rafmacalaba/datause-encoder-data huggingface dataset:
# **7 Trend analysis**

This section presents results for which QFDs are estimated under current performance where data are available for two or more years. Multiyear trend analysis helps to check the robustness of the results— for example, to see if the reference year could have been an outlier—and identify trends within the data collected. In addition, where the AICD data are available, it is informative to compare results and assess longer-term trends. Although methods used are not strictly comparable between the two studies, significant changes are likely to indicate changes in QFD patterns.

## 7.1 Robustness of results in reference year

Multiyear data are available for 31 countries, and three or more years of data available for 23 countries. Detailed results are provided in annex 8. Data are sufficient to estimate the QFD under current performance. Conducting analysis under benchmark performance and breakdown of hidden costs for all years is beyond the scope of the paper.

As illustrated in annex 8, in general QFD estimates are relatively steady from year to year. For countries with three or more years of data, the pattern is generally consistent (improving, worsening, or remaining steady). Most of the countries with low QFDs are also improving over time, while most of the countries with high QFDs are steadily high. These patterns of change are broadly consistent whether QFDs are measured relative to GDP or revenue (Kenya and Tanzania are slightly different, reflecting different growth rates in GDP and utility revenue).

Several significant increases in QFD are noteworthy:

- In **Botswana** , the QFD increased significantly in 2012, following the commissioning of Morupule B coal plant, increasing capex. Further, major capacity constraints with the new plant forced the government of Botswana to rent short-term emergency power, increasing overall costs significantly and driving up the QFD.

- In **Malawi** , the QFD increased in 2013, in part due to the commissioning of a new 50-MW hydropower plant, and in part due to large increases in distribution expenses (particularly staff costs).

- In **Sudan** , the QFD increased significantly after separation from South Sudan.

Tariff increases and improved performance helped reduce QFD in several cases:

- In the **Seychelles** , a 30-percent tariff increase was implemented in 2012, sharply reducing the QFD. A new annual tariff review system was also put in place. A quarterly adjustment of tariffs was introduced in 2014 to pass through changes in fuel prices on the world market.

- In **Tanzania** , a 40-percent tariff increase was implemented in 2014. In addition, T&D losses were reduced steadily from 25 percent in 2011 to 18 percent for the period ending June 2015.

- In **Uganda** , the QFD declined, partly driven by a 48-percent tariff increase in January 2012 following the commissioning of Bujagali, and partly driven by T&D loss reductions from 27 percent in 2012 to 21 percent in 2014.

- Finally, the QFD in **Ghana** is expected to have fallen in 2014. The reference year used in this analysis is 2013, during which time an 80-percent tariff increase was implemented in October.

53
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averages were close to zero (which masked substantial heterogeneity). The overall pattern is once again similar when using median regression.

The results are also similar when restricting the analysis to primary school teachers or to public school teachers. Public sector teachers tend to have either no premium or a positive hourly earnings premium when compared to other public sector employees (13 of the 14 countries for which this could be estimated). Only in Tanzania do they have a deficit relative to comparable civil servant employees. Last, the hourly earnings premiums are typically also found for secondary school teachers (i.e., these are now more in line with teachers overall/primary school teachers).

Across contract types, earnings premia are higher (or less negative) for teachers with permanent contracts than for teachers with fixed-term contracts. For both monthly and hourly wages, there are more statistically significant premia for permanent contract teachers than for fixed-term contract teachers (Table 10).

# **<u>3.5. Correlations</u>**

In this section, we explore the associations between teacher pay and student performance as well as various broader aspects of the economy and the labor market. None of these associations should be assumed to be causal. Other variables, not observed in this analysis, may affect both teacher pay and other aspects of the economy. However, understanding how teacher pay differs across different types of economies both provides a clearer picture of where we observe higher teacher salaries and yields avenues for further exploration.

# **<u>3.5.1. Teacher pay and student performance</u>**

We examine the cross-country association between teacher earnings premiums and student achievement using two measures: first, the harmonized test scores from the Human Capital Index database, recently developed and then updated by the World Bank (2020); second, the rate of “learning poverty” defined as the share of children who are unable to read and understand a simple text by age 10 (World Bank, 2019a). For more details on these measures and our empirical specification, see Appendix 1. Figures 3 and 4 provide visual evidence of the association between teacher earnings differentials and student performance. Figure 3 shows the cross-country 19
party – as well as the scope, density and likely robustness of those networks. In a subsequent section, we go on to look at how network attributes correlate with the firm level variables summarized in Tables 1 and 2.

# **4.1 Some network descriptives**

Our objective is to study how firms and political exposed persons (PEPs) are linked and the economic impact of those links. The main innovation in this paper is to look beyond the binary character of the connection, i.e., whether a firm is connected or not, and identify whether connections are direct or, as in most cases, indirect through a network. So for example, in a binary case, some firms may look as if they are not connected, yet in reality they may be connected to PEPs indirectly as through connections to third parties that are, in turn, directly connected to PEPs. These third parties may be other firms, or political parties, or some other entity. In addition, some firms may be connected to multiple PEPs as well as many other firms, and thus be ‘super’ connected. Likewise, some PEPs may act as hubs with many spokes emanating from them. In a binary approach, all these network characteristics will be lost and the scale and impact of politically connected business may be significantly mis/under-estimated.

By definition, a network is composed of nodes and edges. In our case,<sup>9</sup> the nodes are firms, individuals, and political parties and the edges are the links between these entities. Firms are split into two groups, private and SOEs while individuals can also be separated between political individuals, and others.<sup>10</sup> A network consequently represents relationships between agents, while also providing some form of structure for those relationships.<sup>11</sup> Before deploying all the network analytics – betweenness, clustering, density and so on – it is useful to begin with some simple statistics and to look at the network from the point of view of the firms. In other words, this is to describe the network by looking at how many connections firms have, and with what types of entity.

Table 3 gives the percentage of firms (i.e. nodes) grouped according to the number of connections and whether a firm has connections exclusively with other firms or with firms and/or other entities. It is useful to single out the group of firms that have connections only with other firms, because this is a group where firms are linked with PEPs only by an indirect link (and this indirect link may or may not be intentional). How large is the group

> 9Firms can also be connected through inter and intra-industry trading, but such transactions (as for intermediate goods and services) are not measured in _PEPdata_ nor is this dimension being considered in the paper.

> 10 _PEPdata_ also contain other entities, such as international organizations.

> 11A good review of the wider literature on networks is Ward et al. (2011), see also, inter alia, Do et al. (2015); Goyal et al. (2006).

9
demand is larger amongst poor households. This estimate is statistically significant at the 10% level in column (5), and at the 1% level in column (6).

# _B. Gujarat: Video experiments_

Amongst the 20 Gujarat villages where video treatments were implemented, we randomized the content of the video viewed and the size of the discount coupon the household received. Correspondingly, we regress insurance purchase on the discount amount in rupees and the randomized video features: (i) whether the video featured a strong SEWA brand emphasis, (ii) whether a peer rather than authority figure endorsed the product, (iii) whether the policy is framed positively as paying in 2 of 10 years (rather than not paying in 8 of 10 years), and (iv) whether the product is framed in terms of “safety” rather than “vulnerability”. We also include a dummy for whether the household was part of the 2006 baseline survey.

Results are presented in Table 6. Columns (1) and (2) report basic results with and without village fixed effects, respectively, while (3) and (4) include additional interaction terms. As shown in the table, the overall take-up rate is 29%.

The size of the discount has a large effect on take-up. The coefficient on discount size is positive and statistically significant at the 1% level. The coefficient of 0.005 implies that raising the discount from Rs. 5 to Rs. 30 increases the probability of insurance purchase by 12.5 percentage points (or 43% of the sample average take-up rate). In contrast, none of the framing effects are significant at even the 10% level, and they are also jointly insignificant.

In columns (3) and (4) we interact the size of the discount with each framing effect. While in some cases the price sensitivity of demand does vary with framing treatments, we are unable to reject the null that these interaction terms are jointly zero. Finally, we find across specifications that households who participated in the 2006 baseline survey are significantly more likely to purchase insurance. However, this survey was not randomly assigned, and the identified effect thus includes any effect of being surveyed, combined with the fact that surveyed households were selected in part because they were considered more likely to buy insurance.

Panel B of Table 6 reports the sample average take-up rate in each district broken down by the size of the discount. Consistent with the regression estimates, insurance take-up is monotonically increasing in the size of the discount in each district. Also reported for two of the three policies is the estimated gross rate of return on the insurance policy, calculated as the ratio 15
of villages with at least one private school). The sampled villages were richer and larger than the average village; however, approximately 70% of Punjab’s population lived in such villages at the time. As part of the first survey, 1,807 households were surveyed, including information on 5,865 children between the ages of 5 and 15. These households were then revisited four times between 2004 and 2011.

Between 2016 and 2018, we attempted to contact and resurvey the children of the 2003 sample. Over two years of tracking, we completed in-person surveys for 75.1% of the sample. We have information through phone surveys or from a third-party respondent for another 9.4%. We do not have skill measurements for these “indirect” surveys, although we have data on many other outcomes of interest, including years of education, earnings, and migration. Thus, of the 5,865 children, we have at least partial information on 4,956 children, or 84.5% of the original sample, at the individual level. The implied annual attrition rate of (just above) 1% compares favorably to 10-year panels with the highest retention (Outes-Leon and Dercon, 2009).

Appendix A and Table A1 detail the tracking process, the different instruments used, and the types of attrition in the data. Of the 909 individuals on whom we have no data, 43 had died, 186 were living in four villages that fell into a military zone that our team could not access, 395 respondents refused to participate despite multiple attempts, and 285 could not be located. For an additional 550 individuals, the information does not come from face-to-face interviews with the respondent but from third-party surveys or phone calls. We therefore do not have skills measures for these individuals.<sup>7</sup> Respondents with indirect information and phone surveys were more likely to live outside the village at the time of the survey and more likely to be working (Table A2, Panel A). Attritors are less likely to have ever been married and more likely to be living outside their original district and outside the country (Table A2, Panel B). They were also poorer than other households and more likely to have a father living abroad in 2003 (Table A2, Panel D). We account for these differences using a variety of weighting schemes, Heckman selection models, and semi-nonparametric estimation techniques, all described in Appendix B.

> 7 They also include 15 respondents who answered the survey in person but whose skills measures cannot be used: 4 respondents did not finish the survey and 11 respondents for whom there was a bug with the test on tablets.

8
# **Table 13: Receiving SMS vs. Reporting Social Distancing**

|||Social dista|ncing=1;|Do not=0||||Social dista|ncing=1;|Do not=0||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|<br>source|0.263***|0.248***|0.248***|-0.012|-0.012|<br>week|0.211***|0.236***|0.186***|0.155***|0.155***|
||(0.012)|(0.012)|(0.011)|(0.010)|(0.010)||(0.038)|(0.039)|(0.036)|(0.035)|(0.035)|
|Urban||0.009|0.006|||Urban||0.013|0.010|||
|||(0.012)|(0.012)|||||(0.012)|(0.012)|||
|Ln(income pc)||0.045***|0.044***||-0.001|Ln(income pc)||0.048***|0.047***||-0.001|
|||(0.002)|(0.002)||(0.002)|||(0.002)|(0.002)||(0.002)|
|Constant|0.490***|0.354***|0.073**|0.558***|0.561***|Constant|0.542***|0.392***|0.096***|0.553***|0.555***|
||(0.006)|(0.009)|(0.031)|(0.002)|(0.006)||(0.005)|(0.009)|(0.032)|(0.001)|(0.005)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.046|0.085|0.156|0.000|0.000|R-squared|0.002|0.048|0.118|0.004|0.004|
|<br>Unique individuals|2,153|2,153|2,153|2,153|2,153|<br>Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 19
Coady, David, Margaret Grosh and John Hoddinott, 2004, _Targeting Transfers in Developing Countries: Review of Lessons and Experience_ , Washington D.C.: World Bank.

Del Ninno, Carlo and Bradford Mills. 2015 (Eds.). Safety Nets in Africa: Effective Mechanisms to Reach the Poor and Most Vulnerable. The World Bank, Washington D.C.

Dercon, Stefan and Pramila Krishnan. 2000. “In Sickness and in Health: Risk Sharing within Households in Rural Ethiopia,” _Journal of Political Economy_ 108(4): 688-727.

De Vreyer, Philippe, and Sylvie Lambert. 2016. “Intra-household Inequalities and Poverty in Senegal,” mimeo, Paris School of Economics.

Djuikom, Marie Albertine and Dominique van de Walle. 2017. “Marital Shocks and Women’s Welfare in Africa,” mimeo, World Bank, Washington, DC Duflo, Esther, Michael Greenstone, Raymond Guiteras and Thomas Clasen. 2015. “Toilets can Work: Short and Medium Run Health Impacts of Addressing Complementarities and Externalities in Water and Sanitation,” NBER Working Paper 21521.

Evans, David and Edward Miguel. 2007. “Orphans and Schooling in Africa: A Longitudinal Analysis,” _Demography_ 44(1): 35-57.

Ezememari, Kene, Nazmul Chaudhury and Janet Owens. 2002. “Gender and Risk in the Design of Social Protection Interventions,” Social Protection Discussion Paper Series No. 0231, World Bank, Washington, DC.

Filmer, Deon and Lant Pritchett. 2001. “Estimating Wealth Effects Without Expenditure Data—or Tears: An Application to Educational Enrollments in States of India.” _Demography_ 38(1): 115132.

Filmer, Deon and Lant Pritchett. 1999. “The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries.” _Population and Development Review_ 25(1): 85-120.

Filmer, Deon and Kinnon Scott. 2012. “Assessing Asset Indices.” _Demography_ 49(1): 359-392. Fiszbein, Ariel, and Norbert Schady, 2010, _Conditional Cash Transfers for Attacking Present and Future Poverty,_ World Bank, Washington DC _._ Grogan, Louise and Luc Moers. 2016. “Wealthier is not Healthier in Sub-Saharan Africa.” Mimeo, University of Guelph.

Haddad, Lawrence and Ravi Kanbur. 1990. “How Serious is the Neglect of Intra-Household Inequality?” _Economic Journal_ 100: 866-881.

25
turnover and life insurance premiums to GDP, particularly for high income countries. On the other hand, the data do not show a relationship between stock market turnover and non-life insurance premiums to GDP.

**Table 6: Interaction of Insurance Variables with Private Credit and Stock Market Turnover**

_Sample of 56 countries, 1976-2004 (5-year period observations) Dependent Variable: Growth in real GDP per capita Estimation Method: GMM-IV System Estimator_

||**[1]**<br>**Life insurance**|**[2]**<br>**Non-Life insurance**|**[3]**<br>**(Life + Non-Life)**|**[4]**<br>**Life insurance**|**[5]**<br>**Non-Life insurance**|**[6]**<br>**(Life + Non-Life)**|
|---|---|---|---|---|---|---|
|Constant|0.120 ***|0.137 ***|0.145 ***|0.123 ***|0.167 ***|0.156 ***|
||_0.000_|_0.000_|_0.000_|_0.011_|_0.000_|_0.000_|
|Private Credit to GDP|0.015 **|0.021 **|0.023 ***|-0.002|0.005|0.004|
||_0.032_|_0.013_|_0.000_|_0.810_|_0.379_|_0.560_|
|Stock Market Turnover|0.018 ***|0.031 ***|0.023 ***|0.025 ***|0.033 ***|0.026 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Insurance|0.764 ***|0.478 ***|0.490 ***|0.232 ***|0.300 ***|0.194 ***|
||_0.000_|_0.000_|_0.000_|_0.011_|_0.002_|_0.000_|
|**_Insurance and interactions_**|||||||
|Insurance * Private Credit|-0.893 ***|-0.812 ***|-0.545 ***||||
||_0.000_|_0.002_|_0.000_||||
|Insurance * Stock Market Turnover||||-0.160 *|-0.316 *|-0.120 **|
|||||_0.068_|_0.081_|_0.039_|
|**_Control Variables_**|||||||
|Log. Initial GDP per capita|-0.009 ***|-0.014 ***|-0.012 ***|-0.011 ***|-0.017 ***|-0.014 ***|
||_0.000_|_0.000_|_0.000_|_0.003_|_0.000_|_0.000_|
|Degree of openess|0.490 ***|0.551 **|0.517 **|0.630 ***|0.362 *|0.335|
||_0.003_|_0.025_|_0.033_|_0.004_|_0.104_|_0.162_|
|Governement consumption|-2.494 ***|-1.864 ***|-2.924 ***|-2.175 ***|-1.797 ***|-2.275 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Inflation|-0.003 ***|-0.003 ***|-0.003 ***|-0.003 ***|-0.003 ***|-0.003 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Terms of trade|0.056 ***|0.045 ***|0.047 ***|0.044 ***|0.057 ***|0.050 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Human Capital Investment|0.044 ***|0.052 ***|0.052 ***|0.049 ***|0.059 ***|0.054 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|**_Period shifts_**|||||||
|Period 81-85|-0.015 ***|-0.016 ***|-0.014 ***|-0.015 ***|-0.015 ***|-0.015 ***|
|Period 86-90|-0.009 ***|-0.012 ***|-0.009 ***|-0.012 ***|-0.012 ***|-0.012 ***|
|Period 91-95|-0.020 ***|-0.024 ***|-0.022 ***|-0.023 ***|-0.022 ***|-0.023 ***|
|Period 96-00|-0.021 ***|-0.027 ***|-0.025 ***|-0.026 ***|-0.027 ***|-0.027 ***|
|Period 01-04|-0.030 ***|-0.038 ***|-0.035 ***|-0.037 ***|-0.038 ***|-0.039 ***|
|Countries|56|56|56|56|56|56|
|Observations|263|263|263|263|263|263|
|Specification tests (p-values)|||||||
|<br>-Sargan test|0.456|0.463|0.321|0.459|0.315|0.421|
|-2nd. Order Correlation|0.319|0.525|0.380|0.336|0.464|0.325|

P-values in Italics. ***, **, * denote statistical significance at the 1, 5, and 10 percent level.

14
_EPEQ_ = (0.7) where _EXP_ is total household expenditure and _n_ is household size.<sup>2</sup> The OECD scale reflects economies of scale due to household size but does not incorporate gender differences.

Household expenditures were not deflated by a regional price index to take potential differences in prices within the country into account. The reason is that, except for Russia, the countries in the analysis are all fairly small and regional price differences can be expected to be minor. For example, for Poland (the second largest country in the set), regional price differences were found not to exceed 2 percent (Grootaert, 1995). For Russia, informal calculations suggested that the effect on poverty estimates of corretting for regional price differences was very small. During the period of analysis, several countries experienced significant inflation and in these cases expenditures were deflated with a month-by-month consumer price index. This yields real household expenditure per equivalent adult as measure of household welfare.

A cut-off point needs to be selected to serve as poverty line across the distribution of real household expenditure per equivalent adult. We rejected the use of an absolute line, such as x dollars in PPP-terms, due to the wide variation in income levels across the six countries. Indeed, it is not very meaningful to compare poverty profiles, when for one country the profile pertains to less than 5 percent of the population and for another

> 2 For the household sizes typically found in Eastern Europe and the FSU, this fonnulation is a close equivalent of the more conventional statement of the OECD scale whereby the first adult = 1, other adults = 0.7, and children = 0.5. The exponential formulation however simplifies the calculations.

4