rafmacalaba/datause-encoder-data huggingface dataset:
- 37 -

**<u>Table 9. Electricity demand estimation with different specifications for weather variables</u>**

||Biased|OLS|IV|esti|mator|Nelson|-Olse|n estimator|
|---|---|---|---|---|---|---|---|---|
|_I_|–0.163 ***|–0.156 ***|–1.024|***|–1.137 ***|–1.477|***|–1.583 ***|
||(0.017)|(0.017)|(0.166)||(0.219)|(0.225)||(0.300)|
|ln_MP_|0.565 ***|0.571 ***|–0.088|***|–0.188|–0.123||–0.233|
||(0.037)|(0.037)|(0.189)||(0.210)|(0.268)||(0.284)|
|ln_D_|0.016 ***|0.016 ***|0.031|***|0.032 ***|0.029|***|0.031 ***|
||(0.001)|(0.001)|(0.007)||(0.008)|(0.010)||(0.011)|
|ln_TEMP_|0.145 **|0.284 ***|0.043||–0.171|0.045||–0.169|
||(0.057)|(0.087)|(0.067)||(0.130)|(0.093)||(0.183)|
|_TEMP_<br>_TEMP_<br>_TEMP_<br>_D_<br>ln<br>*<br>)<br>(<br>||–0.012 *<br>(0.007)|||0.028 **<br>(0.011)|||0.029 *<br>(0.016)|
|ln_PREC_|–0.038 ***|–0.073 ***|–0.020|***|–0.031 *|–0.014||–0.028|
||(0.009)|(0.014)|(0.011)||(0.019)|(0.016)||(0.026)|
|_PREC_<br>_PREC_<br>_PREC_<br>_D_<br>ln<br>*<br>)<br>(<br>||0.015 ***<br>(0.005)|||0.011 *<br>(0.006)|||0.012<br>(0.008)|
|ln_Y_|0.020 **|0.021 **|0.035|**|0.037 ***|0.037|***|0.039 ***|
||(0.008)|(0.008)|(0.010)||(0.011)|(0.014)||(0.015)|
|ln_HH_|0.057 ***|0.056 ***|0.040|***|0.038|0.049||0.047|
||(0.021)|(0.021)|(0.024)||(0.025)|(0.031)||(0.032)|
|constant|4.223 ***|3.905 ***|6.303|***|7.198 ***|6.816|***|7.736 ***|
||(0.251)|(0.322)|(0.459)||(0.567)|(0.634)||(0.750)|
|Obs.|4762|4762|4678||4678|4678||4678|
|Adj R-squared|0.552|0.553|0.396||0.352||||
|_F_-statistics|96.78|94.88|54.20||49.86||||
|Pseud R-squared||||||0.213||0.21|
|LR chi2||||||1888.26||1901.12|

Other independent variables are omitted from the presentation: electricity and gas usage dummies for particular purposes, household ownership of 10 appliances, and district dummies.

Quantile regressions are performed to explore the presumably heterogeneous consumer behavior. However, the quantile technique cannot solve the endogeneity problem in price and quantity. Hence, the two-stage quantile regression (2SQR) estimator is used (Arias _et al_ ., 2001). Five quantiles are examined: .10, .25, .50 .75, and .90 (Table 10). The quantile estimation results have not differed significantly from the main results. However, it is found that large- and small-volume consumers are found to have different electricity demand functions. This can be interpreted as distributional impacts, because high-income households generally use more electricity.

Regardless of quantile, metering has been found useful to contain the demand for electricity. But the demand appears less elastic for large-volume customers. They are mostly the rich and
The Self-Organizing Financial Stability Map (SOFSM) is another tool to identify vulnerabilities. The changing nature of the numerical forecasts motivated this initiative. The effort was the “development of tools with clear visual capabilities to complement numerical predictions.” (ECB, 2010, p.1). SOFSM allows a two-dimension representation of a multi-dimensional financial stability space in colorful maps. The systemic financial crisis metrics is based on a FSI from Duca and Peltonen (2011), as above mentioned.

Other example of visual instrument is illustrated in IMF (2010), as the EWE results are aggregated depending on the number of flags in each sector, considering the distance of current situation in standard deviation from the models’ forecast. And then each sector assessments are aggregated with equal weights to an overall rating. Colors are attributed (red, orange or green), meaning different levels of vulnerabilities. In the case of missing data and/or models, judgment and qualitative feedback are also used to provide a final country rating.

# **References**

Agénor, P.-R., Koray, A., and Pereira da Silva, L., 2011. _Capital Regulation, Monetary Policy and Financial Stability_ , Working Paper No 237, Central Bank of Brazil, April.

Agénor, P.-R., and Pereira da Silva, L., 2012. _Macroeconomic Stability, Financial Stability, and Monetary Policy Rules._ Forthcoming, International Journal of Central Banking.

Alfaro, R. and Drehmann, M. _Macro Stress Tests and Crises: What can we Learn?_ BIS Quarterly Review (December 2009), 29-41.

Akerlof and Shiller, 2010. _Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism_ . Princeton University Press. Princiton New Jersey.

Angelini, P., Neri, S. and Paneta, F., 2012. _Monetary and Macroprudential Polices_ . European Central Bank, Working Paper Series No 1449.Basel Committee on Banking Supervision, 2010. _An Assessment of the Long-Term Impact of Stronger Capital and Liquidity Requirements_ . August, www.bis.org/publ/bcbs173.pdf BCBS (Basel Committee on Banking Supervision). 2010. _An Assessment of the Long-Term Impact of Stronger Capital and Liquidity Requirements._ Bank for International Settlements. Basel, Switzerland. August BCBS (Basel Committee on Banking Supervision), 2011. _Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems._ Bank for International Settlements. Basel, Switzerland.

BCBS (Basel Committee on Banking Supervision),.2012. _Models and Tools for Macroprudential Analysis._ Bank for International Settlements, working paper N. 21. Basel, Switzerland.

Bean, C., Paustian, M., Penalver, A. and Taylor, T., 2010. _Monetary Policy after the Fall._ Federal Reserve Bank of Kansas City, Jackson Hole Symposium Bernanke, B., 1983. _Nonmonetary Effects of the Financial Crisis in Propagation of the Great Depression._ American Economic Review, vol. 73(3), pages 257-76, June.

32
in light of the measures taken by the Indian government to promote the political participation of women. In the transcripts we were also able to identify instances where decisions regarding the provision or maintenance of public goods were taken. Using these instances, we ...nd that decisions, and in particular positive decisions, are more likely to be reached for the public goods preferred by the landed class. We want to emphasize that the evidence of inequities is restricted to the deliberative space of the village meetings. We do not have data about the policy outcomes that may follow these meetings, so we cannot say whether the inequities in deliberation translate into inequities in outcomes.

Having found that the preferences of the landed class are more likely to be mentioned and take up more time in the meeting, we also want to investigate whether any village level characteristics accentuate or mitigate this e¤ect. Literacy has been shown to have a positive e¤ect on the outcomes of local governance. For example, Besley, Pande and Rao(2005b) ...nd that increased literacy reduces village leaders’opportunism. Our ...ndings also show that literacy has a positive e¤ect in that it mitigates the power of the landed in village meetings. Political reservations for women and disadvantaged castes have been also documented to play an important role in local governance. The evidence on the role of women’s reservations is mixed. Chattopadhyay and Du‡o(2004b) ...nd that women leaders bene...t their villages while providing the public goods preferred by women. Ban and Rao(2008a), on the other hand, ...nd that women leaders do not in‡uence the provision of public goods and that their performance is hampered by the presence of a large upper caste landowner faction. Chattopadhyay and Du‡o(2004a), and Besley, Pande and Rao(2004b) ...nd that reservations for disadvantaged castes yield bene...ts to the members of these castes in the village. In this paper, we ...nd that reservations for women and disadvantaged castes exacerbate the power of the landed in village meetings. Finally, we examine the 6
# **4 One Solution: Temporary Migration**

## **4.1 High Costs and Returns to Migration**

Temporary international migration is an expensive endeavour for which the BRMS provides a detailed breakdown for the first time. At their median values, the cost of migration exceeds (pre-departure) household income in the sample by a factor of 2.6. More than 10% of individuals require at least 10 years to raise the entire cost through their earned incomes.<sup>9</sup> Accordingly, credit access is critical for migration. The largest part of migration expenses, 56% is composed of the costs charged by the intermediary agencies that match workers with foreign employers.

In this section, we use the BRMS data to provide a cost-benefit analysis of temporary migration. Using a range of measures, we show that expected returns make migration a very profitable investment,<sup>10</sup> which enables returnees to start businesses for which local credit markets do not provide the necessary funds. Specifically, for these calculations, we use information on individuals’ earnings prior to migration, their wages while abroad, the duration of stay, the share of migration costs covered by loans, as well as the saving rates observed in different locations. Table 2 presents details of this information.

First, we compute a simple earnings-to-cost ratio, which we average across _N_ individuals _j_ in the sample: On average, the ratio of total earnings accumulated abroad over the cost of migration, _r_ 1, amounts to 9.3. Total foreign earnings are calculated as the time spent abroad multiplied by the average wage a migrant reports to have earned abroad. Foreign wages and migration _N_ costs imply an average break-even duration of _N_ <u>1</u> � _Cj/Wj_<sup>_a_=1</sup><sup>_._1years.Inourdata,the</sup> _j_ =1 median and the average duration of stay abroad are 4.7 and 6.5 years, respectively. A large majority of migrants (88%) hence earned more than their migration costs, implying a positive log earning-to-cost ratio. Panel (a) of Figure 5 shows the full distribution of this measure.

> 9Appendix Figure A4 displays the full distribution of migration costs relative to pre-migration household income.

> 10Our data in fact suggest that migrants are yet more optimistic about their foreign earnings potential. In the survey, we elicited retrospectively pre-migration expectations about wages to be earned abroad. While this is an imperfect measure of actual ex-ante expectations, the data do indicate a strong overestimation of foreign earnings: 77% of migrants in our sample expected higher earnings before migration than what they actually earned overseas, with both mean and median realized earnings falling short of the initial expectations by about 40%. This fact cannot be explained by recall bias, which would _reduce_ the difference between actual earnings and the retrospectively reported expectations. Our analysis here focuses on realized earnings.

14
|_IPV experience_<br>**World Value Survey Vari**|Ever experienced any form of physical or sexual<br>violence by past or current partner. Coded 1 if a<br>woman has ever experienced any violence and 0<br>for no violence and missing for women selected<br>and interviewed for the domestic violence module.<br>**ables**|D105a-k (has ever been<br>slapped; punched with fist or<br>hit by something harmful;<br>strangled or burnt;<br>threatened with knife/gun or<br>other weapon; CS physical<br>violence by partner; had arm<br>twisted or hair pulled;<br>physically forced into<br>unwanted sex; forced into<br>other unwanted sexual acts;<br>physically forced to perform<br>sexual acts respondent<br>didn't want to.|
|---|---|---|
|_Men have more right to a_<br>_job_|<br>1 if a woman or man agrees that when jobs are<br>scarce, a man has more right to a job than a woman,<br>and 0 if theydisagree or “neither” agree or disagree.|C001_01|
|_University is more_<br>_important for a boy_|1 if a woman or man agrees or strongly agrees that<br>university education is more important for a boy than<br>for agirl,and 0 if theydisagree or stronglydisagree.|D060|
|_A woman has to have_<br>_children to be fulfilled_|1 if a woman or man thinks that for a woman to be<br>fulfilled, it is necessary that she has children, and 0 if<br>theythink that it is not necessary.|D019|
|_Both spouses should_<br>_contribute to household_<br>_income_<br>Source: DHS and WVS|1 if a woman or man agrees or strongly agrees that<br>both spouses/partners should contribute to<br>household income, and 0 if they disagree or strongly<br>disagree.|D058a|

53
financing to these firms. Different transactional technologies that facilitate arms-length lending (such as credit scoring and significantly standardized risk-rating tools and processes, as well as special products such as asset-based lending, factoring, fixed-asset lending, and leasing) are increasingly applied to SME financing. For example, hard information on the SME or its owner obtained from credit bureaus allows banks to infer future loan performance and thus enables the use of credit scoring to process and approve small loans at a scale that makes costs sufficiently low.<sup>20</sup> Moreover, the pledging (as collateral) of assets that do not lose much value over time and are relatively easily liquidated (e.g., equipment and real estate), provide greater assurances of repayment, even when contract enforcement processes are relatively imperfect. Also, reliable accounts receivable can underpin factoring, while the renting of tangible and marketable assets through leasing can help overcome costly contract enforcement processes including ambiguous commercial laws and inefficient bankruptcy procedures (as the creditor retains the property rights over the asset). Thus, when good financial information is not available to gauge capacity or willingness to pay, banks can use other types of hard information and incentive-compatible mechanisms to increase the likelihood of repayment. In this way, banks can compensate for weaknesses in the institutional environment. Moreover, these mechanisms free banks from having to rely on government subsidies to lend to SMEs.

Third, lending is just one part of a larger overall package that banks provide to SMEs. Banks find SMEs profitable through a combination of services; and this places

> 20 See Jappelli and Pagano (2003), Kalberg and Udell (2003), and Miller (2003) for evidence on the value of credit registries.

9
Table <u>5A Monthly median earnings for workers with secondary education by occupation group</u>

|||||Other w|age workers|with seconda<br>Services|ryschool educa<br>Skilled|tion<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
|Country|Teachers|Managers|Professionals|Technicians|Clerks|and sales<br>workers|agricultural<br>workers|Craft<br>workers|Machine<br>operators|Elementary<br>occupations|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Burkina Faso|603|_603_|_777_|464|441|200|_172_|181|-|-|
|Côte d'Ivoire|975|_498_|_699_|_847_|_841_|309|-|_407_|_424_|_212_|
|DR Congo|96|100|91|91|102|109|91|154|182|112|
|The Gambia|254|400|265|273|235|204|255|204|204|153|
|Ghana|278|556|389|367|333|222|194|339|333|233|
|Liberia|207|431|237|279|306|222|278|296|301|176|
|Malawi|258|544|243|-|254|147|176|235|235|180|
|Namibia|1,447|1,537|1,265|1,009|723|362|330|542|814|362|
|Niger|338|_889_|_267_|_886_|544|336|273|367|-|-|
|Nigeria|230|_402_|259|253|368|172|_92_|460|459|239|
|Senegal|688|1,034|690|739|647|345|334|371|323|194|
|Sierra Leone|301|_222_|356|319|323|289|-|278|278|222|
|Tanzania|724|724|-|757|600|563|_402_|644|584|394|
|Uganda|188|_494_|_395_|491|_79_|166|_197_|232|286|158|
|Zambia|1,610|1,073|1,646|1,073|716|304|206|465|465|233|
|**Average**|546|634|541|561|434|263|231|345|376|221|

Notes: All the earnings are in PPP ($,2011). Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other corresponding occupation groups; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. ISCO-08 group definitions are used to classify occupation groups (ILO, 2012). Elementary occupations involve the performance of simple and routine tasks which may require the use of hand-held tools and considerable physical effort. Earnings in _italics_ are estimated based on fewer than 10 observations. Sample only includes workers with secondary education. The top and bottom 1% earnings are trimmed. Source: Surveys listed in Table 1.

37
detrimental to non-farm production, contrary to what is sometimes assumed. This appears to be linked not so much to income effects but rather to reliance on the market for the satisfaction of consumption needs.

These results are partly associated with differences in factor endowments and preferences. Distance between surveyed households and the nearest market is no longer significant in most regressions once we control for household characteristics. Without household panel data, we are unable to tell whether i!t is proximity to market that drives household factor endowments (through internal migration or accumulation) or whether it is the opposite. Distance to towns, however, remains strongly significant even after household characteristics are included. This suggests that distance to towns has an effect on economic activity and market participation beyond that of differences in (observed) factor endowments. These issues deserve further research.

# References

- Abdel-Rahman, Heshani M., "Product Differentiation, Monopolistic Competition and City Size," 1988, 18, 69-86.

- , "Productivity Gains from Geographic Concentration of Human Capital: Evidence from the Cities," _Journal oJ Urban Economics,_ 1993, 34, 380-400.

- ,"Economies of Scope in Intermediate Goods and a System of Cities," _Regional Science and Urban Econ.,_ 1994, 24, 497-524.

- Arthur, W. Brian, "Sell-Reinforcing Mechanisms in Economics," in "The economy as an evolving complex system, SFI studies in the sciences of complexity," P.W. Anderson, K.J Arrow and D. Pines (eds.), 1988.

34
Figure 4.0: Average HLO derived from EGRA and PIRLS, 2000-2017 <!-- Start of picture text --> 330.5<br>Egypt<br>364.6<br>416.6<br>Indonesia<br>411.8<br>442.0<br>Macedonia<br>414.6<br>449.6<br>Honduras<br>432.2<br>0 100 200 300 400 500<br>HLO - PIRLS HLO - EGRA<br><!-- End of picture text -->

# 2.2. PASEC (2014)

The most recent PASEC in 2014 uses Item Response Theory (IRT) and is a high-quality RSAT. Ten countries participated, including Benin, Burkina Faso, Burundi, Cameroon, Chad, Congo, Cote d’Ivoire, Niger, Senegal and Togo. We include these countries using available microdata. Madagascar also participated in 2015 and was scaled to the PASEC 2014 round. We include Madagascar in our database using estimates from reports. To this end, inclusion of the recent PASEC data adds countries as well as enhances the quality of data from sub-Saharan Africa. This marks a significant improvement over past datasets. To provide a link to past PASEC rounds, which use classical test theory and have substantially different test items, we create an inter-temporal comparison using Togo, which participated in all rounds of PASEC. However, given that PASEC did not conduct an intertemporal scaling calibration as is typical for IRT ISATs and RSATs, priority should be given to analyzing the most recent PASEC 2014 data. Any intertemporal comparisons should be conducted with caution.

# 2.3. SACMEQ (2013)

SACMEQ collected its fourth round of data in fifteen countries in East and Southern Africa from 2012-2014. These include: Botswana, Kenya, Lesotho, Mauritius, Malawi, Mozambique, Namibia, Seychelles, South Africa, Tanzania, Uganda, Zambia, Zanzibar and Zimbabwe. SACMEQ was designed and scaled to be comparable to past rounds. We include estimates from reports for SACMEQ since the microdata is pending.

8
# **B.2 Malawi**

The LSMS-ISA data from Malawi includes two separate surveys: (1) Integrated Household Survey, from which we include the first wave and (2) Integrated Household Panel Survey which includes three waves (NSO, 2012; NSO, 2015; NSO, 2017). The two surveys are different in their representation of various households within the country. In this analysis, we rely only on the Integrated Household Panel Survey.

The Integrated Household Panel Survey begins with Wave 1 in 2010 and includes 3,247 households from 204 enumeration areas that were visited as part of the Third Integrated Household Survey 2010/11 and that were designated as “panel” for follow-up, starting again in 2013. The sample was designed to be representative at the national-, urban/rural-, and regional-level at baseline. Wave 2 from 2013 aimed to track all panel households from Wave 1, including all individuals that changed locations between the waves. The Wave 2 household sample size was 4,000, including new households that were formed by split off individuals that were tracked. Finally, Wave 3 from 2016 aimed to track all households and split off individuals that were ever associated with a random half of 204 original enumeration areas that had been visited in 2010. The Wave 3 household sample was 2,500 households, including again new households that were formed by split off individuals that were tracked from previous rounds.

# **B.3 Niger**

The LSMS-ISA data from Niger includes two rounds. In Wave 1. approximately 4,000 households in 270 Zones de D ́enombrement (NIS, 2014). The sample is nationally representative, as well as representative of Niamey, other urban, and rural areas. Households visited in Wave 1 were revisited in Wave 2, including households and individuals who moved after the 2011 survey (NIS, 2016). When the entire household moved within Niger, the household was found and re-interviewed in the second wave. When individuals from the household moved, one individual per household was selected to follow. This forms a sample of approximately 3,600 households in Wave 2.

# **B.4 Nigeria**

The LSMS-ISA data from Nigeria includes three waves (NBS, 2012; NBS, 2014; NBS, 2019). The total sample consists of 5,000 panel households and is representative at the national level. Households are visited twice per wave of the Panel, both post-planting and post-harvest. The post-harvest visit is implemented jointly with a larger General Household Survey of 22,000 households (5,000 panel and 17,000 non-panel households). The sample is representative at the national level and provides reliable estimates of key socio-economic variables for the six zones in the country.

106
Table A5: Attrition

||(1)<br>Attrited from<br>4-month follow-up|(2)<br>Attrited from<br>10-month follow-up|(3)<br>Attrited from<br>15-month follow-up|
|---|---|---|---|
|Offered Training|0.01|0.01|0.02|
||(0.01)|(0.01)|(0.01)|
|Observations|900|900|900|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. We do not test for differences in means for females since the lottery was randomized for each gender separately.

30