rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 35<br>30 28.6<br>25<br>&a<br>22<br>aaoce<br>wo | 24<br>69<br>57 57<br>5<br>° 1 os22 3 4 5 0366 Total<br><!-- End of picture text -->

<!-- Start of picture text --> 25<br>97<br>20<br>a<br>2<br>ce<br>70 es<br>5 44<br>of 04=1=11 2 02—2—22 4 5 [|1561566 Total<br><!-- End of picture text -->

<!-- Start of picture text --> T [] oO 35 25<br>30 28.6 97<br>20<br>25<br>&a<br>22<br>aaoce<br>wo | 24 70 es<br>69<br>57 57 5 44<br>5<br>a oO ° 1 os22 3 4 5 0366 Total of 04=1=11 2 02—2—22 4 5 [|1561566 Total<br>2 2<br>a4 18 47<br>D<br>16<br>14<br>8<br>aozs26=asa4 4 a°Fe171== = FeFa<br>29 os<br>2 2.0 25 23 os} 2 4 04 o3 0:3 oz<br>o2 04<br>°——°<br>12 3 4 5 6 Total 1 2 3 4 5 6 Total<br>30 8<br>#2<br>| 28 256 “4 ee<br>LI 20 .<br>es2s£2<br>3 5es<br>a D1 a6<br>0 24<br>59 4<br>5<br>2 14<br>a a ° —os ° —os [| —04<br>. 1 2 3 4 Totsl 1 2 3 4 Totsl<br>6 14 42<br>54<br>12<br>1 os<br>4<br>EZ2a=&wosZoe&<br>a 20 24 Qos<br>2 o2 o2<br>12 o2<br>’ o | | —<br>O41 oo<br>° — 0.2<br>1 2 3 4 Total 1 2 3 4 Totsl<br><!-- End of picture text -->

<!-- Start of picture text --> 2<br>a4<br>D<br>8<br>zs26=asa26=asa=asaasasaa<br>aozs26=asa4<br>29<br>2 2.0 25 23<br>2 3 4 5 6 Total<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>54<br>4<br>a 20 24<br>2<br>’ 12<br>O41<br>° —<br>1 2 3 4 Total<br><!-- End of picture text --> <!-- Start of picture text --> 14 42<br>12<br>1 os<br>wosZoe&Zoe&&<br>Qos<br>o2 o2<br>o2<br>o | | —<br>oo<br>0.2<br>1 2 3 4 Totsl<br><!-- End of picture text -->
requirements forecastirg that would be replaced in labor market signaiiing. The signalling technique emphasizes economic outcor- s, measured in terms of wages and ei.lployment, and costs for specific education and training programs as a guide to public and private resource commitments to these programs.

# ProducIng Labor Market Signals

The identification and deciphering of labor market signals requires a basic understanding of deniand arnd supply analysis applied to labor markets. This understanding is important as a tool to enable the manpower planner to identify important labor market signals and interpret their meaning. In addition, manpower planners need to be firmly grounded in analytical techniques such as multiple regression analysis, survey research methods, and basic inferential statistics to develop and anaiyze these signals. Finally, manpower planners will benefit from an i,ifrastrunture of labor market information. However, as explained in the section below on labor market information, even in cases where this infrastructure is missing or of poor quality, there are relatively inexpensive techniques available for developing labor market signals. These signals can be used in each of the roles played by manpower planners.

Market Training Decisions. Labor market signals are produced by monitoring labor force activity and the movement of wages and employment for workers with specific levels of schooling and participation in training programs. These movements, interpreted through demand and supply analysis are indicators of the labor market's assessment of the need for additional investments in schooling and skills training. Thus, as the wage or employment opportunities for one level of schooling or training program rise relative to others, this is a market signal of the demand for the skills associated with the schooling or training. The rise in wages or employment opportunities in relative terms is seen as a signal of excess demand for the skills involved, while a decline in wages or employment opportunities in relative terms is viewed as a signal of excess supply. Similar signals are provided by examining labor force activity and unemployment rates by skill level.

9
Table I.7. Robustness Analysis for the Subsidized Healthcare Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.265***|0.312***|0.269***|0.294***|0.309***|0.311***|0.265***|0.312***|0.269***|0.294***|0.309***|0.311***|
||(0.078)|(0.083)|(0.085)|(0.062)|(0.074)|(0.065)|(0.078)|(0.083)|(0.085)|(0.062)|(0.074)|(0.065)|
|Obs. left|447|399|377|450|373|413|447|399|377|450|373|413|
|Obs. right|851|711|694|1,468|1,003|1,353|851|711|694|1,468|1,003|1,353|
|Mean values (Non-RAMV)|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.267**|0.276**|0.249*|0.333***|0.374***|0.343***|0.233*|0.204|0.201|0.292***|0.288***|0.247***|
||(0.118)|(0.139)|(0.145)|(0.080)|(0.086)|(0.082)|(0.131)|(0.187)|(0.130)|(0.090)|(0.094)|(0.088)|
|Obs. left|429|346|355|417|336|352|429|346|355|417|336|352|
|Obs. right|764|564|631|1,550|1,335|1,554|764|564|631|1,550|1,335|1,554|
|Mean values (Non-RAMV)|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.272*|0.250|0.280*|0.291***|0.283***|0.290***|0.255*|0.272*|0.201|0.232**|0.256**|0.224**|
||(0.140)|(0.158)|(0.144)|(0.102)|(0.108)|(0.105)|(0.145)|(0.159)|(0.130)|(0.108)|(0.116)|(0.112)|
|Obs. left|480|422|472|452|409|431|480|422|355|452|409|431|
|Obs. right|981|736|952|1,562|1,460|1,531|981|736|631|1,562|1,460|1,531|
|Mean values (Non-RAMV)|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|0.015|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
- Engle, R.F., 1982. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica 50, 987–1007. doi:10.2307/1912773

- Enke, S., 1951. A Equilibrium Among Spatially Separeted Markets: Solution by Electrical. Econometrica 19, 40–47.

- Fafchamps, M., 1992. Cash Crop Production, Food Price Volatility, and Rural Market Integration in the Third World. Am. J. Agric. Econ. 74, 90–99. doi:10.2307/1242993

- Gilbert, C.L., Morgan, C.W., 2010. Food price volatility. Philos. Trans. R. Soc. B Biol. Sci. 365, 3023–3034. doi:10.1098/rstb.2010.0139

- Im, K.S., Pesaran, M.H., Shin, Y., 2003. Testing for unit roots in heterogeneous panels. J. Econom. 115, 53–74. doi:10.1016/S0304-4076(03)00092-7

- Jordaan, H., Grové, B., Jooste, A., Alemu, Z.G., 2007. Measuring the Price Volatility of Certain Field Crops in South Africa using the ARCH/GARCH Approach. Agrekon 46, 306–322. doi:10.1080/03031853.2007.9523774

- Kaminski, J., Christiaensen, L., Gilbert, C.L., 2014. The end of seasonality ? new insights from Sub-Saharan Africa (Policy Research Working Paper Series No. 6907). The World Bank.

- Kaminski, J., ELBEHRI, A., ZOMA, J.-B., 2013. Analyse de la filière du maïs et compétitivité au Burkina Faso: politiques et initiatives d’intégration des petits producteurs au Marché

- Kilima, F.T.M., Chanjin, C., Phil, K., Emanuel R, M., 2008. Impacts of Market Reform on Sptial Volatility of Maize Prices in Tanzania. J. Agric. Econ. 59, 257–270. doi:10.1111/j.1477-9552.2007.00146.x

- Maître d’Hôtel, E., Le Cotty, T., Jayne, T., 2013. Trade Policy Inconsistency and Maize Price Volatility: An ARCH Approach in Kenya. Afr. Dev. Rev. 25, 607–620. doi:10.1111/1467-8268.12055

- Minot, N., 2014. Food price volatility in sub-Saharan Africa: Has it really increased? Food Policy 45, 45–56.

- Minten, B., Kyle, S., 1999. The effect of distance and road quality on food collection, marketing margins, and traders’ wages: evidence from the former Zaire. J. Dev. Econ. 60, 467–495.

- Minten, B., Randrianarison, L., 2003. Etude sur la Formation des prix du Riz Local à Madagascar.

- Piot-Lepetit, I., Mbarek, R., 2011. Methods to analyze agricultural commodity price volatility, Springer. ed.

- Prakash, A., 2011. Safeguarding food security in volatile global markets. xxii + 594 pp.

- Samuelson, P.A., 1952. Spatial price equilibrium and linear programming. Am. Econ. Rev. 283–303.

- Schnepf, R.D., 2005. Price determination in agricultural commodity markets: A primer. Congressional Research Service, Library of Congress.

- Shively, G.E., 1996. Food Price Variability and Economic Reform: An ARCH Approach for Ghana. Am. J. Agric. Econ. 78, 126–136. doi:10.2307/1243784

- Stifel, D., Minten, B., 2008. Isolation and agricultural productivity. Agric. Econ. 39, 1–15. doi:10.1111/j.1574-0862.2008.00310.x

- World Bank, 2012. Africa Can Help Feed Africa/ Document Removing barriers to regional trade in food staples/ The World Bank

- Yang, J., Haigh, M.S., Leatham, D.J., 2001. Agricultural liberalization policy and commodity price volatility: a GARCH application. Appl. Econ. Lett. 8, 593–598. doi:10.1080/13504850010018734

25
<!-- Start of picture text --> WB Portfolio by Region<br>Number of Active Active Projects per FY<br>AFR FY01-06Fy07-13Fy07-13<br>EAP FY01-06Fy07-13Fy07-13<br>ECA FY01-06Fy07-13Fy07-13<br>LOR FY01-06Fy07-13Fy07-13<br>MNA FY01-06 Fy07-13 Fy07-13<br>SAR FY01-06Fy07-13Fy07-13<br>) 200 400 600 800<br>MEE FCSIDA «=NNNN non-FCSS IDA-FCSS IDA IDA<br>MEE Other<br><!-- End of picture text --> <!-- Start of picture text --> WB Portfolio by Region<br>Net Commitment Commitment of Active Active Projects per FY FY ($ m) m)<br>AFR FY01-06Fy07-13Fy07-13<br>EAP FY01-06Fy07-13Fy07-13<br>ECA FY01-06Fy07-13Fy07-13<br>LOR FY01-06Fy07-13Fy07-13<br>MNA FY01-06 Fy07-13 Fy07-13<br>SAR FY01-06Fy07-13Fy07-13<br>) 10,000 20,000 30,000 40,000<br>MEE FCSIDA «=NNNN non-FCSS IDA-FCSS IDA IDA<br>MEE Other<br><!-- End of picture text --> <!-- Start of picture text --> WB Portfolio by Region WB Portfolio by Region<br>Number of Active Active Projects per FY Net Commitment Commitment of Active Active Projects per FY FY ($ m) m)<br>AFR FY01-06Fy07-13Fy07-13 AFR FY01-06Fy07-13Fy07-13<br>EAP FY01-06Fy07-13Fy07-13 EAP FY01-06Fy07-13Fy07-13<br>ECA FY01-06Fy07-13Fy07-13 ECA FY01-06Fy07-13Fy07-13<br>LOR FY01-06Fy07-13Fy07-13 LOR FY01-06Fy07-13Fy07-13<br>MNA FY01-06 Fy07-13 Fy07-13 MNA FY01-06 Fy07-13 Fy07-13<br>SAR FY01-06Fy07-13Fy07-13 SAR FY01-06Fy07-13Fy07-13<br>) 200 400 600 800 ) 10,000 20,000 30,000<br>MEE FCSIDA «=NNNN non-FCSS IDA-FCSS IDA IDA MEE FCSIDA «=NNNN non-FCSS IDA-FCSS IDA IDA<br>MEE Other MEE Other<br>WB Portfolio by Network WB Portfolio by Network<br>Number of Active Active Projects per FY FY Net Commitment Commitment of Active Active Projects per FY FY ($ m)<br>FPD FY01-06 FY01-06<br> ev07-13 FPD ev07-13<br>HDN FY01-06 FY01-06<br> Ey07-13 HDN Ey07-13<br>PREM FY01-06 FY01-06<br>507-13 PREM 507-13<br>SDN FY01-06 FY01-06<br>5y07-13y07-13 SDN 5 y07-13<br>0 500 1,000 1,500 0 50,000 100000<br>HN «&FCS'|DA)«=- HJ nn -FCS -FCS IDA HN «&FCS'|DA)«=- HJ nn -FCS -FCS IDA<br>ME Other ME Other<br><!-- End of picture text -->

<!-- Start of picture text --> WB Portfolio by Network<br>Number of Active Active Projects per FY FY<br>FPD FY01-06<br> ev07-13<br>HDN FY01-06<br> Ey07-13<br>PREM FY01-06<br>507-13<br>SDN FY01-06<br>5y07-13y07-13<br>0 500 1,000 1,500<br>HN «&FCS'|DA)«=- HJ nn -FCS -FCS IDA<br>ME Other<br><!-- End of picture text -->

<!-- Start of picture text --> WB Portfolio by Network<br>Net Commitment Commitment of Active Active Projects per FY FY ($ m)<br>FY01-06<br>FPD ev07-13<br>FY01-06<br>HDN Ey07-13<br>FY01-06<br>PREM 507-13<br>FY01-06<br>SDN 5 y07-13<br>0 50,000 100000<br>HN «&FCS'|DA)«=- HJ nn -FCS -FCS IDA<br>ME Other<br><!-- End of picture text -->
<!-- Start of picture text --> Complete Network )<br>2011-2022<br>\,<br>Lr. 2e— se a<br>andClusteringhigh corruptionof low ABNERala ofTESEN Nee opGh Mixedcenter activity of the in the<br>risk ties at the peeee | SHON? OMY network<br>ee Sees BP ATI TANS > \<br>\ = Mk? a eae lt erste Nah<br>SSSS SRESa NORA)AON WAGNi teleSears STON<br><!-- End of picture text -->
# **Table 2: Assumptions** **<u>on GDP and population growth</u>**

|||GDPgrow|th,%<br>Ave. 2005-|Population|(million), gro|wth(%)|
|---|---|---|---|---|---|---|
|**Countries/Regions**|2005|2020|2020 /a/|2004|2020|Growth /a/|
|China|10.1|7.2|8.4<br>|1,303|1,431|0.59|
|Japan|1.9|0.8|1.2<br>|128|123|-0.24|
|Indonesia|5.7|4.5|5.0<br>|218|259|1.09|
|Malaysia|5.0|4.3|4.8<br>|25|31|1.39|
|Thailand|4.5|3.5|3.9<br>|64|69|0.52|
|India|9.2|6.4|7.4<br>|1,080|1,292|1.13|
|Canada|3.1|1.5|1.9<br>|32|34|0.39|
|United States|3.1|2.1|2.1<br>|294|330|0.73|
|Argentina|9.2|4.3|5.2<br>|38|44|0.89|
|Brazil|2.9|3.5|3.7<br>|184|217|1.03|
|France|1.7|1.2|1.5<br>|61|63|0.27|
|Germany|0.9|1.3|1.6<br>|83|79|-0.29|
|Italy|0.2|0.8|1.0<br>|58|54|-0.42|
|Spain|3.6|2.2|2.7<br>|43|42|-0.03|
|United Kingdom|1.8|1.8|2.0<br>|60|60|0.05|
|Russia|6.4|4.9|5.6<br>|144|131|-0.57|
|South Africa|5.0|4.3|4.6<br>|46|50|0.43|
|Rest European Union and EFTA b/|2.9|2.2|2.6<br>|196|194|-0.09|
|Rest of Latin America and Caribbean|4.5|3.4|3.8<br>|326|401|1.30|
|Australia and New Zealand|2.8|2.2|2.6<br>|24|27|0.67|
|Rest of East Asia and Pacific|5.0|6.0|6.4<br>|343|404|1.02|
|Rest of South Asia|6.7|4.8|5.4<br>|398|527|1.78|
|Rest of Europe and Central Asia|7.3|5.7|6.1<br>|227|247|0.51|
|Middle East and North Africa|5.2|3.7|4.1<br>|360|469|1.66|
|Rest Sub-Saharan Africa|6.2|5.7|6.0<br>|690|942|1.97|

/a/ Growth growth /b/ EFTA - European Free Trade Area Source: World Bank‟s Development Data Group The third set of assumptions under the BaU scenario is on the population in each of the countries and regions in the model also shown in Table 2. Growth rates of population were based on the population growth projection of the United Nations. One can observe that developed countries have generally declining population growth rates.

The fourth set of assumptions under the BaU scenario is that existing biofuel policies (e.g. already implemented mandates, subsidies and import duties) will continue throughout the study horizon (2010-2020). Biofuel penetration in the BAU scenario, which is defined as the 14
in local versus central elections, or as grants-based decentralization driven by greater effectiveness of vote mobilization in local elections.<sup>15</sup>

# **3. Evidence on characteristics of local jurisdictions**

This section provides evidence from several countries around the world of characteristics of local jurisdictions that is consistent with the implications of a theory of political capture of decentralization explored above. The purpose here is to show that the proposed theory has empirical or practical relevance, rather than to formally test predictions.

Table 1 provides some data on characteristics of local jurisdictions in selected countries. These countries have been selected on the basis of available reports on the nature of decentralization to local jurisdictions. There is little systematic data available on fiscal characteristics of local jurisdictions such as villages, municipalities, and districts. The main source of comparable cross-country data employed in the literature on subnational finance is the Government Finance Statistics (GFS) database compiled by the International Monetary Fund (IMF); but although this provides reasonably reliable estimates of finances of states, regions, or provinces, it is widely acknowledged as not reliable for measuring finances of the lower level jurisdictions which are the focus of this paper.

For this paper, detailed reports on local government finances were located for several large countries spread across four major regions of the world. There is clear evidence from three large countries, India, Indonesia, and Nigeria, where responsibility for public service delivery is being increasingly decentralized to local governments, that such decentralized spending is financed by fiscal grants from higher tiers. The average share of own-source revenue in the total revenues managed by local jurisdictions in these three countries is less than or barely equal to 10 percent. This average in fact masks even higher dependence on grants by the majority of local jurisdictions in these countries. In Nigeria, for example, a detailed survey of local government finances in the rural state of Kogi finds that own revenues are only 1-2 percent of total revenues managed by rural

> 15 Even with such local political differences, decentralization to locally elected governments might be trumped by other strategies of centralized targeting of local precincts of central electoral districts.

18
A second policy implication is the role of policies to absorb this sub-optimal smoothing. In Cambodia, for instance, this emphasizes the role of social safety nets. While the economy becomes increasingly open (and therefore vulnerable to global shocks) while moving away from subsistence agriculture to industry and services – the paper suggests that these emerging sectors won’t be able to provide full smoothing for temporary shocks. Therefore additional safety nets will be needed to protect workers during economic crises.

Finally, our findings have implications for macroeconomic policies, through the role of expectations. Indeed insufficient smoothing can be self-fulfilling through a demand effect: if all firms perceive the shock to be permanent, they do a minimal amount of smoothing, which further depresses demand, making the shock indeed more permanent. In fact, a negative demand feedback loop might also arise from limited financial development or poorly regulated financial development (one or the other would increase the likelihood of firms being credit-constrained, which would constrain smoothing, hence depressing demand further). This emphasizes the role of credible policies (at least nationally, but possibly also through international coordination) to send a credible signal that the shock is temporary.

# **References**

- Arne Bigsten, Paul Collier, Stefan Dercon, Marcel Fafchamps, Bernard Gauthier, Jan Willem Gunning, Abena Oduro, Remco Oostendorp, Cathy Pattillo, Mans Söderbom, Francis Teal, and Albert Zeufack (2003), “Risk Sharing in Labor Markets”, _World Bank Economic Review_ , 17: 349-366.

- Borensztein, E. and J.-W. Lee (2002): "Financial crisis and credit crunch in Korea: Evidence from firmlevel data", _Journal of Monetary Economics_ , vol. 49, pp.853-75.

- Campello, M., J. Graham and C. Harvey (2010): "The real effects of financial constraints: Evidence from a financial crisis", _Journal of Financial Economics_ , vol. 97, pp.470-87.

- Claessens, S., S. Djankov and L. Xu (2000): "Corporate Performance in the East Asian Financial Crisis", _The World Bank Research Observer_ , vol.15, no.1, pp.23-46.

- Correa, Paulo and Iootty, Mariana (2010), “The Impact of the Global finance Crisis on the Corporate Sector in Europe and Central Asia: Evidence from a Firm-Level Survey”, Enterprise Note Series, 9, the World Bank Group, available at <u>http://www.enterprisesurveys.org/documents/ECA-Note.pdf.</u>

- Dercon, S. (2002): "Income Risk, Coping Strategies, and Safety Nets", _The World Bank Research Observer_ , vol.17, no.2, pp.141-66.

- Dollar, D. and M. Hallward-Driemeier (2000): "Crisis, Adjustemnt, and Reform in Thailand's Industrial Firms", _The World Bank Research Observer_ , vol.15, no.1, pp.1-22.

- Fafchamps, M. (2000): "Ethnicity and credit in African manufacturing", _Journal of Development Economics_ , vol. 61, no. 1, pp. 205-35

Fafchamps, M., J. W. Gunning, and R. Oostendorp. 2000. “Inventories and Risk in African 30
internal electricity demand for many decades to come. Without expansion of cross-border transmission interconnection and regional cooperation for electricity trade, most of the hydropower potential in Nepal and Bhutan would remain untapped in future as well. Besides the surplus hydropower potential in Bhutan and Nepal, as well as Afghanistan, there are many other factors to justify regional electricity trade and cooperation. These include, among others, the role of hydropower to provide flexibility in power system balancing; the potential contribution of hydropower for sharing of reserve margins and reducing peak loads. In addition, large hydropower projects can have other benefits, such as irrigation and flood control, that can be shared between the neighboring counties (e.g., India and Bangladesh, Nepal and India).

At present, regional electricity trade is limited to small volumes of bilateral trade between India and its eastern and northern neighbors (Bangladesh, Bhutan and Nepal). Current capacity of cross-border transmission interconnection is small as compared to the potential hydropower capacity that can be traded. Expansion of cross-border transmission interconnection is expected to evolve over time, which will benefit Bhutan and Nepal by increasing their scope for making cost-competitive hydro investments. Similarly, development of Indian hydro capacity in the remote northeast would benefit from opportunities to export power to Bangladesh and to transmit power through Bangladesh to other parts of India. The Governments of India and Bangladesh have already agreed to build a multi-terminal transmission system from the North East of India to other parts of India passing through Bangladesh.

This paper first provides some background information on the global status of hydropower development using the data provided by the International Hydropower Association (IHA, 2017). It compares the growth of hydropower capacity with that of other power generation technologies including other renewable energy technologies over the last 17 years (2000-2016).

3