rafmacalaba/datause-encoder-data huggingface dataset:
# **VI. Conclusion**

In this paper, we develop a new method to monitor the quality of power supply using high-frequency satellite imagery of nightly lights data. We define PSI as the excessive variability in light output relative to the predicted variability in light output. Variability is measured as the deviation from the mean brightness of a locality observed from a lengthy time series of nighttime satellite imagery. To test the validity of the method, we compute the PSI index for all 600,000 villages in India each year from 1993 to 2013. We find that power outages predicted by the PSI index are consistent with those reported by alternative ground-based measures of power supply reliability in India.

Our method of monitoring PSI opens new opportunities for policy research, power system planning, and citizen engagement. First, previous analyses of the impact of power outages have often relied on shortage data at the district or state level. Our research shows that PSI varies significantly across villages. Using the PSI index, researchers can capture the spatial granularity in supply disruption to better estimate the effects of power outages on welfare and growth. Second, the method developed in this study can potentially be used to facilitate near real-time monitoring of power supply disruption by utilities and government agencies. Power system planners can rely on this information to identify outage-prone areas in a cost-effective way. Finally, when information on PSI is made available to consumers, it could help consumers to better engage with utilities to improve the quality of power supply.

In the future, we plan to expand the approach to other parts of the world and to develop a global database of the PSI index for other countries across time.

21
probability of having borrowed in the past 6 months for the business<sup>29</sup> . For the first follow-up, at least, this seems to mask a difference between men and women - men are less likely to have borrowed, while women are more likely to have taken a loan (this latter effect is significant at 10 percent). In addition to the information provided in table 9, we also do not see any change in the outstanding debt of the firms in the treatment groups relative to the control group.

Nonetheless, firms in the group offered bank information sessions seem to be less credit constrained at the first follow-up survey than those in the control group, as there is an economically and statistically significant impact of the activities on the amount of money they say their firms can borrow if suddenly facing an unexpected situation needing extra funds for the business in two weeks. This increased financing capacity seems to be driven by the opportunities of using formal financing institutions rather than depending on family and friends – while 59 percent of businesses in the group offered bank information sessions said they would borrow through a bank to respond to this unexpected financing need (62 percent for men and 54 percent for women), that would only be the case for 46 percent of the control group (47 percent for men and 44 percent for women)<sup>30</sup> . By the time of the second follow-up, the effects on the third group of the amount businesses can borrow in 2 weeks is no longer statistically significant, although still large for women.

The treatment group offered BRC + bank information sessions, which received business bank accounts through the SME Department of a local bank, also had significantly large impacts on the use of insurance schemes in the name of the business. The control group access to insurance schemes was of 1 percent at the first and the second follow-up, but was 9 percent for the third treatment group. Within the firms in the group offered bank information sessions with insurance schemes, 56 percent of them had insurance against weather incidents, 24 percent against fire<sup>31</sup> , 20 percent against theft, and 16 percent for life/health coverage of the business owner (data from the second follow-up).

> 29 A recent study in Chile (Kast and Pomeranz, 2014) suggests savings and short-term credit could be substitutes. 30 Conversely, while 23% of group three business owners would borrow from family and friends for this situation of the unexpected need of money (22% for men and 24% for women), that would be the case for 32% of the control group (34% for men and 28% for women). 31 In 2014, there was a large fire in one of the main markets in Lilongwe where our study is operating: http://www.nyasatimes.com/2014/07/30/fire-guts-lilongwe-tsoka-market/.

24
MENA countries have highly centralized government fiscal structures. This is despite our observation that there is a potentially rich deconcentrated and decentralized government systems. In the next section, we will examine the reasons behind this heavy centralization using different regression analyses.

# **4. Determinants of Centralization and Decentralization**

# **4.1. Measurement Issues and Review of the Literature**

Decentralization is difficult to measure. Despite the popularity of decentralization as a research topic in recent literature, there are serious problems with the measurement of decentralization. Ebel and Yilmaz (2003) note that this is mainly due to data imperfections, particularly in the IMF’s Government Finance Statistics (GFS). Problems with data availability and quality get worse in the case of the MENA countries. Hence an empirical investigation of decentralization in the MENA region is difficult due to aforementioned data problems. As so many other studies did before us, we started with data from the GFS. We have realized however that data on conventional fiscal decentralization measures such as share of local governments in total government expenditures or revenues are simply nonexistent. That left us with using data on total central government expenditures relative to an economic base such as gross domestic product (GDP). Thus, we first used total budgetary central government expenditures as share of GDP of the country. While this measure captures mainly the size of the central government, it will also be closely related to the degree of delegation of central government powers to lower levels of government. A reduction in the size of the central government could be one avenue for decentralization. Indeed, we get a negative correlation (-0.47) between the share of local government spending in GDP for the 12
# (b) <u>Descriptive statistics</u>

In **Table 3** we report school characteristics by geographical sample. The information corresponds to the 2014 School Census (Panel A to D) and ECE 2015 (Panel E). Differences between both samples ( _metropolitan_ versus _regional_ ) are substantial. Schools in the _metropolitan_ sample are larger –with more classrooms, and more students per classroom– more likely to have an afternoon shift, and in most cases have better physical infrastructure (access to sewage, existence of a science laboratory) than those in the _regional_ sample; they are also less likely to have bilingual schools and to be in districts where the main Peruvian social programs operate. Concerning students’ achievement in these schools, attainment is higher in the _metropolitan_ sample, but grade attainment is low in general; in _Mathematics_ and _Reading Comprehension_ , only 6% and 11% attain a satisfactory level in each subject, respectively.<sup>18</sup> Based on this information, we also report results from the balancing tests (see **Table A1** in the Web Appendix). Treatment and control schools appear to be similar in most observed dimensions. Some differences are observed (by day shift and sex), however, these differences are not substantial in magnitude. Unfortunately, no ECE was available before treatment to confirm balancing in the outcome variables, as 2015 was the first year in which ECE was administered in schools at the secondary level.

**Figure 2** presents kernel distributions for _Mathematics_ and _Reading Comprehension_ standardized test scores in 2015, separately for treatment and control schools, **Figure 3** presents analogous information for 2016, including also scores from _History, Geography, and Economics_ . A clear pattern arises whereby improvements are observed in all areas and both years in the _regional_ sample. In contrast, no such improvements are observed in the _metropolitan_ sample. This graphical analysis strongly mirrors the parametric analysis presented later in the paper.

> 18 Results correspond to the control group.

13
# **Table 9: Effects of Peer Adherence Support (PAS) and Food on Timeliness of Visits**

||Clinic - delay (days)|Clinic - delayed<br>(yes/no)|Hospital - delay<br>(days)|Hospital - delayed<br>(yes/no)|
|---|---|---|---|---|
|**A. Intent-to-treat analysis**|||||
|Treatment|6.197|0.089|10.321|0.192 *|
|Treatment * ITT PAS only|-0.589|-0.105|7.372|-0.110|
|Treatment * ITT PASnFood|-5.794|**-0.177 ***|**-22.587 *****|**-0.456 ****|
|n (N)|192 (2,108)|192 (2,108)|114 (844)|114 (844)|
|F-test (p-value)|2.07 (0.082)|2.37 (0.050)|3.60 (0.006)|1.78 (0.131)|
|Treatment|6.163|0.089|10.091 *|0.189 *|
|Treatment * ITT AnyPAS|-2.979|**-0.138 ***|-3.235|-0.232|
|n (N)|192 (2,108)|192 (2,108)|114 (844)|114 (844)|
|F-test (p-value)|2.34 (0.071)|2.95 (0.031)|1.79 (0.147)|1.26 (0.288)|
|**B. Instrumental variable (IV) analysis**|||||
|Treatment|9.798|0.335|24.396 **|0.525 **|
|Treatment * PAS only|-0.650|-0.301|-1.047|-0.408|
|Treatment * PASnFood|-10.623|**-0.439 ***|**-39.827 ****|**-0.821 ****|
|n (N)|192 (2,108)|192 (2,108)|108 (801)|108 (801)|
|F-test (p-value)|374.48 (<0.001)|793.73 (<0.001)|219.99 (<0.001)|420.28 (<0.001)|
|Treatment|12.134|0.367|17.127|0.447 **|
|Treatment * AnyPAS|-8.924|**-0.415 ***|-14.296|**-0.549 ****|
|n (N)|192 (2,108)|192 (2,108)|108 (801)|108 (801)|
|F-test(p-value)|374.24(<0.001)|795.81(<0.001)|214.44(<0.001)|423.11(<0.001)|

_Note_ : Results are for Fixed Effect (FE) Instrumental Variables (IV) linear panel regression models.

Results are adjusted for age, sex, education and treatment duration.

_Source_ : Authors’ own calculations.

33
The results suggest that bilateral export growth is positively correlated with both the supply shocks in the exporting country (column (1)) and demand shocks in the partner country (column (2)). Specifically, a 1 percentage point decline in worker mobility relative to January 2020 is associated with a 0.42 percentage point decline in annualized export growth on average (column (1)), while a 1 percentage point decline in retail mobility in the partner countries is linked to a 0.30 percentage point decline on average (column (2)).

**Table1: Supply, demand and third-country shocks and bilateral export growth, OLS regression results**

|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|(5)<br>**_growth_**_ijkt_|(6)<br>**_growth_**_ijkt_|(7)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|---|---|
|**_work mobilityit(supply)_**|**0.419*****|||**0.439*****|**0.319*****||**0.347*****|
||-0.085|||-0.086|-0.088||-0.089|
|**_retail mobilityjt(demand)_**||**0.300*****||**0.313*****||**0.299*****|**0.306*****|
|||-0.048||-0.048||-0.048|-0.048|
|**_competition shockijkt_**|||**-0.337****||**-0.372****|**-0.358****|**-0.390****|
||||-0.165||-0.161|-0.165|-0.161|
|**_upstream shockilt_**|||**2.035****||**1.634*****|**2.075****|**1.485*****|
||||-0.843||-0.379|-0.843|-0.376|
|**_global outputmt_**|**1.085*****|**1.040*****|**0.915*****|**1.058*****|**0.952*****|**0.878*****|**0.926*****|
||-0.144|-0.144|-0.155|-0.144|-0.15|-0.154|-0.15|
|**Constant**|**0.949*****|**0.924*****|**0.984*****|**1.045*****|**1.029*****|**1.071*****|**1.108*****|
||-0.026|-0.02|-0.077|-0.029|-0.044|-0.078|-0.045|
|Observations<br>|631,111<br>|631,111|631,111<br>|631,111|631,111<br>|631,111|631,111|
|R-squared|0.400|0.399|0.400|0.398|0.400|0.399|0.398|
|Exporter-Time FE|No|Yes|Yes|No|No|Yes|No|
|Importer-Time FE|Yes|No|Yes|No|Yes|No|No|
|Exporter-Partner-Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Cluster|Exporter-|Exporter-|Exporter-|Exporter-|Exporter-|Exporter-|Exporter-|
||Partner|Partner|Partner|Partner|Partner|Partner|Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Exporter-partner-time FEs could not be included due to collinearity with the country shocks. Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

By contrast, competition shocks are negatively associated with bilateral export growth (column (3)) which is in line with expectations, because reduced production in third countries could be expected to boost export growth. Last, the coefficient capturing the upstream shock is positive and significant, suggesting that declines in industrial production in an exporter’s source countries, weighted by a sector’s reliance on imported inputs from these source countries, negatively affected export growth. The findings hold for different combinations of these shocks in columns (4) to (6) and also when all shocks are simultaneously 15
small and never statistically significant, meaning that IPT-G did not reduce moderate or severe depression in this population.

Finally, Table 6 Panel C shows that there are no effects on any of the primary or secondary mental health outcomes at the 24-month follow-up. The rates of minimal depression, as measured by the PHQ-8, appear to have bounced back from the decline during COVID-19, but the impact of therapy is now only 0.004 pp using the PHQ-8 (q = 0.889) and 0.014 using the GHQ-12 (q = 0.889). Similarly, the impact of IPT-G on either discrete score is positive, small, and not statistically significant. There are no effects on self-esteem, resilience, or locus of control at any follow-up round.

## **3.2.2 Impact of adding cash to IPT-G**

Table 7 presents the marginal impact of providing lump-sum cash transfers, announced and disbursed soon after the completion of IPT-G (and the completion of the _rapid resurveys_ ), on mental health. Hence, the impacts are only analyzed at the 12- and 24-month followups. At the 12-month follow-up (Panel A), i.e., approximately 6–8 months after the cash transfers, the marginal effects on the primary outcomes are large and negative: the likelihood of minimal depression is reduced by approximately 10 pp using either measurement scale (columns 1 & 2). Increases in the discrete scores are moderate, around 0.18 SD using either scale (columns 3 & 4). All of these estimates are statistically significant (q _<_ = 0.001). At the 24-month follow-up, the negative effects remain large and statistically significant for the primary outcomes - approximately 7 pp using either scale (q = 0.025). The increases in the discrete measures are smaller and not statistically significant. While there are no effects on self-esteem and locus of control at endline, individuals in the combined therapy (IPT-G+) arm also have significantly lower resilience than those in the IPT-G arm (0.17 SD; q = 0.062). We discuss the negative impacts of cash in Section 3.4, which may be linked to the COVID-19 pandemic, and unexpected negative shocks more broadly.

# **3.3 Treatment effects on human capital**

## **3.3.1 Impact of IPT-G**

Assuming that the pathway to increased human capital in the treatment group is through reductions in psychological distress and/or improved self-esteem, resilience, and self-efficacy, one would not expect to see large improvements in human capital indicators, given the small effects of IPT-G on mental health reported above. However, if simply the act of participating in IPT-G plays a role in increasing human capital, we may see some effects immediately after the completion of IPT-G, especially on outcomes measuring expectations and aspirations.

15
**Table A14: Perceived vaccine acceptance**

|**Estimate**|Burkina Faso|Malawi|Nigeria|Tanzania|
|---|---|---|---|---|
|Estimated national acceptance|74.4|75.1|78.4|63.3|
||(71.5 to 77.2)|(71.1 to 79.1)|(75.8 to 80.9)|(60.2 to 66.3)|
|Average perceived acceptance|43.2|47.7|59.1|43.6|
||(40.6 to 45.8)|(45.1 to 50.3)|(56.8 to 61.3)|(40.6 to 46.7)|
|Perceived acceptance (hesitant)|22.7|32.5|25.7|25.6|
||(19.2 to 26.2)|(27.8 to 37.2)|(20.9 to 30.5)|(20.8 to 30.3)|
|Perceived acceptance (willing)|49.3|52.5|64.8|51.5|
||(46.7 to 51.9)|(49.9 to 55.1)|(62.5 to 67.0)|(48.1 to 55.0)|
|_N (national)_|1,847|1,447|2,934|2,196|
|_N (perceived)_|1,551|1,287|1,995|1,416|
|_N (hesitant)_|389|315|280|407|
|_N (willing)_|1,162|972|1,714|1,009|

Note: Average perceived acceptance of COVID-19 vaccines in one's community. All values in percent. 95% confidence intervals in parentheses.

**<u><mark>Table A15: Decisionmaker within the household</mark></u>**

|**Decisionmaker **|BurkinaFaso|Malawi|Nigeria|Tanzania|
|---|---|---|---|---|
|Each adult for themselves|50.9|42.0|18.3|37.1|
||(46.7 to 55.2)|(37.6 to 46.5)|(15.2 to 21.5)|(34.0 to 40.3)|
|All adults together|6.6|17.9|6.6|18.2|
||(4.9 to 8.3)|(15.3 to 20.5)|(5.2 to 8.0)|(15.8 to 20.5)|
|Household head|42.3|39.4|69.0|44.4|
||(37.9 to 46.8)|(35.4 to 43.4)|(65.5 to 72.6)|(41.1 to 47.7)|
|Other household member|0.1|0.6|6.0|0.3|
||(0.0 to 0.3)|(0.0 to 1.4)|(4.3 to 7.8)|(0.1 to 0.6)|
|_N_|1,744|1,343|2,386|2,131|

Note: Decisionmaker within the household about vaccine uptake of adult household members. All values in percent. 95% confidence intervals in parentheses.

27
> c (2) ii<sup>= (BBPP</sup> ii<sup>−NNPPNN rrc</sup> ii<sup>) × GGrrTTP</sup> ii<sup>× (100% −NNPPNN PPrrTTPPMMPPMMTTMM MMc</sup> ii<sup>/T</sup> ii<sup>)</sup> IIMMc MMR<sup>MMTTNNTTccMMPPMMTT) × (100% −MMRccc RRPrrccPP</sup>

where the exposure net of provisions is risk-weighted using as a weight the difference between 100%<sup>6</sup> and the risk weight attached to the original loan before turning NPL (assumed to be the same as the risk-weight density, the aggregate risk weight to total assets: RWA/Total Assets).

For banks in EMDEs, problem loans are often divided into three main categories according to the credit quality of each loan: substandard, doubtful, and loss loans. Each category requires a different level of provisioning.<sup>7</sup> In line with previous work (see, for example, Cihák, 2007, Ong et al., 2010; and Ong, 2014), we assume that under an adverse scenario, the provisioning rate for new NPLs ( _NPL Provision Ratio_ ) is 55%. This ratio represents an average and assumes that new NPLs are equally distributed among the three categories and that the provisioning rates are 20% against substandard loans, 50% against doubtful loans, and 100% against loss loans (the exact average is 56.7%). We apply this provisioning ratio to the entire NPL exposure, thus ignoring the value of any collateral.<sup>8</sup> We also assume that provisioning requirements reduce both the value of regulatory capital as well as risk-weighted assets (RWA).<sup>9</sup> We calculate a bank’s Break Point (BP) as the NPL ratio that causes a decrease in total regulatory capital (TRC) to the minimum capital threshold (MCR). Using Equation (1), the BP for bank _I_ can be computed as follows: We compute a bank's Distance from Break Point (DBP) by comparing a bank’s BP to its actual NPL ratio: , 0} ii ii ii DDBBPP = max {BBPP −BBccMMBB NNPPNN MMc MMTT We set the DBP to 0 if a bank is already undercapitalized (i.e., when a bank’s BP is lower than its NPL ratio).

To aggregate the individual bank results to the country-level, we calculate the consolidated DBP (CDBP) for a subset of the most vulnerable banks. We do so by sorting banks according to their DBP. We define

> 6 According the Basel Committee on Banking Supervision (2019), the unsecure portion of any loan for which provisions are no less than 20% of the outstanding amount of the loan should carry a weight of 100%.

> 7 See the Bank Regulation Supervision Survey – available at https://www.worldbank.org/en/research/brief/BRSS - for detail on asset classification systems across 161 jurisdictions around the world, see Anginer et al. (2019) for a detailed explanation. 8 In general, provisioning is applied to the outstanding amount of NPLs minus the value of collateral. The value of collateral is in turn reduced by pre-defined haircuts established by supervisors. As we miss key information on the value of collateral and supervisory haircuts, we apply the value of provisioning to the entire NPL exposure.

> 9 This approach is in line with Ong (2014, p. 29).

5
16 contemporaneously, than an anticipated shock, but has a less lasting effect. For example, the changes in the GNPs are much larger in the case of the unanticipated shock. In both the cases, the expansionary monetary policy in the United States initially increases its own GNP and reduces other countries' GNPs. Among the industrialized blocks, Japan showed stronger reaction to the monetary changes in the United States than others. The changes in the GNPs gradually return to their normal position as the prices adjust.

# _3. MULTIMOD and Other Rational Expectations Models_

The above results are in broad conformity with the the results oI' simulations performed with John t .ylor's Multi-Country Model using the same assumptions (Table 4). Two points of difference between the two models, however, may be noted:<sup>16</sup> First, Taylor's results show larger increases in GNP deflators for the unanticipated monetary shock, while the MULTIMOD results show the reverse for all countries and regions. An implication cf the rational expectations hypothesis is that when a policy change (e.g., a monetary expansion) is expected to take place in some distant future (i.e., anticipated) and is taken into account in current policy evaluations, prices respond :,n advance of the policy changes. In other words, prices should rise faster in the case of anticipated shocks.

The second point of difference between the results of the two models is that prices in the United States are more responsive to a monetary expansion (anticipated or unanticipated) in Taylor's model than in MULTIMOD. However, higher output growth rates for Taylor (both for anticipated and unanticipated) might be only due to the difference in initial conditions.<sup>1 7</sup> '¶aylor, John B. 'The Treatment of Expectations in Large Multi-Country Econometric Models," in Bryant, Ralph C., _etal,_ (Eds.) _Empirical Macroeconomics for Interdependent Economics,_ The Brookings Institution, 1988, p. 16'-182.

17rhe time periods for the shocks reported in table 3 for MULTIMOD are different from the time periods in used for shocks in table 4 for Taylor's model.
**<mark>Table S1. Knowledge of government actions undertaken to curb the spread of COVID-19</mark>**

||Stay at<br>home|Restricted<br>travel|Close<br>schools|Lockdown|Close<br>businesses|Limit social<br>gatherings|
|---|---|---|---|---|---|---|
|Ethiopia|0.203***|0.167***|0.031|0.139***|0.059***|0.225***|
||(0.024)|(0.016)|(0.024)|(0.013)|(0.009)|(0.025)|
|Nigeria|0.506***|0.319***|0.128***|0.446***|0.332***|0.080***|
||(0.024)|(0.020)|(0.026)|(0.019)|(0.020)|(0.027)|
|Uganda|0.025|0.656***|0.242***|0.705***|0.329***|0.214***|
||(0.022)|(0.017)|(0.024)|(0.015)|(0.017)|(0.025)|
|Ethiopia-Nigeria|0.000|0.000|0.000|0.000|0.000|0.000|
|Ethiopia-Uganda|0.000|0.000|0.000|0.000|0.000|0.591|
|Nigeria-Uganda|0.000|0.000|0.000|0.000|0.898|0.000|
|Observations|9,113|9,113|9,113|9,113|9,113|9,113|
|R-squared<br>_Note_: Each column re|0.157<br>ports results fro|0.123<br>m a single regres|0.021<br>sion of a binary|0.178<br>variable equal to|0.096<br>1 if the respond|0.029<br>ent was familiar|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent was familiar with the government action and 0 otherwise. The binary variable is then regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

28