rafmacalaba/datause-encoder-data huggingface dataset:
# **Contractual, information and macroeconomic framework**

While the literature has established a robust relationship between macroeconomic stability, the efficiency of the contractual and information framework and bank lending, this framework might have a differential impact on firm and household credit with repercussions for the composition of overall bank credit. A priori, the relationship between the contractual framework and the share of household credit is ambiguous. On the one hand, lenders might have to rely more on the contractual framework for “small” borrowers than in the case of large business borrowers. On the other hand, the sums at play in household credit might be too small for the lender to engage the legal system in the first place. Similarly, it is a –priori not clear whether there is any differential effect of inflation on the composition of total private sector lending. On the one hand, most consumer lending is short-term and might therefore be less affected by monetary instability than firm credit. On the other hand, household credit in many countries includes mortgages, which are mostly at the long-end of the yield curve. When it comes to systems of credit information sharing, we would expect a stronger effect on household credit, as households are typically more informationally opaque than medium-size and large enterprises. Consumer lending in both advanced and emerging markets has increasingly relied on historic information about borrowers and credit scoring models, both of which rely on effective credit registries and bureaus (Berger, Frame and Miller, 2005).

We will use an indicator of **creditor rights** in and outside of bankruptcy, an indicator of the **cost of contract enforcement** and an indicator of the efficiency and breadth of **credit information sharing,** using data from the World Bank’s Doing Business database. We will also include an overall measure of **institutional development** , as developed by Kaufman, Kraay and Mastruzzi (2004) and the **inflation rate** , as measured by the log difference of the Consumer Price Index.

# **Banking sector development and structure**

We also assess whether credit composition varies with the development and structure of a country’s banking system. A priori it does not seem clear whether private or government-owned banks and domestic or foreign-owned banks should be more inclined towards household or firm credit. On the one hand, foreign banks might be more inclined towards the consumer credit market if they can apply innovative and cost-effective technologies from their home market in 14
education levels. Changing demographics can have important impacts on the demands for public sector investments and incentives for private sector investments, public services and the political power to lobby for support, and on labor markets. As a result, it is important to look at recent changes in demographic patterns in Paraíba. The following describes demographic changes that have taken place in Paraíba from 1991 to 2000. All raw population data are from IBGE, with Census data used for 1991 and 2000. The focus is on changes in population size, geographic dispersion, and age distributions.

In Paraíba, the population was 3.4 million in 2000. Compared to other states in the Northeast, the population growth rate in Paraíba was relatively low—about 1 percent per year in 1996-2000 (Table 2.2).

|**Table 2.2: Demographics in the Northeast**|
|---|

||Population,<br>2000<br>(1,000)|Annual<br>Population<br>Growth Rate<br>1996-2000*<br>(Percent)|Urban Population,<br>2000<br>(percent)|Female Population,<br>2000<br>(percent)|
|---|---|---|---|---|
|**Sergipe**|1,780|2.31|71|51|
|**Ceará**|7,417|2.16|72|51|
|**Rio Grande do Norte**|2,771|2.01|73|51|
|**Maranhão**|5,638|1.94|60|50|
|**Alagoas**|2,818|1.71|68|51|
|**Pernambuco**|7,911|1.69|77|52|
|**Piauí**|2,841|1.53|63|51|
|**Bahia**|13,067|1.03|67|51|
|**Paraíba**|**3,437**|**0.98**|**71**|**51**|
|**TOTAL Northeast**<br>_Source:_calculations based on Cen|48,680<br>sus 2000. Note: * Aver|---<br>agegeometric rate of a|---<br>nnual increment.|---|

**<u>Table 2.3: Population and Age Structure, 1991-2000</u>**

||**Population**<br>**1991**|**Population**<br>**2000**|**Population**<br>**growth rate**<br>**1991-2000**|
|---|---|---|---|
|**Total Population**|3,201,114|3,443,825|7.6%|
|**Less than 5 years of age**|393,573|338,321|-14.0%|
|**5 – 9**|412,218|355,392|-13.8%|
|**10 – 14**|407,913|389,897|-4.4%|
|**15 – 19**|360,981|388,789|7.7%|
|**20 – 24**|279,416|323,117|15.6%|
|**25 and above**|1,347,013|1,648,309|22.4%|

_Source:_ based on IBGE, Census 1991 and 2000.

As are the other Northeastern states, Paraíba is highly urbanized, and became even more so in 1991-2000, with the percentage of the population living in urban areas counting 71 percent in 2000 (Table 2.2).

14
# **Economic Transformation in Africa: The Role of North-South and South-South Trade**

Woubet Kassa _World Bank, Washington DC, United States_ Gideon Ndubuisi _Delft University of Technology, the Netherlands_ Solomon Owusu _Frederick S. Pardee School of Global Studies, Boston University, United States_

**Keywords** : North-South Trade, South-South Trade, Economic Transformation, Structural Change, Productive Efficiency **JEL** : F14 F21 F43 F63
# **Table A9.2. The Effect of the PMT on the Probability of Stunting Among Children 12 to 24 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poole<br>|d Linear<br>|Linear<br>|Probability<br>|
|---|---|---|---|---|
||Prob|ability|w/Com|munityFEs|
|Year (2000=1)|-0.065|-0.072*|0.001|0.003|
||[0.039]<br>|[0.040]<br>|[0.053]<br>|[0.056]<br>|
|_Exposed Share_*Year|0.026|0.028|-0.145*|-0.143*|
||[0.047]|[0.048]|[0.082]|[0.084]|
|Gender (boy=1)|0.037|0.037|0.072**|0.075**|
||[0.028]|[0.028]|[0.034]|[0.035]|
|Age|0.008*|0.008*|0.002|0.001|
||[0.004]|[0.004]|[0.005]|[0.005]|
|Mother education (years)|-0.001|-0.001|0.004|0.004|
||[0.003]|[0.003]|[0.004]|[0.004]|
|Father education (years)|-0.000|0.001|0.001|0.001|
||[0.003]|[0.003]|[0.004]|[0.004]|
|Mother height (cm)|-0.010***|-0.010***|-0.014***|-0.014***|
||[0.003]|[0.003]|[0.003]|[0.003]|
|Father height (cm)|-0.011***|-0.011***|-0.012***|-0.012***|
||[0.003]|[0.003]|[0.003]|[0.003]|
|Head of HH (male=1)|-0.018|-0.021|0.064|0.065|
||[0.046]|[0.046]|[0.058]|[0.058]|
|Number of children 5-14 yo in HH|-0.001|-0.002|-0.012|-0.012|
||[0.014]|[0.014]|[0.017]|[0.017]|
|HH owns private toilet (Yes=1)|-0.062**|-0.058*|-0.055|-0.051|
||[0.030]|[0.030]|[0.040]|[0.041]|
|HH has sanitation (Yes=1)|-0.125***|-0.089**|-0.011|-0.001|
||[0.037]|[0.043]|[0.060]|[0.064]|
|Type of community (urban=1), C|-|-0.044|-|0.016|
|||[0.039]||[0.075]|
|Distance to district capital (km), C|-|0.000|-|0.000|
|||[0.001]||[0.001]|
|Distance to bus station (km), C|-|-0.003|-|-0.001|
|||[0.002]||[0.005]|
|Land w/ technical irrigation (%), C|-|-0.119|-|0.054|
|||[0.080]||[0.144]|
|Comm. has asphalt road (Yes=1), C|-|0.033|-|-0.099|
|||[0.039]||[0.073]|
|C h Y=1 C||0029||0036|
|omm. as sewerage system (es),|-|-.|-|.|
|||[0.032]||[0.058]|
|Comm. has piped water (Yes=1), C|-|<br>-0.038|-|<br>-0.049|
|||[0.035]||[0.066]|
|Constant|3.636***|3.674***|4.245***|4.294***|
||[0.607]|[0.609]|[0.705]|[0.711]|
|R-squared|0.08|0.09|0.38|0.38|

Notes: _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. n is 1042. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

47
consumption and total fertility data from the World Development Indicators. In our sample of 85 countries, the average value of children is around 37% of household utility.<sup>28</sup> The value of children as a fraction of household utility in our model is computed as where _u_ ( _·_ ) is the pre-pandemic steady-state level of utility of a household in state _k_ ; _k_ = 1 is the state in which all the members are alive, and _k_ = 4 is the state in which the children are deceased (see Appendix Table B.1 for the list of household states). This exercise yields _σ_ = 2 _._ 5.

Finally, we could assess sensitivity by treating _σ_ as a free parameter. Appendix Table B.6 compares the basic results from using the baseline _σ_ , the fertility literature-based _σ_ , as well as a wider range of _σ_ ’s from 1 to 5. As expected, a lower _σ_ implies that both the reference and the optimal lockdowns lead to fewer children’s lives lost per COVID-19 fatality averted (top panel). This is because a lower _σ_ implies a stronger private labor supply response, making the government-imposed lockdown less needed (bottom panel). Qualitatively the results are unchanged. Even with _σ_ very close to 1, there is a substantial number of children’s lives lost per COVID-19 fatality averted in the lower-income countries.<sup>29</sup>

**Simple adjustments to the reference lockdown** While the optimal lockdown involves a high-dimensional global optimization problem, an interesting question is whether governments could have done better than the reference lockdown by varying either the duration or the onset timing of the reference lockdown. Appendix Table B.7 investigates this. It summarizes our key statistic, the ratio of children’s deaths per adult COVID-19 fatality averted under alternative reference lockdown scenarios. The middle panel shows what happens when

> 28We abstract from the distinction between skilled and unskilled children in Doepke (2004) and compute the value of children as a fraction of household utility as 0 _._ 132 _∗_<sup>_√_</sup> _<u>n/</u>_ (<sup>_√_</sup> _<u>c</u>_ +0 _._ 132 _∗_<sup>_√_</sup> _<u>n</u>_ <u>), where</u> _n_ is the number of children and _c_ is consumption. We approximate the number of children using the total fertility rate and consumption using the per capita consumption in WDI.

> 29 We cannot lower _σ_ below 1 because as often happens in similar CES settings, the model is not continuous at _σ_ = 1. Instead it’s a hyperbola. Values of _σ <_ 1 have some perverse implications, as they imply that household members’ consumptions are complements. In this environment losing a family member actually _raises_ household utility conditional on the same level of consumption expenditure – the mirror image of the “love for variety” effect.

44
unemployed increased from 6% to 10%. These figures are in line with the decline in GDP per capita and the rise in unemployment rates reported in national statistics and the worsening regional conditions, reported in Table 6. Furthermore, the deteriorating objective economic conditions are reflected in deteriorating economic perceptions. Personal economic optimism declined from a 2.6 to a 2.4. Whereas in the period from 2010 to 2014 67% of the people felt their life was getting better, this share declined to 57% in the period from 2015 to 2020. Positive expectations regarding the local economy plunged from a 2.4 to a 2.0, with the percentage of people with positive expectations regarding the local economy declining from 55% to 42%. In addition, leadership approval plunged from 52% to just 23% of respondents approving the government; confidence in national institutions declined too.

At the same time, not all conditions deteriorated considerably. Social support declined but remained high (at 90%), while reported education levels, civic engagement, acceptance of diversity, digital access, satisfaction with socializing opportunities and health care, and perceived safety improved in the second half of the 2010s.<sup>20</sup> Yet, as shown in Figure 6, only improvements in digital access partly mitigated the negative effects of the economic crisis on SWB.

**Figure 5. SWB decline by demographic group**

<!-- Start of picture text --> SWB decline<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2<br>Age<br><30<br>30-45<br>45-60<br>60+<br>Gender<br>Men<br>Women<br>Partner<br>Partner<br>No partner<br>Education<br>Elementary<br>Secondary<br>Tertiary<br>Income<br>Poorest 20%<br>Second 20%<br>Middle 20%<br>Fourth 20%<br>Richest 20%<br>Employment status<br>Full-time for employer<br>Self-employed<br>Part-time employed<br>Unemployed<br>Out of workforce<br>Urban vs. rural<br>Urban<br>Rural<br>Region<br>Northeast<br>North<br>Central-West<br>Southeast<br>South<br><!-- End of picture text --> _Notes:_ 95% confidence intervals shown. N=10,228

> 20 There were no notable differences between groups with regard to changes in perceived safety.

23
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**19**

# **Table 4: Dynamic linkages of the agricultural sector**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|**Agricultural**||||||
|**GDP/capita**|**Full sample**|**Middle inc**|**Middle inc**|**Low inc**|**Low inc**|
|**Non-agr. GDP t-1**|0.00956|-0.0329|-0.0386|-0.0449|0.0811|
||_0.489_|_0.230_|_0.154_|_0.240_|_0.101_|
|**Non-agr. GDPt-1 * DSSA **|||-0.0452||-0.148|
||||_0.288_||_0.031_|
|**Non-agr. GDP t-2**|-0.215|||||
||_0.001_|||||
|**Agr. GDP t-1**|1.21|1.04|1|1.04|0.802|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|**Agr. GDPt-1 * DSSA**|||0.0595||0.147|
||||_0.354_||_0.077_|
|**Precipitation**<sup>**1)**</sup>|0. 0974|0. 0675|0. 0639|0. 2900|0. 2080|
||_0.077_|_0.230_|_0.357_|_0.000_|_0.005_|
|**Constant**|-0.0527|0.0404|0.3|0.0725|0.54|
||_0.598_|_0.909_|_0.311_|_0.825_|_0.036_|
|**Observations**|734|350|350|526|526|
|**# of countries**|104|44|44|74|74|
|**# of instruments**|81|50|74|50|50|
|**P-value f/ no AR(1)**|0.00204|0.0221|0.0236|0.00395|0.00294|
|**P-value f/ no AR(2)**|0.466|0.143|0.141|0.168|0.191|
|**Sargan test**|0.392|0.748|1.000|0.207|0.559|

1) Deviation of actual rainfall (in mm) in t from long run average (in mm), normalized by the country-specific standard deviation of rainfall, divided by 1000. Source: Authors’ calculations.

# **5 Participation Effect**

There are several reasons why the contribution to poverty reduction from growth might differ across sectors. First, connecting to the growth process might be easier for the poor if growth happens where the poor are located. Indeed, much of the literature underscoring the importance of agriculture in poverty reduction has argued that the poor stand to benefit much more from an increase in agricultural incomes than from an increase in non-agricultural incomes because many of the poor live in rural areas and most of them earn their living in agriculture or agriculture related
particular focus on financial infrastructure features such as creditor rights and credit information systems) are associated with more credit and more long-term credit.

Estimating the effect of institutional variables is problematic due to endogeneity challenges. This is because countries with higher financial development tend to also have betterquality institutions and, in particular, better financial infrastructure. However, some studies have established causality by exploiting changes in the quality of financial infrastructure and found that better financial infrastructure is associated with credit as well as more long-term credit (Djankov, McLiesh, and Shleifer 2007; Gopalan 2014; Love 2013; Peria and Singh 2014).

Our paper is also related to the few papers that explore the determinants of long-term credit using aggregated bank balance sheet data. Using cross-country data on bank loan maturity, Valev and Tasić (2008) find that the share of long-term credit is higher in countries with strong institutions, low inflation, large financial markets, and credit information systems. However, they did not find any effect of bank concentration on debt maturity. In a second paper on the determinants of long-term finance, Tasić and Valev (2010) confirm the positive effects of the political and institutional environment, the rate of inflation, the levels of economic and financial development, and the establishment of credit information sharing institutions. While bank privatization is associated with higher long-term debt, the share of foreign-owned banks and banking sector competition have no influence on credit maturity. Gbenyo and Kpodar (2010) find that macroeconomic stability, a creditor-friendly environment, political stability, and the availability of long-term financial resources are factors that encourage banks to provide long-term financing to firms.

The rest of the paper is organized as follows. Section 2 discusses the data and section 3 explores the representativeness of the data. Section 4 describes the patterns of credit allocation in LAC and compares it with that of countries elsewhere. Section 5 explores what factors are associated to the patterns of credit allocation and to what extent these factors affect LAC countries on a different way than in other countries. Section 6 provides conclusions.

6
Table 5: First stage regression from Table 4

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable: computer|s-per-worke|r|||||
|IV1|0.090***|0.111***|0.095***|0.099***|0.102***|0.102***|
||(0.019)|(0.023)|(0.021)|(0.021)|(0.021)|(0.024)|
|IV2|8.357***|7.867***|6.433***|5.877***|5.037**|5.001**|
||(2.076)|(2.118)|(1.945)|(1.956)|(1.958)|(2.106)|
|First stage F-value|26.3|20.5|17.2|17.1|17.1|11.4|
|Underid p-value|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|
|Sargan p-value|0.107|0.106|0.171|0.323|0.365|0.359|
|Dependent variable: share of l|abor with I|nternet|||||
|IV3|0.413***|0.418***|0.377***|0.391***|0.380***|0.314***|
||(0.059)|(0.069)|(0.065)|(0.064)|(0.064)|(0.069)|
|IV4|0.663***|0.640***|0.566***|0.489**|0.518***|0.369*|
||(0.207)|(0.213)|(0.199)|(0.198)|(0.198)|(0.207)|
|First stage F-value|41.8|26.7|24.3|24.7|24.7|12.1|
|Underid p-value|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|
|Sargan p-value|0.84|0.92|0.94|0.87|0.77|0.68|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. 1 The hypothesis of weak instruments cannot be rejected for specification (6) Source: Authors’ calculations using data from ENTIC, INEGI 19
# **Appendix Table 3: The Classification of Other Special Group Countries**

|Oil Exporters(27)|Small Islanders(30)|Civil Conflict Countries(12)|
|---|---|---|
|Algeria|Antigua and Barbuda|Afghanistan|
|Angola|Aruba|Algeria (see oil exporter)|
|Azerbaijan|Bahamas, The|Angola (see oil exporter)|
|Bahrain|Barbados|Azerbaijan (see oil exporter)|
|Brunei|Bermuda|Bosnia and Herzegovina|
|Cameroon|Cape Verde|Congo, Dem. Rep.|
|Congo, Rep.|Comoros|East Timor|
|Ecuador|Cook Islands|El Salvador|
|Equatorial Guinea|Dominica|Eritrea|
|Gabon|Eq. Guinea (see oil exporter)|Haiti|
|Iran, Islamic Rep.|Fiji|Iran (see oil exporter)|
|Iraq|Grenada|Iraq (see oil exporter)|
|Kazakhstan|Kiribati|Lebanon|
|Kuwait|Maldives|Liberia|
|Libya|Marshall Islands|Nigeria (see oil exporter)|
|Nigeria|Micronesia, Fed. Sts.|Russian Fed. (see oil exporter)|
|Norway (see industrial countries)|Nauru|Serbia and Montenegro|
|Oman|Netherlands Antilles|Sierra Leone|
|Qatar|New Caledonia|Somalia|
|Russian Federation|Niue|Yemen Rep. (see oil exporter)|
|Saudi Arabia|Palau||
|Sudan|Samoa||
|Syrian Arab Republic|Sao Tome and Principe||
|Trinidad and Tobago|Seychelles||
|Turkmenistan|Solomon Islands||
|United Arab Emirates|St. Kitts and Nevis||
|Venezuela|St. Lucia||
|Yemen Rep.|St. Vincent & Grenadines<br>Tonga<br>Trinidad & Tobago (see oil exporter)<br>Tuvalu<br>Vanuatu||

Notes: Oil exporters are based on at least 40 percent of fuels in total merchandise exports from UN COMTRADE Statistics. Small islanders are those small economies with independent or autonomous administration, excluding colonized or dependent territories.

Civil conflict countries are drawn from countries with recent serious war outbreaks from the study by Paul Collier on "Economic Cause of Civil Conflict and their Implications for Policy", 2000.

22
70. Gilbert and Palaskas (1990) estimated three equation 'mini-models' for six commodities (cocoa, coffee, copper, natural rubber, sugar and tin) using annual data. They estimated both the price adjustment specification (46) and the specification which utilizes lagged stock data (49). For three of the commodities (copper, natural rubber and sugar) the latter specification gave a superior fit, but for two of these commodities there was nD evidence of any forward-looking behavior. Only in the case of copper was there clear evidence of forward-looking behavior, confirming the results obtained by Ghosh _et al._ (1987). For the remaining three commodities the model did not appear to give an adequate description of the sample data. As Wallis (1990) noted in his comment this may be because the Gilbert and Palaskas model ignores the major interventions by international stabilization agencies in the coffee and tin markets. Indeed, of the six commodities considered by Gilbert and Palaskas, only copper was completely free from intervention over their sample period.

71. A second reason for the greater evidence of forward-looking behavior in the Ghosh _et al._ (1987) study may derive from the fact that their model was quarterly and incorporated substantial institutional detail. In particular Ghosh _et al._ assumed that the market correctly anticipated both the timing and duration of labor disputes in the US copper industry (these strikes at the end of three year labor contracts resulted in major shortages in periods of tight demand), and the timing and extent of releases of metal from the US strategic stockpile (the GSA stockpile). By contrast, the Gilbert-Palaskas mini-models were insufficiently detailed to permit incorporation of this sort of information.

72. Trivedi (1990) estimated equations of the form (44) for tea, cocoa, coconut oil and palm oil all estimated on samples of annual data by Instrumental Variables. Only in the case of cocoa was he able to find strong evidence of a robust price-sensitive inventory relationship. Furthermore, as Trivedi himself noted, it is unclear that the so coefficients on the exogenous variables can be interpreted as confirming the presence of 42