rafmacalaba/data-use-mentions-extended huggingface dataset:
## 2.4 Closing conditions

Aggregate demand is the sum of private and public consumption and is dened as: We assume that the cost of opening new vacancies are socially shared.

Assets are in zero net supply, which implies the equilibrium condition: Net foreign assets are determined by: ̃ ̃ where the last term in brackets represents the trade balance: _TBt_ = _QtNx,tρx,tyx,t − N_<sup>_∗_</sup> _x,t_<sup>_ρ_ ̃</sup><sup>_∗_</sup> _x,t_<sup>_y_ ̃</sup> _x,t_<sup>_∗_.</sup>

# 3 Calibration

We calibrate the model using quarterly data from the U.S. and Brazilian economies. We believe that Brazil is an illustrative example of an emerging country which, starting form high level of informality in the late 1990s, has adopted a set of policy initiatives to facilitate the move to formality.<sup>12</sup> A rst program, called SIMPLES, was launched in 1996 and was followed by a second one, the SUPERSIMPLES program, in 2006. Since, in Brazil there is a strong correlation between size of company and prevalence of informality, these programs aimed at reducing the costs of formalization through a simplication and a reduction of tax rates and tax regulations for Brazilian micro rms with no more than ve paid employees.<sup>13</sup> Since the SUPERSIMPLES came into force in July 2007, some 9 million businesses have joined this system of taxation and the formal rate has increased by 11 percentage points (see Fajnzylber et al. (2011)).<sup>14</sup> In this section we discuss the calibration strategy. Broadly speaking, we choose some parameter values from the literature, while other parameters are set so to match macroeconomic series observed for the United States and Brazil. We assume that the two
determine the average claim payment rate for the district, with this total rate split between village Panchayats or Blocks using high resolution remote sensing imagery or local weather data. As another example, insured farmers could receive a claim payment based on block or district level yield estimates, with potential for a top up if local weather or remote sensing imagery data suggests unusually low local yields.

Third, the insurance unit size could be reduced, for example from the level of the block to the village Panchayat. However, without village Panchayat-level historical data it would not be possible to compute the actuarial premium rates for village Panchayat-level insurance units and so any reduction in insurance unit size would have to be considered to be a ‘social benefit’ paid for by government and not be passed onto the insurer, at least till the time a sufficient data series is created. Any reduction in the insurance unit size would also require a large increase in the number of crop cutting experiments to be conducted from 500,000 to an estimated 5.5 million (Joint Group 2004), and possibly even up to 10 million (author estimates). This may make it challenging to concurrently increase the quality, speed and reliability of crop cutting experiments.

# **4. The pilot modified National Agricultural Insurance Scheme (mNAIS)**

In September 2010, the Government of India approved the modified National Agricultural Insurance Scheme (mNAIS), moving from a social crop insurance program with ad-hoc funding from the Government of India to a market-based crop insurance program with actuarially sound premium rates and product design. Given the technical and operational challenges associated with moving from the NAIS to the mNAIS, implementation began with a three-season pilot, starting with 34 districts across 12 states for the Rabi 2010-11 crop, and scheduled to increase to 50 districts (around a tenth of India). In Rabi 2010-11 approximately 340,000 farmers purchased policies under this scheme, with a premium volume of approximately US$10 million, and over time it could be expanded to India’
time that the first MDG was set, there were very few time-series observations to draw on. Fortunately, the situation has changed dramatically. Although the data are still far from ideal, we now have a firmer empirical basis for considering alternative goals that are within the range of experience.

The benchmarks proposed here rest on explicit scenarios for future economic growth and distributional change, informed by knowledge of recent past performance in reducing poverty and current expectations about economic growth prospects across the developing world. Unavoidably, there is a measure of conjecture in the establishment of any targets. But it is hoped

> 2 The only careful attempt to benchmark performance at the country level appears to be Newman et al. (2010), who draw on time-series evidence across developing countries. They use the empirical distribution of absolute changes in the poverty rate as data to inform the establishment of performance benchmarks for countries in Latin America and the Caribbean.

> 3 The MDGs were not the first such effort at goal setting; Hume identifies a number of antecedents, including the UN Declaration of Human Rights.

2
# Introduction

Consumer subsidies in the Middle East and North Africa (MENA) region are widespread. All of the countries in the region administer energy subsidies, and most countries administer food subsidies on at least a few items. These subsidies are important for households in that they constitute a sizable part of household expenditure and represent an important share of governments’ expenditure or forgone revenues. Consumer subsidies are also larger in this part of the world compared to other regions (Clements _et al._ 2013; Sdralevich _et al._ 2014) and they are more heterogeneous in many respects. The initial origins, types, profile, administration, and cost and beneficiaries of subsidies vary significantly across the countries of the MENA region. This heterogeneity makes comparisons across countries more complex, but also provides an opportunity to derive lessons on subsidies and subsidy reforms.

This paper aims at comparing the distribution of subsidies and the impact of subsidy reforms on welfare across countries in the MENA region. Specifically, the paper does this offering a standardized analysis of consumer subsidies in 2014. We use household budget survey data for five selected case studies and standardize the key variables for the analysis, including expenditure per capita on individual products and a basic set of household characteristics. We also update all surveys to 2014 using information on production, prices, and population growth and transform all values in purchasing power parity (PPP) using the latest round of the PPP survey (2011). We then use a version of the microsimulation model “SUBSIM,” (www.subsim.org) which is designed to make comparisons across countries, to provide a comparative distributional analysis of subsidies and simulations of subsidy reforms. This version of the software is designed to compare individual products across countries and allows researchers to see how any two countries compare in the distribution of subsidies and in the outcomes of subsidy reforms. In this way, we are able to simulate the same subsidy reforms in different countries and compare the outcomes across countries in terms of household welfare and government revenues.
In 2022, there were a total of 7,697 manifestations (4,612 protests and 3,085 riots) in SSA according to the Armed Conflict Location & Event Data Project (ACLED), compared with 2,188 a decade earlier and 359 two decades earlier. SSA witnessed the largest increases in anti-government protests in the world over the last decade increasing by 23.8% each year (more than twice the global average).<sup>31</sup> Since 2000 most demonstrations and protests have been unarmed and peaceful – even if they can turn violent often in response to state crackdowns.

> 31 ACLED data confirms that the general trend of falling fatality rates also holds for all forms of political activity including armed rebellion and insurgency. When total fatalities are adjusted for Africa’s rapid population growth to represent the ratio of fatalities per million people in the population, it is clear that fatality rate has slowly come down over long-time horizons.

24
# **5.3 Existing Support Programs and Projects**

100. With very limited available information, a comprehensive list and description of programs and projects targeted at the informal sector, including HEs, could not be drawn. Nevertheless, the stocktaking undertaken for this report reveals useful information on a number of programs and projects by various agencies, both government and nongovernment. Table A1.20 shows a list of programs and projects on which information was available, albeit with several gaps that limit an assessment of their impact.<sup>26</sup> Consequently, this study relies on useful insights gained from the FGD study to highlight key issues in general, while providing a detailed assessment of some of the programs and projects, whenever possible.

101. Most of the programs for HEs and MSMEs have overlapping objectives and target beneficiaries, reflecting weak coordination among implementing authorities. A large number of them focus on issues that involve access to credit, training on entrepreneurship skills, and socioeconomic empowerment of women and youth. A few others are much broader in scope and seek to address larger issues related to the business environment. Such programs include the MKURABITA (the formalization of properties owned by HEs), BARA (simplification of registration and licensing), and BEST (simplification and rationalization of regulations affecting businesses). The study of Johanson and Wanga (2009) on informal sector training discussed the limitations of the impact of SIDO and UDEC in entrepreneurship development, and VETA on vocational training and technical education. Another recent study analyzed the effects of both training and microfinance on small enterprises (Box 5.2).

**Box 5 2: Studying the Impact of Microfinance and Training on Small Enterprises**

In order to understand the needs of MSEs, an experimental study was conducted in Dar es Salaam in 2008-2009, which involved randomly selected male and female entrepreneurs who sought loans from PRIDE, the biggest microfinance institution in Tanzania. The members, all of whom had already had one successful loan from PRIDE, were
cutoffs (which represents 97% of applicants)<sup>1</sup> . For all analyses, we study “medium-term” and “short term” effects by using data on program recipients that began the five-year program in 2011-2012 (34 years in the program) and 2013-2014 (1-2 years in the program), respectively.

With respect to environmental impacts, we found that the program increased management activities by participant communities to protect land cover--such as patrolling for illegal conversion, building fire breaks, or combatting soil erosion. Compared to controls, we found a substantial and statistically significant increase in an index of community land cover management activities of 48% (or 0.60 standard deviation). We also found a statistically significant increase of approximately 2.7 days per year per household devoted to land management activities. This substantial increase in land-cover management activities suggests that the program is achieving its primary goal of behavior change to support ecosystem service provision.

Mexico’s program has sought to overcome the inherent adverse selection problem common to PES by targeting payments to certain eligible areas of the country, prioritizing land with higher risk of deforestation, and introducing differentiated payments depending on risk level and ecosystem type. Our analysis of avoided forest cover change found suggestive evidence that the program as a whole reduced the rate of loss of tree cover, with point estimates indicating a 30-34% reduction for the early cohorts and a 20-24% reduction overall, but not being statistically significant. Due to data limitations<sup>2</sup> and the overall low rates of forest cover change during this time period, the minimum detectable effect sizes for the full sample were of roughly the same order of magnitude as the rate of forest loss among controls, making it difficult to detect anything less than a 70% impact of the program, which is higher than prior estimates of PES impacts in the literature, and is unlikely to occur because of remaining natural forest losses due to events such as fires or hurricane damage.
addition, the wealth distribution, the probability of becoming unemployed, and other risks are similar on both sides of the border. Eugster _et al._ use data from referenda over the period 1980-2009 to document a persistent difference in the demand for social insurance – the German group expresses a much lower demand than the Latin group. These two groups are at opposite ends of scale defined by average responses in countries to the question whether government should do more to redistribute income. Eugster _et al.’s_ findings suggest that even among groups with the same economic fundamentals, there can be large and long-lasting differences in the demand for social insurance.

A neat example of how the welfare state may shape preferences with regard to labor supply is Lindbeck and Nyberg (2006). They explicitly examine the influence of the welfare state on parents‘ incentive to instill preferences for hard work (―work norms‖ for short) in their children.

17
# **3 Results**

## _Fact 1. Firms facing higher competition have increased more their use of ICT._

First of all, we analyzed whether firms that faced more competition, measured as a value above the median in our competition variable, experienced higher increases in terms of ICT use. Figure 1 shows that for the period 2008-2012 this is indeed the case. Firms producing goods that experienced higher import competition from China invested more in ICT; the difference is statistically significant at the 10% level for computers-per-worker and the share of labor with Internet.<sup>5</sup> A possible mechanism behind the relation between ICT use and competition is trade-induced technical change. Under this hypothesis, competition generates incentives for innovation and the adoption of new technologies, speeding up the creative destruction process (Bloom et al., 2015). In this sense, considering all the side investments in terms of changes in processes and organization that entail increasing ICT use, firms that face higher competition pressures have more incentives to pursue these investments.

Figure 1: Mean change in ICT use 2008-2012 <!-- Start of picture text --> Computers−per−worker *<br>Share of labor with computer<br>Share of labor with internet *<br>0 .02 .04 .06 .08<br>Low Chinese competition High Chinese competition<br><!-- End of picture text --> Source: Authors’ calculations with data from ENTIC 2009 and 2013, INEGI. Differences in means significant at the: * 10% level, ** 5% level, *** 1% level Note: High Chinese competition is defined as a value above the median in the change of the share of China in total Mexican imports between 2000 and 2008.

_Fact 2. Firms that become more intensive in their use of ICT exhibit higher increases in productivity._ In order to test whether the predictions from previous studies regarding the relation between ICT and firm
Table 5: Early Life Exposure to Coffee Mills and Test Scores: Effects by Location

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Rural|||||
|CatchmentArea X Post|0.0799***|0.0890***|0.0730***|0.0604**|0.0585***|0.0450**|0.0867***|0.1027***|0.0860***|
||(0.0273)|(0.0229)|(0.0217)|(0.0241)|(0.0211)|(0.0203)|(0.0286)|(0.0239)|(0.0225)|
||[ 0.005 ]|[ 0.001 ]|[ 0.001 ]|[ 0.015 ]|[ 0.008 ]|[ 0.009 ]|[ 0.004 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3112|0.3588|0.3588|0.3017|0.3450|0.3451|0.2925|0.3423|0.3424|
|Observations|1314120|1313807|1313807|1314123|1313810|1313810|1314119|1313806|1313806|
||||||Urban|||||
|CatchmentArea X Post|0.1524|0.
In the second case, road level of service (technical standard, width...) should be reduced<sup>24</sup> aiming at ensuring passability of intermediate means of transport, which would mean the importance of working exclusively on critical points/obstacles (small bridges for instance).

Like pointed out in Raballand et al. (2010) using Cameroon, Burkina Faso and Uganda examples, there is a continuum of integration to markets for most households in Africa. In some cases, a road may be non-passable for cars, a motorcycle driver may, for instance, dismount the motorcycle and walk it around the trouble spot in the road and then continues his trip. Therefore, from an economic perspective, most rural populations are _somehow_ connected to markets whereas most data analysis or policy prescriptions are based off of binary classifications of connectivity as either 0 or 1. Hence, from a public policy perspective, investments in roads might have a lower impact on economic development than expected due to the fact that transport connectivity is only one component of rural development, and sometimes not the most important.<sup>25</sup> It may also explain why despite major investments in rural roads in some countries, poverty reduction has not reduced significantly.

In any case, economic and social data (such as traffic data, vehicle operating costs for minibuses/IMTs, purpose of minibus usage) need to be collected for policy-makers to decide on which investment choice is required to enable which type of service provision, based primarily on demand assessment. Surveying transport demand in the Western part of the country, Zambia JCTR (2008) found that 92% of the local farmers surveyed do not produce more than 50 bags of 50 kilos each of agricultural products in a year, which means that without consolidation, the current demand does not justify transport services to develop.

There is a recurrent lack of data collection on the demand side and many investments are carried out on the multiple assumptions that roads improvement translates into reduced
0.660|0.571|_0.182_|_0.554_|0.660|0.619|_0.556_|
|Number of Observations|Baseline|223|116|107||204|106|98|||209|218||
||Follow-up|480|251|229||440|227|213|||460|460||
|P-value ofjoint F-test that all coefficients are 0<br>Note: This table shows descriptive statistics using individual level data f<br>beneficiaries of the NAIVS voucher program in 2010/11 in lottery villag<br>compare households in meeting and no meeting villages. Due to imple<br>beneficiary status (Columns 5 and 9) or meeting dummy (Column 13) <br>values for categorical variables are given in parenthesis. (0/1) indicates d|rom the household<br>es. We report anal<br>mentation issues, t<br>against characteris<br>ummy variables, e|survey. Colu<br>ogous figure<br>he number o<br>tics are zero.<br>qual to one i|mn 1 describes<br>s for no lottery<br>f surveyed hous<br> All p-values ar<br>f the response i|when the data<br>villages in Colu<br>eholds at baseli<br>e computed usi<br>s yes. See Appe|_0.903_<br>were collected,<br>mns 6-9. In Co<br>ne is lower th<br>ng the t-asymp<br>ndix Table 1 fo|either at base<br>lumn 10 we<br>an that at fol<br>totic wild clu<br>r definition o|line or follow-u<br>report the p-valu<br>low-up. P-value <br>ster bootstrap p<br>f
results indicate that the willingness of an investor to rate a country is not correlated with its perceived or experienced readiness.

# **4. Results**

This section provides the main results from our investor survey. This section is structured along the three main research questions set out in the introduction. Section 4.1 therefore sets out the importance of different risk factors when evaluating power sector investments, whereas Section 4.2 gives an overview of the readiness of different segments of the power sector for private solutions. Section 4.3 then reviews the results in terms of the readiness of different countries.

# **4.1. Ranking of Risk Factors When Evaluating Power Sector Investments**

Investors and financiers assign the greatest importance to policy and regulatory risks (average (avg.) 6.8), followed by country context risks (avg. 4.6) and risks related to the wider power sector context (avg. 4.0). Among power sector policy and regulatory risks, the certainty of cash flow (avg. 7.4) is the most important for investors, with ease of market entry (avg. 6.6) and clarity of investment priorities (avg. 6.5) following closely behind. In the category overall power sector context risks, investors see the sectoral growth potential as the most important (avg. 4.7), whereas neither the sectoral track record nor the investors’ own track record are as important (avg. 3.6 and 3.7, respectively). In terms of country context risks, governance and political risks are the most critical for investors (avg. 6.7), followed by the general business environment (avg. 5.6), and the macro-economic framework (avg. 4.3). Risks related to domestic banking and capital markets are not considered important (avg. 1.7).

These results underscore that even in difficult country and sector contexts, policy makers have the
impact of shocks vary across regions and household livelihoods? This is particularly useful for the analysis of vulnerability and resilience of households to shocks. Therefore, we augment this basic model by relating exposure to shocks with regions, household livelihoods, and other household characteristics related to vulnerability and resilience (such as intensity of using coping strategies and access to credit and help) as follows: The extent to which ρ is significantly different from zero illustrates the degree of heterogeneity in the effect of shocks across household characteristics. We examine these results through the lens of vulnerability and resilience of Sudanese households to shocks.

# 3.2 Results

## 3.2.1 Impact of Shocks on Household Welfare

In this subsection, we discuss the estimated impact of the effect of shocks on household welfare. To capture the multidimensionality of the impact of shocks, we consider various indicators of household welfare such as consumption per capita, the FCS, assets, poverty, and dietary diversity.

The FCS is calculated by weighting the different food groups (cereals, meat/fish, fruits, oils, sugar, and so on) consumed by the household. This indicator combines indicators of dietary diversity, food frequency, and nutritional importance of food groups. Food groups such as meat/fish, eggs, and so on are allocated higher weights whereas food groups such as sugars, coffee/tea, mineral water, and so on receive lower weights. Therefore, higher values of the FCS imply that households consume more healthier food items. The household asset index is constructed by weighting different assets (such as motor vehicles, mobile phones, television, radio, and so on) owned by households. It is an indication of household wealth and accumulation of assets. Higher values indicate that households have more assets. Low dietary diversity is identified from the number of food groups consumed by the household. Households who consumed less than 7 of the 13 food groups have low dietary diversity. The coping strategy index (CSI) is a measure of the severity in