithmic monthly changes, RP is divided by 1200, to allow for a monthly measure.

7
19. Another challenge is the social and environmental costs associated with mining.<sup>10</sup> Few of the developmental benefits expected to accompany the exploitation of minerals have materialized in the host communities. The population in mining areas suffers from such problems as displacement of indigenous communities, loss of livelihoods, and adulteration of local culture, conflicts and human rights abuses, diversion of watercourses, and loss of biodiversity due to the environmental destruction (Akabzaa, Seyire, and Afriyie 2007; Salami 2001).<sup>11</sup> These problems also have been highlighted by the NCOM, an umbrella organization comprising development-oriented nongovernmental organizations (NGOs),<sup>12</sup> which advocates for the communities. In addition to environmental degradation, NCOM points at the limited compensation for expropriated land. Considerable environmental problems are associated primarily with the old mines—that is, mines developed before 1990.<sup>13</sup>

# **c. Regulatory Institutions**

20. Since the 1980s, the government increasingly has privatized the mining sector and it now plays a regulatory role. The institutional framework for mining governance is complex.<sup>14</sup> At the central level, it includes the presidency, Parliament, central government ministries, and various departments and agencies. The Ministry of Lands, Forestry and Mines—through the Mineral Commission (MC), the Geological Survey Department (GSD), the Inspectorate Division of Minerals Commission (IDMC), and the Precious Minerals Marketing Co. Ltd. (PMMC)— regulates Ghana’s mining industry. The MC regulates the use of mineral resources and coordinates mineral policy. The GSD is responsible for keeping geological information and a repository of geoscientifc data. The IDMC is in charge of monitoring and enforcing health, safety, and environmental standards in mines as established by the mining and mineral laws. PMMC markets the country’s precious minerals and jewelry industry. At the local state level, there are District Assemblies (DAs) and Traditional Institutions (see Table 2).

**Table 2: List of Mining Sector Players**

|State level|Executive President, Parliament, Judiciary, MoFEP, MLFM, EPA, MoSE MLGRDE, OASL,<br>BoG, MC, IRS, LTU, GEITI|
|---|---|
|Local level|14 District Assemblies, which have mineral deposits|
|Non-state|National Coalition on Mining, WACAM, TWNG ISODEC, Chamber of Mines|
|Mining companies|21 mining companies such as Newmont, Ashanti Goldfields, Anglogold Ashanti, Bogoso,<br>Chirano|
|International<br>players|Bilateral and multilateral institutions the CIDA, DfID, World Bank, African Development Bank<br>(AfDB)|
|Collective players|Citizens<br>Constituencies; Communities|
|Traditional<br>authorities|Traditional councils, chiefs|

12
good match. All financial institutions are required by law to obtain information about a borrower from the CIB before granting or renewing a loan. This provision was introduced in the effort to reduce non-performing loans in the banking system.

While the CIB has a long and positive track record, several shortcomings of the existing system should be noted. First, the system is still limited by the lack of computization. Second, banks effectively only obtain current information about existing borrowers, but no historic information, i.e. information about past payment performance. One can imagine defaulting borrower paying up to one institution before taking out new loans with another institution – the CIB reports do not seem to be able to capture such behavior. Third, only financial institutions participate in the information sharing agreement, other non-financial institutions such as utility companies, which are a source of valuable credit information about customers in other countries are excluded. Most importantly, however, the CIB has a stability focus – credit information sharing has the objective to prevent future loan losses, which is an important and valid aspect of credit information sharing. As important, however, is the function of credit information sharing in enhancing competitiveness and access to credit by allowing borrowers to create “reputation collateral”, i.e. allow them to use their track record as reference in future loan applications. The lack of historical information limits such a role of the CIB. More importantly, the threshold of 50,000 BDT (800 USD) excludes the class of borrowers that around the world rely most on reputation collateral – small and opaque enterprises. While technical problems are certainly recognized, a move towards including all borrowers irrespective of the size of their loans should be envisioned in the medium-term. Another important step would be to link the existing CIB for banks and financial institutions to the credit registry to be created for MFIs. Finally, there does not seem any reason to prevent private credit information sharing, i.e. to change the legal 18
taking appetite of DFI boards and shareholders must match the risk involved in changing the narrative and trajectory of fragile states. Only this will reduce the risk that fragile states remain an obstacle to achieving sustained global prosperity.

# **Bibliography**

- Basu, Kaushik. 2014. “Fiscal Policy as an Instrument of Investment and Growth.” _World Bank Policy Research Working Paper No. 6850_ , Washington DC: The World Bank

- Boulding, William, and Markus Christen. 2003. “Sustainable Pioneering Advantage? Profit Implications of Market Entry Order.” _Marketing Science_ 22 (3): iii–436.

- Carter, Paddy. 2015. “Why subsidize the private sector?”, London: Overseas Development Institute.

- Collier, Paul. 2007. _The Bottom Billion: Why the Poorest Countries Are Failing and What Can Be Done About It._ Oxford: Oxford University Press.

- ———. 2017. “Culture, Politics, and Economic Development.” _Annual Review of Political Science_ 20 (1): 111–25.

- DFI Working Group on Blended Concessional Finance for Private Sector Projects. 2017. _Summary Report._ October. Washington, DC: International Finance Corporation (IFC).

- LSE-Oxford Commission on State Fragility, Growth and Development. 2018. “Escaping the Fragility Trap.” LSE-Oxford Commission on State Fragility, Growth and Development. London and Oxford, UK.

- Golder, Peter N., and Gerard J. Tellis. 1993. “Pioneer advantage: Marketing logic or marketing legend?” _Journal of Marketing Research_ 30 (2): 158–70.

- Fernandes, Ana Margarida, Caroline Freund, and Martha Denisse Pierola. 2016. “Exporter Behavior, Country Size and Stage of Development: Evidence from the Exporter Dynamics Database” _Journal of Development Economics_ 119 (C): 121–37.

- Hauser, John, Gerard J. Tellis, and Abbie Griffin. 2006. “Research on Innovation: A Review and Agenda for Marketing Science.” _Marketing Science_ 25 (6): 551–765.

- Hoff, Karla. 2000. “Beyond Rosenstein-Rodan: the modern theory of underdevelopment traps.” Washington, DC: The World Bank.

- Kabuth, Oliver. 2003. “Pioneering Versus Following in Emerging Markets: The Case of the Automotive Industry in China and Brazil.” Doctoral Dissertation, University of St. Gallen, Switzerland.

Kay, John and Mervyn King. Forthcoming. _Radical Uncertainty._ Kenny, Charles. 2018. “Aid Transparency and Private Sector Subsidies at the IFC.” Center for Global Development (blog). January 17.

<u>https://www.cgdev.org/blog/aid-transparency-and-private-sector-subsidies-ifc.</u> 26
national population survey data also show that 16 percent of rural youth at the age of 16– 17 have participated in the labor market.

- **Regional and provincial gap.** The sample size in the 1 percent population data may not be large enough to derive meaningful provincial trends. However, provincial labor statistics indicate that variation of the provincial-level average years of schooling of employed labor force is large. Beijing (13.3 years) and Shanghai (12.5 years) have the highest education attainment, while Tibet (5.8 years), Guizhou (8.2 years), and Yunnan (8.4 years) are among the provinces with the lowest education attainment of their labor force (Figure 3.9).

Figure 3.9 Average years of schooling of employed labor force, 2015 <!-- Start of picture text --> 13.3<br>14.0<br>12.5<br>11.8<br>12.0 10.610.5<br>10.0 10.1 10.5 10.0 10.4 9.9 10.2 9.99.610.09.89.99.9 10.5 9.6 9.99.8 10.1<br>10.0 9.0 9.0 9.19.1 9.610.4<br>8.2 8.4<br>8.0<br>5.8<br>6.0<br>4.0<br>2.0<br>0.0<br>Years<br>National Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang<br><!-- End of picture text --> _Source:_ National Bureau of Statistics of China 2016a.

- **Gender gap.** Comparison between 2014 and 2015 shows that the average years of schooling attained is increasing, but the male-female gap is noticeable (Figure 3.10). Similarly, the real gap can be even larger given that female labor force participation is lower than that of males. In 2015, the data of employed labor force comprised 58 percent males and 42 percent females. In 2014, it was 55 percent and 45 percent, respectively.

30
- Industry consolidation

- New demand from vertical services

- Expansion into new locations

- Payment based on performance.

# **V. The Effect of the Recession on the Offshore Services Value Chain**

The economic crisis has impacted all the segments of the offshore services value chain, especially vertical offshore activities, in different ways.<sup>8</sup> Sectors negatively impacted by the global recession generally faced lower demand for offshore services. In other cases, clients in industries less affected by the recession show similar demand patterns as before the financial downturn, and in some cases there is demand for even more services.

The impact of the crisis also depends on the maturity of the industry in different countries, making it difficult to generalize across the value chain. For example, India saw similar declines in both IT and BPO activities, while the Philippines IT services sector was more affected than the BPO segment in which the country has more experience. In this section, two case studies of India and the Philippines, the two leading countries in these segments, provide further analysis of the impact of crisis on the value chain.

In general, KPO services have been more insulated from the economic downturn. Business intelligence and legal services continued to perform well during the crisis. According to Gartner’s annual EXP Worldwide Survey-2009, covering 1,500 Chief Information Officers (CIOs) worldwide, Business Intelligence was the top investment priority. This has been the top priority for four consecutive years and companies in the sector continue to grow, taking advantage of new opportunities afforded by the crisis. For example, MAIA Intelligence, an Indian Market Intelligence provider, launched affordable reporting and analytics solutions for small and medium enterprises. In the past this service was only available for large enterprises and MAIA Intelligence found the niche and opportunity to offer this innovative product (NASSCOM Newsline, 2009).

Legal services, referred to as legal process outsourcing (LPO) in the value chain, are still mostly limited to India. The annual growth rate slowed from 40% pre-crisis, initially, reaching a still very promising growth of 28% in 2008 and then in 2009 becoming just 16% in comparison with the previous year (India PRwire, 2009). The two largest companies in the segment, Mindcrest and CPA, saw positive growth during the crisis, likely the result of early and rapid consolidation of the still infant industry segment, with 20% of the providers in India exiting the industry (BPOWatch India, 2008). Leading companies expect the sector to expand significantly in 2010 due to the tremendous cost-saving opportunities afforded for law firms (Crain's Chicago Business, 2010).

> 8 See Figure 2 for a diagram of the offshore services value chain.

22
# **_Table 9: Perceptions of Microfinance Banks_**

||If I apply to an M<br>application wil|FB for a loan my<br>l be accepted|Taking a loan is|too risky for me|I would tr|ust an MFB to keep|my money|MFBs|treat people with|respect|
|---|---|---|---|---|---|---|---|---|---|---|
|Trust in MFBs||||||dl|dl||dl|<br>dl|
||Screening|Endline|Screening|Endline|Screening|Enine (agree<br>strongly)|Enine (agree &<br>agree strongly)|Screening|Enine (agree<br>strongly)|<br>Enine (agree &<br>agree strongly)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|_Treatments_|||||||||||
|Movie|0.06**|0.04|‐0.01|0.01|0.15***|0.08**|0.01|‐0.05|0.03|0.01|
||(0.026)|(0.033)|(0.038)|(0.039)|(0.034)|(0.038)|(0.033)|(0.031)|(0.038)|(0.034)|
|MFB|0.10***|‐0.01|‐0.02|0.01|0.26***|0.05|0.01|0.01|0.06|0.02|
||(0.027)|(0.034)|(0.039)|(0.041)|(0.035)|(0.039)|(0.034)|(0.032)|(0.040)|(0.035)|
|Movie + MFB|0.08***|0.05|‐0.02|0.01|0.27***|0.08**|0.06*|0.10***|0.10**|0.06*|
||(0.027)|(0.034)|(0.039)|(0.040)|(0.034)|(0.039)|(0.033)|(0.031)|(0.039)|(0.035)|
|_Gender disaggregated in_|_teraction effects (_|_female base)_|||||||||
|Movie|0.04|0.00|‐0.01|‐0.01|0.08**|0.06|0.01|‐0.08**|‐0.00|‐0.01|
||(0.031)|(0.038)|(0.044)|(0.046)|(0.039)|(0.044)|(0.038)|(0.036)|(0.044)|(0.040)|
|MFB|0.10***|‐0.03|‐0.03|‐0.01|0.25***|0.07|0.03|‐0.01|0.07|0.01|
||(0.032)|(0.040)|(0.046)|(0.048)|(0.041)|(0.046)|(0.040)|(0.038)|(0.047)|(0.042)|
|Movie + MFB|0.08**|0.05|‐0.02|‐0.02|0.22***|0.12***|0.05|0.07*|0.13***|0.05|
||(0.032)|(0.040)|(0.045)|(0.047)|(0.040)|(0.046)|(0.039)|(0.037)|(0.046)|(0.041)|
|_Gender disaggregated in_|_teraction effects (_|_male interaction)_|||||||||
|Male|‐0.03|‐0.02|‐0.08|‐0.10|‐0.17***|0.01|‐0.00|‐0.09|0.03|‐0.02|
||(0.046)|(0.057)|(0.066)|(0.068)|(0.058)|(0.066)|(0.057)|(0.054)|(0.066)|(0.059)|
|Male*Movie|0.06|0.13*|‐0.01|0.10|0.28***|0.04|0.03|0.14*|0.11|0.08|
||(0.061)|(0.075)|(0.087)|(0.090)|(0.077)|(0.087)|(0.075)|(0.071)|(0.087)|(0.078)|
|Male*MFB|‐0.02|0.07|0.03|0.08|0.04|‐0.06|‐0.06|0.08|‐0.01|0.03|
||(0.062)|(0.077)|(0.089)|(0.092)|(0.078)|(0.089)|(0.076)|(0.074)|(0.089)|(0.080)|
|Male*(Movie + MFB)|0.01|0.00|‐0.02|0.11|0.19**|‐0.14*|0.02|0.11|‐0.09|0.03|
||(0.061)|(0.076)|(0.087)|(0.090)|(0.077)|(0.087)|(0.075)|(0.071)|(0.088)|(0.079)|
|p‐values δ1+ γ1≠0|0.05|0.04|0.81|0.21|0|0.18|0.55|0.36|0.17|0.28|
|for F‐tests δ2+ γ2≠0|0.11|0.56|0.98|0.14|0|0.93|0.63|0.27|0.48|0.61|
|δ3+γ3≠0|0.1|0.42|0.58|0.25|0|0.77|0.23|0|0.66|0.21|
|_Observations_|_1,215_|_1,261_|_1,223_|_1,261_|_1,226_|_1,261_|_1,261_|_1,174_|_1,261_|_1,261_|
|_R‐squared_|_0.04_|_0.05_|_0.05_|_0.04_|_0.11_|_0.05_|_0.05_|_0.06_|_0.05_|_0.05_|
|_Controls_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|
|_Restricted Model_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|_YES_|
|_Control Mean:_|_0.820_|_0.754_|_0.356_|_0.495_|_0.586_|_0.581_|_0.757_|_0.808_|_0.559_|_0.722_|

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

33
Note that the 10-year government bond rate, shown in Figure 3, fell to a low approaching 3 percent in mid-2005. Since most insurance providers in Sweden discount liabilities with a rate of 3 percent, examination of Figure 3 shows that for a short period in 2005 there was a possibility that the _market rate_ for 10-year bonds had come close to falling below the rate used for discounting liabilities, and, in fact, did so for the few companies using a discount rate of 3.5 percent.

**Figure 3.  10-year government bond rate Monthly values, January 1987-March 2008**

<!-- Start of picture text --> Figure 3. 10-year government bond rate<br>Monthly values, January 1987-March 2008<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Year<br>Percent<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Source. The Swedish National Debt Office An interesting question in this context is what happens to competition among annuity providers if the whole insurance market has access to and holds long bonds with the same returns. Logically, an investment portfolio that provides a good longevity match between assets and liabilities will inevitably restrict competition among insurance companies. The counterfactual is a situation with portfolios with higher equity content and higher rates of return for customers, but also with more volatility and risk. It can be concluded that present Swedish practice enables companies to assume greater risks – and for annuity recipients to achieve higher returns - than would be the case if annuity liabilities were more or less exactly matched by long bonds with a duration close to the longevity of annuities.

Finally, we note that periods with falling interest rates have two effects on the portfolios of insurance companies. The first is that a lower discount rate increases the present value of a company’s commitments. As a consequence, the required technical insurance reserve, that is bond content, also increases. Second, companies’ bond portfolios increase in value as coupon rates fall, but this increase will not match the increase in liabilities to the insured. This is a result of the fact that liabilities have a longer duration than assets. The average duration to maturity of government bonds has been around 4 to 5 years<sup>27</sup> , whereas the average duration to

> 27 The duration of the stock of government bonds is 5.5 years, calculated from Table B7 in the appendix. The duration of treasury bills is around a year. The data indicate debt rolls over completely about every four years.

46
ECONOMIC ANALYSIS OF SMALL WATER SUPPLY PROJECTS -- INDONESIA All data refer to the first year --year(1)--after project completion, except where Indicated otherwise. Input data in the highlightedlcoloured areas. To reach a paragraph directly, press F5 and type PARAx , when x is the number you want. To see the results, press F5 and type RESULT. Arrow keys move the cell pointer.

A. 'WITH' project situation:

1. Population served and piped water delivered by the project:

||house|yard|public|non-||total(pres|s F9)|
|---|---|---|---|---|---|---|---|
||connec|<br>tion|<br>standpipe|residenti|al|||
|no. of<br>persons|0|<br>0|<br>0|n/a||0||
|cubic<br>meter/yea|0<br>r|<br>0|<br>0|0||0||
|2.|Investrme|ntcost(conn|ection exclu|ded) on con|stant prices:|||
||year(-4)|year(-3)|year(-2)|year(-1)|year(O)|total va<br>(in ear|lue<br>1))|
|million<br>ruplah|0|<br>0|0|0|0|y|<br>0.00|
|3.|Connectio|n cost (inclu|ding house|hold storage|tank):|||
|MRp|year(-4)|year(-3)|year(-2)|year(-1)|year(0)|year(1)|total<br>(rv)|
|house|0|<br>0|0|0|0|0|p.<br>0.00|
|yard|0|<br>0|0|0|0|0|0.00|
|standpipe|0|0|0|0|0|0|0.00|
|non-res.|0|0|0|0|0|0|0.00|

4. Operation and maintenance cost of supply system (including company overhead cost) on a cubic meter delivered basis: rupiah/ cub.met. 0 5. Do water vendors or the consumers themselves carry the watef from standpipes to the place of consumption ? (if answer is consumers, change Indicator to "1", if vendors, leave as f0 ) vendor indic. 0 18