rafmacalaba/datause-encoder-data huggingface dataset:
Sisben 1 and/or 2. For social security benefits, the worker’s occupation and before-benefit wages determine benefits and contributions. However, there are a number of deviations from the regular practice, which substantiates the complexity of the system. Salaried workers pertaining to Sisben 1 and 2 not participating in the compulsory contributive pension and health care systems qualify for noncontributive subsidized pension and health care regimes. So do independent workers, since they are also obliged legally to insure against health and old-age risks (but not labor related accidents). Hence, workers earning one minimum wage or more who do not comply with their obligation to insure qualify for subsidized social security provided their Sisben categories are 1 and 2. Note that Sisben is a household’s welfare index, rather an individual index. If the individual pertains to Sisben 1 and 2 and remains uninsured (either on contributive or subsidized regimes), they also qualify for _Familias en Acción_ , a conditional cash transfer whose benefits depend (and vary accordingly) on family composition, location and special circumstances such as being displaced by armed conflict. Although benefiting from a noncontributive subsidized insurance means automatic exclusion from _Familias_ , in practice, no cross-reference of beneficiaries’ databases take place. Interestingly, other social assistance programs such as those offered by ICBF are received by both formal and informal workers independently of their labor status (and only depending on being part of the targeted group measured by Sisben). The same tenet applies to job training programs by SENA. Other assistance programs, such as the unemployment subsidy, are only received by formal salaried workers and by informal workers (so says the law, which uses the term “informal worker”), but not by selfemployed formal workers. Other social assistance benefits, instead, such as family compensation funds, a family allowance, are received according to the income level and composition of the household (whether or not the household has children) and are offered regardless of work status if household income does not exceed four minimum wages. At the other end, provisional benefits such as severance payments, service premiums and holidays are benefited exclusively by formal salaried workers, regardless of earnings.

11
4 A major hurdle in analyzing the response of the Polish economy to the reforrns is the need to disentangle the structural determinants of the performance of the economy in the period 1990-94 from the cyclical ones. There is no issue that demand factors were at play during those years, in addition to the more permanent effects of the market-oriented reforms. Aggregate demand collapsed in 1990 and 1991, for a number of reasons, and strongly recovered in 1992-94. GDP decreased sharply in the first two years, and recovered in the latter two (see Table 1). From 1989 to 1994, total employment in Poland decreased by some 15 percent, and unemployment rose from 0 to 16 percent of the labor force.

|**Table 1. Pol**|**and: M**|**ain Eco**|**nomic I**|**ndicator**|**s**|||
|---|---|---|---|---|---|---|---|
||1989|1990|1991|1992|1993|1994|1995|
|Real GDP (%, change)|0.2|-11.6|-7.2|2.6|3.8|5.2|7.0|
|Rate of Inflation (Yearly Avg.)|245.6|585.8|<br>70.3|<br>43.0|<br>35.3|<br>32.2|<br>27.8|
|GDP in Industry (% change)|-2.1|-22.0|-17.1|<br>2.6|8.6|<br>10.0|<br>12.0|
|Employment (millions)|17.4|<br>16.5|<br>15.8|<br>15.4|<br>15.1|<br>15.3|<br>15.2|
|Unemployment (% Labor Force)|0.0|<br>6.3|<br>11.8|<br>13.6|<br>16.4|<br>16.0|<br>14.9|
|Source: GUS, authors' calculation|s|||||||

There is also strong evidence, however, that the demand developments just discussed were accompanied by a progressively stronger supply adjustment. Table 2 shows two sets of indicators in this regard: (i) sectoral distribution of employment and production; and (ii) ownership structure.

destination reveal an impressive redirection of exports and imports. The share of total exports accounted for by European Union countries increased sharply from less than 30 percent in 1989 to more than 60 percent in 1993. Accordingly, the share of exports to former CMEA countries collapsed from 37 percent in 1989 to 13 percent in 1993. Clearly, the different sectors of Polish manufacturing benefited to a very diversified extent from such dramatic redirection of foreign trade, with a selected bunch of them (such as food processing and clothing) benefiting the most.
**Figure 3 - Estimated dwell time distributions in the port of Durban and impact of dwell time reduction**

<!-- Start of picture text --> capacity<br>increase:<br>53%<br><!-- End of picture text --> <!-- Start of picture text --> capacity<br>increase:<br>118%<br><!-- End of picture text --> <!-- Start of picture text --> capacity capacity<br>increase: increase:<br>53% 118%<br>50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>Day 1 Day 2 Day 3 Day 4-8 Day 8-28 Over 28<br><!-- End of picture text --> Source: Local interviews (TPT, MSC and Maersk) and own estimates.

By using a simulation model of operational movements in container yards we show in addition that using information about dwell time for the optimal allocation of containers in the container yard would lead to additional capacity release of up to 40% in a congested container yard. Containers that are expected to be released quickly are indeed best positioned on the top of container piles to avoid double-handling. On the contrary the long stay containers should be stored in the lower levels of the piles or separately to avoid multiple repositioning before clearance.

# **5. Lessons for Ports in SSA**

As articulated in the State of Logistics (SoL) surveys conducted annually by CSIR and others, “the performance of the supply chain depends on the quality of the service provided by the private sector, in collaboration with the proficiency and diligence of public agencies”(CSIR, 2006).

18
discussion of disaster risk measures in the household, and community resources to deal with disasters), we first show basic descriptive charts by country. Subsequently, we display the results of simple linear regressions, mostly examining the association between the main outcome variables and basic socio-demographic characteristics of the household (location, composition, and economic situation) and the household head (sex and educational attainment).<sup>8</sup> We present results separately by country (instead of pooling the data) because, as discussed in the next section, countries’ socioeconomic contexts differ and the disaster risks to which countries are exposed are not identical. Although straightforward, the approach provides useful insight into ability to cope with major disasters, with important policy implications discussed in the closing section of the paper.

# **3. Context**

# **3.1 Socioeconomic context**

In this subsection we describe the socioeconomic context of the countries included in the analysis. We do this by combining descriptive statistics based on socioeconomic variables derived from the phone survey with readily available information from the World Bank’s World Development Indicators<sup>9</sup> and the United Nations Development Programme’s (UNDP) Human Development Index.<sup>10</sup> We first discuss the location and population of the five countries, going from west to east on the map. Belize is located in Central America. Bordered by Mexico, Guatemala, and the Caribbean Sea, Belize had a total population of about 405,000 in 2022. Haiti makes up the western part of the island of Hispaniola in the central north of the Caribbean Sea. With an estimated 11,585,000 inhabitants in 2022, it has the biggest population of the five countries included in this study. Dominica and Saint Lucia are small island states, located on the eastern edge of the Caribbean Sea. They respectively had a population of about 73,000 and 180,000 in 2022. Suriname is located to the south-east of the Caribbean Sea. It borders Brazil, Guyana, French Guyana, and the Atlantic Ocean. It had a total population of about 618,000 in 2022. Although located South America, in cultural and economic terms Suriname is usually grouped

> 8 The regressions all have binary outcome variables. We apply simple OLS. However, the results are robust to applying Probit regressions instead.

> 9 <u>https://data.worldbank.org/</u> (accessed on April 14, 2024).

> 10 - - <u>https://hdr.undp.org/data center/country insights#/ranks (accessed on April 14, 2024).</u>

6
# **1. INTRODUCTION**

**This report explores avenues for fruitful regional economic integration in the Lower Congo region** , meaning primarily the Democratic Republic of Congo (DRC) and the Republic of Congo (RC). We seek to quantify the latent trading opportunities in the Lower Congo region, to identify the principal obstacles to their realization and to formulate practical policy proposals for mutually advantageous cross-border integration.

**DRC and RC offer enormous economic potential** . Provided that the political situation remains comparatively stable and the remaining security problems abate further, DRC and RC export sectors are poised to increase production. Such a prediction is of course fraught with risks, and given the turbulent history and complex ethnic and social makeup of the two countries, reversals must be considered a real possibility. To mention just three examples: the river Congo is considered by experts as the world’s largest untapped source of hydroelectric energy; the value of the rainforest as a carbon sink is bound to increase; and further discoveries may well beckon in the Kizomba oil field off the Congo estuary. Moreover, the DRC’s manufacturing base has been severely diminished by misrule under the Mobutu régime and by the ensuing wars, and some industrial activity is bound to resume if stability can be maintained in the DRC as well as in RC. Severe constraints including weak institutions, low human capital and a depleted capital stock all but rule out rapid industrialization, but the mediumterm economic forecast for the two Congos must surely be positive (see also Ulloa _et al_ ., 2009).

**This report offers analysis based on new primary data and develops a set of policy recommendations.** We first provide a descriptive account of the economies of the Lower Congo region. Such an exercise faces severe challenges due to an almost complete absence of systematic data, but we can nonetheless draw on some informative statistical information from international as well as local sources. In Section 3, we then present and analyze two data-collection efforts conducted specifically for the present report. In a first exercise, we compare retail prices in Kinshasa and Brazzaville as a gauge for the degree of market integration. Our second approach consists of a set of structured interviews among trading firms in Kinshasa and Brazzaville. These interviews allow us to calculate some rough estimates of latent trading potential between the two cities, and they point us towards the main existing constraints on the realization of this potential. We then provide more general discussions of the main barriers to cross-border integration in Section 4, focusing on river-based transport. Finally, Section 5 develops a set of policy recommendations, and Section 6 concludes.

7
Slemrod, J., Blumenthal, M., & Christian, C. (2001). Taxpayer response to an increased probability of audit: Evidence from a controlled experiment in minnesota [cited By 346]. _Journal of Public Economics_ , _79_ (3), 455–483. https://doi.org/10.1016/S0047-2727(99)00107-3

- Slemrod, J., & Yitzhaki, S. (1987). The optimal size of a tax collection agency [cited By 69]. _Scandinavian Journal of Economics_ , _89_ (2), 183–192.

Slemrod, J., & Yitzhaki, S. (2002). Chapter 22 tax avoidance, evasion, and administration [cited By 429]. _Handbook of Public Economics_ , _3_ , 1423–1470. https://doi.org/10.1016/S15734420(02)80026-X

- Stefano M. Iacus, G. K., & Porro, G. (2011). Multivariate matching methods that are monotonic imbalance bounding. _Journal of the American Statistical Association_ , _106_ (493), 345–361. https://doi.org/10.1198/jasa.2011.tm09599

- Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. _Journal of the American Statistical Association_ , _113_ (523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839

- World-Bank. (2020). Indonesia - public expenditure review : Spending for better results. https: / / doi . org / http : / / documents . worldbank . org / curated / en / 154721588612658163 / Indonesia-Public-Expenditure-Review-Spending-for-Better-Results

- Yitzhaki, S. (1987). On the excess burden of tax evasion [cited By 64]. _Public Finance Review_ , _15_ (2), 123–137. https://doi.org/10.1177/109114218701500201

29
<!-- Start of picture text --> IRG<br>100%<br>80%<br>(eeccccocosooes<br>s<br>® 60%<br>Fa<br>&<br>x)<br>o<br>&<br>c<br>§ 40%<br>Fd<br>‘esetslo<br>0% | i I"| PUTT | !il| tla ! | | I | uhi| flI lMk.halaooo il fullecccces<br>2010 2012 2014 2016 2018 2020 2022<br>Date<br>@ Population % at risk of experiencing a deterioration into IPC Phase 4+ @% of pop in IPC Phase 4+ (FEWS) @ % of pop in IPC Phase 4+ (IPC) @% of pop in IPC Phase 4+ (FEWS and IPC averaged)<br><!-- End of picture text -->

<!-- Start of picture text --> AA<br>100%<br>80%<br>§<br>=s ecco<br>& 60%<br>2<br>g<br>x=)<br>g<br>£c<br>$ 40%<br>é (<br>20% oPecceees<br>2010 2012 2014 2016 2018 2020 2022<br>Date<br>@ Population % at risk of experiencing a deterioration into IPC Phase 4+ @% of pop in IPC Phase 4+ (FEWS) @% of pop in IPC Phase 4+ (IPC) @% of pop in IPC Phase 4+ (FEWS and IPC averaged)<br><!-- End of picture text --> a significant leap forward from traditional heavy loaded assessment techniques, which often suffer from insecurity emergencies. The selection of indicators—focusing on inflation, conflict, and agricultural alarms and detecting critical emergencies. In contrast, the conflict and displacement indicators, with a
washes hands with soap after defecation, and whether human or animal fecal matter is observed near the home. _Hindu_ is an indicator variable equal to one if the head of household is Hindu.

Household characteristics are denoted by _Xi_<sup>_h_,forwhichsummarystatisticsarereported</sup> in Table 1 (IHDS), Appendix Table 1 (DLHS-3), and Appendix Table 2 (NFHS-3). The list includes sociodemographic characteristics that may influence latrine ownership and other sanitation practices in India, which are: caste, occupation, and education of household head; household size, number of female members in the household, and age of survey respondent; whether the household has piped water; and proxies for household wealth (indicator variables for whether the household owns their house; indicator variables for the presence of electricity, a cell phone, television, bicycle, car, motorcycle; and distance to fuel source).<sup>13</sup> Characteristics of the household’s location are denoted by _Xjd_<sup>_l_,whicharedrawnfrom</sup> the 2011 Census.<sup>14</sup> These variables include measures of urbanization and local demographic composition: an indicator for urban area; log population density; share of population working in agriculture; share of population that are minority religious groups (Jain, Sikh, Christian, Buddhist); share of population that is Muslim; share of population belonging to a Scheduled Caste (SC) or Scheduled Tribe (ST); fraction of women who report being literate; and the sex ratio. These control variables are included because urbanization affects the availability of sanitation infrastructure and the returns to improved sanitation practices.<sup>15</sup> Further, the average demographic composition of areas is associated with different economic environments.<sup>16</sup>

> 13The data sets vary slightly in which of these variables are available. The IHDS data do not include information on homeownership. Only the IHDS data include the occupation of the household head and the distance to the closest fuel source.

> 14These variables are measured at the district level, reported separately for urban areas of the district and rural areas of the district.

> 15Urbanization impacts the returns to hygienic behaviors since communicable disease is more easily transmitted in dense areas. By contrast, low population density might reduce the risk of infectious disease. A more agriculturally oriented area might also value the fertilizer content of human waste.

> 16Muslims live disproportionately in more urban areas, and the presence of other minority groups (Jain, Sikh, Christian, Buddhist) can also be associated with different economic environments. Note that in controlling for the Muslim share of districts, the analysis still compares Hindu and Muslim households but within locations that have similar demographic shares. Also included is the sex ratio, which varies

9
1.38 -

-

# 2. Policy Simulatkns

In order to assess cocoa producer response to price Incentives In a dynamic context, several different sconarios are required since producer response is also a function of the available supplies of the inputs needed for cocoa investment and productlon, as well as the state of the Ghanaian economv and other policies of the Goverrvnent of Ghana. in general, the continued bmpiementation of the policy measures integral to the structural adjustment program Is taken as given. However, the progress of the Ghanalan economy Is stll subject to external economic and climatic influences as the history of the recent decade attests. Therefore, the following sequence of sconarios was simulated:

1. The effects of price incentives, given zero growth in population and per capita icome.

2. The effects of prico incentives, given population growth, but no growth in per capita income.

3. The effects of price incentives, given population increase and relatively slow growth in r capita income with some agricultural protection.

4. The effects of price incentive, given growth In populatlon and per capital income and a policy of food self sufficiency.

Scenarlo 1 Is included skmply to assess the effects of price incentives without the complications of growth In population and per capita Income. Scenar5o 2 introduces the affects of population growth alone, although with a proportionate Increase in land available for cultivation (as discussed above). This scenario essentially projects a continuation of the lack of growth In per capita Income of the past two decades, but without the distorted price incentives. It also Inpiles Investment in Infrastructure sufficient to extent the margin of cultivation proportionately with population growth. Scenario 3 Introduces the effects of slow growth In per capita income. As already noted, this scenario has two variants: one with no growth In the wage of unskilled labor, and another In which unskilled labor **receives** a 50 percent share of the Increment in per
This has as its main consequence that all demands and supplies for consumption and labor are functions only of the observable market prices and non-labor incomes. The household chooses its farm production plan by maximizing profit from farm production, and it then maximizes its utility subject to a budget constraint defined on the given prices, and an income found by adding its maximized farm profit to its full income<sup>3</sup> plus any non-labor income.

The separation result is crucially dependent on two assumptions: that the relevant decision variables internal to the household have perfect substitutes on outside markets, and that these markets are perfectly competitive. Relaxation of either of these assumptions destroys the separation and, while this presents no great difficulties for theoretical analysis,<sup>4</sup> it greatly increases data requirements for empirical estimation.<sup>5</sup> Optimal consumption and production decisions will be mutually interdependent, the household equilibrium will generally be characterized by implicit internal prices unequal to observable market prices, these prices may be household specific, i.e. dependent on preference and productivity parameters that may differ across households, and may be dependent on quantities. Estimation of models with these characteristics clearly requires individual household data.

# **2.2 Household production**

Becker (1965) developed the idea that consumption could be viewed as a production activity in which the (single-person) household combined its time and bought-in market commodities to produce the goods that ultimately entered its utility function. These latter are more abstract than market commodities, consisting of goods such as warmth, nutrition and health, which, though in general measurable (albeit in some cases perhaps only with some ingenuity), are not observable on markets. This fact, and the extreme simplicity of the assumed production technology, provoked strong critiques, perhaps the most

> 3 The market wage rate times total time available.

> 4 See for example Hirshleifer's (1970) classic analysis of the investment decision in the presence of capital market imperfections.

5
# **2. Data**

The empirical analysis in the paper merges information from two large databases of European firms: the Business Environment and Enterprise Performance Survey (BEEPS) and AMADEUS databases.

## **_2.1 Business Environment and Enterprise Productivity Survey (BEEPS_** _)_

For the measurement of the business environment, the analysis draws on firm-level data from the BEEPS, which was conducted by the World Bank in conjunction with the European Bank for Reconstruction and Development (EBRD). BEEPS covers establishments of all sizes in many industries and provides a wide array of qualitative and quantitative information regarding the business environment in all countries of Central and Eastern Europe, the former Soviet Union, and Turkey. Topics covered in the BEEPS include the obstacles to doing business, infrastructure, finance, corruption and red tape, legal and judicial issues, labor market regulations, and the skills and education of available workers. Taken together, the qualitative and quantitative data capture all aspects of the business environment within countries that affect firm productivity and performance.

In an effort to track changes of evolving business environments and benchmark the effects of reforms, the survey was conducted in 2002 and again in 2005. An identical core set of questions (covering 367 variables) was asked in all countries in both rounds to ensure comparability across countries and years, and all questionnaires in every country in both rounds of the BEEPS were implemented through face-to-face interviews with managers and owners. In each country, the sectoral composition of the sample in terms of industry (ISIC codes 10-14, 1537, 45) versus services (ISIC codes 50-52, 55, 60-64, 70-74) was determined by their relative contribution to GDP. Furthermore, the sampling design in both rounds included quotas for a set of firm characteristics to ensure sufficient numbers for statistical analysis, specifically, city/town (i.e., large, medium, small), firm size (i.e., small=2-49, medium=50-249, large=250-9,999), ownership (i.e., domestic, foreign, state), and exporters/non-exporters. The sampling approach was the same in both rounds of the BEEPS and was implemented nationwide.<sup>4</sup>

> 4 The BEEPS 2002 and 2005 datasets in Stata and CSV format as well as documentation on sampling and implementation are available for download from the following World Bank website:

5