rafmacalaba/datause-encoder-data huggingface dataset:
# **INTRODUCTION**

Africa’s ports are vital to the continent’s domestic economies. Maritime trade accounts for more than 90 percent of the continent’s imports and exports and ports therefore play a fundamental role in facilitating Africa’s integration to international trade. However both importers and exporters face high costs for sea transport and substantial inefficiencies in port clearance operations. UNCTAD reports that the average freight rate for imports for example is 47 percent higher than in other developing countries and twice the rate in developed countries, estimated at 5.21 percent (UNCTAD, 2003). For containerized imports, cargo dwell time - defined as the time between vessel arrival and container exit from the port facilities - exceeds 20 days in average for most ports in the continent which makes African ports the most time-inefficient ports in the world. This is an added burden on business as compared to other regions in the world, as businesses relying on just in time shipment are less likely to develop in such environment, and local importers need to integrate higher storage and inventory costs. This paper aims at understanding the long cargo dwell time issue in Sub-Saharan African ports. It focuses on containerized imports through the port of Doula in Cameroon and leans upon extensive customs and port data collected locally. It also seeks to provide some theoretical background and methodological tools to address a crucial obstacle to Africa’s international trade.

The choice of Douala as a case study builds on its critical position as the gateway to several Central African countries and as one of the largest ports in Western and Central Africa. Thanks to a longstanding partnership between Cameroon (and especially the Cameroon Customs, the Facilitation Committee and the Single Window for External trade), World Customs Organization (WCO) and the World Bank, the port platform performance including cargo dwell time has been long monitored (tracking of delays within the logistics chain in Douala have been thoroughly monitored by several sources since the end of the 1990’s, first as part of adjustment and debt reduction initiatives and then at the initiative of Cameroonian stakeholders).

The methodology used has been to combine readily available data from the business and port community in Douala, with statistical analysis from ASYCUDA data provided by Cameroon Customs and analyzed at WCO headquarters. To our knowledge, this had never been carried out as comprehensively in developing countries. Cameroon is a specific case since ASYCUDA has been implemented for all customs procedures, from the manifest lodging to the exit note, which enables a very complete follow-up of import processes. These analyses have been complemented by interviews with consignees, port operators, clearing and forwarding (C&F) agents and shippers. It is 2
93 males, even though women might do some of them better and probably at less cost. 29 Entirely new public policies may bo appropriate to deal with the health problems of women and children. Nutritional supplementation in conjunction with child and maternal health **is** one such option (Winikoff, 1988). The large gap from region to region in maternal mortality rates Lllustrated in Table 7 **shows** that this **serious** health problem **is** not being resolved by traditional public policies (Herz and Measham, 1987). Some have even concluded that in **subSaharan** Africa, public health is unlikely to **make** any progress until the level of women's education improves (Anonymous editorial, Lancet, 1987).

# 6. ConclusLons

Reliance on traditional market-oriented national income accounts **has** introduced a systematic bias in development economics that has asxigned too low a value to the economic contribution of women in the development process. In reorganizing our approach to economic development, and refocusing our analysis of women and development, it is essential that a family economic framework incorporate both rsonmarket and market activities of the members of the household. In what **way** would such a reappraisal of women's contribution to society clarify aeglected opportunities for public policies? Women are usually enmeshed in a family that trades off the competing interests of its members and coordinates the uses of its members' tim and resources in market and nonmarket production. To affect women's status one must generally work through the family. Society can transfer resources to families to raise current consumption levels, but **the** family can reallocate its resources In a compensatory manner. Thus, this intervention vill not
In the U.S. economists and planners have debated the issue arguing around the edges, and coming to different conclusions about the desirability and sustainability of Los Angeles type of development. Although Los Angeles type lock-in may become common around the world and especially in the rapidly growing and spreading urban areas of the developing countries, it is not clear that this type of urban form is inefficient or unsustainable. The existence of a lock-in effect might be a legitimate concern for supporting transit-oriented development and all would agree that transit does much better in higher density areas and corridors. But there is less agreement on whether the sprawl of Los Angeles type development creates an unsustainable urban form. Some planners have defended the value of having compact cities (e.g. Ewing (1997)), but those who looked at data (Gordon and Richardson, 1989, 1996) have found that the average commuting time in Los Angeles is not unusually high, and that, furthermore, the average commute time in the U.S. as well as in L.A. has remained stable at somewhat above 20 minutes one way since WWII.

The possible existence and quantification of a lock-in effect induced by road investments can be sorted out with a properly structured simulation model of transportation and land use. In this paper we demonstrate how this can be done by using a simple model, calibrated with data from Beijing representative of the year 2005. Based on the data, Beijing is divided into two areas: the completely built-up urban core (containing about 6.8% of the land) and the periphery. We may think of the core as the equivalent of a Central Business District (or CBD) that is commonly assumed to hold all the jobs in the theoretical analyses that are fashionable in conventional urban economics. Actually, as we will explain in more detail in section 3, the data shows that only 20% of the jobs are in this core, the remaining 80% being distributed in the periphery. Thus, Beijing has little resemblance to a monocentric city. Neither does Beijing look a lot like L.A. where only a few percent of all the jobs are in the downtown. In addition, Beijing – despite the rapid growth of auto ownership in recent past – is not yet a fully auto dependent metropolis. In 2005, only 20% commuted by car, 35% walked or bicycled, while 46% used public bus or subway.

To properly capture the diversity of commuting arrangements in Beijing, we use a discrete choice approach inspired by the general equilibrium models introduced into urban economics by the first author (Anas and Xu (1999), Anas and Rhee (2006)). The model allows workers in Beijing to choose in which of these two areas they will work and in which area they will reside 4
# **3.4 Descriptive statistics**

The survey captures the occurrence of shocks and the ways households cope with them. Given the available information, we can define two types of violence-related shocks. The first are deliberate aggressions on particular households, idiosyncratic shocks, such as the destruction or theft of household goods or direct victimization. It is important to stress that those households that are directly victimized with overt violations of human rights or who face a high risk of victimization frequently become displaced, either in seeking to avoid being victimized or because they have already been the victim of an attack. This implies that the households in our survey were likely not victimized in this fashion; thus, we are dealing with a sample of ―stayers,‖ who have a low likelihood of facing idiosyncratic shocks. In fact, two of the CLS-WILL regions report high rates of forced displacement (see Table 1), showing that the households currently residing in the rural districts are those that face a low risk of direct aggression<sup>10</sup> .

The victimization profile of forcefully displaced persons diverges significantly with the reports of idiosyncratic shocks directed at ―stayers.‖ Ibáñez (2008) shows that rural households forced to flee report a high incidence of direct victimization—54.5 percent of households were directly threatened, 34.5 percent experienced the killing of a household member, and 17.3 percent had a household member forcefully recruited by armed groups.

Given the high probability of underreporting, we include a detailed conflict module on the rural district questionnaire that collects information on the presence of armed groups and the incidence of conflict shocks. We expect higher response rates, as responses are general for the community and do not identify particular community members. We use the responses to this module to construct the second type of violencerelated shocks. These shocks are covariate risks, and include such things as cattle theft, murders, kidnapping, extortions and threats from armed groups. This information comes 10 Therefore, the incidence of idiosyncratic shocks in our sample is low, around 1% of the sample; our sample is restricted to less serious shocks, such as the destruction or theft of household goods. These shocks are not necessarily related to the conflict, and may be due to violent crime. The low incidence of idiosyncratic shocks may also result from fear and apprehension about reporting having been the victim of a direct aggression. More specifically, this apprehension might be stronger among households residing in regions in which the presence of armed groups is strong.

18
Benefits in Russia 16 property and this share rose to over 75% in the case of the second largest firm size class (see Table 2).

While housing has normally been financed through the Social Fund, the survey indicates some recent departures from this practice. Firms with large housing programmes hare tended to shift to financing directly from profits or by borrowing. This is likely to reflect in part some of the risk sharing that firms are creating for new construction projects.

Figure 4 is instructive for showing a signifi:ant decline in the real value of construction by firms across most firm size classes when comparing 1991.1-4 with the same period of 1992; the decline is in excess of 60%. We also observe a fall in maintenance charges which, considering the limited scale of the intervening privatisation of housing -- barely 2% of tenants had privatized their apartments by November 1992 -- largely relates to the same housing stock. Over the same reference period, the decline is less than 30% and is not uniformly distributed over size classes. Putting the two expenditure items together, the result is more than halving of housing expenditures by firms comparing 1992 with 1991. The fall is considerably steeper among the firm size classes with higher frequencies for housing programmes. Relating housing outlays to total benefits expenditures captured by the firms' Social Fund, we again see a reasonably significant reduction in the share. For all firms housing expenditures declined from around 45% in 1991.1 to under 20% in 1992.3. A similar decline in the share of gross profits -- from 9% to 5% -- can also be observed.

The motivation behind the fall in housing expenditure is not difficult to fathom; indeed one might perhaps have expected a sharper decline. Rents remain regulated and yield derisory current incomes. Moreover, whiile housing programmes were commonly built up in the Soviet period to reduce labour mobility, this is certainly not an objective they attain at the present. We can see, in fact, that nearly 40% of current tenants in the housing stock of the survey were not current firm employees. This share was moreover rather stable across all firm size classes with significant housing stock.

It is interesting to estimate t.e likelv effect of reduci-;? rent contro s. Recent figures suggest large negative shocks to current -ncome given the extremely low base from which adjustments will be made. For-una-ely, :ne survey
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**Figure 2: Share of working population in different business size classes, versus GDP per capita and Gini coefficient**

<!-- Start of picture text --> 3 β: -0.799 3 β: 0.517<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>3 β: -0.598 3 β: 0.280<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>3 β: 0.560 3 β: -0.487<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>3 β: 0.946 3 β: -0.412<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>Latin America Europe and U.S. Asia and Australia Linear fit<br>Share of workers who are<br>self-employed without employees<br>Share of workers who are<br>in businesses with up to 10 workers<br>Share of workers who are<br>in businesses between 11-50 workers<br>Share of workers who are<br>in businesses with more than 50 workers<br><!-- End of picture text -->

**Note** : Both variables in each graph have been standardized. Each dot represents a country in the sample. Japan and Nepal are missing in the right-hand panels because their Gini indices are unavailable in the WDI. Argentina, Costa Rica, Japan, India, and Nepal are missing in the bottom two panels because of the lack of separate reports for the 50+ category. The coefficient below each panel corresponds to the linear regression coefficient of the y- axis variable on the x-axis variable. **Sources** : Data on Gini and GDP per capita are from World Development Indicators (World Bank, 2023). GDP per capita and Gini Indices are the last publicly available for each country between 2016 and 2019.

11
<!-- Start of picture text --> a Electricity provision costs<br>0.80<br>= 070<br>& 0.60<br>8<br>2 050<br>=<br>2<br>§ 040<br>Cy<br>5 0.230<br>8 o20<br>°o<br>2 0.10<br>0<br>PEELEFI eeEPa EL AOLee PS S<br>ge Rs os * v<br>IN Price of grid electricity lm Cost of backup electricity<br>b Annual costs of backup power<br>180 3,000<br>8 160 e<br>: 8 2.500<br>S 140 8<br>iS =<br>g 120 @ 2,000<br>8 3<br>& 100<br>2 4,500<br>% 8 i<br>ec D<br>: 60 33 1,000<br>aE 40 3<br>5 2 :TM<br>0<br> FF EF EEE EE EEE SE S ° 2<br>Kg OS IS ELE SE ES é<br><!-- End of picture text -->
# **Invitations, Incentives, and Conditions: A Randomized Evaluation of Demand-Side Interventions for Health Screenings in Armenia**

Damien de Walque, Adanna Chukwuma, Nono Ayivi-Guedehoussou and Marianna Koshkakaryan The World Bank Keywords: Non-communicable diseases; hypertension; diabetes; health screenings; invitations, incentives, conditional cash transfers, primary care.

JEL codes: I12; I15; O15
# _Omitted variable bias_

The above estimates may suffer from omitted variable bias if the occupational gender wage gap is affected by other factors other than economic development, trade and FDI net inflows. Here we will investigate two different types of omitted variable bias, namely omitted variable bias due to observed and omitted variable bias due to unobserved variables. With respect to the observed omitted variable bias we investigate three types of omitted variables, namely wage-setting institutions, intra-country trade, and occupational segregation and inequality. With respect to the unobserved omitted variable bias we will investigate the impact of omitted unobserved country and occupation characteristics.

# **Wage-setting institutions**

The first type of observed factor we consider is the impact of wage-setting institutions. It has typically been found that pay differentials are reduced with strong institutional determination of wages and/or prices (Freeman and Oostendorp 2000). Here we test whether this is also the case for the wage differentials between female and male workers in the same occupations.

We have two types of measures of the role of institutions in wage-setting. First, we have obtained estimates of union density for a large number of countries in 1990-95 from Visser (1999); these numbers are subject to considerable measurement error. But perhaps the biggest problem with them is that union density does not necessarily translate into collective bargaining and wage determination. Some countries, such as the Netherlands or Austria have modest union density but near universal collective bargaining. Other countries, such as the Ukraine or China have high union density, but little or no collective bargaining. Accordingly, we use the union density figure from Visser except for OECD and (ex-)communist countries. For OECD countries, we have replaced unionism with OECD estimates of collective bargaining coverage from OECD (1997). For communist countries such as China and for ex-communist countries, where 24
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capture some demand shifts caused by the ways of using different energy sources. As will be seen, our estimation results indicate that the use of electricity or LPG has certain but not much explanatory power to the electricity demand.

**Sample bias.** Household surveys often have a sample bias problem. Respondents can always choose not to provide answers (Yoo, 2007). It is often pointed out that the amount of electricity consumption reported in household surveys might be deviated from the amount that households actually consumed. In Georgia, for instance, it is found that household surveys overstated the actual payments received by a power company (Lampietti _et al_ ., 2007). In our sample, there is no crucial evidence that Albania’s household surveys suffer from the sample bias problem.

**Instruments.** The current paper adopts three instrumental techniques to deal with the endogeneity associated with the marginal price and Nordin’s difference variable. First, the marginal price and Nordin’s difference variables are instrumented by their predicted values according to the simple expansion of the definition of the Nordin’s difference variable. To obtain the predicted values, the actual bill payment is regressed on a constant term and the quantity of consumption:

where _Billij_ is the amount of bill payment for customer _i_ , who is faced with _j_ th block marginal rate. _εij_ represents an error term. The marginal price and Nordin’s difference variable can be instrumented by<sup>ˆ</sup> 2<sup>andˆ</sup> 1<sup>, respectively (Hewlett, 1977). However, there</sup> may remain concern about the fact that the total bill is still a function of quantity and marginal price. Therefore, total bills may still feedback onto quantity (Deller _et al._ , 1986).

The second technique is the Terza’s (1986) two-stage probit approach, which essentially transforms the endogeneity question to the consumer self-selection problem. It is shown that
**Table 5 Working Conditions Regressions for Cambodia and Sri Lanka**

||**Cambodia**||**Sri Lanka**||||
|---|---|---|---|---|---|---|
||**Household Su**|**rveys**|**Labor Force**|**Surveys**|**Household S**|**urveys**|
||**1**|**2**|**3**|**4**|**5**|**6**|
|**Female**|0.031***|0.032***|0.066***|0.059***|0.010***|0.038***|
||(0.002)|(0.002)|(0.002)|(0.004)|(0.003)|(0.004)|
|**Year dumm**|0.045***|0.046***|−0.009***|−0.014***|−0.451***|−0.435***|
|**y**|(0.002)|(0.003)|(0.002)|(0.003)|(0.003)|(0.003)|
|**Fl*T&G**||−0.036***||−0.029*||−0.008|
|**emae**||(0.012)||(0.015)||(0.014)|
|**Fl***||−0.001||0.015***||−0.060***|
|**emaeyear**||(0.004)||(0.005)||(0.005)|
|**Year*T&G**|−0.052***|−0.069***|0.023***|0.01|−0.030***|−0.062***|
||(0.008)|(0.018)|(0.009)|(0.017)|(0.009)|(0.016)|
|**Y*T&G*fl**||0.022||0.01||0.085***|
|**earemae**||(0.020)||(0.020)||(0.020)|
|**Ctt**|0.779***|0.779***|0.737***|0.739***|0.630***|0.631***|
|**onsan**|(0.016)|(0.016)|(0.015)|(0.015)|(0.029)|(0.029)|
|**R-squared**|0.09|0.091|0.122|0.123|0.559|0.561|
|**Number of observations**|84,724|84,724|49,348|49,348|34,585|34,585|

_Source:_ Authors’ calculationsbased on Cambodia Socio-Economic Surveys and the Sri Lankan 2002 and 2006 Household Income and Expenditure Surveys and the 2002 and 2008 Labor Force Surveys _Note:_ Standard errors in parentheses. Additional controls include age, age squared, marital status, education, industry, and occupation dummies.T&G = textiles and garments; MFA = Multi-fibre Arrangement. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

We measure working conditions as an average of two dummies—a dummy for a worker above age of 14, and a dummy for a workweek that is less than or equal to a 40hours (see table 5, columns 1–4). We have to use such a narrow measure of working conditions due to the lack of a more comprehensive measurement in the data. Thus, a reader should keep in mind that this measure of working conditions reflects only two specific aspects of the job quality. In both cases, regressions are pooled across different survey years.

The results show that the working condition index in the Cambodian textile and garment industry deteriorated post-MFA by 5.2 percent, compared to other industries (table 5, column 1). Even though the post-MFA changes in female working conditions relative to male working conditions and compared to other industries are positive (see table 5, column 2), they are not significant, suggesting that women’s working conditions were not affected differently than those of men as a result of the MFA phase-out. These findings arenot far from the theoretical predictions that working conditions in apparel sector should deteriorate as a result of declining prices on apparel.

On the other hand, working conditions in Sri Lanka improved by 2.3 percent post-MFA as compared to other industries, which might be a sign of the impact of the _Garments without Guilt_ campaign. Similar to Cambodia, in Sri Lanka the post-MFA changes in female working conditions relative to male working conditions and compared to other industries are positive, 16
Table 4: First stage of the ZIP regression for all transport, transport services and private transport.

|_Dependent variable: _|_Whether transp_|_ort expenditure is no_|_n-zero_|
|---|---|---|---|
||All transport|Transport Services|Private transport|
|Log per cap. exp.|-0.669<sup>∗∗</sup>|-2.698<sup>∗∗</sup>|-0.208<sup>∗</sup>|
||(-6.32)|(-34.59)|(-2.35)|
|Square Log per cap. exp.|-0.0371<sup>∗∗</sup>|0.143<sup>∗∗</sup>|-0.0617<sup>∗∗</sup>|
||(-5.01)|(27.83)|(-10.54)|
|Age of head|0.00696<sup>∗∗</sup>|0.00397<sup>∗∗</sup>|0.00314<sup>∗∗</sup>|
||(12.77)|(8.65)|(6.63)|
|Complete secondary education|-0.152<sup>∗∗</sup>|0.0296|-0.152<sup>∗∗</sup>|
||(-6.65)|(1.64)|(-8.81)|
|Log household size|-1.342<sup>∗∗</sup>|-0.518<sup>∗∗</sup>|-1.518<sup>∗∗</sup>|
||(-79.17)|(-38.28)|(-97.82)|
|Urban|0.207<sup>∗∗</sup>|0.0157|0.301<sup>∗∗</sup>|
||(9.86)|(0.95)|(18.20)|
|Female|0.535<sup>∗∗</sup>|-0.165<sup>∗∗</sup>|1.095<sup>∗∗</sup>|
||(24.87)|(-8.89)|(51.98)|
|Subnational FE|Yes|Yes|Yes|
|N. of observations|153133|153133|147153|

_t_ statistics in parentheses,<sup>+</sup> _p_ < 0 _._ 10,<sup>∗</sup> _p_ < 0 _._ 05,<sup>∗∗</sup> _p_ < 0 _._ 01 26