rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: panel regression of CPIA clusters A and B average on cumulative loans

|equation no.|(1)|(2)|(3)|
|---|---|---|---|
|number of cumulative loans|.073|.134|.|
||(.023)<sup>_∗∗∗_</sup>|(.047)<sup>_∗∗∗_</sup>||
|number of cumulative loans (squared)|.|-.005|.|
|||(.003)<sup>_∗_</sup>||
|log of number of cumulative loans|.|.|.406|
||||(.112)<sup>_∗∗∗_</sup>|
|year|-.023|-.024|-.026|
||(.008)<sup>_∗∗∗_</sup>|(.008)<sup>_∗∗∗_</sup>|(.008)<sup>_∗∗∗_</sup>|
|log GDP per capita (PPP)|.805|.799|.814|
||(.152)<sup>_∗∗∗_</sup>|(.149)<sup>_∗∗∗_</sup>|(.148)<sup>_∗∗∗_</sup>|
|aid over GDP|1.618|1.577|1.512|
||(.531)<sup>_∗∗∗_</sup>|(.519)<sup>_∗∗∗_</sup>|(.515)<sup>_∗∗∗_</sup>|
|Political Rights|-.016|-.015|-.011|
||(.021)|(.021)|(.021)|
|_country fxed efects_|_yes_|_yes_|_yes_|
|Observations|1761|1761|1761|
|Countries|139|139|139|
|_R_<sup>2</sup>|.134|.139|.147|
|Adjusted _R_<sup>2</sup>|.131|.137|.144|
|AIC|1113.115|1103.17|1086.232|
|BIC|1140.483|1136.012|1113.601|

Note: * significance at 10%; ** significance at 5%; *** significance at 1%.

23
_Table 8. Coefficients of Student-Level Variables Estimated in Individual Multi-Level Regressions on English and Mathematics Achievement_ (Standard Errors)

|||_Engl_|_ish_||||_Mathe_|_matics_|||
|---|---|---|---|---|---|---|---|---|---|---|
|**_Variable_**|**_Perc_**<br>**_Cffiir_**|**_en_**_Vara_<br>_Sh_|_nce Ep_<br>_Cl_|_lained_<br>_Std_|_T_|_Perc_<br>**_Cffii_**|_ent Vari_<br> _h_|_ance E_<br>|_rplained_<br>||
||**_oece_**|_c._|_._|_u._|_o:._|**_oecet_**|_Sc._|_Cl._|**_Stud._**|_Tot,_|
|**Female (O=male)**|.01 (.04)'|-||-||**-.05**(04)-|||**-**|**-**|
|**ASe**|**.04**(.02)|**3**|**1**|**I**|**-1**|**.15**(.02)|13|2|1|**4**|
|**Log Age**|**-6.29 (.65)**|**16**|2|**8**|**1 1**|**4.65**(.72)|16|**4**|**6**|7|
|**Repeater (O= Non-Repeater)**|**-.13 (.06)**|**1**|-|2|1|-.49 (.06)|8|1|3|4|
|**Non-English**|**-.11**(.08)'|-|-|-||.16 (.09)'|-|-|||
|**Family Sze**|**-.04 (.01)**|5|**2**|**4**|**4**|-.03 (.01)|**6**|-|2|2|
|**Father's Educatiort**<br>||10|-|6|7|-|17|-|**3**|**5**|
|**(Reference**=**No Education)**<br>|||||||||||
|**Prinary**<br>|**.10 (.07)'**<br>|||||**.11 (.07)'**<br>|||||
|**Jun-ior Certificate**<br>**''**|.21**(.06)**<br> '|||||.12 (.07)'<br>|||||
|**0 Level**<br>**''**|**.16**(.09)<br>|||||.09 (.10)'<br>|||||
|**A Level**|**.46**(.12)|||||.39 (.13)|||||
|**Mother's**Education<br>|||||||||||
|(Reference=No Education)<br>|||||||||||
|**Primary**<br>|.08 (.06)'<br>|||||.02 (.07)'|||||
|Junior Certificate<br>''|-.05 (.07)'<br>|||||-.21 (.08)'|||||
|O Level<br>''|-.10 (.11)'<br>|||||-.30 (.12)'|||||
|A Level|.04 (.14)'|||||-.04 (.16)'|||||

_Notes:._ Not significant (p > _.05)_

**b** Chi-square (p=.0003) for English and (p=.02) for m.athematics for comparisons across utegories. Counterintuitive.

Measured **froin** grand mean except where noted.

41. A pupil's family size had a small influence on both her/his English and mathematics achievement. Children from smaller families did better on both examinations.

42. Father's education had the strongest effect on achievement in both subjects, explaining 7% and 10% of the total variance in English and mathematics achievement, respectively. Although individual dummy variables were not always significant, the contrasts across the categories were significant."' The highest increments in achievement were estimated for children whose fathers had an 'A' level education, as could be expected.

43. Several student level variables were found to be insignificant predictors of Grade 7 examination results: gender, home language, and mother's education. Although gender was not found to be a i4 The chi-square tests produced results of (p= .0003) and (p=.02) for English and mathematics, respectively.

**15**
_ANNEX 6 CHARACTERISTICS OF EDUCATION PERSONNEL IN INDONESIA_ Number and Percent of Part-Time and Full-Time Teachers in Secondary Education

||**Headmasters**|**%**|**Full-time Teachers**|**%**|**Part-time Teachers**|**%**|**Total**|
|---|---|---|---|---|---|---|---|
|**Junior Secondary**|**22,240**|**4**|**343,575**|**63**|**176,776**|**33**|**542,591**|
|Public|12,037|3|274,668|75|78,925|22|365,630|
|Private|10,203|6|68,907|39|97,851|55|176,961|
|**Senior Secondary**|**14,366**|**3**|**220,133**|**51**|**200,967**|**46**|**435,466**|
|Public|4,673|2|140,582|73|47,269|25|192,524|
|Private|9,693|4|79,551|33|153,698|63|242,942|

_Source:_ MoNE 2005.

Number and Percent of Primary Teachers Per Responsibility

|||**T**|**eacher Responsibility**|||
|---|---|---|---|---|---|
|**Primary level**|**Headmasters**|**Class teachers**|**Religion teachers**|**Sport teachers**|**Total**|
|Number of teachers|146,045|934,479|167,449|87,113|**1,335,086**|
|Percent of total|11%|70%|13%|7%|**100%**|

_Source:_ MoNE 2005.

37
18 FIGURE 1B (cont'd.)

# BRAZIL PROJECTED POP. PROFILE 2100

<!-- Start of picture text --> 9.0X<br>8.03 _<br>Z 7.03<br>5L 6.03<br>3.o<br>2.0%-<br>0.03<br>20-24 40-44 6D-<br><!-- End of picture text --> NIGERIA PROJECTED POR. PROFILE 2100 <!-- Start of picture text --> Stox<br>8.03<br>z 7.03<br>tog<br>I-<br>aozo<br>20-24 ~ 40-"46-<br><!-- End of picture text -->
# 3.3 The International Cocoa Agreements

As in sugar and tin, the four cocoa agreements also struggled against a background of major supply problems. The price during the first two ICCAs (1972-79) was always substantially above the ceiling of the support range as the result both of low rainfall in west Africa, reducing yields, and maladministration by the military government of Ghana (then the largest producer). The high prices in 1976-79 not only made a mockery of the 2nd ICCA but also indirectly undermined the 3rd ICCA by suggesting a stabilization range which, _ex post,_ appears to have been too high, and by stimulating excessive planting in the C6te d'Ivoire, which displaced Ghana as the largest producer, and also in Brazil, Indonesia and Malaysia. The situation in the 3rd and 4th ICCAs was therefore the reverse of that in the earlier agreements, with the price in general beneath the ICCA stabilization range (see Figure 2).'4 The 3rd ICCA, which was chronically under-financed (neither the largest producing country, the C6te d'Ivoire, nor the largest consuming country, the USA, were members) exhausted its entire financial resources within three months of the start of the agreement in purchasing 100,000 tons of cocoa. The 4th ICCA limited the size of the buffer stock to 250,000 tons, the equivalent of around six weeks consumption. Total stocks at the end of the 1986-87 crop year were estimated as 650,000 tons, and these rose to 1,376,000 tons by the end of the 1991-92 crop year. But because the ICCO inherited 100,000 tons from the 3rd agreement, it was only able to accumulate a maximum of a further 150,000 tons. Against this background, it was clearly unrealistic to expect the agreement to have any significant effect on the cocoa price. Although the 4th ICCA made provision for an increase in the maximum allowable size of the buffer stock to 350,000 tons, this provision was never invoked.

The 3rd and 4th ICCAs specify floor and ceiling prices, but the effective price range is defined by the gap between the lower intervention "must buy" price (the LIP) and the upper "4 Source: International Cocoa Organization, _Quarterly Bulletin of Cocoa Statistics,_ (ICCO, London), various issues.

16
Gregorio Impavido, Craig Thorburn, Mike Wadsworth

- [30] **Knox, David M.** , 2000, The Australian Annuity Market. _World Bank Policy Research Working Paper_ , No 2495.

- [31] **Kotlikoff, Laurence J. and Lawrence H. Summers** , 1981, The Role of Intergenerational Transfers in Aggregate Capital Accumulation. _Journal of Political Economy_ , 89(4): 706-32.

- [32] **Laitner, John and F. Thomas Juster** , 1996, New Evidence on Altruism: A Study of TIAA-CREF Retirees. _American Economic Review_ 86(4): 893-908.

- [33] **Lindbeck, Assar and Mats Persson,** 2003, The Gains from Pension Reform. _Journal of Economic Literature_ XLI: 74-112.

- [34] **Mitchell, Olivia S., and James F. Moore** , 1988, Can Americans Afford to Retire? New Evidence on Retirement Saving Adequacy. _Journal of Risk and Insurance_ 65(3): 371-400

- [35] **Mitchell, Olivia S., James M.  Poterba, Mark J.  Warshawsky, and Jeffrey R. Brown** , 1999, New Evidence on the Money’s Worth of Individual Annuities. _The American Economic Review_ , 89(5): 1299-1318.  Also in Brown _et al_ ., 2001. _The Role of Annuity Markets in Financing Retirement_ .  (The MIT Press, Cambridge, MA.).

- [36] **Murthi, Mamta, J. Michael Orszag, and Peter R. Orszag** , 1999, The Value for Money of Annuities in the UK: Theory, Evidence and Policy. _Birkbeck College Working Paper_ 99-19.

- [37] **Oeppen, Jim and James W. Vaupel,** 2002, Broken Limits to Life Expectancy, _Science_ , 2916: 1029-31.

- [38] **Olshansky, S. Jay, Bruce A.Carnes, and  A. Désesquelles** , 2001, Prospects for Human Longevity in an Aging World. _Science_ , 291: 1492-92.

- [39] **Palacios, Robert, and Rafael Rofmann** , 2001, Annuity Markets and Benefit Design in Multipillar Pension Schemes: Experience and Lessons from Four Latin American Countries. _World Bank Social Protection Discussion Paper_ , No 0107.

- [40] **Petrova, P.** , 2002, The Annuity Puzzle Gets Bigger.  Mimeo.

- [41] **Poterba, James M.** , 2001, A Brief History of Annuity Markets.  in Brown, Jeffrey R., Olivia S.  Mitchell, James M.  Poterba and Mark J.  Warshawsky: _The Role of Annuity Markets in Financing Retirement_ (The MIT Press, Cambridge, MA.).

- [42] **Poterba, James M., and Mark J. Warshawsky** , 2001, The Cost of Annuitizing Retirement Payouts from Individual Accounts.  in Brown, Jeffrey R., Olivia S. Mitchell, James M.  Poterba and Mark J.  Warshawsky: _The Role of Annuity Markets in Financing Retirement_ (The MIT Press, Cambridge, MA.).

- [43] **Shiller, Robert J.** , 2003, Social Security and Individual Accounts as Elements of Overall Risk-Sahring. _American Economic Review_ , 93(2): 343-47.

- [44] **Schnabel, Reinhold** , 2002, Annuities in Germany Before and After the Pension Reform of 2001. _CERP Working Paper_ , No.  27/02.

- 33 -
30 What Factors Explain School<sup>Enrollment?</sup> We estimated equation (1) for<sup>the youth sample, with the dependent</sup> variable being school participation, as a logistic function. Table<sup>11 shows</sup> the parameter estimates for girls and<sup>boys.</sup>

# Table 11: Determinants of School<sup>Enrollment: Logistic Regression</sup> Results for Youth Sample, Males<sup>and Females</sup>

||Fema|les|Ma|les|
|---|---|---|---|---|
|Variables|Beta|s.e.|Beta|s.e.|
|YEARS OF SCHOOLING|0.426|0.31|0.584|0.32|
|AGE SPLINES:|||||
|8 TO 10|-0.070<br>|0.13<br>|-0.052<br>|0.14<br>|
|11 TO 13|-0.448<br>|0.08<br>|-0.185<br>|0.09<br>|
|14 TO 16|-0.543<br>|0.06<br>|-0.527<br>|0.07<br>|
|17 TO 19|-0.497|0.06|-0.610|0.06|
|MOTHER'S YEARS OF SCHOOLING|0.077<br>|0.02<br>|0.021<br>|0.02<br>|
|FATHER'S YEARS OF SCHOOLING|0.054<br>|0.02<br>|0.095<br>|0.02<br>|
|LIVED WITH MOTHER AT AGE 10|0.087<br>|0.20<br>|0.049<br>|0.20<br>|
|LIVED WITH FATHER AT AGE 10|0.286|0.14<br>|0.249<br>|0.14<br>|
|HAS ELECTRICITY AT HOME|0.76<br>|0.15<br>|0.602<br>|0.16<br>|
|NUMBER OF ROOMS IN HOME|0.141|0.03|0.075<br>|0.04<br>|
|DRY LAND FARMED BY HOUSEHOLD|0.001|0.00|0.000<br>|0.00<br>|
|WET LAND FARMED BY HOUSEHOLD|0.025|0.02|0.016|0.02|
|RESIDES IN OTHER URBAN AREA<sup>(DUMMY)aJ</sup>|-0.304<br>|0.15<br>|-0.320<br>|0.16<br>|
|RESIDES IN RURAL AREA (DUMMY)|-0.641|0.19|-0.296|0.21|
|HAD READING AND/OR MATH BOOK(S)|0.277<br>|0.14<br>|-0.005<br>|0.15<br>|
|NUMBER OF GRADES IN SCHOOL|0.282<br>|0.06<br>|0.278<br>|0.06<br>|
|SCHOOL HAD FURNITURE|-0.062<br>|0.22<br>|0.045<br>|0.21<br>|
|SCHOOL SERVED FREE FOOD|0.116<br>|0.11<br>|-0.048<br>|0.11<br>|
|NUMBER OF TEACHERS IN SCHOOL|0.109|0.04|0.087<br>|0.04<br>|
|4-6 TEACHERS|-0.109|0.06|0.047<br>|0.06<br>|
|6.+|-0.105|0.05|-0.079|0.05|
|-2 Log-likelihood|1445.5||1065.46||
|Sample size|3855||3960||
|Note: Data is for youths aged 8<sup>to 19.</sup>|||||

a/ Omitted category is Resides in<sup>Lima.</sup>
**Table 5: Networks and export participation, alternative estimators**

|Dependent variable:||export pa|rticipation||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln firm labor productivity|0.0014***<br>(0.0002)||||
|ln firm employment||0.0022***<br>(0.0003)|||
|ln GDP|0.0020***|0.0015***|0.0007***|0.0203***|
||(0.0003)|(0.0003)|(0.0000)|(0.0023)|
|ln GDP per capita|0.0011**|0.0008**|0.0004***|0.0124***|
||(0.0004)|(0.0003)|(0.0000)|(0.0015)|
|ln distance|-0.0049***|-0.0037***|-0.0020***|-0.0566***|
||(0.0010)|(0.0008)|(0.0000)|(0.0074)|
|EU member|0.0036**|0.0025*|0.0011***|0.0274***|
||(0.0018)|(0.0014)|(0.0000)|(0.0035)|
|common currency|0.0009|0.0007|0.0004|0.0066***|
||(0.0011)|(0.0009)|(0.0000)|(0.0013)|
|landlocked|-0.0024*|-0.0018*|-0.0009***|-0.0353***|
||(0.0014)|(0.0010)|(0.0000)|(0.0048)|
|common language|0.0853*|0.0793*|0.0699***|0.0682***|
||(0.0464)|(0.0462)|(0.0016)|(0.0091)|
|ln emigrants|0.0006***|0.0005***|0.0002***|0.0067***|
||(0.0002)|(0.0001)|(0.0000)|(0.0009)|
|Estimator|Probit|Probit|RE Probit|Cond. Logit|
|Firm effects|No|No|Random|Fixed|
|Observations|2852926|2852926|2852926|2852926|
|Firms|14782|14782|14782|14782|
|Notes: Marginal effects repor<br>Destinations|ted. In column<br>193|s (1) and (2), r<br>193|obust standard<br>193|errors clustered<br>193|

<u>Notes: Marginal effects reported. In columns (1) and (2), robust standard errors clustered</u> by importing country are in parentheses. In columns (3) and (4), robust standard errors are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

4
# **8. APPENDIX 1:**

# **Table A.1: Local Budget module of the Life in Kyrgyzstan survey for 2012**

# **8.D. LOCAL BUDGET**

_Now I would like to ask your opinion and involvement in the process of budget decision of local_ _<u>government authorities</u>_

|||Very well 1|
|---|---|---|
||How do you think, how informed are you about the|Well 2|
|I828|budget of your local government body (Aiyl Aimak<br>or municipal government)?|Badly 3|
|||Absolutely not informed 4|
||Please estimate, how much it is interesting to you,|Very interesting 1|
||how your local government body (Aiyl Aimak or<br>municipal|Interesting 2|
|I829|government) spends your money from taxes and|Little interesting 3|
||<br>payments?|Absolutely not interesting 4|
||Do you know that your local government body should<br>provide local population with|Yes 1|
|I830|<br>~~informationabout local budget?~~|No 2|
|||Easily accessible 1|
||Estimate, please, how accessible is information about|Accessible 2|
|I831|the<br>budget of Aiyl Aimak for population?|Accessible with difficulties 3|
|||Inaccessible 4|
||How do you think, can an ordinary citizen influence<br>budget process of his/her local|Yes 1|
|I832|~~t bdAil Aik iil~~|No 2|
||~~governmen oy (y ma or muncpa~~<br>Did you or members of your household try to influence<br>the budget process of your local|Yes 1|
|I833|<br>government body (Aiyl Aimak or municipal government||
|<br>durin|<br>g the last 12 months|<br>No 2|
||~~(since November2011)?~~||
||<br>When was the last budget hearing in your local<br>government body (Aiyl Aimak or<br>|_(month ear)_|
|I834|municipalgovernment)?|._ _. y_|
||Did you participate in budget hearing of your Aiyl Aimak<br>during the last 12 months|<br>Yes 1|
|I835|||
||(since November 2011)?|No 2|
|||Influences strongly 1|
||In our oinion how much does the articiation of||
||y p, pp<br>citizens in the process of local budget production<br>and|Influences few 2|
|I836|||
||implementation influence on quality of governmental<br>services, provided on local level?|Doesn't influence 3|
|||Do not know<br>99|

23
# **A Quarter Century Effort Yet to Come of Age:**

**A Survey of Power Sector Reforms in Developing Countries**

**_Tooraj Jamasb, Rabindra Nepal and Govinda R. Timilsina_**<sup>**_#_**</sup>

Keywords: energy, reform, efficiency, poverty JEL Classification: L52, L92, Q48

# Tooraj <u>(tooraj.jamasb@durham.ac.uk) is a Professor of Energy Economics at Durham University</u> Business School, Mill Hill Lane, Durham, DH1 3LB, United Kingdom; Rabindra (r.nepal@uq.edu.au) currently at The University of Queensland is soon commencing a Lectureship in Economics at the School of Business, Charles Darwin University and Govinda (gtimilsina@worldbank.org) is a Senior Economist at Environment and Energy Unit, Development Research Group, World Bank, Washington, DC.

We acknowledge inputs from John Besant-Jones, Subhes Bhattarchryaa, Anupama Sen, Jevgenijs Steinbuks and Mike Toman. The financial support was provided by Development Research Group of the World Bank.
This paper has assembled data at the all-India level and for the village of Palanpur, Uttar Pradesh, to document the growing importance, and influence, of the non-farm sector in the rural economy. The suggestion from the combined NSS and Palanpur data is of a slow process of nonfarm diversification, whose distributional incidence, on the margin, is increasingly pro-poor. The rich insights offered by a detailed village study suggest that the non-farm sector is not only increasing incomes and reducing poverty, but appears as well to be breaking down long-standing barriers to mobility amongst the poorest segments of rural society. Efforts by the government of India to accelerate the process of diversification could thus yield significant returns in terms of declining poverty and increased income mobility. It is important to recognize however that rising income inequality could, at least initially, accompany the declining poverty and expanding social mobility. At the all-India level this process may not be so apparent as rising rural incomes, even if they are associated with increases in village-level inequality, could contribute to a narrowing of the gap between rural and urban areas, and thus a reduction in inequality at the all-India level (see Elbers and Lanjouw, 2000). What this paper has documented, however, is that at the level of the village, non-farm diversification could well be associated with significantly higher income inequality.

Rising village-level inequality can be of potential concern as a growing body of evidence shows that inequality can shape the fabric of village society, the way in which village institutions function and evolve, and the scope for collective action at the village level (Mansuri and Rao, 2012, provide an extensive review). Going forward it will be important to monitor distributional outcomes at the local level, and there may be a need to adopt measures to counter the more adverse impacts on local level inequality. The concern is that failure to keep such inequalities in check could threaten the pro-poor impacts that we currently see from the ongoing process of structural transformation underway in rural India.

25
making of villages with dominant family clans than in those without.<sup>22</sup> As shown in Figure 7, when more households are in the largest family lineage clan, the village representative assembly is more likely to be authorized to examine village financial records (left axis) and is also more likely to report instances in which it exerted monitoring authority by overturning decisions of the governing party or villagers’ committees (right axis). Information from village elections also suggests other dimensions in which villages with large lineage clans may be more “democratic,” as these villages tend to have more candidates and candidates are more likely to make public speeches during their election campaigns (Figure 8).

If both more land rental and better monitoring occur in villages with larger family lineage clans and these factors contribute to a reduction in the average incidence of land reallocation, then the relationship between the timing of elections and the timing of land reallocations, will likely exhibit different patterns across villages with different shares of residents in the main lineage group. As in Figure 2 (shown earlier) when the sample is stratified into two groups based on whether 60% or more of the households are in the largest lineage group, we see that homogenous villages have less incidence of land reallocation before or during election years, and are more likely to support reallocations in the period after (or alternatively, two years before) an election.

Differences in timing of reallocations relative to election timing in more and less homogenous villages is consistent with improved use of elections to monitor leaders through competitive elections in more homogenous villages. In particular, a village leader in a more homogenous village may be less likely to initiate a reallocation if he (or she) is more at risk of losing an election should one occur in the

> 22By law, the village assembly supervises the work of the village committee (see article 18 of the 1998 Organic Law for Village Committees) and can be convened with a simple majority participation of the villagers at or above the age of 18 or with the participation of the representatives from at least two‐thirds of the households in the village. As a form of direct democracy, the village assembly is an unwieldy governing institution, especially in villages with large populations. In practice, the majority of villages (93% in our sample) adopt a more manageable village representative assembly instead. The real power of village (representative) assembly is questionable, however, given its infrequent meetings and its tendency to be controlled by village cadres (Oi and Rozelle, 2000).

_Land Tenure Insecurity and Migration, Page 22_
|in constant 2011 international $ (PPP)|(0.029)|(0.029)|(0.029)|(0.029)|
|---|---|---|---|---|
|Scenario: Solar Home System (1 = Yes)|‐0.212***|‐0.222***|‐0.226***|‐0.088|
||(0.051)|(0.052)|(0.050)|(0.283)|
|Scenario: Solar Lamp (1 = Yes)|‐0.612***|‐0.614***|‐0.620***|‐0.038|
||(0.059)|(0.056)|(0.055)|(0.262)|
|Number of household members||‐0.005|‐0.006|‐0.006|
|||(0.007)|(0.007)|(0.007)|
|Respondent’s age||‐0.001|‐0.001|‐0.001|
|||(0.002)|(0.002)|(0.002)|
|Respondent is female (1 = Yes)||‐0.068|‐0.072|‐0.074|
|||(0.049)|(0.051)|(0.049)|
|Respondent’s education:||0.120**|0.127**|0.118**|
|higher than primary school (1=Yes)||(0.055)|(0.055)|(0.055)|
|Household has a bank account (1=Yes)||0.242***|0.272***|0.245***|
|||(0.062)|(0.062)|(0.062)|
|Household contracted credit within last year||‐0.052|‐0.038|‐0.054|
|(1=Yes)|||||
|||(0.052)|(0.052)|(0.052)|
|Constant|0.601***|0.951***|1.752***|0.696***|
||(0.190)|(0.203)|(0.170)|(0.264)|
|Observations|1,854|1,792|1,792|1,792|
|R‐squared|0.308|0.318|0.315|0.320|

Notes. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered on the village level.

# _4.3 Comparing Household Benefits and Costs of Service Provision_

The results in section 4.2 clearly demonstrate that the household WTP for grid electricity increases with higher income and electricity demand. As households become wealthier, they put greater value on access to the grid, so that they can consume larger and more reliable amounts of electricity. Below we perform a simple cost‐benefit analysis to determine whether the estimated magnitude of the difference in WTP for the different electricity access technologies is high enough to cover the difference in investment costs. In doing so, we divide the investment cost of electricity access technologies by the estimated households’ monthly willingness to pay and obtain the hypothetical amortization period of the corresponding 24