rafmacalaba/datause-encoder-data huggingface dataset:
country at each point in time. Usually, these variables consist of housing attributes, infrastructure use, and a range of other assets owned by households<sup>4</sup> . The asset or wealth quintiles are created using this asset index.

The standard categories of the infrastructure variables are presented in Table 2. In addition to standard categories, additional categories can be used to follow water and sanitation indicators, for example by computing access to so-called improved or unimproved water sources, which is an indicator for the Millennium Development Goals. For example, ‘access to safe drinking water’ is defined as the ‘percentage of the population using improved water sources’ and is monitored by the JMP. The fact that infrastructure categories are added in the DHS and sometimes changed in each phase makes it difficult to track the same categories over time in some cases. Therefore, in addition to the improved and unimproved water and sanitation source categorization adopted in the JMP, we use also in this paper a categorization based on modern, intermediate and basic service options suggested by Komives et al. (1999) (Table 2).

It would be interesting to know whether the service received meets some quality standards. Unreliable and infrequent electricity provision costs households and businesses losses and may lead to a dependence on alternative and often more expensive sources of energy. Also, households may be receiving water for only two hours in a day even when they are connected with piped supply. The availability of this information has significant policy implications. In Eastern Europe and Central Asia, where there is universal access, household surveys report quality of service delivery in some countries. Unfortunately, this type of information is not available in the DHS or MICS surveys for Africa.

# **3. Access Trends**

# **_3.1. Country level estimates_**

Table 3 provides the estimates of access at the national level to piped water, flush toilets, electricity, and landline phones. Rather than detailing the findings for each country, it is easier to look at Figures 1 through 4 which provide the same information in terms as access rates at the

> 4 In most countries, the asset index is based on the following variables: type of main floor, wall and/or roof material, radio, television, refrigerator, bicycle, car/motorcycle, livestock ownership, farmland/other land, number of persons sleeping per room, car/truck, telephone, type of cooking fuel, bed net for sleeping, access to electricity, source of drinking water, type of toilet facility, and time to get to closest water source. We recognize that including access to electricity as well as the type of water and sanitation facilities used creates an endogeneity issue since these are also the variables for which we are conducting a benefit incidence analysis, but the bias is likely to be small due to the fact that many other variables are included in the asset index.

6
by households appear to be regressive since non-lifeline consumers will in principle receive high-income transfers due to their relatively high consumption. However, we show that the extension of the subsidy from the initial three months to 12 months, for [only] lifeline customers, made the subsidy progressive than regressive.

Using the actual consumption of both consumer groups, we compute the [expected] subsidy (implicit transfer) to the median lifeline and non-lifeline customers (households) during the period as shown in Figure **??** . First, we compute the monthly subsidy received by the median household in each consumer group between April and December 2020 by multiplying the subsidy rate by their respective average monthly consumption. Averaging this over the period, our estimates suggest that on average, the median lifeline and non-lifeline household received GHS 12 (USD 2.14) and GHS 29 (USD 5.18) per month these over the 12 months that lifeline consumers respectively.<sup>26</sup> By aggregating period received the subsidies, the median lifeline customer received a total of GHS 139 (USD 25). On the other hand, non-lifeline consumers received only GHS 86 (USD 15) over their three-month subsidy period. Thus, cumulatively, the total subsidies to lifeline customers were about 67% higher than those to non-lifeline customers. Admittedly, these estimates are only indicative, and may not reflect the true amount received by households largely because the subsidies were administered at the meter level, and given the prevalence of shared metering particularly among low-income households, the share of the transfer received by households may in practice be much lower (Berkouwer et al., 2022).

# **6 Discussion and Conclusion**

This paper examines the effect of the COVID pandemic on electricity consumption patterns among households and firms in Sub-Saharan Africa using administrative data on billing records from Ghana and Rwanda. We also explore the effects of SAH orders (lockdown) and pandemic relief measures such as electricity subsidies on household consumption.

Six main results emerge from the paper. First, residential electricity consumption during the pandemic increased in both Ghana and Rwanda, albeit the rate of increase was high in Ghana than in Rwanda. Specifically, average household consumption increased by about 23% in Ghana compared to 4% in Rwanda. Second, consumption in the nonresidential sector declined by about 11% in Rwanda, while rising marginally by about 6% in Ghana. Third, industrial consumption declined in both countries, highlighting the ef-

> 26Based on an exchange rate of USD 1: 5.6 GHS

16
_Differences across income groups_ Finally, in column (9) we investigate whether our results vary across countries with different levels of development. For example, developing countries’ MNEs could be less sensitive to SFD than developed countries’ MNEs because a large number of developing countries’ MNEs are state-owned.<sup>64</sup> They may benefit from a preferential access to external finance denied to private firms. To test this possibility, we interact our two main interaction terms, and the ED variable, with a developing country dummy variable.<sup>65</sup> We do not find evidence that the effects of SFD and DFD on the relative volume of bilateral FDI in financially vulnerable sectors differ across country income groups.

Overall, the results presented in Table 2 indicate that the overall effects of higher SFD and DFD on the relative volume of bilateral greenfield FDI in financially vulnerable sectors are large and positive. In Appendix C, we show that these findings hold when we use M&A FDI data.

# **5.1.2**

Table 3 provides additional robustness checks and extensions.

_Excluding sectors with outlying financial vulnerability_ In column (1) of Table 3, we remove from the sample the observations related to the “Food and Tobacco” (ED=-0.16) and “Plastics” (ED=1.14) sectors. Their ED values are very different from the other ED values, which may distort our results. Column (1) shows that this omission leads to an increase in the size of the _ED of young vs. mature firms_ In columns (2) to (3) we replace our ED variable with ED measures which have been calculated by Rajan and Zingales (1998) for young firms (firms that went public less than 10 years ago) and mature firms (firms

> 64The World Investment Report 2006 (UNCTAD, 2006) reports that one-quarter of the 100 largest developing countries’ MNEs were state-owned in 2004, whereas the largest developed countries’ MNEs are mostly privately-owned.

> 65Developing countries are those countries classified by the World Bank as low-income and middle-income countries, as reported in the “AAA codes” dataset available at http://graduateinstitute.ch/md4stata. Results are qualitatively similar if we define developing countries as all countries which are not considered to be traditional industrial countries.

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In many evaluation studies, this is complicated by the presence of a plethora of programs targeting the same universe of firms and lack of centralized administrative records on beneficiaries from all the different programs. One consequence is that some part of the control group may actually have participated in other programs, thus possibly contaminating the counter-factual and biasing impact estimates of the specific SME program under study.

The paper finds a consistent set of results on the impacts of program participation using propensity score matching (PSM) and difference-in-difference (DID) models. First, using PSM models on 2004 cross-section data, we find positive and significant impacts of treatment on intermediate outcomes – increased investments in training, introduction of new products and processes, ISO certification and links with other public and private institutions – as well as final outcomes such as sales, wages and labor productivity, reversing lower average group means of these outcomes in the treatment group. Second, using difference-in-differences (DID) models together with PSM to control for observed and unobserved heterogeneity, we find robust positive and statistically significant program impacts on sales, production, employment, labor productivity and wages. We draw the inference that level estimates of final outcomes may be confounded by unobserved heterogeneity motivating selection into programs of firms with relatively lower productivity levels. Third, we test and find positive outcomes for programs providing subsidized technical assistance, support for cluster formation and technology upgrading but not financing programs. Finally, we find evidence of positive and growing time-effects from program participation, typically between 4-10 years after starting participation for final outcomes such as sales, production and labor productivity but not for employment, wages and exports.

The remainder of this paper is organized as follows. Section II begins with an overview of SME programs in Chile during the mid-1990s and early 2000s and several studies that have evaluated some of these programs. Section III describes the data used in this paper. It provides estimates of program use by manufacturing establishments canvassed in the 2004 Chile Investment Climate Survey (ICS) and compares the characteristics of firms that participated (the “treatment group”) with firms that did not (the “control group”). Section IV describes the estimation approach and reports propensity score matching estimates of the impacts of participation in any SME program on intermediate and final outcomes measured in 2004. Section V focuses on the panel data over the 1994 to 2006 period, using panel regression models to estimate treatment effects on final outcomes measured in levels and in differences. It tests for differences across SME programs, the post-treatment time effects of program participation, and sensitivity analysis bounding the impact estimates for the potential role of support programs in inhibiting exit from the panel by inefficient firms. The final section VI concludes.

3
**FIGURE O.6** Gujarat and Maharashtra follow significantly different growth paths

|Within-sector<br>Andhra Pradesh|productivity growth and structural change b|y state (percentage), 1988–2004<br>Within-sector|
|---|---|---|
|Assam<br>Bihar||Structural|
|Gujarat|||
|Haryana|||
|Karnataka|||
|Kerala|||
|Madhya Pradesh|||
|Maharashtra|||
|Orissa|||
|Punjab|||
|Rajasthan|||
|Tamil Nadu|||
|Uttar Pradesh|||
|West Bengal|||
|–2|0<br>2|4<br>6|
||Percent||

Source: Ahsan and Mitra, Chapter 1 in this book.

to a little more than 60 percent (still the dominant sector at 40 percent of GDP), while the share of employment in manufacturing increased by a meager 2.2 percentage points to 4.1 percent (accounting for only about 10 percent of GDP). Over this same period, average annual productivity growth was 4.5 percent for the nonpetroleum economy, but the lion’s share of this growth (3.5 percent) was accounted for by within-sector productivity improvements. If petroleum (oil and gas)—which employs less than 1 percent of the labor force but accounts for 20–30 percent of GDP—is included, productivity rose less but structural change played a bigger role (Table O.1). The authors suggest that productivity gains could have been as much as 54 percent greater had structural change been greater. They see the key levers for this to occur as (1) stimulating agricultural production, (2) liberalizing trade policies, (3) upgrading infrastructure, and (4) improving human capital.

A worrying feature of the Nigerian economy is that productivity growth in manufacturing between 1996 and 2009 was actually negative relative to agriculture. The reasons for this are unclear. One explanation may be that people entering the manufacturing workforce are in the informal sector, as in Ghana and several other African countries. Another explanation 22
reputational collateral can increase access to finance. Credit bureaus in about 65 percent of the 94 countries for which this variable is available share positive information (Table 1).

Figure 3 plots the two measures of depth of credit bureau information across country income groups. Both measures follow a similar pattern in that middle income countries have the greatest depth of information, followed by low income countries and then high income countries.

Finally, the _transparency index_ is a tally of affirmative responses to five questions. These questions ascertain whether borrowers are guaranteed access to their credit history data by law, whether they can inspect their data in practice, and whether there is a cost for inspecting one’s own credit information. The average of this index is about 2.5 (table 1). Middle income and high income countries have higher levels of credit bureau transparency on average than low income countries (figure 4).

# **3.2 Measures of competition and concentration**

As a measure of banking competition, we chose to use the log minimum capital requirement (in millions of USD) for banks in 2001 from the World Bank’s 2013 Global Financial Development Report (GFDR) database. We prefer this measure of bank entry regulation over measures of market power in banking, such as the Lerner index or the Boone indicator for two reasons. First, the variable is conceptually closest to the entry barriers that Pagano and Jappelli (1993) include in their theoretical model and also to the way in which Brown and Zehnder (2010) vary entry barriers in the lab setting (i.e. through transactions costs for entering a new market). Second, market power is a function of entry regulation as well as other factors, including the existence and depth of credit information, and is thus more likely to be endogenous to our outcome variables (see section 4 for a discussion about reverse causality).

We have data on the minimum capital requirement for 137 countries, ranging from zero to USD 15 billion. Close to 90 percent of the sample have a minimum capital requirement below USD 20 million, with a median of 5.7 million. Figure 5 shows the distribution of minimum capital requirements below USD 20 million. Since the full distribution of the variable has a long left tail, 10
The negative contribution of capital to growth in Zimbabwe may reflect capital decay in recent years. More than excess population growth, the deterioration of capital stock has clearly been a major drag on growth in Zimbabwe over the last decade. Since the turn of the millennium, TFP growth has been negative, contributing to a reduction of growth by more than two full percentage points. However, the overall contribution of TFP to growth over the reference period remains positive, owning in large part to investments which increased the intensity of factors utilization and efficiency gains accumulated in the pre-crisis years.

In contrast to TFP and capital deepening which both exhibit a large variance in their relative contribution to growth, population growth (measured by raw labor) is a consistent drag on the economy. Regardless of the country and growth regimes, its contribution to growth is negative. It also has the largest negative contribution to per-capita growth, with adverse effects on growth exceeding two full percentage points in many countries. The magnitude reflects both labor surplus and more importantly, the deficit of technology and infrastructure which are essential for enhancing the productivity of labor.

**Figure 3 – Baseline case: Contributions of male and female labor force to growth**

<!-- Start of picture text --> 2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>-0.50<br>-1.00<br>-1.50<br>GDPPCGR Cont of LM Cont of LF<br>1980-84 1985-89 1990-94 1995-99 2000-04 2005-10 1980-2010<br><!-- End of picture text --> Although land, human capital and labor force participation are growth-enhancing, irrespective of the reference period, female labor force participation exhibits a large relative contribution to growth. On average, it accounts for over 0.66 percentage point of growth during the period 1980-2010. This figure represents approximately 0.64 out of 0.75 aggregate per capita GDP growth averaged over the period. On the other hand, the contribution of male labor force participation to growth, although positive, has been much lower, at about half a percentage point of growth, which accounts for about 0.56 out of 0.75 total growth (see Figure 3 above).

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Since the decline in poverty between 2007 and 2011/12 using the HBS data may be due to the changes in the survey design, different imputation methods are used to address this issue and check whether the reduction in poverty is a reality. The different prediction approaches supported the decline in poverty between 2007 and 2011/12, although they revealed a slightly lower pace of poverty reduction, suggesting that the improvements of survey methods in the HBS are not the cause of the difference in poverty trends between HBS and NPS data.

# **C. Comparison of household characteristics and asset ownership**

Table 4 compares the main household socio‐economic characteristics, asset ownership, and access to basic services between HBS and NPS. The comparison of the household sizes and number of dependents shows lower levels in HBS 2007 and NPS 2012/13 than in the other surveys. This suggests potential problems of under‐reporting and non‐compliance by the poor and/or changes in sample design.

Both HBS and NPS show similar results and reveal improvements in asset ownership and access to basic services, consistent with a decline in poverty measured using HBS data.<sup>6</sup>

**<u>Table 4: Comparison of household characteristics and assets ownership</u>**

||**H**<br>|**BS**<br>||**NPS**<br>||
|---|---|---|---|---|---|
||**2007**|**2011/12**|**2008/09**|**2010/11**|**2012/13**|
|**Demographic structure**||||||
|Mean adult equivalent of the household|3.8|4.0|4.1|4.1|4.0|
|Mean household size|4.8|5.0|5.0|5.1|4.9|
|Number of children (14 years and below)|2.1|2.2|2.3|2.2|2.1|
|Dependency ratio (%)|43|43|47|44|42|
|Female‐headed households (%)|25|25|26|26|26|
|<br>**Ownership of assets**||||||
|Radio (%)|65.6|54.6|64.8|66.2|57.8|
|Television (%)|8.1|13.8|10.8|14.0|15.6|

6 Table 4 shows a decline in radio and bicycle ownership which is often largely driven by households’ acquisitions of substitute “upgraded” goods, such as televisions or motorbikes. The rise in television and motorbike ownership, coupled with the decline in radio and bicycle ownership, supports the notion that these goods are substitutes. See the Poverty Assessment for mainland Tanzania and Seff et al. (2014) for more details.

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||**Log**|**[Self-report**|**ed Productiv**|**ity (XOF/ha)**|**]-Export cro**|**ps**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|Household head has Secondary||||||||||
|education|(0.020)|(0.015)|(0.015)|(0.018)|(0.021)|(0.020)|(0.022)|(0.025)|(0.034)|
|Household head has Tertiary education|0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household head disability|0.032|0.013|-0.006|-0.040**|-0.040*|-0.001|0.026|0.025|-0.007|
||(0.026)|(0.021)|(0.016)|(0.019)|(0.022)|(0.021)|(0.022)|(0.022)|(0.027)|
|Dependency ratio|0.103|-0.053|-0.104|-0.066|0.055|-0.009|-0.018|-0.003|0.025|
||(0.145)|(0.095)|(0.086)|(0.080)|(0.084)|(0.082)|(0.088)|(0.079)|(0.082)|
|Household Male Labor Use (Number)|<br>-0.038|<br>-0.041|<br>-0.131**|<br>-0.057|<br>-0.044|<br>-0.015|<br>-0.046|<br>-0.005|<br>-0.045|
||(0.075)|(0.055)|(0.052)|(0.054)|(0.054)|(0.053)|(0.048)|(0.049)|(0.077)|
|Household Female Labor Use|0.608*|0.297|0.519**|0.577***|0.511***|0.273|0.401***|0.245*|0.302|
|(Number)|(0.360)|(0.237)|(0.229)|(0.214)|(0.195)|(0.178)|(0.140)|(0.140)|(0.186)|
|Household Child Labor Use (Number)|0.002|-0.005|-0.017|0.001|0.015|0.003|0.014|0.001|0.046|
||(0.026)|(0.020)|(0.023)|(0.019)|(0.020)|(0.016)|(0.019)|(0.016)|(0.032)|
|Hired Labor Use (Number)|-0.084*|-0.071**|-0.031|0.013|-0.015|-0.047|-0.042|-0.009|-0.019|
||(0.049)|(0.035)|(0.031)|(0.040)|(0.035)|(0.032)|(0.036)|(0.030)|(0.033)|
|Number of Fields Managed by the|<br>-0.885|<br>-0.462|<br>-0.799|<br>-1.378***|<br>-0.777**|<br>-0.336|<br>-0.696**|<br>-0.539*|<br>-1.078**|
|<br>household|(0.744)|(0.515)|(0.508)|(0.414)|(0.374)|(0.335)|(0.329)|(0.300)|(0.447)|
|Total number of crops produced|1.077*|0.337|0.914|1.128**|0.416|0.040|0.231|0.277|0.663|
||(0.633)|(0.568)|(0.561)|(0.449)|(0.380)|(0.343)|(0.324)|(0.341)|(0.458)|
|Household used any purchased seed|0.055|-0.011|0.040|0.008|0.044*|0.028|0.021|0.001|0.001|
||(0.067)|(0.038)|(0.030)|(0.023)|(0.026)|(0.023)|(0.021)|(0.022)|(0.024)|
|Plots that Use Chemical Fertilizer (%|-0.016|-0.003|0.014|0.014|0.027|0.018|0.001|0.016|0.049|
|<br>of total)|(0.022)|(0.020)|(0.022)|(0.022)|(0.024)|(0.023)|(0.024)|(0.031)|(0.036)|
|<br>Plots that Use Pesticide (% of total)|<br>0.046|<br>0.027|<br>0.004|<br>-0.003|<br>-0.005|<br>0.012|<br>0.007|<br>-0.039|<br>-0.033|
||(0.040)|(0.032)|(0.028)|(0.032)|(0.047)|(0.042)|(0.043)|(0.045)|(0.048)|
|Constant|<br>-0.277|<br>0.411|<br>0.381|<br>0.505|<br>0.093|<br>1.093|<br>0.828|<br>1.579*|<br>1.318|
||(1.008)|(1.007)|(0.778)|(0.737)|(0.763)|(0.858)|(0.981)|(0.820)|(1.027)|
|Observations|3588|3588|3588|3588|3588|3588|3588|3588|3588|

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