rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Trends in citizens' trust<br>Trust in army Trust in police Trust in president<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>45%<br>2 0 0 5 / 2 0 0 6 2 0 0 8 / 2 0 0 9 2 011/ 2013 2014/ 2015 2016/ 2018 2019/ 2021<br><!-- End of picture text --> Note: Sample of 16 countries: Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar (except missing data in 2019/21), Malawi, Mali, Namibia, Nigeria, Senegal, Tanzania, Uganda, Zambia, and Zimbabwe Source: Afrobarometer database available at https://www.afrobarometer.org/online-data-analysis/

# Political stability

## **<mark>Long-term trend 4. Political instability is on the rise in Africa and constitutes a major threat to the continent’s ability to claim the 21</mark>**<sup>**st**</sup> **<mark>century.</mark>**

<mark>Political violence is at a historical high in Africa – and the continent fares poorly in regional comparative perspective. As illustrated in Figure 15, after a short-lived decrease in the early 2000s, violence has reached new highs in the last decade. This violence is likely the product of the long-term trends identified above, and reflects the fractured nature of the social contract, and the challenge of ensuring state control over the territory, in particular in fragile states with weak institutions. It remains a core binding constraint to growth on the continent: instability creates policy volatility, shortens policy horizons leading to suboptimal macroeconomic policy decisions, and decreases investors’ confidence. Estimates of the cost of conflict in Nigeria alone in 2021 was $15 billion, and total costs between 2008 and 2021 were $113 billion. Fang et al. (2020) estimates that in SSA annual growth in countries in conflict is about 2.5 percentage points lower on average, and that the impact on per capita GDP is cumulative and increases over time. They also show that conflict negatively impacts public finances, including by lowering revenue and raising military spending, and incentivizes a shift in resources away from social spending.</mark> 28
supply or outsourcing connections. This helps these banks overcome the asymmetric information problems that relationship lending tries to solve.

In terms of risk management, large, universal banks are better suited to conduct lending based on automated scoring models for small loans (since they have the knowhow and models to do so) and template-type rating systems for larger loans (based on streamlined, standardized versions of corporate rating). Also, they generally have more advanced risk management systems. Due to their size and to their presence in many different markets, large and foreign banks are better able to diversify away the idiosyncratic risks of SME lending. Also, they probably have advanced methods to assess the value of collateral, better recovery units, and more efficient ways to execute collateral.

# **5. SME Side**

To complement the information analyzed so far from the bank side, we present evidence on the demand side from surveys of SMEs. This rich dataset, collected independently by FRS (Inmark Group), gives a different but complementary perspective of the relation between banks and SMEs, corroborating many of the findings obtained from the bank surveys. In particular, the data show that SMEs interact with banks using a variety of products. Furthermore, SMEs do not exclusively obtain financing via “relationship loans” but also access financing products that do not depend on the bank processing “soft” information on the firm.

Figure 15 shows data on the average number of “distinct” bank products and services used by SMEs in Latin American countries collected by FRS (Inmark Group) via 35
is not enough precision about the term ”institution” itself. There is a big use of performance indicators measuring how certain institutional systems function, e.g. when it comes to political stability or governance issues (Gradstein/Milanovic 2004: 516).<sup>5</sup> Such performance indicators then are often mixed up with public policies. But, the performance and the policies together are the outputs of underlying structures and procedures as well as contextual factors. These underlying (formal) structures and procedures can be subsumed under the heading political system. This is what we understand under political institutions.

According to the rational choice strand of the new institutionalism in political science or the field of new institutional economics and political economy, political institutions are the rules which govern the political game (e.g. Peters 1999, Hall/Taylor 1996, Persson/Tabellini 2000). They not only determine via electoral rules which actors and preferences can access the political arena and get heard. They also provide the means to aggregate those preferences by establishing procedures for decision-making and distributing political power, i.e. the right to decide (Persson 2002: 886). The common output of institutions and preferences are policies. Although actors and other environmental constellations may change over time, policies in general will reflect the political institutions that produced them (Persson/Tabellini 2006: 321, Peters 1999). We distinguish between two types of policies that may be favorable to human development: policies for the protection of property rights and policies for redistribution. Policies for the protection of property rights encourage economic investment and contribute to economic development and economic growth (e.g. Acemoglu/Johnson/Robinson 2002). Growth is assumed, under certain conditions, to increase the welfare of the population by reducing poverty (Klasen 2004). Policies for redistribution have an equalizing impact on the distribution of wealth in a society. Especially through broad-based programs

> 5 See for example the Worldwide Governance Indicators (Kaufmann/Krayy/Mastruzzi 2007).

7
focused on whether the Indian industrial strategy, which involved extensive use of industrial licensing and tariff and non-tariff barriers, led Indian manufacturing to perform poorly relative to other countries in particular those in East Asia (Bhagwati and Desai 1970, and Bhagwati and Srinivasan 1975).<sup>8</sup> Nationwide policies, however, have limited scope to explain the marked cross-state variation in manufacturing performance that we observe in Figure 4.

To understand these patterns we need to focus on state specific policies and conditions. We examine three examples of research on how 1<sup>st</sup> advantage factors and the interplay between 1<sup>st</sup> advantage and 2<sup>nd</sup> advantage factors determine the pattern of manufacturing growth in India. These examples illustrate how the framework we have developed can be used build up a microeconomic evidence base on what matters.

_Example [1]: 1_<sup>_st_</sup> _advantage: labor regulation and registered manufacturing_ Labor regulations have been identified as an important element of the investment climate in India (Stern, 2001; Dollar, Iarossi and Mengitsae, 2001, Sachs et al, 1999). Besley and Burgess (2002) examine whether labor regulation is an important source of 1<sup>st</sup> advantage which could help explain differences in manufacturing performance across Indian states. To do this they exploit two key facts: (i) labor regulations only apply to firms in the registered manufacturing sector, and (ii) the Indian constitution empowers state governments to amend central legislation. The main piece of central legislation is the Industrial Disputes Act of 1947. This Act has been extensively amended by state governments during the postIndependence period. Besley and Burgess (2002) read the text of each amendment and coded each as pro-worker (+1), neutral (0) or pro-employer (-1). The pattern of change across Indian

> 8 Between 1960 and 1995, manufacturing as a share of GDP grew from 9 percent to 24 percent of GDP in Indonesia, 8 percent to 26 percent in Malaysia and 12.5 percent to 28 percent in Thailand. In contrast, manufacturing output in India constituted 13 percent of GDP in 1960  but grew to only 18 percent of GDP by 1995. Source: World Development. Indicators.

25
Table 4: Summary statistics: Employees

||All|Social net<br>use|works are<br>d?|
|---|---|---|---|
|||Yes|No|
|Age at hire|24.21|23.56|25.33|
||(4.89)|(5.05)|(4.40)|
|Schooing in years|9.01|7.19|12.15|
||(4.64)|(3.84)|(4.19)|
|Tenure in years|4.32|4.23|4.47|
||(3.35)|(3.36)|(3.34)|
|Earnings at hire (monthly, in Taka)|5745.66|4758.19|7445.25|
||(4213.63)|(2865.85)|(5443.01)|
|Earnings (monthly, in Taka)|9146.08|7448.94|12067.13|
||(7108.49)|(4418.45)|(9510.92)|
|_Occupation at hire_||||
|Manager|0.06|0.05|0.06|
||(0.23)|(0.22)|(0.23)|
|Professional|0.20|0.16|0.28|
||(0.40)|(0.37)|(0.45)|
|Semi-skilled worker|0.61|0.62|0.58|
||(0.49)|(0.49)|(0.49)|
|Elementary job|0.14|0.16|0.08|
||(0.34)|(0.37)|(0.28)|
|Search spell till fnding the current job (in weeks)|8.60|5.56|13.85|
||(12.94)|(5.91)|(18.76)|
|No. of vacancies applied for|3.60|2.28|5.87|
||(5.36)|(2.79)|(7.53)|
|Promoted ever in the current frm (1=yes)|0.04|0.02|0.06|
||(0.18)|(0.15)|(0.23)|
|Observations|3396|2148|1248|

_Note_ . Shown are means and standard deviations for permanent male employees aged 40 and below. Standard deviations are in parentheses. The second column is the statistics of those who found jobs through social networks, and the third is of those who did not. Earnings include bonuses, overtime, and other compensations.

26
# On Hunger and Public Action

# by Martin liavallion

# Table of Contents

|1.|Concepts|2|
|---|---|---|
|2.|How is this approach different?|4|
|3.|Data and methods|10|
|4.|Famines|13|
||4.1 Markets and famines<br>4.2 "Cash" versus "food"<br>4.3 Reaching the vulnerable|15<br>18<br>2a|
|5.|Chronic hunger|23|
|6.|On the role of the international community|29|
|7|Conclusions|32|
|Endn|otes|35|
|Refe|rences|36|
and Melinda Gates Foundation, n.d.; OHCHR, n.d.; Plan International, n.d. ; UNICEF, n.d.; Woodon et al., 2018) and in Nigeria (see UNICEF, 2017; UNGEI, 2008; World Bank, 2018), education deprivation shares do not display noteworthy differences across gender neither in the rural nor urban areas, nor when comparing north to south, when solely considering national or regional figures, as displayed in Figure 6 d) to i) and Figure A.2 in the Appendix.<sup>22</sup> One exception is the higher share of rural girls aged 15-17 that has never been to school in the NW compared to boys, 20% versus 11% in wave 4. For the North East, the graph however displays the opposite.

As severe education deprivation however does not inform about the length of education and early dropout rates of those not deprived, estimates for the share of children not in school while of school going age are presented in Figures 11 to 15.

Apart from a rural – urban education deprivation divide, there is an even more extreme north-south divide, with children living in the North-East and North-West regions facing the highest though over time decreasing severe education deprivation rates, as displayed in Figure 7. Nevertheless, still even in the fourth GHS wave, close to 30 percent of children in the oldest rural age group in these regions compared to less than 1 percent of the children of this age group in the South-South have never received any education. While the pattern of the GHS and MICS estimates across regions is generally consistent, the GHS data suggests higher education deprivation rates for the rural North-West, whereas the MICS data suggests even higher rates in the rural North-East. This discrepancy may be related to differential access to conflict affected areas of the rural North-East by the survey teams of the GHS and the MICS survey, which may suggest that the temporarily inaccessible areas display notably higher education deprivation rates than the more accessible ones. Such a discrepancy would confirm findings by Bertoni et al. (2018) that the conflict does notably effect schooling of children in the North-East. It is also worth noting that while children in the rural South-West similar to the children in the northern regions, appear to start schooling later and used to have slightly higher education deprivation rates than the other southern regions, towards GHS wave 3 also in the South West almost all children have been at some point in school by the time they reach the oldest child category.

Figure 6: National Level Severe Education Deprivation

> 22 UNICEF (2017) evaluates for instance the unconditional cash transfer program ranging from 2014-2016 with the primary objective of increasing the enrolment, retention and completion of basic education of girl’s in specific areas of Niger and Sokoto States, presented in.

19
Developing Countries' Exports of Manufactures: Past and Future Implications _oi_ Shifting Patterns of Comparative Advantage

# Alexander J. Yeats*

# I. Introduction

Considerations relating to the determinants of comparative advancage for countries or groups of countries have played a major role in the formulation of both national and international economic and trade policies. For example, international organizations like the World Bank and International Monetary Fund have given considerable attention to the need for formulating positive structural adjustment policies under which countries adopt trade, _inancial, fiscal and other measures to facilitate the transfer of resources from industries where they have lost a comparative advantage in production to those where it has been gained or maintained. 1/ Problems relating to contemporary protectionism are also linked to changing comparative advantage

- Senior Economist, Inteinational Economics Department, World Bank, Washington. The views expressed in this paper need not reflect those of the World Bank or its staff. The author would lke to thank Alasdair Sinclair, Paul Meo and Bela Balassa for many helpful comments and suggestions.

- 1/ See World Bank (1986) (1987) (1988) or OECD (1979) for examples of the types of issuea normally addressed in these discussions. A key element often stressed is the need for developed countries to implement measures to facilitate the flow of resources from industries experiencing longer term structural adjustment problems (i.e., agriculture, textiles, clothing, etc.) into sectors like chemicals and machinery where comparative advantage has not shifted to developing countries.
Table 7 Logistic Regression to Estimate Propensity Score for overdraft facility <u>policy.</u>

|Dependent<br>Variable||||||Dum|my for H|TAP Subsid|y, =1 if treated by th|e program|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Industry**||**Beverages**||||**Food**||||**Garments**||||**Metals and machi**|**nery**||
|**Independent**<br>**variables**|**Coefficient**|**Standard**<br>**error**|**Z**|**P>z**|**Coefficient**|**Standard**<br>**error**|**Z**|**P>z**|**Coefficient**|**Standard**<br>**error**|**Z**|**P>z**|**Coefficient**|**Standard**<br>**error**|**Z**|**P>z**|
|Age|-0.01245|0.024976|-0.5|0.618|0.12828**|0.062587|2.05|0.04|-0.01708|0.026247|-0.65|0.515|-0.00263|0.01533|-0.17|0.864|
|Age squared|0.000164|0.000178|0.92|0.359|-0.00135|0.000882|-1.53|0.126|0.000147|0.000269|0.54|0.586|1.79E-05|0.000155|0.12|0.908|
|Dummy Large|||||||||||||||||
|firm|0.447817|0.557797|0.8|0.422|||||1.305646***|0.45932|2.84|0.004|0.522057*|0.279631|1.87|0.062|
|Dummy Medim|||||||||||||||||
|firm|0.327875|0.45841|0.72|0.474|-0.43002|0.679557|-0.63|0.527|||||||||
|Dummy small<br>firm|||||0.194275|0.659108|0.29|0.768|0.326923|0.41772|0.78|0.434|-0.1526|0.254381|-0.6|0.549|
|Dummy high/<br>upper-middle<br>income country|-0.42417|0.469876|-0.9|0.367|0.046765|0.562043|0.08|0.934|-0.05657|0.366503|-0.15|0.877|-0.08481|0.248774|-0.34|0.733|
|Dummy<br>domestic<br>owners|-0.44578|0.565572|-0.79|0.431|0.182613|0.579717|0.32|0.753|-0.28575|0.371397|-0.77|0.442|-0.34143|0.279259|-1.22|0.221|
|Constant|-0.82554*|0.427795|-1.93|0.054|-2.64108***|0.875654|-3.02|0.003|-1.38919***|0.439206|-3.16|0.002|-0.93797***|0.315|-2.98|0.003|
|Number of obs.||135||||116||||243||||515|||
|LR chi2||6.55||||9.29||||12.20||||12.07|||
|Prob > chi2||0.2128||||0.1577||||0.0576||||0.0604|||
|Loglikelihood||0.0351||||0.0725||||0.0469||||0.0205|||
|Pseudo R2||-79.974592||||-59.461831||||-123.85812||||-288.14436|||
|Note:|||||||||||||||||

* significant at 10%; **significant at 5%; and *** significant at 1%. Dummy variables take on the value of 1 if the firm has the stated characteristic and 0 otherwise.

Source; Authors estimation 20
# **Table 5: The Determinants of Evasion Gap Quantity and Price Gaps**

|||**Evasio**|**n Gaps – Pri**|**ces or Qua**|**ntities?**||||
|---|---|---|---|---|---|---|---|---|
|_Dependent Variable:_||_Log Qua_<br>_(by HS6-co_|_ntity Gap_<br>_untry-year)_|||_Log P_<br>_(by HS6-c_|_rice Gap_<br>_ountry-year)_||
||Q|Q|Q|Q|P|P|P|P|
||(1)<br>**coef/se**|(2)<br>**coef/se**|(3)<br>**coef/se**|(4)<br>**coef/se**|(5)<br>**coef/se**|(6)<br>**coef/se**|(7)<br>**coef/se**|(8)<br>**coef/se**|
|Log (Tariff+1)|0.056**|-0.016|0.063**|-0.007|-0.001|0.031|0.012|0.052|
||(0.025)|(0.063)|(0.026)|(0.067)|(0.009)|(0.030)|(0.011)|(0.033)|
|Ben Ali %|0.104|-0.150|0.021|-0.009|0.297***|0.321***|-0.890***|-0.786**|
||(0.134)|(0.168)|(0.548)|(0.595)|(0.081)|(0.102)|(0.227)|(0.324)|
|Offshore %|0.034|-0.372***|0.123|-0.221|-0.068***|-0.092**|0.124*|0.218*|
||(0.067)|(0.070)|(0.204)|(0.226)|(0.020)|(0.038)|(0.064)|(0.117)|
|Public %|0.093|0.006|0.041|-0.361|-0.418***|-0.470***|-0.295*|-0.274|
||(0.091)|(0.119)|(0.408)|(0.638)|(0.050)|(0.069)|(0.170)|(0.308)|
|Ben Ali % * Log (Tariff+1)|||0.024|-0.041|||0.347***|0.327***|
||||(0.153)|(0.170)|||(0.069)|(0.093)|
|Offshore % * Log (Tariff+1)|||-0.026|-0.045|||-0.055***|-0.093***|
||||(0.058)|(0.064)|||(0.017)|(0.033)|
|Public % * Log (Tariff+1)|||0.016|0.114|||-0.037|-0.061|
||||(0.119)|(0.189)|||(0.051)|(0.091)|
|Log (NTB + 1)|0.026|-0.024|0.025|-0.023|-0.012|-0.047*|-0.014|-0.045*|
||(0.025)|(0.060)|(0.025)|(0.060)|(0.011)|(0.025)|(0.011)|(0.026)|
|Country*year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country*product FE||Yes||Yes||Yes||Yes|
|<br>N|48724|48724|48724|48724|48724|48724|48724|48724|
|Number of products|1386|1386|1386|1386|1386|1386|1386|1386|
|<br>R2|0.032|0.611|0.032|0.611|0.034|0.495|0.036|0.496|
|AdjustedR2<br>Note: Standard errors are c|0.030<br>lustered by|0.513<br>product. ***|0.030<br>p=<0.01, **|0.513<br><0.05, * p=|0.032<br>0.10. The sa|0.368<br>mple is confin|0.033<br>ed to product-|0.369|

Note: Standard errors are clustered by product. *** p=<0.01, ** p<0.05, * p=0.10. The sample is confined to productsource-year combinations(i) in which imports reported in Tunisia and exports reported in partner countries are strictly positive, (ii) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (iii) products which account for at least 0.01 percentage points of cumulative import value (either as declared by partners or as declared by Tunisian customs) over this period.

42
**_Energy demand models for policy_** **_<u>formulation</u>_**

Energy demand analysis has attempted to capture these aspects in different ways: the traditional economists’ approach relies on optimizing behavior within the neoclassical tradition of economics. Another approach follows the engineering tradition and criticizes the limitations of the optimizing and rational behavior assumed in the traditional analysis. Instead, they introduce other behavioral assumptions (such as “satisficing” approach in the sense of Herbert Simon or evolutionary approach for technological change) and beliefs.<sup>1</sup> This divergence in the views has dominated the energy literature in the past and led to the emergence of two distinct traditions of energy analysis – namely the econometric approach and the engineering end-use approach.

# **_3.1 Economic foundations of energy demand_**<sup>**_2_**</sup>

The factors driving energy demand differ across economic agents and sectors. Households consume energy to satisfy certain needs and they do so by allocating their income among various competing needs so as to obtain the greatest degree of satisfaction from total expenditure. Industries and commercial users demand energy as an input of production and their objective is to minimize the total cost of production. Therefore the motivation is not same for the households and the productive users of energy and any analysis of energy demand should treat these categories separately.

# **3.1.1 Household energy demand**

The microeconomic basis for consumer energy demand relies on consumers’ utility maximization principles. Such an analysis assumes that consumers know their preference sets and ordering of preferences. It also assumes that preference ordering can be represented by some utility function and that the consumer is a rational one in that she will always choose a most preferred bundle from the set of feasible alternatives.

> 1 See Worrel et al (2004) and Wilson and Dowlatabadi (2007). 2 This section relies on Bohi (1980), Chapter 2, Estimating the demand for energy: Issues and Methodologies. Similar treatments are also provided in Hartman (1979) and Munasinghe and Meier (1992).

17
In this paper, we take a different approach to estimating the impact of climate change on tropical cyclones by relying on a geographically detailed Tropical Cyclone Integrated Assessment Model (TCIAM) (Mendelsohn, Emanuel, and Chonabayashi 2010). The model begins with the A1B SRES global emissions trajectory that stabilizes greenhouse gas concentrations at 720 ppm (IPCC 2000). This path is one of the well established scenarios used by the IPCC in their Fourth Assessment Report (IPCC 2007a). There are consequently several climate model runs that were available using this scenario. We use this emission path in conjunction with four climate models to predict four global climate scenarios for both the present and the future (2100). These climate scenarios are then used to predict how tropical cyclones may change into the future using a newly developed tropical cyclone simulator (Emanuel et al. 2008). Potential storms are seeded into each ocean basin. Given both current climate and future climate conditions, the storms then either develop into tropical cyclones or they die off. A total of 3000 tropical cyclones are generated per basin<sup>2</sup> . This provides an extensive data set of tropical cyclones from which to examine whether current (2000) versus future (2100) storm patterns change. Normally, it would take three hundred years to observe this many tropical cyclones in nature.

The historic relationship between aggregate damages and the magnitude of each storm is estimated using data from storms that have hit the United States since 1960. Aggregate damages include the value of all lost structures and infrastructure. Damages do not include fatalities. Fatalities are estimated separately. Impacts in different years are updated to current dollars using the GDP deflator. The damages are matched with characteristics of the storm including minimum barometric pressure, maximum wind speed, and location at landfall (NOAA 2009). Estimates of county income and population density are inferred from Census data for the five counties closest to the point of contact. A regression estimates the relationship between storm intensity and damages. We then use international data (EMDAT 2009) to estimate the relationship between storm damages and national income and population density (vulnerability). The limitation of the international data is that it has no information about the intensity of each storm.

> 2 5000 tropical cyclones are seeded into the North Atlantic to get extra accuracy of the changes in this ocean.

3