rafmacalaba/datause-encoder-data huggingface dataset:
|S/N|Table A3: ODIN d<br>Data dimension|ata dimension,catego<br>Data category|ryand indicators<br>Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|1|Social Statistics|Population and Vital<br>Statistics|(1.1) Population by 5-year age<br>groups<br>(1.2) Birth rate<br>(1.3) Death rate|(1.1) Sex; marital status<br>(1.2) Sex; marital status<br>(1.3) Sex|To receive full point: Must have all indicators disaggregated<br>by sex.<br>To receive half point: Must have at (1.1) with one<br>disaggregation, or (1.2) and (1.3) with one disaggregation<br>each.|
|2||Education Facilities|(2.1) Number of schools or<br>classrooms;<br>(2.2) Number of teaching<br>staff;<br>(2.3) Education budget data.|(2.1) School stage; school type.<br>(2.2) School stage; school type;<br>(2.3) School stage; functional<br>categories.|To receive full point:Must have all three indicators<br>disaggregated as follows: (2.1) and (2.2) by school stage (see<br>notes) and one other disaggregation and (2.3) with one<br>disaggregation.<br>To receive half point:Must have one indicator with two<br>disaggregation or two indicators with one disaggregation<br>each.|
|3||Education Outcomes|(3.1) Enrollment rate;<br>(3.2) Completion or<br>graduation rate;<br>(3.3) Competency exam<br>results.|(3.1, 3.2) Sex; school stage;<br>age; school type.<br>(3.3) Sex.|To receive full point: Must have all indicators<br>disaggregated as follows: (3.1) and (3.2) by sex and one<br>other disaggregation, and (3.3) by sex.<br>To receive half point: Must have (3.1) or (3.2) with two<br>disaggregations, or (3.3) disaggregated by sex. Two indicators<br>with only one disaggregation each is not enough.|
|4||Health Facilities|(4.1) Number of health<br>facilities;<br>(4.2) Number of beds or data<br>on health care staff;<br>(4.3) Health budget data.|(4.1) Facility type;<br>(4.2) Facility type; department<br>type; staff type<br>(4.3) Functional categories|To receive full point: Must have two indicators with one<br>disaggregation each.<br>To receive half point:Must have one indicator with one<br>disaggregation.|

**<mark>Table A3 continued</mark>**

35
<!-- Start of picture text --> B Upward mobility gap HIC B Upward mobility gap ECA<br>c 50.00 | 50.00<br>3fo}<40.00 | 40.00<br>2<br>2 |<br>Faoa 30.00 | 30.00<br>= 20.00 20.00<br>8 10.00 | 10.00 ae<br>iS2 0.00 | 0.00<br>-0.01 0.00 0.01 0.02 -0.35 -0.30 -0.25 -0.20 -0.15<br>B Upward mobility gap LAC B Upward mobility gap Other<br>c 50.00 50.00<br>g<br>= 40.00 40.00<br>2<br>3=> 30.00 30.00<br>oa<br>> 20.00 20.00<br>822 10.000.00 ae 10.000.00 a<br>0.22 0.24 0.26 0.28 0.30 0.32 0.20 0.25 0.30 0.35 0.40<br>Range of estimated coefficient Range of estimated coefficient<br><!-- End of picture text -->

<!-- Start of picture text --> B Probability of higher education HIC B Probability of higher education ECA<br>< 1.60 | 1.60<br>2 |<br>g<br>= 1.20 | 1.20<br>F- |<br>2o0.80 |0.80<br>2 |<br>22 0.40 | 0.40<br>2© 0.00 | 0.00<br>-1.00 -0.80 -0.60 -0.40 -0.20 0.00 4.00 4.50 5.00 5.50 6.00<br>B Probability of higher education LAC B Probability of higher education Other<br>< 1.60 1.60<br>&<br>2<br>3 1.20 1.20<br>2<br>7]<br>3 0.80 0.80<br>Tv<br>2<br>2<br>2*° 0.400.00 ZN 0.400.00 aN<br>3.00 4.00 5.00 6.00 5.00 6.00 7.00 8.00 9.00 10.00<br>Range of estimated coefficient Range of estimated coefficient<br><!-- End of picture text -->
<!-- Start of picture text --> t P __ a<br>Wd gach 0-19<br>b ‘ ee Hl 2-2<br><!-- End of picture text --> <!-- Start of picture text --> “i ‘ No GSMA MMU services<br>(0) No cata avaiiabie<br>- i account services included in the GSMA<br>et - FD a | APPL 2008<br>igi e<br><!-- End of picture text -->
the most consistent factors associated with poor mental health (Bogic et al., 2015).<sup>9</sup> This is further supported by recent evidence from the economic literature that early-life exposure to war has a persistent effect on mental health (Singhal, 2019), but the impact may fade out with time (Moya, 2018).

The following simple example can help illustrate the rationale behind our identifying assumption. Consider a refugee living in Australia who was exposed to war/conflict in his home country. A possible IV may be a time-variant indicator representing the refugee’s past exposure to these traumatic experiences. Indeed, plotting the OLS estimates of the impacts of traumatic experiences on standardized mental health scores and their 95% CIs (controlling for other variables), Figure A1 (Appendix A) generally shows a strong correlation between refugees’ exposure to traumatic events and mental illness. Specifically, we find that while the correlation between mental illness and certain specific events (such as physical or sexual violence, imprisonment or kidnapping, and political or religious persecution) is not statistically significant, it is strongly statistically significant and positive for most traumatic events such as lack of food, water, shelter, or medicine, war or other conflict, natural disasters (e.g., floods or drought), and/or other events. Overall, there is a strong correlation between refugees’ exposure to _any_ of these traumatic events and mental illness (as shown by the 95% CI at the bottom of Figure A1, Appendix A).

However, we argue that such instrument does not fully take into account the fact that refugees’ traumatic experiences are time-varying. That is, these traumatic experiences may decline over the course of resettlement (Steel et al., 2002). Our IV improves on this instrument by exploiting both the differences of traumatic experience across refugees and its variation over time after the refugee settles in the host country. This strategy is, in fact, consistent with the

> 9 Studies in Australia have documented that traumatic experience of refugees leads to an increased risk of experiencing psychological trauma, including major depression, chronic anxiety, and PTSD (e.g., Steel et al., 2002).

11
Policy Research Working Paper 10330

# **Abstract**

The COVID-19 pandemic caused significant disruptions to economies around the world. In response to this, some developing countries offered reliefs such as electricity subsidies while others did not. How did the pandemic affect the electricity consumption of households and firms? Did the utility subsidies enable a quick recovery from the pandemic? And what are the distributional impacts of the utility subsidies? This paper leverages unique administrative billing data on electricity consumption from two African countries, Ghana and Rwanda, with differing policy responses to the pandemic to document the demand response of households and firms to the COVID-19 pandemic, and the role of utility subsidies during the period. Findings from the paper indicate that the pandemic led to higher consumption of electricity in both countries, albeit with variations across countries and sectors. While residential consumption soared, consumption of non-residential customers such as hotels and industries declined during the period. Electricity subsidies in Ghana during the pandemic explain the sharp increase in residential consumption. These findings highlight the potential effects of pandemic relief measures on household welfare.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jmensah2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 5. Market Concentration, Contract Enforcement, and Transaction Costs**

|Dependent<br>variable|(1)<br>Mgmt.<br>practices|(2)<br>Mgmt.<br>practices|(3)<br>Mgmt.<br>practices|(4)<br>Mgmt.<br>practices|(5)<br>Skills|(6)<br>Skills|(7)<br>Skills|(8)<br>Skills|
|---|---|---|---|---|---|---|---|---|
||All firms|All firms|All firms|All firms|All firms|All firms|All firms|All firms|
|HHI (industry)|0.524||||1.083*||||
||[0.517]||||[0.576]||||
|HHI (industry-<br>provincial)||0.415||0.314||0.865||0.815|
|||[0.317]||[0.302]||[0.574]||[0.586]|
|HHI (industry-<br>local)|||-0.219||||-0.148||
||||[0.293]||||[0.209]||
|Has contracts||||0.263**||||0.343***|
|||||[0.117]||||[0.110]|
|Transportation is<br>severe obstacle?||||0.592***||||0.209|
|||||[0.156]||||[0.141]|
|Industry dummies|N/A<br>(collinear)|No|No|No|N/A<br>(collinear)|No|No|No|
|Provincial dummies|Yes|No|No|No|Yes|No|No|No|
|Age control (>= 5)<br>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Size controls<br>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Ownership control<br>(foreign owned)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Female ownership|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|639|626|556|626|639|626|556|626|
|R-squared|0.200|0.188|0.192|0.246|0.169|0.167|0.152|0.231|
|Adj R^2|0.189|0.179|0.182|0.235|0.157|0.157|0.141|0.213|

Note: Standard errors in brackets. Management practices and skills are reported as normalized scores (z-indices). *** p<0.01, ** p<0.05, * p<0.1.

# **7 Firm capabilities and performance in Mozambique**

Table 6 shows that enterprises in Mozambique with better firm capabilities perform better. A one standard deviation increase in management practices is associated with 63 percent<sup>14</sup> increase in sales per employee after accounting for standard controls: sector of operations, location, firm’s age, and manager’s age. A one standard deviation in skills is associated with 38 percent larger sales per employee when accounting for similar factors. These differences are statistically different from zero at various measures of performance and productivity. Although the coefficient on management practices is larger than the one on educational skills, we cannot reject that it is not larger.

> 14 The coefficients in Table 6 represent logarithmic percentages. A coefficient of 0.490 (49.0 “log” percent) corresponds to −1 = 63.2 percent. TT<sup>0.490</sup>

> 14 The coefficients in Table 6 represent logarithmic percentages. A coefficient of 0.490 (49.0 “log” percent)

24
with the true schooling differentials. Suppose, for example, that the government makes primary school attendance compulsory for all children between 5 to 10 years of age in year t. Then comparing the adjusted schooling attainment of a girl who is 10 years old in year t+5 with a boy who is 15 in the same year is likely to understate<sup>the actual</sup> difference in the schooling of girls. Conversely, comparing<sup>a boy who</sup> is 10 in year t+5 with a girl who is 15 in the same year will<sup>overstate</sup> this difference. It is important to weed out these cohort-specific effects.

To resolve the first problem, we have included family characteristics in the regression. Regarding the second problem,<sup>the</sup> comparison is not between attained schooling but _shortfalls_ in schooling attainment of boys and girls. This shortfall, for a child J, is defined<sup>as</sup> (AgeJ - 5) - (Schooling in Years) - (Schooling Shortfall) , for j - f,m. That is, the shortfall is equal to potential schooling (age-5)<sup>minus actual</sup> 13 schooling (alternatively, the highest grade completed). The nice thing about this measure is that it is a familiar one: it is identical<sup>to Mincer's</sup> (1974) definition of potential work experience for adults. The<sup>only</sup> 13Within the household, it is also possible to compare the quality of schools attended by male children versus the quality of schools<sup>attended</sup> by female children. For example, in the PLSS, it is possible to determine whe-her the child attended a private or a public school. Indices of quality of schooling of and school-related expenditure on male and female offspring can be incorporatod to make the schooling differential variable better approximate differential investment in schooling rather than differential schooling attainment.

18
assumption of the flexible accelerator model about the availability of credit supply by the banking sector cannot be taken for granted in developing countries. This discrepancy also occurs because of the public deficits and public debt, which can lead to financial repression and to eviction of private investment. On the empirical side, the impact of financial development on private investment is now well documented<sup>23</sup> . In his survey of investment functions in developing countries, Rama (1993) presents the positive effect of financial development on private investment in 21 of the 31 papers surveyed.

Trade reforms constitute another factor that can stimulate private investment decisions. Trade openness increases competitiveness and provides access to enlarged markets (Balassa, 1978; Feder, 1982). Trade openness can be at the origin of economies of scale and of productivity gains. In addition, trade openness influences the availability of external credit -- considering the general consensus on the role of tradable goods in providing positive externalities in the form of collateral for external financing (Caballero and Krishnamurthy, 2001).

All these factors create favorable conditions for the enterprises to invest. However, as mentioned in the introduction, economic reforms are also expected to affect private investment through their impact on the quality of governance institutions. There is, in particular, some evidence that greater openness to trade and stronger competition are conducive to governance improvement<sup>24</sup> . Opening up markets may help to weaken vested interests and reduce rents derived from prevailing economic and institutional arrangements. Trade openness may also lead to demands for governance institutions more suited to an increasingly varied and complex range of transactions (See IMF, 2003).

15
# **1. Introduction**

The process of financial globalization, which accelerated in the 1990s, has brought many changes to developing countries’ financial sectors. Countries have opened up their stock markets to foreign investors, allowed domestic firms to cross-list and issue debt overseas, and welcomed foreign direct investment into their local financial sectors. When it comes to the banking sector, arguably no change has been as transformative as the increase in foreign bank participation in developing countries. On average, across developing countries, the share of bank assets held by foreign banks has risen from 22 percent in 1996 to 39 percent in 2005.<sup>1</sup> At the same time, foreign bank claims on developing countries, which together with the loans extended by foreign bank branches and subsidiaries include cross-border loans, increased from 10 percent of GDP in 1996 to 26 percent in 2008.

There is significant debate surrounding the implications of foreign bank participation for developing countries. Supporters of this process argue that foreign banks can bring much needed capital as well as technical skills, and product innovation to developing countries. Also, they highlight the potential gains in terms of increased competition and improvements in the efficiency of the banking sector. On the other hand, the critics of foreign bank entry argue that foreign banks can destabilize the local banking sector due to a number of reasons. First, foreign banks can “import” shocks from their home countries and/or spread shocks from other developing countries in which they operate. Second, fierce competition with foreign banks can threaten the survival of the local banks. Finally, foreign banks can lead to reduced access to finance for a majority of domestic firms and consumers, if they only concentrate on a top and selected segment of the market.

This paper documents the increase in foreign bank participation in developing countries and, through an exhaustive literature review, explores the drivers and consequences of this phenomenon, paying particular attention to the differences observed across regions both in the degree of foreign bank participation and in the impact of this process. The increase in foreign bank participation has not been even. While the share of assets held by foreign banks has

> 1 These statistics come from data collected by Claessens, Gurcanlar, Mercado Sapiani, and Van Horen (2008a). Though they also collect data for 2006, we do not include data up to that year because information on foreign bank ownership is missing for many countries in 2006.

2
research within - existing health intervention programmes methodological improvements are much Tnre likely to result from indepth study over many years linked to input-output monitoring of programe efficiency than from a "one-off' survey.

However, there are various issues related to health services which would form a more appropriate focus for investigations based on single-round or repeated single-round surveys. Access and coverage, for example, can be much more clearly identified as outcomes of investments in particular health programmes and so are more appropriate for economic evaluation than impact on health status. For the same reason, data on these issues can also be more easilv compared across countries. Although the dangers of transferring results of evaluations between countries without reference to differing country situations remains, the impact of such differences on the results are themselves also of interest to health planners. (How do different strategies of health service delivery affect numbers of population covered?). Data collection could concentrate both on areas where improvements In the methodology of economic evaluation might result, and on other useful economic/planning information relating to health services (for eacL country and to make international comparisons). Possible areas of interest would be: - data on patient costs for use of health services: differences between urban and rural areas, differences between age and sex groups; - data on the patient costs of different health financing mechanisms eg.user fees, tax financing and social security; - data on patient utilisation of health services/programmes: differences between urban and rural areas, differences between age and sex groups; effects of fees, transport costs and time cost; - data on the determinants of demand for health care; comparing demand for different types of providers (public, private-for-profit, traditional healers, charitabl **e** tc.); - data on the adequacy of care: relating adequacy to different factors such as level of facility, educational level of health worker, availability of drugs, availability of supervision and management.

Such household-base **d** ata would need to be complemented by data from the health system itself before it would form the basis for economic 22
interest rates and low black market premium for the currency. This situation is however, unsustainable as the monetary base starts growing at rates inconsistent with price stability and the black market exchange rate starts to depreciate. Eventually the system breaks down as the Central Bank is forced to devalue the official exchange rate and to allow the raise in Ml in order to reduce interest rates and the pressure of the quasi-fiscal deficit.

The inherently unstable system implies wide oscillations in real interest rates and the real exchange rate as depositors try to anticipate sudden devaluations (the preferred instrument for the melting down of the quasifiscal deficits) by exchanging their deposits for cash with which to buy dollars in the black market. If the government tries to stop the portfolio shift it must resort to higher interest rates in order to force the roll over of the deposits. This mechanism reduces the short run pressures on the currency but increases the rate of growth of the monetary base and therefore generates expectations of even larger devaluations. The run feeds on itself until authorities give up to pressures and devalue.

# VI.2 Empirical Estimates

The magnitude o'f the changes in the interest rate needed to accommodate fluctuations in the demand for deposits is bound to depend on the interest elasticity of such demand. The more inelastic the demand is, the larger should be the required increase in the interest rate necessary to induce the roll over of the deposits in the face of an exogenous fall in the demand for them. We have attempted to estimate this elasticity using data for the period following the financial reform of 1977 since then it took place a fundamental structural change by allowing the free market determination of interest rates. The result was an unprecedented change in the ratio of M2 to MI as a result of an increase in the demand for the former and a fall in the demand for the latter as it can be appreciated in Figure 11 showing the time path of the (M2-M1)/M1 ratio. The increase in the ratio of interest earning deposits to Ml started in 1977 and was approximately completed by mid-1980. Since then this ratio has oscillated in response to the relative returns to both assets.

43
- 25 -

Table 1

|l_______________<br>DISTRIBU|TION OF UNE<br>M|MPLOYED BY<br>en|AGE 1988|Wom|en|
|---|---|---|---|---|---|
|Age<br>Standard|Definition|Alternativ|e Definition|Standard D|efinition|
|12-15<br>4.2||10.7||2.5||
|16-20<br>33.3||33.5||36.6||
|21-25<br>24.7||19.3||||
|26-30<br>13.3||10.5||38.1||
|31-40<br>8.8||9.0||11.7||
|41-50<br>8.0||9.T||6.9||
|_51-60_<br>_5.1_||5.9||2.5||
|61-70<br>2.6||1.6||1.7||
|<br> <br>DISTRIBUTION OF|UNEMPLOY<br>M|<br>ED BY EDUCA<br>en|TION LEVEL,|1988<br>Wom|en|
|Years of school<br>Standard|Definition|Alternativ|e Definition|Standard D|efinition|
|0|2.4||4.4||1.6|
|1-5 years|9.7||12.5||8.6|
|6|16.0||20.0||14.6|
|7-8|12.9||12.8||8.6|
|9|20.4||18.8||18.9|
|10-12|20.2||18.1||35.6|
|13 and more|18.4||13.4||12.1|