rafmacalaba/datause-encoder-data huggingface dataset:
of 425 (Table D2). SOFIB has had very low capacity utilization during recent years while it did not process any cottonseed during the 2009/10 season. A third major company, JOSSIRA Industries, entered the market in 2001 with annual ca‐ pacity of 45,000 tons, but has not been in operation since 2007. Both these com‐ panies have been in arrears with SOFITEX.

**Table D2**

**Size of Major Seed Processing Companies in Burkina Faso**

||**_Labor force_**<br>||**_Capacity (tons/year_**<br>|**_)_**<br>|
|---|---|---|---|---|
||**_(all employees)_**|**_Seed_**|**_Oil_**|**_Meal_**|
|**SN‐CITEC**|990|120,000|20,000|70,000|
|**SOFIB**|425|36,000|5,000|30,000|
|**JOSSIRA**|165|45,000|6,000|35,000|
|**Total**|1,580|201,000|31,000|135,000|

**Notes:** JOSSIRA has not been in operation since 2007 while SOFIB did not process any cottonseed during 2009/10.

**Source:** Zanfongnon (2009).

Numerous smaller cotton seed processing companies joined the industry during the mid‐2000s, as the result of the policy reforms and the subsequent ex‐ pansion of the cotton sector. These firms are scattered throughout the country, with a high concentration in Bobo‐Dioulasso, the country’s key cotton producing region. The expansion of the industry, which also reflected strong demand for cotton meal by neighboring countries, was aided by investments made by expa‐ triates who returned to Burkina Faso because of the civil conflict in Côte d’Ivoire.<sup>**22**</sup> Because the smaller processors had difficulty obtaining seeds from SOFI‐ TEX during their early years of operation, they formed an association (APHB, _Association des producteurs dʹhuile des Hauts‐Bassins_ ) in 2003 to facilitate their supply of seeds from SOFITEX, as well as easing their access to funds from fi‐ nancial institutions and promoting the quality of cotton oil through a national labeling system. In 2005 a competing association was formed, GTPOB ( _Groupe‐ ment des transformateurs des produits oléagineux du Burkina_ ), which acquired most of APHB’s membership and eventually replaced its competitor. Internal conflicts led some members to form two new associations in 2008: GHH ( _Groupement des_

> **22** The minimum equipment requirement (i.e., one cold pressing machine which is produced do‐ mestically) costs about 2 million CFAf (equivalent to about US$4,200, in 2010 US dollars). There‐ fore, setting up an average‐size operation with five pressing machines costs a little more than US$20,000.

**_Page 48_**
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# 1. Introduction

Banks dominate credit intermediation and savings mobilization in most Emerging Markets and Developing Economies (EMDEs). A key driver of lending interest rates is the lending-deposit interest spread, the difference between the lending and deposit interest rates, which captures the efficiency with which banks allocate society’s savings to its most productive uses. High lending rates and spreads pose a challenge for policy makers: they can affect monetary policy transmission, hinder private investment and job creation, inhibit financial development and inclusion, and can ultimately compromise financial stability.

This paper quantitatively analyzes the main factors that are correlated with lending rates and spreads in EMDEs. The empirical literature suggests that rates and spreads are driven by three factors: _macro-fiscal conditions_ , _banking sector characteristics_ , and the _business environment._ These affect four components that make up the spread: _bank operational costs_ , _risk premia_ , _quasi-taxes_ , and _returns_ .

Using data from 140 EMDEs, we show that the country median nominal lending rate and spread declined in EMDEs in the 15-year period between 2003 and 2017. However, country experiences differ: in the decade 2007-2017, rates and spreads have increased in 33% and 40% of the countries, respectively. The analysis also shows that rates and spreads have been consistently higher in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (AFR) and are also a policy challenge in many countries in Europe and Central Asia (ECA), including Turkey and the Russian Federation. They are typically lower in East Asia and Pacific (EAP) and Middle East and North Africa (MENA), but exceptions exist (e.g., EAP: the Lao People’s Democratic Republic, Mongolia, Indonesia. MENA: the Arab Republic of Egypt, Lebanon, Tunisia).

In addition, based on a quantitative analysis of lending rates and spreads, we find that:

- First, less economically and financially developed countries tend to exhibit higher lending rates and spreads. These higher rates tend to be driven by higher spreads, not deposit rates, suggesting that intermediation efficiency is what matters most in less developed countries.

2
**Figure 1: Coverage of land lines and mobile phones in sub-Saharan Africa, 1960-2007**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Telephone mainlines (per 100 people)<br>Mobile phone subscribers (per 100 people)<br>1960196319661969197219751978198119841987199019931996199920022005<br><!-- End of picture text --> Source: World Bank, World Development Indicators

**Figure 2: Electric Power Consumption (kWh per capita), 2005**

<!-- Start of picture text --> 10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>HIC EAP ECA LAC MNA SAS SSA SSA ex<br>RSA<br><!-- End of picture text --> HIC: High-income countries; EAP: East Asia and Pacific; ECA: Eastern Europe and Central Asia; MNA: Middle East and North Africa: SAS: South Asia; SSA: Sub-Saharan Africa; RSA: Republic of South Africa Source: World Bank, World Development Indicators 30
<mark>major urban areas, namely Dushanbe (the capital), Khujand, Istaravshan, Kurganteppa, Kulyab, and Horog. Because of few observations in the household surveys for individual urban areas we group all urban areas into one. We estimate composite urban vulnerability using the same methods and data as for rural areas with the exception that for urban areas the agricultural variables are not included in the calculation of the sensitivity index.</mark><sup>2</sup>

# **<mark>4. Results</mark>**

<mark>The results show that vulnerability varies according to socio-economic and institutional development in ways that do not follow directly from exposure, geography, or elevation. Urban areas are by far the least vulnerable while the eastern RRS mountain zone is the most vulnerable and the remote GBAO mountains rank in the middle. In the following we first present results for rural agro-ecological zones, then for the urban areas, and finally for oblasts.</mark>

# **<mark>Results for agro-ecological zones</mark>**

<mark>The results show substantial and sometimes surprising variation in vulnerability and its components. Overall vulnerability varies much less than exposure, sensitivity, and adaptive capacity; this is because the sub-indices tend to cancel each other out. For example, while the remote GBAO highlands experience the highest exposure, it also benefits from the highest adaptive capacity of any area combined with medium sensitivity. Overall, GBAO therefore has medium vulnerability according to these estimates. The lesson is that a full understanding of the determinants of vulnerability alters the results from what analysis of exposure to the impacts of climate change alone would have led to; such analysis would have placed GBAO in the top as the most exposed region.</mark> <mark>The most vulnerable areas are the eastern RRS (Region of Republican Subordination) mountains, Southern Sughd hills, and Khatlon hills and lowlands (Figure 2 and Figure 3). These are areas of varying elevation and population density. The combined population of the three most vulnerable zones exceeds 500,000 (9% of the total) while that of the four most vulnerable zones—that is, including Khatlon lowlands’ population of more than 1 million—exceeds 1.6 million (27% of the country’s total). Although the zones are vulnerable for somewhat different reasons, they share a high degree of sensitivity to climate change, particular food insecurity, disaster sensitivity, and reliance on</mark>

> 2 <mark>Measures of crop diversification and irrigation per capita are only meaningful in the rural context. We use these variables to compare the rural areas within themselves, but they are not defined in the absence of agricultural land. Not using these values in calculating the index value for urban areas is algebraically equivalent to using the country average values of these variables.</mark>

<mark>10</mark>
Table 11: Effects on Main Outcomes, BDM sample

||(1)<br>Business<br>practices|(2)<br>Personal<br>initiative|(3)<br>N. correct<br>answers|(4)<br>Has<br>empl.|(5)<br>Pos.<br>sales|(6)<br>Pos.<br>profts|(7)<br>Sales|(8)<br>Profts|
|---|---|---|---|---|---|---|---|---|
|**Panel A. Main Effects**|||||||||
|At least 1 class|0.16<sup>_∗∗_</sup>|0.03|0.38|0.13|0.22|0.44<sup>_∗∗∗_</sup>|71,813|43,371<sup>_∗_</sup>|
||(0.08)|(0.21)|(0.33)|(0.14)|(0.13)|(0.16)|(63,697)|(24,299)|
|R-squared|0.106|0.096|0.054|0.126|0.025|-0.101|0.173|-0.003|
|**Panel B. Heterogeneous effects by**|**WTP**||||||||
|At least 1 class|0.16<sup>_∗∗_</sup>|0.03|0.29|0.15|0.23|0.46<sup>_∗∗∗_</sup>|75,568|44,505<sup>_∗_</sup>|
||(0.08)|(0.20)|(0.36)|(0.14)|(0.14)|(0.17)|(65,176)|(25,567)|
|At least 1 class * WTP demeaned|-0.00|-0.01|-0.17<sup>_∗∗∗_</sup>|0.03|0.02|0.03|14,410|1,810|
||(0.01)|(0.04)|(0.06)|(0.03)|(0.02)|(0.03)|(10,706)|(4,115)|
|Observations|300|300|300|300|289|289|289|281|
|R-squared|0.107|0.095|-0.078|0.126|0.017|-0.135|0.177|-0.001|
|Mean Y if did not attend any class|0.48|5.81|2.22|0.54|0.78|0.41|120,403|15,921|

Panel A presents 2SLS regressions with inverse probability of attrition weights of outcome variables on an indicator for attending at least 1 class of the course instrumented with the price offered for the course. We control for WTP dummies and the baseline value of the outcome; we replace missing values at baseline with 0s and we include an indicator for missing values. Panel B includes also interactions of the indicator for attending at least 1 class with WTP (demeaned), and adds the interaction of price and demeaned WTP as another instrument. See Appendix B for details on the definitions of outcome variables. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 40
or recover, structural shocks or policy changes with a clear economic interpretation. While there are several ways to impose these identification restrictions, the most compelling ones are those that use economic reasoning to disentangle structural shocks from the set of reduced form shocks.

Before describing our approach to identification, we outline the main features of the PVARX that we use to model the dynamics of GDP growth. The starting point is the autoregressive-distributed lag equation Here _y_ it denotes the growth rate of country _i_ ’s real GDP in period _t,_ and _aj_ are coefficients capturing the effects of lagged growth. In turn, **x** **_t-h_** is an _Mx_ 1 vector of external (or global) real and financial variables, and **b** _h_ is a vector containing their corresponding coefficients. Finally, ��� is an i.i.d. residual with mean zero and variance �<sup>�</sup> .

Equation (1) allows for rich dynamics of the endogenous variable, reflecting its own history as well as feedback effects from current and lagged external variables. External real and financial shocks impact on growth through their effect on the global variables **x** . Among these, we include a measure of global GDP growth, to capture world economic activity; a measure of world interest rates, to capture international financial conditions; and a measure of global commodity prices, recognizing the fact that many emerging and developing countries exhibit a commodity-intensive pattern of specialization. We also include a measure of global inflation for two reasons, first to summarize the stance of global markets beyond that captured by the measure of global economic activity and commodity prices, and second, and most important, because it allows us to differentiate among global structural shocks, as we discuss below, 6
# **4.  Poverty, Income Inequality, and Quality of Life**

As Section 2 shows, growth is important, but is not the sole component of a poverty alleviation strategy. Programs are needed to ensure that the poor can take advantage of job opportunities and to protect some vulnerable groups that are not able to participate fully in the economy. In order to design these programs, information on the poor is needed. This sector addresses headcount poverty and its depth, other poverty indicators, inequality, and the quality of life. Due to lack of data and information, this section does not address the broader issues of inequality of assets and opportunities.

In the last decade Bahia has made creditable headway in reducing poverty. During 1993-2001, the state’s extreme poverty, measured by P0, fell 14 percentage points (Figure 4.1 and Appendix A show P0, P1, and P2 for Brazil, and the state of Bahia for 1981-2001). There is little room for complacency, however, because extreme poverty is still very high at 41 percent. This translates to over 5 million people who still live in extreme poverty, which means that they do not have sufficient income to buy a minimum basket of food. This is almost twice the poverty rate for Brazil as a whole (21.9 percent).

**Figure 4.1:   Indigent Poverty Rate (P0), selected** **<u>years, 1981-2001</u>**

<!-- Start of picture text --> 70<br>Brazil<br>60 Northeast region<br>Bahia<br>50<br>40<br>30<br>20<br>10<br>0<br>1981 1985 1988 1990 1993 1995 1997 1998 1999 2001<br><!-- End of picture text --> _Source:_ Calculations based on selected years of PNADs in 1981-2001.

Poverty did not fall monotonically over the last two decades. The P0 increased from 40.7 in the beginning of the 1980s, increased before the federal government’s implementation of the inflation-beating Cruzado Plan of 1986, increased when the plan collapsed, and increased even more during the crisis of 1990 where it hit an all-time high of 54.9 percent poverty rate. The P0 embarked on a downward path only after the most recent inflation-beating Real Plan of 1994 was implemented and the minimum wage increased. There was a sharp decline in poverty to a level lower than at any point during the previous decade. After the Real Plan was implemented, the P0 fell 7 percentage points in Brazil. But Bahia managed to reduce the P0 by the 12
|**Share of**<br>**tertiary**<br>**migration to**<br>**US**|**(d)**<br>**(7) and(8)**|0.490<br>[1.578]<br>0.405**<br>[0.187]<br>0.223<br>[0.779]<br>1.536**<br>[0.706]<br>-1.361<br>[3.833]<br>10.903<br>[6.854]<br>98<br>0.62<br>0.16<br>1.93<br>0.09||
|---|---|---|---|
|**Ln aggregate**<br>**migration to**<br>**US**|**(c)**<br>**(7) and(8)**|0.256***<br>[0.094]<br>0.028***<br>[0.010]<br>0.262***<br>[0.064]<br>-0.163***<br>[0.053]<br>0.24<br>[0.369]<br>-0.849***<br>[0.318]<br>98<br>0.68<br>0.37<br>12.86<br>0.00||
|**Share of**<br>**tertiary**<br>**migration to**<br>**US**|**(d)**<br>**(5) and(6)**|0.065<br>[1.467]<br>0.368**<br>[0.182]<br>-0.153<br>[0.716]<br>1.358*<br>[0.712]<br>98<br>0.61<br>0.14<br>2.27<br>0.07||
|**Ln aggregate**<br>**migration to**<br>**US**|**(c)**<br>**(5) and(6)**|0.285***<br>[0.096]<br>0.031***<br>[0.010]<br>0.296***<br>[0.063]<br>-0.147***<br>[0.051]<br>98<br>0.67<br>0.36<br>12.66<br>0.00|cant at 1%|
|**Ln tertiary**<br>**migration to**<br>**US**|**(b)**<br>**(3) and(4)**|0.165*<br>[0.099]<br>0.258***<br>[0.067]<br>-0.111**<br>[0.045]<br>98<br>0.64<br>0.25<br>7.96<br>0.00|at 5%; *** signifi|
|**Ln aggregate**<br>**migration to**<br>**US**|**(a)**<br>**(1) and(2)**|0.096<br>[0.071]<br>0.298***<br>[0.070]<br>-0.132***<br>[0.049]<br>98<br>0.64<br>0.28<br>8.64<br>0.00|0%; ** significant|
||**No. of the corresponding column in Table 5**|Ln total migration to EU<br>Ln tertiary migration to EU<br>Share of tertiary migration to EU<br>Ln population density<br>Share of passport cost in GDP per capita<br>Legal restrictions on migration<br>Restrictions on women’s travel<br>Observations<br>R-squared<br>Shea partial R-squared<br>F test<br>p-value|Robust standard errors in brackets * significant at 1|
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111. The current account balance of a country reflects its savings-investment balance. The above analysis explains China‘s large current account surplus. The reasons for the large and growing trade imbalance between the U.S. and China were, however, mainly the relocation of many of the low value-added labor-intensive products from other East Asian economies to China. The U.S. has long had a trade deficit with East Asia because of imports of labor-intensive products. This relocation concentrates the trade surplus of East Asia with the U.S. on China, which can be observed in the changing patterns of the United States‘ trade deficits with Japan and China from 1985 to 2009 (Figure 53).

<!-- Start of picture text --> Figure 53: Share of US-Japan and US-China Trade Deficits in Total US trade<br>Deficit<br>50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>China<br>15%<br>Japan<br>10%<br>5%<br>0%<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: United Nations Comtrade, World Integrated Trade Solution database; authors' calculations.

112. This relocation as well as the regional integration through production networks also caused China‘s trade deficit with other East Asian economies. Figure 54 illustrates the rising importance of China in U.S. imports from East Asia. Related to this, Figure 29 above shows how China‘s trade balance with other East Asian economies deteriorated, as China increasingly imported raw materials and semi-finished products from these countries. Many subcomponents of these commodities are produced in advanced countries and assembled in China, as a result of relocation of labor-intensive production to China. Figure 55 shows the decomposition of the production of an iPhone assembled in China, which illustrates the small share of value added in China versus inputs from other countries, notably Japan, Korea, Germany and the United States.

70