rafmacalaba/datause-encoder-data huggingface dataset:
the consumption bundle of household _h_ . Following Deaton (1989, 1997), we can safely ignore the private marginal utility of income _∂V_<sup>_h_</sup> _/∂_ ln _y_<sup>_h_</sup> in policy evaluation. In addition, letting _τi_ be the instrument of tariff protection for sector _i_ and assuming perfect price pass-through elasticities,<sup>5</sup> so that _d_ ln _pi_ = _−τi/_ (1 + _τi_ ), the estimable welfare effects are given by: The interpretation of this equation is straightforward. After a price change caused by tariff cuts _d_ ln _pi_ = _−τi/_ (1 + _τi_ ), the first-order effects on real income can be well-approximated with the corresponding income and expenditure shares. In the language of Deaton (1989), because we are working with tariff cuts and price declines, net-consumers benefit while net-producers suffer. Though this is well-known, it is important to clarify that the Deaton first-order approximation captures direct, short-term effects of tariff liberalization. In particular, it does not take into consideration second-order effects such as consumption and production adjustments (which may become progressively more important over time), labor or investment decisions, or any dynamic effect more generally.

The goal is to calculate the welfare effects generated by the _entire_ structure of tariff protection. To obtain a measure, we sum the changes in welfare in (4) over all traded goods _i_ to get: where _V_<sup>�</sup><sup>_h_</sup> is the proportional change in household real income. Next, we use this expression to assess trade policy with the HIT database.

> 5Note that we are making the assumption of perfect pass-through for convenience and ease of exposition. The data in principle accommodate richer and more realistic pass-through assumptions, see e.g. Nicita (2009), Ural-Marchand (2012), and Bergquist (2017) for pass-through estimates for selected commodities in selected countries.

7
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|**Table 2: Prevalence strata for **<br>|**validation listing**<br>|||||
|---|---|---|---|---|---|
|**Prevalence**|**Unions**|**Census**<br>**HH**|**Rohingya**<br>**HH out of**<br>**camps**|**Camps**|**Rohingya**<br>**HH in**<br>**camps**|
|Prevalence= 0|42|122305|0|0|0|
|Prevalence<.1|45|193626|7508|0|0|
|Prevalence>.1|18|59445|10251|0|0|
|With camps|5|38026|3571|27|281150|
|Total|110|413402|21330|27|281150|

Source **:** Union-level prevalence using the number of local households (based on the 2011 census) and the number of <u>out-of-camps Rohingya (based on the IPA population count). Note: HH = household.</u> This validation exercise implemented a full household listing. This involves going to each household in the chosen villages with a set of 12 questions that included: recontact details (household head name, phone numbers); demographic information on adolescents; and questions related to how long the household had been at that location, as well as details on where they had come from. If a village had a total population of fewer than 500 households, the full village was listed. This was the case for 27 out of the 33 villages. In the remaining six cases, the villages were divided into segments of 100-120 households each, and three segments were randomly chosen for the listing. If key informants at the village level indicated that the Rohingya households lived in particular segments, then one such segment was chosen as one of the three segments where a full listing was to be implemented. Additional segments indicated by the informant might also be included for full listing through random selection.

Table 3 summarizes Rohingya prevalence by upazila. In addition to a low average prevalence of 1.3 percent in the validation sample, a significant proportion of villages reported zero prevalence. Figure 2 and Table 3 let us compare the refugee prevalence based on the IPA population count and the household listing in the validation sample. The full household listing reported different numbers compared to the key-informantbased IPA population count, both for the number of households and refugee prevalence. However, the full listing confirmed the low prevalence of Rohingya displaced outside camps.

**<u>Table 3: Prevalence by upazila, validation household listing</u>**

8
Policy ReseaRch WoRking PaPeR 4638

# **Abstract**

Capitalizing on recent improvements in the availability of cross-country financial sector data, this paper proposes a standard methodology for benchmarking the policy component of financial development. Systematic controls are introduced to isolate main structural country characteristics and a principal components analysis is used to help identify a parsimonious set of ten “core” outcome indicators from a broader set of twenty seven potential indicators covering different dimensions of development in both financial institutions and financial markets. Such a broad-based approach helps reveal important determinants and regularities of the process of financial development. The paper also identifies some of the main data gaps that will need to be filled to allow further progress in financial benchmarking looking forward.

This paper—a product of the Financial Policy Development Unit, Financial and Private Sector Development Vice Presidency—is part of a larger effort in the department to understand the process of financial development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tbeck@ worldbank.org, efeijen@worldbank.org, fmoizeszowicz@worldbank.org and aize@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
34 Table 4.23: Incidence of Poverty by Employment Status of Head of Household, 1985-93 (Poverty Line=N395.41)

||||**_RURAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Employment**|||||||
|Status of Head|**P0**|**PI**|P2|**P0**|**P,**|**P2**|
|Employer|0.282|0.089|0.038|0.000|0.000|0.000|
|<br>Wage Earners|0.515|0.176|0.089|0.277|0.097|0.046|
|<br>Self Employed|0.514|0.114|0.000|0.367|0.157|0.093|
|<br>Others|0.453|0.053|0.012|0.368|0.163|0.094|
|_All Rural_|_0.495_|_0.169_|_0.085_|_0.364_|_0.167_|_0.098_|
||||_URBAN_||||
|||**1985/86**|||**1992193**||
|**Employment**|||||||
|Status of Head|**P0**|**P,**|**P2**|**Po**|P1|P2|
|Employer|0.256|0.069|0.027|0.000|0.000|0.000|
|<br>Wage Earners|0.336|0.143|0.083|0.278|0.112|0.062|
|<br>Self Employed|0.574|0.204|0.127|0.319|0.139|0.076|
|<br>Others|0.359|0.191|0.104|0.238|0.107|0.043|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|

Source: NCS, 1985/86 and 1992/93

# **Table 4.24: Incidence of Extreme Poverty by Employment Status of Head of Household, 1985-93 (Poverty Line=N197.71)**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|Employment Status|P0|P,|P2|**Po**|P1|P2|
|Employer|0.038|0.008|0.003||||
|Wage Earner|0.134|0.034|0.013|0.104|0.054|0.020|
|Self Employed|0.206|0.019|0.000|0.142|0.075|0.030|
|Others|0.079|0.000|0.000|0.125|0.079|0.029|
|_Al/Nigeria_|_0.120_|_0.042_|_0.016_|_0.136_|_0.085_|_0.034_|

**Decomposition of Extreme Poverty Incidence by Employment Status**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Employment Status**|**P0**|**P,**|**P2**|**P0**|**PI**|**P2**|
|Employer|4.9|1.1|**1.0**|0.0|0.0|0.0|
|Wage Earner|5.1|3.0|0.0|11.9|9.7|9.1|
|Self Employed|88.9|95.9|99.0|82.7|84.9|85.8|
|Others|1.1|0.0|0.0|5.4|5.4|5.0|
|_All Nigeria_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|

Source: NCS, 1985/86 and 1992/93.
In addition to affecting how including county-level GDP in the revenue sharing formula, in the case of Kenya, can considerably affect resource allocation among individual counties, estimating subnational economic activity also has other uses. For one, counties themselves may want to know and understand what drives growth within their borders, and how they stack up against others. And another important reason is that, with a better understanding of county-level growth dynamics, such information would help private investors assess more accurately where to undertake private investments.

This paper proceeds as follows: The next section summarizes the data and sketches the relationship between nightlights and GDP using observations from the sample. Section 3 specifies the empirical model and presents the main results. Section 4 applies the model estimates to subnational GDP growth in Kenya and Rwanda, while section 5 focuses on subnational GDP levels. The final section concludes.

# **2. The Link between Night Lights and GDP**

## **2.1 Data**

Data on night lights are provided by the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The DMSP satellites circle the earth 14 times a day and record the intensity of Earth-based lights. The data picked up by the satellites have been digitized since 1992. We used stable light imagery of SSA derived from scores of orbits of the DMSP OLS in from 1992-2013 since this product inter-calibrated where fires and other ephemeral lights have been removed, although there are noteworthy blunders associated with over-glow effects where lighting spreads to neighboring pixels (and hence economic activity is wrongfully attributed to certain places). The stable lights imagery has annual quantized pixels with values (Digital Numbers) with integers ranging from 0 to 63. This product is optimal especially for SSA where ephemeral lighting is minimal during the night where gas flares are a rare phenomenon. Gas flares are combustion devices used mainly in oil wells and big offshore platforms to burn flammable gas (mostly methane) released during the operations of oil extraction.

There are other satellite sensors of the same resolution and scale but not appropriate for economic activity studies. Landsat, for instance, depicts settlement expansion, but is not available for frequent temporal coverage.

Data on GDP and other indicators used in the analysis (surface of country territory, electricity consumption, etc.) are provided by the World Development Indicators (2014). Following HSW, we use constant GDP in local currency units.

Night lights data are available for all 47 Sub-Saharan African countries between 1992 and 2012, resulting in 987 country-year observations. South Sudan is not included since it only became an independent country in 2011. GDP data are not available for Somalia, resulting in a final sample of 966 country-year observations.

3
# **1 Introduction**

Electrification rates in Sub-Saharan Africa are the lowest of any region in the world. In 2012, 35.3 percent of the population had access to electricity, compared to 22.8 percent in 1990.<sup>1</sup> Lack of electricity can stunt economic development through various channels including education, labor supply, and productivity (Dinkelman, 2011; Fisher-Vanden et al., 2015; Allcott et al., 2016; Chakravorty et al., 2016).

Many factors contribute to this highly complex problem of limited access to electricity. This paper is concerned about one of these factors, namely electricity connection charges.<sup>2</sup> These are the fixed upfront amount that new customers pay to connect to an existing distribution network. In many African countries, electricity connection charges are very high, both relative to local incomes and relative to connection charges in other parts of the world. Golumbeanu and Barnes (2013) argue that high electricity connection charges may be the most significant obstacle to improving electricity access in the Sub-Saharan Africa region.

In this paper, we develop a model of household and distribution utility behavior to describe how high connection charges and low access rates can arise from regulated electricity tari↵s that are set too low.<sup>3</sup> Wholesale costs of generating and supplying electricity in many parts of Africa are high. However, in many countries, regulated tari↵s are set below a level that would allow utility firms to recover these costs (Trimble et al., 2016). The potential losses from connecting additional customers make it optimal for distribution utilities to set high connection charges. These high charges both reduce the demand for connections and recover future losses from those customers who do connect.

Our analysis requires the estimation of a household-level model of the joint demand for electricity and electricity connections. Households are assumed to choose between several alternatives for their primary fuel source, including kerosene, grid electricity, and modern o↵grid electricity sources such as solar.<sup>4</sup> This choice will depend on, among other factors, the

> 1World Bank Global Electrification Database, `http://data.worldbank.org/indicator/EG.ELC.ACCS. ZS` .

> 2Other important factors include low households’ willingness to pay for electricity service at market prices, high costs of electrification, and inefficient and dysfunctional utilities (Kojima et al., 2016; Trimble et al., 2016).

> 3Other potential explanations for these high charges include geographical challenges for network construction, inefficient procurement practices, and excessive stringency of technical standards (Golumbeanu and Barnes, 2013). As we demonstrate below, these explanations are not inconsistent with our framework. Rather they tend to exacerbate the problem we discuss.

> 4In practice, many households use multiple energy sources for di↵erent purposes, such as charcoal for cooking and kerosene for lighting. Because households rarely cook or heat with electricity in our setting, we focus on the choice of energy source for lighting.

2
_innovative and knowledge‐intens_ ive service sectors and a negative one in the case of financial‐related sectors and most traditional services like trade and transport. Greenan and Guellec (2000) using French manufacturing sector data over 1986‐90 find a positive relationship between innovation and employment at the firm level, but at the sectoral level only product innovation was associated with job creation. Simonetti et al, in Vivarelli and Pianta (2000), do a macroeconometric study comparing Italy, Japan, the United States and France, specifically looking at the impact of process innovation<sup>15</sup> during 1965‐93. They conclude that countries lagging behind technologically and therefore depending more on innovation developed in other countries (Italy and Japan in their sample) see a strong negative relationship between employment (and process) innovation, in the period that productivity rises. During the period of analysis, France and the United States did more basic research (R&D). Investment was also more strongly related to innovation in Italy and Japan; innovation was embodied in gross investment. However, the relationship of innovation and investment is negative in the United States and France during that period.<sup>16</sup>

# **_R&D fosters product innovation within firms and supports employment growth across sectors_** _._

In sectoral analysis, Bogliacino and Vivarelli (2012) examine one of the compensation mechanisms by which employment may grow after labor‐saving innovation is adopted. They study 25 manufacturing and service sectors for 16 European countries over the period 1996‐2005 and find that R&D expenditures fostering product innovation have a job‐creating effect. Mario Pianta (in Pianta and Vivarelli ,ed.), find that at the firm level, R&D expenditures generally reflect product, rather than process innovations and are associated with employment growth.<sup>17</sup>

> 15 They use patent data from the U.S. Patents and Trademarks Office to distinguish between process and product innovations.

> 16 Innovation was not embodied in new capital goods or was in sectors that were not physical capital ‐intensive. 17 Piva and Vivarelli (2004), studying a sample of 318 manufacturing firms in Italy over 1992‐97, find innovation raises employment within the firm in Italy.

14
# **4. Empowering Forest-Dependent Women**

PRIME offers a broad conceptual framework for creating opportunities for the poor as household units. However, a great deal of inequality lies within households. How, why and where men and women access, use and manage forests differs across the world.<sup>19</sup> Globally, women contribute just as much to households’ forest income as do men.<sup>20</sup> However, evidence from the Poverty and Environment Network global study points to distinct male and female roles in collecting forest products that vary across regions (Sunderland _et al_ . 2014). In Africa, women are the main collectors of subsistence-oriented forest products; whereas in Latin America, men dominate firewood collection. And in all regions, men are more involved in hunting, wood harvesting, and minerals extraction.

Persistent gender gaps in access to services, markets and value-addition activities, land and tree tenure, voice and agency, and hiring labor result in forestry program outcomes that often marginalize women (Colfer _et al._ 2016).<sup>21</sup> Thus, appreciating gendered differences matters for both fair and effective design of interventions and institutional arrangements. For instance, Agarwal (2009) shows that enhancing women’s presence in community forestry institutions improved resource conservation and regeneration in Nepal and India. Yet, in East Africa and Latin America, forest-user groups that were predominantly female were found to perform less well than mixed or male-dominated groups, due to gender bias in technology access and dissemination, women’s labor constraints and limitations in women’s sanctioning authority (Mwangi _et al_ . 2011). Thus, gender analysis is critical to identify gender gaps that may be important for both an efficient and fairer distribution of outcomes.

There are many constraints to achieving gender-sensitive outcomes in forest and agroforestry efforts (Kiptot 2015, Colfer _et al_ . 2015). Common challenges include gendered norms and cultural prejudices that reinforce forestry as a male profession, lack of evidence-based research and genderdisaggregated data, limited technical capacity and budgets to implement gender-focused activities and women’s limited representation in decision-making (Buchy 2012). Overcoming these challenges may require changing norms and management practices that go beyond the forestry sector. Successful strategies have included participatory consultations to discuss gender gaps in forest policies and practices (Aguilar _et al_ . 2011), creating gender working groups and learning networks (Gurung _et al._ 2011, WOCAN 2016, Agarwal 2015), taking gender-transformative research approaches (Colfer _et al._ 2016), re-engineering management structures and setting up gender-sensitive monitoring and evaluation systems (Buchy 2012, WOCAN 2016) as well as institutional arrangements supporting equitable benefit-sharing (Shames _et al_ . 2012).

14
On average, countries with higher policy volatility were the ones that fell-behind the most in terms of long-term growth. The linear correlation between both measures is -0.63 (and statically significant at the 10% level). In fact, Argentina and the República Bolivariana de Venezuela, the two countries with the lowest values of policy stability, were the ones that diverged the most with the US economy. On the other hand, Spain, the country with the second highest policy stability, was the one that converged the most with the US. On average, policy volatility explains about 40% of the cross-country variability in long-term growth, as measured by the R-squared.

It is important to stress that the interpretation is not necessarily causal, but rather suggestive. The relationship between policy stability and long-term growth can go both ways, or could also be driven by a third (omitted) variable. On the one hand, more policystability can lead to higher average levels of growth. But as countries grow, policy stability can also be affected through several channels (e.g., better institutions).

In the next subsection, we show that the strong correlation between policy volatility and growth is robust to different decisions underlying the creation of our policy volatility variable, and to different measures of policy volatility and long-term growth.

# **5.1 ROBUSTNESS CHECKS**

In the process of calculating our policy volatility variable we took several arbitrary decisions: how to define the topics of each speech; whether we should omit topics with a low frequency in the process of creating our variables; and what should be the relevant timeframe of analysis We now show that our main result is robust to a wide variation of these parameters.

In our baseline measure of policy volatility, we used the LDA output obtained by running the model in the pooled sample of speeches. This guarantees that the number of topics, and the content of the topics are constant between countries—increasing the crosscountry variability—while the topic probabilities vary both across countries and across speeches within countries. This methodology assumes there are ‘common’ topics in the speeches across countries of the region, and that the relative relevance of each of those common topics varies across countries (as measured by the topic probabilities).

As a robustness check, we use an alternative approach. Instead of using LDA on the pool of speeches, we use it on a country by country basis. This implies that the optimal number of topics used to run the model, and the content of each of those topics vary across countries. This makes cross-country comparisons more difficult, but it improves how well the topics selected fit the speeches of each country. Figure 9 shows the cross-country correlation using this alternative way of creating our policy volatility measure.

20