rafmacalaba/datause-encoder-data huggingface dataset:
||**CDT betwee**<br>**da**<br>|**n 31 and 90**<br>**ys**<br>|**CDT > 9**<br>|**0 days**<br>|
|---|---|---|---|---|
||Observed<br>odds ratio|Estimated<br>odds ratio|Observed<br>odds ratio|Estimated<br>odds ratio|
|**Container type = Last trip**|1.56|1.74|1.82|1.57|
|**Container type = Others**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|
|**Last port of call = Antwerp**|0.93|0.84|0.95|0.952|
|**Lastport of call = Dubai**|1.48|1.30|1.94|1.93|
|**Last port of call = Algeciras**|1.02|0.90|0.72|0.72|
|**Lastport of call = Singapore**|0.80|0.63|0.75|0.73|
|**Last port of call = Others**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|
|**Region of origin = Europe **|0.60|0.55|1.29|1.082|
|**Region of origin = Asia**|0.68|0.69|1.20|1.082|
|**Region of origin = MENA**|0.66|0.60|1.28|1.062|
|**Region of origin = Sub-Saharan Africa**|0.61|0.67|1.11|0.932|
|**Region of origin = others**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|
|**Fiscal regime = Finished goods**|1.35|1.51|1.23|1.26|
|**Fiscal regime = Semi-finished goods**|1.16|1.23|1.14|1.13|
|**Fiscal regime = Raw materials**|0.88|0.982|1.24|1.19|
|**Fiscal regime = Necessity goods or duty free**|0.96|1.042|1.06|1.022|
|**Fiscal regime = duty free**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|
|**Density of value superior to 6500 FCFA/kg**|1.46|1.66|0.87|0.912|
|**Density of value from 1000 to 6500 FCFA/kg**|1.12|1.12|1.12|1.13|
|**Density of value inferior to 1000 FCFA/kg**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|
|**Full container load**|1.56|1.07|1.82|0.93|
|**Less than container load**<br>**2 Model estimates are not significant at 5% level**|Ref Cat|Ref Cat|Ref Cat|Ref Cat|

**Table 13– Observed and estimated Odds ratio using logistic regression model** Source: Cameroonian Customs data- statistical analysis using SAS Software

# **5. CONCLUSION AND POLICY RECOMMENDATIONS**

The variety and thoroughness of databases and statistical elements made available in Cameroon both in customs and with terminal operators for this study is such that only a fraction of what can be analyzed has been summarized above. The main findings of the study comprise both elements related to the specific Douala situation and to the methodology used.

Cargo dwell time in the port of Douala for containerized imports is very significant. An aggregate analysis shows that cargo dwell time exceeds 20 days for a significant proportion of traffic and average dwell time has been consistently about 20 days in the last 10 years. From a customs clearance standpoint, the two main contributors to long dwell times are time between ship arrival and lodging of declaration, and time between payment of customs dues and gate exit. The payment 27
divided either by the corresponding working age population (denoted as HCPworking), or by the whole population (denoted as HCPwhole).

Let the sub-population of youth (0-14), working age (15-64) and the elderly (65 and above) be denoted respectively as POPyoung, POPworking, and POPelder; and the corresponding average lifetime income as LINyoung, LINworking and LINelder. As defined, The last identity holds because LINelder is assumed to be zero in the OECD project due to the assumption made there that people with age 65 and above will have withdrawn from the labor market. It follows that: Clearly, we know that HCPwhole < HCPreal , but uncertain about the relationship between HCPworking and HCPreal .

In general, LINyoung > LINworking , simply because younger people will have longer remaining working years and thus more lifetime incomes than their elder counterparts. If the sub-population of the elderly people POPelder is very small, then it may lead to HCPworking < HCPreal . However, if POPelder is large enough, we may expect that HCPworking > HCPreal.

Therefore, depending on a country’s demographic structure, the use of HCPworking could overestimate or underestimate the real human capital per capita, while the use of HCPwhole will surely underestimate it. To align the OECD and World Bank assumptions, this paper calculates the human capital per capita as HCPwhole instead of HCPworking; this measure is an approximation and represents an underestimation of human capital per capita.

# **_4.2. The choice of real discount rates_**

The second issue is the choice of two key parameters in the OECD project, i.e. the annual real income growth rate and the real discount rate, compared with the assumptions about growth and discounting used in the World Bank estimates.

7
residents’ income tends to be very volatile. It is conceivable that households may want to borrow money or buy insurance to ensure their consumption is stable even though their income varies over time. However, there are no formal credit or insurance institutions such as banks. Therefore, it is highly likely that these households will resort to informal activities to stabilize their consumption, consistent with the evidence that consumption is much more stable over time than income.

One such activity may be to share resources with other households in the village. Households may share assets or labor, grant each other loans, or give each other gifts. Abundant evidence suggests that these informal activities are common and frequent among the poor (for example, Rosenzweig 1988, Townsend 1994, Udry 1995, Fafchamps and Lund 2003, Dubois, Jullien, and Magnac 2008, Fafchamps 2008). In particular, we have reason to believe that these informal resource sharing networks may be important in the villages in which PROGRESA is implemented. For example, these villages have very low migration rates (in 1999, only about 5 percent of households had at least one member leave in the previous five years, of which, 20 percent moved within the same village). Subsequently, about 80 percent of households have relatives in the same village. In these villages, groups of related households (parents’ offspring and siblings, as well as more distant relations) live and work in close proximity. Therefore, it is highly likely that when one household experiences high earnings, be it a good harvest or a government transfer, it might share some of those earnings with extended family members.

This discussion suggests the following: First, to evaluate the effect of PROGRESA on eligible households, researchers should not compare their consumption with that of ineligible households living in the same village, but randomly select a group of treatment and control villages, choosing eligible households in these villages (if one believes these spillover effects occur mainly at the village level) and collecting data on eligible households in both treatment and control villages. Second, to measure spillover effects, researchers should also collect information on ineligible households living in both treatment and control villages.

The design of the experiment is exactly as described. Because of logistical constraints, the program was started in a limited number of localities and gradually expanded to cover all targeted localities. The evaluator exploited this gradual phasing in of the program to create the following experiment. Out of a sample of 506 villages, 320 were randomized to receive the program from May 1998. The remaining 186 villages started receiving the program at the end of 1999. A village level census conducted before the randomization ensured there were data on all households in the 506 villages. Each household was classified as eligible or
independent of any other type of seasonality or imperfections (for instance, individual preferences or labor and product market imperfections) that may cause variations in consumption.

Can the finding of a lack of consumption smoothing be maintained with seasonal panel analysis where we control for bias due to common seasonal and aggregate income shock? Consider the seasonal panel data analysis of equation (3.4). As indicated, constructing thana level panel will invariably loses a few thanas as the HIES data collection over the survey periods of 2000 and 2005 is not meant to be even at the thana level. Therefore, a test of attrition bias for losing thanas in panel data analysis is done. The test results are shown in table 7. From the results we see that at 1 percent level, the null hypothesis of excluded thanas is the same as the included thanas cannot be rejected for both survey periods. Thus, we do not think attrition bias is an issue for the study and, therefore, we overlooked this issue in our estimation of thana-level panel data. In a panel household study, Ziliak and Kniesner (1998) reached the same conclusion, “...nonrandom attrition is of little concern ..., because the effect of attrition is absorbed into the fixed-effects”.

The FE-IV estimates of equation (3.4) are shown by the last two columns of table 6. The FE-IV estimates confirm that crop income seasonality still matters in consumption. For example, a 10 percent increase in seasonal crop income shares increases household’s per capita total consumption by 16.6 percent and food consumption by 11.9 percent respectively. The results also confirm that households spend more than seasonal shares of total income to support consumption. This perhaps suggests that households either draw income from non-crop sources not included in seasonal income or resort to distress and advance sale of labor, crop, or assets or to inter-family transfers not reported in income data to smooth consumption.

21
for both cross-sectional and panel data, respectively. Both tables 5 and 6 show that the log of GDP per capita is positively correlated with the SCI score, statistically significant at the 1 percent level.

The proxy variable used for data transparency is the World Bank’s Statistical Capacity Indicator (SCI). The overall SCI indicator is based on a diagnostic framework to assess the capacity of national statistical systems over time. This indicator is ideal as it covers both the quantity (frequency) and to some extent the quality aspects of data, building on studies that have mostly focused on the former (Williams, 2009; Hollyer et al, 2011). The framework has three dimensions: (i) Source data, (ii) Methodology, and (iii) Periodicity and timeliness of socioeconomic indicators. Each dimension is evaluated on criteria based on metadata information from the World Bank, IMF, UN, UNESCO, and WHO. The overall SCI score is the average of the three sub-indicators calculated for each dimension.<sup>5</sup> The source data dimension reflects whether a country conducts data collection activity in line with internationally recommended periodicity, and whether data from administrative systems are available and reliable for statistical estimation purposes. This dimension covers the micro-data aspect of data transparency that is essential given microdata are foundational for a country’s data system. Specifically, the criteria used are the periodicity of population and agricultural censuses, the periodicity of poverty and health-related surveys, and completeness of vital registration system coverage. A country can achieve a perfect score if it has conducted 1 or more population census in the last 10 years, 1 or more agricultural census in the last 10 years, 3 or more health surveys in the last 10 years, 3 or more health surveys in the last 10 years, and has a complete vital registration system.

The statistical methodology dimension measures a country’s ability to adhere to internationally recommended standards and methods. This aspect is captured by assessing guidelines and procedures used

> 5 The composite score ranges from 0 to 100. For our analysis, we rescale this score to be between 0 and 1.

15
business-to-business connectivity, e-commerce and e-government systems, and other ICT services. Indeed, Senegal is well positioned to become a regional hub for 3G and 4G services.

# **4.1.EXPORT COMPETITIVENESS**

Historically, tourism and ICT have been the key services exports in Senegal. While export statistics suggest competitive performance of the ICT services sector, the tourism sector has lost competitiveness. It has been noted that Senegal was only second to Kenya on the African continent as a tourism destination during the 1980s. Currently, many countries in western and eastern Africa have surpassed the two countries in terms of contribution of tourism to exports. The challenges faced by tourism in Senegal include beach erosions, lack of product diversification, lack of effective strategy, and dependence on a narrow consumer market.

A deliberate strategy to diversify services export beyond tourism and ICT is needed. Financial services, health and education services are gaining steam in terms of contributions to GDP growth (see figure 4). Meanwhile, these three sectors contribute substantially to the low ranking of Senegal in the Global Competitiveness Report (GCR) 2014-2015. In other words, the sectors hold potential contributions to export growth and competitiveness if appropriate strategies are implemented to sustain their growth. These sectors should be listed as priority.

It is essential that Senegal seeks to expand its markets, one of the binding limitations also identified in the GCR. A narrow market limits competitiveness by limiting the possibilities for “learning-tocompete” by firms that would otherwise be essential in scenarios of enlarging markets. Although these limitations may be associated with membership in an economic and monetary union, a growing list of emerging markets around the world provide potential markets for Senegal’s services.

To begin, Senegal can capitalize on its unique position in West Africa to tap into rapidly expanding regional markets. Senegal is the institutional and financial center of WAEMU and home to several major research institutions, all of which can be leveraged to promote innovations in financial, education and health services. Regional demand for education services is increasing rapidly, driven foremost by Nigeria’s rapidly growing youth population. Tapping into this market through a competitive education system will boost Senegal’s exports while also accelerating the growth of 24
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 24: In a pure DC reform, rate of return on assets makes a big difference on the replacement rates* in the DC component for civil servants****

<!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>ref I.1A ref I.1B<br>Replacement rate<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage; Comparison of replacement rates in the DC component for civil servants – Reform I.1A with I.1B. **Source** : WB calculations based on PROST.

- 57 -
<!-- Start of picture text --> Product Standards, Harmonization, and Trade:<br>Evidence from the Extensive Margin<br>Ben Shepherd<br>Development Research Group—Trade<br>The World Bank, Washington, D.C.<br>bshepherd@worldbank.org<br><!-- End of picture text --> <!-- Start of picture text --> I am grateful to Bernard Hoekman, John S. Wilson, and an anonymous referee for comments<br>on a previous draft. This work is part of a project on trade facilitation and development supported<br>through a Trust Fund of the U.K. Department for International Development (http://econ.worldbank<br>.org/projects/trade_ costs). The findings, interpretations, and conclusions expressed in this paper<br>are entirely those of the author. They do not necessarily represent the view of the World Bank, its<br>Executive Directors, or the countries they represent.<br>1<br><!-- End of picture text -->
Kee, H.L., A. Nicita and M. Olarreaga, 2008. Import demand elasticities and trade distortions. Review of Economics and Statistics 90(4): 666-682.

- Kee, H.L., A. Nicita and M. Olarreaga, 2009. Estimating trade restrictiveness indices. Economic Journal 119(534): 172-199.

- Madsen, J. 2001. Trade Barriers and the collapse of world trade during the great depression, Southern Economic Journal 67(4), 848-868.

- Rodrik, Dani, 2009. The real exchange rate and economic growth. Brookings Papers on Economic Activity, <u>www.brookings.edu/economics/bpea/bpea.aspx.</u>

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Policy Research Working Paper 10384

# **Abstract**

This paper uses a unique dataset with matched information at the firm-bank level covering 13,000 firms and 550 banks in 36 emerging and developing economies over 2012–20. The analysis tests whether government-owned banks fulfill their social mandate by targeting credit constrained firms or firms that are more likely to generate positive externalities. The findings show that credit constrained firms are more likely to borrow from government-owned banks, and that this is especially the case in countries with good institutions. However, the paper does not find any evidence that government-owned banks target innovative firms or “green” firms. The findings show that in firms that borrow from government-owned banks, employment reacts less to business cycle conditions relative to firms that borrow from private banks. The paper further shows that employment is more stable in credit constrained firms that have a relationship with a government-owned banks with respect to credit constrained firms that borrow from a private bank.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mdifilippo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure A11 <!-- Start of picture text --> Distribution of project capital expenditure<br>M&A | Developing countries<br>.2<br>.15<br>.1<br>.05<br>0<br>-10 -5 0 5<br>FDI (log)<br>2003 2012 2022<br>Density<br><!-- End of picture text -->

Note: The analysis relies on M&A capital expenditure, without any exclusion from the full sample. Countries are considered developing if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

23
# Figure 1: Informal<sup>Mining in Brazil</sup>

<!-- Start of picture text --> Roraima ~ ~ ~ ~ ~ ~ ~ ¶ cer<br>g ~ ~ ~~~~~ ~ ~ ~~~ ~~~~~~~ Ca is Rfs -Gi<br>S 1 Amazonas v v @ Part @ MaranZ*o<br>* ; *°3 Ron<br>, Piaui Per'af.n bO<br>_ .* .. R a ...........<br>0~~~~~~~~~~~~~~~c<br>Mato G!a o To taGons Bah.ia . C,<br>A .......<br>ri~ ~~~~~~~r<br>* :00~~~<br>OURO - Pequena concentra;aO att o .;ss S S a . ,<br>* GEMASgKuende garimpoS °J<br>OURO - grande concentracao<br>de garimpos<br>SJaneiro...<br>(3 GEMAS - pequena concentraCAo .<br>de garimpos 0<br>oGEMAS - grandisconcental4O 0l<br>A MINERAIS METAUCZS<br>Rio Grance do Sul<br><!-- End of picture text --> Source: Globo. March<sup>24, 1991</sup>
The results are reported in Table 3. All capital ratios ( _Capital/RWA_ , _Tier 1/RWA_ , and _Equity/TA_ ) are statistically significant and economically meaningful in reducing bank risk. These results are reported in columns (1) to (3). In the regression reported in column (4), we include the _Tier 1/Regulatory Capital_ variable as an additional covariate. Since we control for the overall level of regulatory capital, the coefficient on this variable provides the marginal impact of having a greater proportion of capital in the form of Tier 1. Similarly, in the regression reported in column (5), we include the _RWA/TA_ variable as an additional covariate. The coefficient on this variable quantifies the impact of having a higher proportion of bank assets with higher risk-weights, above and beyond the size effect measured by the natural logarithm of total assets.

**Table 3: Impact of risk-weights and capital quality on bank risk**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES||Z|-score[4year|]||NPL over|Total Loans|
|ln (Assets in 2012 USD)|-0.964|-1.054|-1.856**|-0.720|-1.290**|0.125***|0.156***|
||(0.674)|(0.662)|(0.736)|(0.620)|(0.496)|(0.036)|(0.033)|
|Loans/TA|-0.441***|-0.452***|-0.509*|-0.434***|-0.255***|-0.002|-0.014***|
||(0.125)|(0.098)|(0.271)|(0.088)|(0.077)|(0.003)|(0.004)|
|Short-term funding/TA|-0.407***|-0.414***|-0.253**|-0.406***|-0.412***|0.000|0.001|
||(0.100)|(0.118)|(0.122)|(0.114)|(0.106)|(0.005)|(0.003)|
|Liquid assets/TA|-1.025***|-1.082***|-0.883***|-1.072***|-1.078***|0.001|-0.001|
||(0.182)|(0.167)|(0.286)|(0.154)|(0.170)|(0.002)|(0.002)|
|ROA|6.256***|7.385***|3.881*|7.242***|8.035***|-0.129*|-0.153**|
||(2.028)|(1.663)|(1.983)|(1.603)|(1.511)|(0.066)|(0.065)|
|Regulatory Capital/RWA|1.378***|||1.406***|1.275***|-0.002|0.003|
||(0.204)|||(0.173)|(0.164)|(0.003)|(0.003)|
|Tier 1/RWA||1.475***<br>(0.175)||||||
|Equity/TA|||0.637***<br>(0.215)|||||
|Tier 1/Regulatory capital||||0.511***<br>(0.066)||-0.023***<br>(0.009)||
|RWA/TA|||||-0.447***||0.025***|
||||||(0.092)||(0.003)|
|Observations|153,353|137,884|205,857|134,748|127,062|152,925|144,751|
|R-Squared|0.041|0.047|0.019|0.048|0.05|0.028|0.042|
|Number of Country_Year|2,523|2,006|3,227|1,943|1,759|2,078|1,801|

Note: *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Standard errors clustered at country level appear in parentheses. Off-shore financial centers included in the computations.

31