rafmacalaba/datause-encoder-data huggingface dataset:
_Africa’s Macroeconomic Story_

# **Figure 23: CPIA Ratings for African Countries, 2005 and 2012**

_Rating scale from very poor (1) to very strong (6) performance_ <!-- Start of picture text --> CPIA Cluster Averages Economic Management Cluster<br>3.5 3.5<br>3.0 3.0<br>2.5 2.5<br>2.0 2.0<br>Economic Structural Social Public sector Macroeconomic Fiscal policy Debt Policy<br>management policies inclusion management management<br>2005 2012 2005 2012<br><!-- End of picture text --> _Source_ : World Bank _World Development Indicators_ (WDI) database.

The economic management cluster was found to be statistically significant in cross-country growth regressions (Table 4, regression [1]).<sup>23</sup> Each of the three components—macroeconomic management, fiscal policy and debt policy—are significant (regressions [2] to [4]), as are the two clusters-structural policies and public sector management and institutions (regressions [5] and [6]). The regression with the economic management cluster has the best fit and in this sense provides a better explanation for cross-country differences in growth outcomes than the alternative CPIA measures. The three components of the economic management cluster each become insignificant when entered simultaneously (regression [7]), indicating that they are highly collinear making it difficult to disentangle their separate influences.

> 23 Note that the regression results reported in Table 4 are generated using IDA-eligible African countries (where CPIA ratings are available) whereas those reported in Table 3 are based on all African countries.

32
one. We further assume that the error term has zero mean and a variance that is the same across countries, but differs across indicators, that is, _E[(_ j,k)2]= _c.(k)._ The disturbance term, &(/, _k),_ captures two sources of uncertainty in the relationship between true governance and the observed indicators. First, the particular aspect of governance covered by indicator k is imperfectly measured in each country, reflecting either perception errors on the part of experts (in the case of polls of experts) or sampling variation (in the case of surveys of citizens or entrepreneurs). Second, the relationship between the particular concept measured by indicator _k_ and the corresponding broader aspect of governance may be imperfect. For example, even if the particular aspect of graft covered by some indicator k (such as the prevalence of improper practices) is perfectly measured, it may be a noisy indicator of graft if countries exhibit differences in the perceived nature of improper practices. Both of these sources of uncertainty are reflected in the indicator-specific variance of the error term, a _2(k)._ Given estimates of the model's parameters, _a(k), 3(k),_ and _a(k),_ we can compute estimates of governance for each country, as well as measures of the precision of these estimates. Formally, the estimate of governance for a country produced by the unobserved components model is the mean of the distribution of unobserved governance conditional on the K(j) observed data points for that country. This conditional mean is the following weighted average of appropriately rescaled scores of each of the component indicators:

## _y (i,k -_a (k)_ (2) _E>(J)_ y(ij4)...y[ijK(i)} _w(k) ()_

> variance of the error term of that source. We also report the standard deviation of this conditional distribution as an indicator of the confidence we can have in this estimate, which is

# (3) SD _tQ) l(ij,1)...,y[i,K(j)]=_ '1+ _ta(k)j_

This standard deviation is declining in the number of individual indicators in which a particular country appears, and it is increasing in the variance of the disturbance term on each of these indicators.

The assumptions of the unobserved components model ensure that the distribution of governance in each country is normal, conditional on the data for that country. Therefore, these conditional means and standard deviations for each country have a natural interpretation. For example, a useful interpretation of 11
priority of creditors vis-a-vis third parties (Alvarez de la Campa, 2011).<sup>4</sup> Therefore, without a well-functioning registry for movable assets, even the best secured transactions laws could be ineffective or even useless.

Given the importance of collateral registries for moveable assets, 18 countries have established such registries in the past decade. However, to our knowledge there is no systematic empirical evidence on whether such reforms have been effective in fulfilling their primary goal: improving firms’ access to bank finance.<sup>5</sup> This paper seeks to fill this gap in the literature.

Specifically, this paper explores the impact of introducing collateral registries for movable assets on firms' access to bank finance using firm-level surveys for 73 countries. Following a difference-in-difference approach, we compare access to bank finance pre and post the introduction of movable collateral registries in seven countries (i.e., the reform or treatment sample) against three different “control” groups: a) firms in all countries that did not implement collateral reforms during our sample frame (59 countries), b) firms in a sample of countries matched by location and income per capita to the countries that introduced movable collateral registries (7 countries), and c) firms in countries that undertook collateral legal reforms but did not set up registries for movable assets (7 countries). This difference-in-difference approach controlling for fixed country and time effects allows us to isolate the impact of the introduction of movable collateral registries on firms’ access to bank finance.

Overall, we find that introducing movable collateral registries increases firms' access to bank finance. In particular, our baseline estimations indicate that the introduction of registries for movable assets is associated with an increase in the likelihood that a firm has a bank loan, line of

> 4 Specifically, three conditions are required for banks to be able to accept movable assets as collateral: the creation of security interest, the perfection of security interest and the enforcement of security interest (see Fleisig, 1995). The movable collateral registry is a necessary component as it allows for the “perfection” of security interest. 5 There is some anecdotal evidence based on case studies reported in IFC (2010) that two countries that have introduced electronic registries have reported an increase in registrations and cheaper credit.

3
# **6.2 Differences in consumption levels**

# _6.2.1  A consumption model_

Using the PSF survey, we can document women’s average consumption levels by marital status for each of the five groups considered (widow ( _W_ ), remarried widow ( _MW_ ), divorcee ( _D_ ), remarried divorcee ( _MD_ ), first marriage ( _M_ )). Simple descriptive statistics show that unconditionally, current divorcees and widows have the highest average cell per capita consumptions and, along with ex-divorcees, reside in higher per capita consumption households (Table 14).<sup>19</sup> Remarried widows fare the worst on average, and particularly when they are in a levirate marriage with log average cell consumption of only 12.06 as opposed to 12.22 for all remarried widows. Widows remarried outside the lineage of their late husband enjoy a level of consumption more than 30% higher than those in levirate unions. This may reflect the fact that remarried widows are different from other women in a few dimensions. As we saw in Section 6, they indeed cumulate a double negative selection: selection into widowhood, which suggests they were from relatively poorer backgrounds to begin, and selection into remarriage, with rural dwellers among them remarrying more often. Negative selection is even stronger for those remarrying one of their late husband’s kin. Such disadvantage could also be due to remarriage itself, if entering into a second union after widowhood confines women to weak bargaining positions within their new households.

To isolate the consumption implications of marital status itself from those of selection on the basis of observable characteristics, we conduct a decomposition analysis.

Table 15 presents regressions for women by marital status group of the log of cell consumption per person for the _i_ th woman against a vector of attributes<sup>_x_</sup> _i_<sup>. This set of</sup> regressions can be written as: Here, all parameters are marital status-specific,  _ij_ is an error term, and _S ij_  1 if woman _i_ is a member of group _j_ and _S ij_  0 otherwise. Noting that  _S ij_  1, we can re-write (1) such that  _j_ the parameters for marital-status groups are evaluated as deviations from mean points for a given reference marital-status group _k_ :

> 19 Descriptive statistics on the five groups are given in Tables A6-A8.

18
# sustainable forest management (ITTO, 1994).6

The **European Union's** EC commission sees TC as a first step towards a more comprehensive eco-labeling scheme based on life cycle analysis. The commission supports the harmonization of TC within the EU eco-labeling scheme and agreed that TC should be applied to all timbers, based on the principles of non-discriminating, transparency and acceptability for all parties concerned.

Other developed countries that are looking into the issue of TC and/or plan to implement a TC program are Belgium, Finland, Switzerland, Denmark, France, Canada, and Australia.

# **Producing Country Certification Initiatives**

In addition to the above activities of importing countries, a few other initiatives are being developed by producer countries. African producing countries have become very aware of European campaigns against tropical timber. They are therefore keen to develop a unified position in response, and promote a positive image for African timber. In response, a roundtable meeting of several members of the African Timber Organization (ATO) was held in March 1993.7 It recommended that a certificate of origin be developed and introduced into the market.' The ATO was proposed as the governing body for the label, with ratification from ITTO. The conference agreed to launch a promotional campaign to generate support for the African labeling scheme among European NGOs and donors particularly, and to develop standards for forest management as the basis for evaluating forest operations. No timetable has been set yet to implement the labeling scheme.

**Indonesia** is the main producing country already developing a national system for timber certification. Indonesia has announced its intention to establish an "Ecolabeling Institute". Teams of experts are currently working to develop criteria of forest management appropriate to Indonesia and an institutional structure and financing plan for the agency. The Indonesian

> 6However, there are signs of growing environmental awareness with regard to timber. Some Japanese prefectural and city govemments have expressed their willingness to reduce or eliminate the amount of tropical timber products they use. Also, the Japanese Building Contractors Society has decided to reduce the consumption of tropical timber used for plywood **forms** by 35% within five years. However, this decision could be due to the fact that Japan is facing a rapidly reduced supply of tropical logs and sawnwood as a result of export bans and reduced logging by East Asian producers _(Japan Lunber Journal,_ July 31, 1994 P. 9).

> 7Cameroon, Congo, C8te d'lvoire, Gabon, Ghana and Nigeria

> **3The** wording "African Timber of Controlled Origin" was suggested.

11
|**Table 4. The linkage between domestic and foreign **|**banking **|**developm **|**ent**|
|---|---|---|---|
|Depvar=Commercial bank branches|[1]|[2]|[3]|
|Real GDP per capita (in logs)|9.59***|8.58***|9.63***|
||(2.64)|(2.62)|(2.92)|
|Control of Corruption|3.13**<br>(1.24)|2.74**<br>(1.16)|3.23**<br>(1.29)|
|Regulatory Quality|1.51|1.24|0.49|
||(0.93)|(0.87)|(0.92)|
|Rule of Law|-0.44|-0.25|-0.67|
||(1.20)|(1.20)|(1.21)|
|Commercial bank branches in the region|0.83***<br>(0.15)|||
|Distance-discounted commercial bank branches (regional)||0.82***<br>(0.13)||
|Distance-discounted commercial bank branches (global)|||2.95***<br>(0.63)|
|Observations|2,526|2,495|2,497|
|Number of countries|180|177|177|
|R-squared|0.26|0.30|0.20|
|AdjR-sq|0.26|0.30|0.19|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Fixed-effect models are estimated here. All regressions include year dummies. The data are for the period 2004-18. Commercial bank branches are in per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See section 2 for detailed data descriptions. Data are between 2004 and 2018.

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<!-- Start of picture text --> co<br>2<br>S<br>g<br>8<br>go<br>E=<br>1S}<br>0 20 40 60 80 100<br>Minimun percentile rank, migrant outflow residual<br><!-- End of picture text --> <!-- Start of picture text --> 82<br>Be<br>s<br>ey<br>1S}<br>°<br>0 20 40 60 80 100<br>Minimun percentile rank, migrant outflow residual<br><!-- End of picture text --> <!-- Start of picture text --> co2<br>8<br>8<br>can<br>Sa<br>ec<br>1S}<br>S<br>0 20 40 60 80 100<br>Minimun percentile rank, migrant outflow residual<br><!-- End of picture text -->
# **Abstract**

Sea level rise (SLR) due to climate change is a serious global threat: The scientific evidence is now overwhelming . Continued growth of greenhouse gas emissions and associated global warming could well promote SLR of 1m-3m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 5m SLR. In this paper, we have assessed the consequences of continued SLR for 84 developing countries. Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated <u>global</u> data on critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5m SLR. Our results reveal that hundreds of millions of people in the developing world are likely to be displaced by SLR within this century; and accompanying economic and ecological damage will be severe for many. At the country level, results are extremely skewed, with severe impacts limited to a relatively small number of countries. For these countries (e.g., Vietnam, A.R. of Egypt, and The Bahamas), however, the consequences of SLR are potentially catastrophic. For many others, including some of the largest (e.g., China), the absolute magnitudes of potential impacts are very large. At the other extreme, many developing countries experience limited impacts. Among regions, East Asia and Middle East/North Africa exhibit the greatest relative impacts. To date, there is little evidence that the international community has seriously considered the implications of SLR for population location and infrastructure planning in developing countries. We hope that the information provided in this paper will encourage immediate planning for adaptation.

2
Table **5:** The effect of student background characteristics, pretest achievement, student expectations, perceived ^rental support, perceived ability,<sup>attitudes</sup> and effort on Grade 8 mathemat..cs poattest achievement in Thailand, 1981-82

|||Alt|ernative sp|ecificatio|ns||
|---|---|---|---|---|---|---|
|dependent<br>ariables|(1)|(2)|(3)|(4)|(5)|(6)|
|etest math achievement|<br>.78***|<br>.80***|<br>.75***|<br>.77***|<br>.78***|<br>.79*|
|x (2- boy)|(51.94)<br>.43|(53.51)<br>.35|(49.06)<br>.14|(50.93)<br>.42|(52.52)<br>.61**|(53.04)<br>.36|
||(1.93)|(1.56)|(.63)|(1.89)|(2.72)|(1.61)|
|e|.16|.10|.23|.03|.10|.16|
||(.69)|(.44)|(1.01)|(.14)|(.45)|(.68)|
|e squared|-.00|-.00|-.00|-.00|--.00|-.00|
||(.95)|(.74)|(1.31)|(.42)|(.73)|(.96)|
|ather's occupation|-.05|.04|.08|.03|-.01|.09|
||(.41)|(.29)|(.64)|(.27)|(.09)|(.71)|
|thers education|.19|.24|.23|.23|.19|.21|
||(1.28)|(1.59)|(1.60)|(1.54)|(1.29)|(1.3,|
|Calculator at home<br>District PCI (1000 bh)|.22<br>(.89)<br>.12***<br>(3.45)|.23<br>(.89)<br> <br>.13***<br>(3.61)|.26<br>(1.03)<br> <br>.13***<br>(3.63)|.21<br>(.84)<br> <br>.14***<br>(4.13)|.26<br>(1.04)<br> <br>.12***<br>(3.57)|.27<br>(1.05<br> <br>.14*<br>(3.89)|
|.ang. of inst. at home|<br>1.09|1.13|.96|1.27*|1.10.|1.31*|
||(1.70)|(1.74)|(1.51)|(1.99)|(1.72)|(2.02)|
|Dist. PCI x Lang of inst.|<br>-.00*|-.00*|-.00|-.00*|-.00|_.00*|
||<br>(2.06)|(2.10)|(1.77)|(2.26)|(1.91)|(2.35)|
|Educational<br>expectations|<br> <br>.80***<br>(6.58)|<br> <br>--|<br>--|--|--|--|
|arental<br>encouragement||-.02<br>(.83)|--|--|--|--|
|'erceived<br>math ability|||-1.47***<br>(11.47)|<br>__|||
|Future importance of math||||1.22***<br>(9.45)|<br>--|--|
|Motivation|||||1.10***<br>(7.06)|<br>--|
|Student effort||||||.13***<br>(4.74)|
|Constant|-8.78|-.71|-5.37|2.41|-7.53|-6.53|
|Adjusted R-squared|.49|.49|.51|.50|.49|.49|
||3609|3579|3581|3564|3599|3555|

***p < .001, ** p < .01, * p <.05.

Note: Unstandardized regression coefficients; t-statistics in parentheses.

- 28 -
|**Occupation**||||||**Employment**<br>|
|---|---|---|---|---|---|---|
|**Name**|**Code**|**Digitalization**<br>**level**|**Factor**<br>**1**|**Factor**<br>**2**|**Factor**<br>**3**|**share in**<br>**Malaysia**|
|Domestic, hotel and office cleaners and helpers|911|Very low|0.0|-0.8|0.0|2.1|
|Veterinarians|225|Very low|-0.1|-0.6|0.0|0.0|
|Other personal services workers|516|Very low|-0.6|-0.6|-0.1|0.1|
|Textile, fur and leather products machine operators|815|Very low|-0.4|-0.2|-0.2|0.1|
|Vehicle, window, laundry and other hand cleaning workers|912|Very low|-0.4|-0.2|-0.2|0.2|
|Legal professionals|261|Very low|1.8|-0.4|-0.3|0.2|
|Other health professionals|226|Very low|0.1|-0.5|-0.4|0.3|
|Car, van and motorcycle drivers|832|Very low|-0.8|-0.2|-0.7|1.5|
|Nursing and midwifery professionals|222|Very low|-0.6|-0.8|-0.7|0.2|
|Other elementary workers|962|Very low|-1.0|-0.3|-1.1|1.5|
|Mobile plant operators|834|Very low|-0.8|-0.3|-1.4|0.8|

_Source_ : Authors’ calculations based SEAD data.

_Notes_ : The factor analysis is based on varimax rotation for orthogonal factors of nine digital, cognitive and socioemotional subsets of skills (three each). Clusters are described in section 6. Darker red and blue colors show higher and lower magnitudes, respectively.

56
**<u>Table 2: Estimates of stochastic frontier production function and technical efficiency</u>**

||**Total output**|**Crop production only**|**Livestock only**|
|---|---|---|---|
|**Frontier production function**||||
|<br>Rainfed arable area (log)|0.3856***|1.0508***|0.3134***|
||(0.1308)|(0.2660)|(0.1167)|
|Irrigated arable area (log)|0.2249*<br>|1.6946***<br>|0.2075<br>|
||(0.1207)|(0.2728)|(0.1263)|
|Rainfed orchard area (log)|0.8972***|1.8292***|0.4648*|
|<br>|(0.2825)<br>|(0.4856)|(0.2632)|
|Irrigated orchard area (log)|0.5424<br>|0.8880<br>|0.3428<br>|
||(0.4618)|(0.8485)|(0.4356)|
|Pasture area (log)|0.1291|0.5538|0.0891|
||(0.1629)|(0.3582)|(0.1533)|
|Head's education (log)|-0.0000|0.1625|-0.0190|
||(0.0429)|(0.0999)|(0.0397)|
|Value of hired labor (log)|0.0416*|-0.0149|0.0300|
||(0.0253)|(0.0473)|(0.0216)|
|No. of family workers on farm|-0.1331**|-0.0475|-0.0440|
||(0.0638)|(0.1335)|(0.0614)|
|Value of other inputs (log)|0.3561***|0.1725***|0.1977***|
||(0.0252)|(0.0463)|(0.0244)|
|Total value of animals (log)|0.2630***||-5.1026***|
||(0.0311)||(0.4023)|
|Any animals|-3.0831***||0.3973***|
||(0.4168)||(0.0294)|
|**Technical efficiency**||||
|Total land area owned (log)|0.2678***|-0.2507|-0.1229|
||(0.0636)|(0.2151)|(0.1430)|
|Total irrigated area owned (log)|0.9756**|-0.5961|-0.0712|
||(0.4007)|(0.5043)|(0.3869)|
|Dummy: any irrigated area|0.3719**|0.0694|0.0808|
||(0.1581)|(0.4356)|(0.2789)|
|# plots owned|<br>0.0885**|<br>-0.0560|<br>-0.0115|
||(0.0429)|(0.0899)|(0.0695)|
|Head's education (log)|0.7430***|-2.5842**|1.2583***|
||(0.2501)|(1.0508)|(0.4092)|
|Head's education square (log)|-0.2555***|0.7946***|-0.5323***|
||(0.0902)|(0.2806)|(0.1486)|
|Head's age (log)|0.6440***|1.1383*|-0.1701|
||(0.2220)|(0.5924)|(0.3831)|
|No. of family workers on farm|0.3236**|0.4260|-0.1966|
||(0.1259)|(0.3377)|(0.2576)|
|Received extension advice for crops|0.2281|0.6836**|0.2236|
||(0.1658)|(0.3417)|(0.2491)|
|Received veterinary advice|<br>0.8679***|<br>-0.9138***|<br>0.4505**|
||(0.1473)|(0.3301)|(0.2194)|
|Income share from local non-farm|-1.8847***|0.2491|-1.8947***|
||(0.1904)|(0.4620)|(0.3662)|
|Income share from transfer/remit.|-2.5877***|-0.7431|-3.0338***|
||(0.2131)|(0.5407)|(0.4344)|
|Land was inherited|0.4898***|-0.0457|0.1133|
||(0.1224)|(0.3129)|(0.2082)|
|No. of observations|1,788|682|1,280|
|Loglikelihood|-3256.65|-4000.37|-3741.99|

Note: Constants as well as (insignificant) dummies on whether the farm had a particular type of land are omitted. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 22
Table 5: Robustness - Alternative methodology and falsification test

||2SC|ML|Falsif|cation test|
|---|---|---|---|---|
|Dependent variable: Tax morale|(1)|(2)|(3)|(4)|
|Trust in public institutions|0.664***|0.532***|0.128|0.131|
||(0.220)|(0.231)|(0.325)|(0.340)|
|Trust in the neighbourhood|0.677***|0.718***|0.033|0.022|
||(0.122)|(0.143)|(0.277)|(0.272)|
|Residual - Trust public institutions|-0.590***<br>(0.222)|-0.489***<br>(0.233)|||
|Residual - Trust in the neighbourhood|-0.633***<br>(0.122)|-0.663***<br>(0.149)|||
|F-stat of excl. IVs|||252.34|230.11|
|Individual characteristics|yes|yes|yes|yes|
|Country fxed efects|yes|yes|yes|yes|
|Location fxed efects|no|yes|no|yes|
|Observations|3 506|3 506|32 490|32 490|

Notes. The table reports estimates from the two-step conditional maximum likelihood and a falsification test in columns (1) to (2) and (3) to (4) respectively. All estimates include all individual characteristics described before and country fixed effects. All estimates use the weights. Robust standard errors clustered at the country level in parenthesis. * _p <_ 0.1; ** _p <_ 0.05; *** _p <_ 0.01.

that did not experience the slave trade. To assess the validity of the identification strategy, I present in this section a falsification test. The latter consist of estimating the impact of trust on tax morale using a sample of countries that did not experience the slave trade. The sample comes from the same wave of the WVS 2010-14 excluding African countries. The sample has the advantage of having the identical questions for all countries and includes 43 them in four continents.<sup>15</sup> As both trust in public institutions and the neighborhood are endogenous, I employ the same identification strategy as previously, except the intensity of the slave trade that is not used here as an instrumental variable. The two last columns of Table 5 report the reduced form estimates of the relationship between trust and tax morale. As it can be seen, there is no evidence that both trust in public institutions and the neighborhood and affect attitude toward taxes. The regression coefficients are highly insignificant. The F-statistic of the excluded instruments from the first stage regression is largely above the usual threshold.

> 15Azerbaijan, Australia, Armenia, Belarus, Chile, China, Taiwan, China, Colombia, Cyprus, Ecuador, Estonia, West Bank and Gaza, Germany, Iraq, Japan, Kazakhstan, Jordan, Republic of Korea, Kuwait, Kyrgyzstan, Lebanon, Malaysia, Mexico, Netherlands, New Zealand, Pakistan, Peru, Philippines, Poland, Qatar, Romania, Russian Federation, Singapore, Slovenia, Spain, Sweden, Trinidad, Turkey, Ukraine, United States, Uruguay, Uzbekistan, and Republic of Yemen

29