rafmacalaba/datause-encoder-data huggingface dataset:
receive the SCTS. However, not all village groups within the four TAs had been reached by the SCTS in March 2007, given that the scheme was scaling up through the district as time and human resources allowed. In February 2007 the District Assembly identified the next 8 village groups eligible for the SCT according to the scale-up plan. At that time, the impact evaluation team randomly assigned 4 village groups to an “intervention” group and 4 to a “control” group. While the choice of the village groups to be assigned to treatment and control was random, the selection of beneficiaries within each village group was based on a multi participatory community based targeting, which enables local community volunteers to determine which households among them are the worst off and most vulnerable<sup>6</sup> .

The evaluation team followed both groups for one year until the comparison group of eligible households began to receive the transfer. Baseline data were collected in March 2007, before households received the cash transfer according to the government's schedule for rolling out the scheme. The second follow-up was in August 2007 and the final round in March 2008. The study included a longitudinal household survey, consisting of three rounds of collection with a panel of intervention and control households in cash transfer recipient and non-recipient village groups.

The household survey, used in the three data collection rounds, was adapted from existing household surveys used with similar populations throughout Malawi, such as the Integrated Household Survey (IHS), Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS). The household questionnaire documents basic health, demographic and economic conditions, including anthropometric measurements; food security (including the quality and quantity of meals, food diversity, and satisfaction with meals based); educational data; health, hygiene and health seeking behaviors; economic data; time use and adult and child care patterns; emotional wellbeing; mobility and migration; coping behaviors; and characteristics of the house and inhabitants. The household questionnaire was administered to the person registered to receive the cash transfer (Miller, Tsoka, Reichert 2010b).

> 6 The District Secretariat trained a Community Social Protection Committee (CSPC) to help implement the scheme. The CSPC made a list of ultra poor labor constrained households based on community knowledge and the local Village Headman signs on this list. The CSPC then visited the households to fill out an application for each household. Next, the CSPC ranked households and chose the 10% poorest. A community meeting occurred where the ranking was discussed.  Then Village, District, and National Committees approved the list of eligible households. Once fully approved, recipients started receiving transfers on a monthly basis.

9
Praise has been high. One attorney says, “ _Since the registry started operating I did not have to appeal at all against their decisions, while before I had to do it very often_ ” (Jersild and Skopljak 2007). The results of the reforms were also recognized by the World Bank’s Doing Business Report and Serbia was ranked top reformer in the world in 2006. During its first year, the Serbian Business Registry Agency registered almost 11,000 new companies, 70% more than in 2004. In 2 years, the number of registered businesses more than doubled.

While the reform included several components, this impact evaluation focuses on the move of company registration away from courts and to the SBRA.

# **3. Data**

We use firm level data from Serbia’s Financial Account Registry. Firms are required by law to submit their annual financial statements to the Financial Account Registry. Before the creation of the SBRA, the Financial Account Registry was operated under the Central Bank of Serbia. It was then moved to the SBRA to enable that financial statements are held together with the business registry data and files.

The data set we have covers only companies that appeared in the database for the first time between 2003-2011 (it does not include sole proprietors). The data set provides the address of each firm. Cities listed in this address were hand matched to the 16 courts that handled company registration. Here, we only merged firms in cities with more than 50 firms, which corresponds to about 87% of all firms in the database (66,063 out of 75,771 unique firms).

We use two different pieces of information to calculate the number of new firms in each region that is handled by one of the 16 courts. First, the database includes the year when the firm reports that it registered for the first time. Second, we construct the year when the firm first entered the database.

7
part of an Africa-wide study currently carried out by the World Bank in relation to the determinants of Port delays in Africa, which also comprises case studies in several other Sub-Saharan African countries.

After a review of alternative perspectives on the issue of cargo dwell time in ports in section 1, we describe the importance of the issue in the Port of Douala in section 2 and formulate early conclusions on main patterns observed. Using conclusions of qualitative research in Cameroon we identify in section 3 structural and behavioral grounds to long cargo dwell time in the Port of Douala. Explanatory statistics are then used in section 4 to test the early conclusions formulated in section 2 and identify lines of inquiry for further statistical modeling. We finish by few recommendations on alternative ways to tackle the long cargo dwell time issue in Sub-Saharan African ports.

1. LONG CONTAINER DWELL TIME<sup>2</sup> IN PORTS, THEORETICAL BACKGROUND

# **A transport perspective**

From a transport science perspective, container terminals are nothing more than intermodal nodes in global transport chains. Their basic function is then to transfer efficiently unitized cargo from a maritime transport mode (container ship) to a land transport mode (rail or truck) and viceversa. The efficiency of this transfer operation is then assessed against performance objectives which are in general berth, yard and gate productivity objectives. If we focus specifically on time performance of gateway ports for containerized imports we can however simply look at the rapidity at which containers are physically transferred from the containership to the land transport mode via the container yard. And this total time for the physical transfers only plus the necessary idle time between operations is defined as **operational dwell time** .

# **Political economy perspective**

From a political economy perspective however, gateway ports are also the place where the customs clearance process is completed to allow goods to enter definitively or temporarily into the

> 2 We define container dwell time in ports as the total time lapse between the maritime and the land transport moves of an imported or an exported container through SSA gateway ports. We focus in this paper on containerized imports for domestic consumption (as opposed to transit to third countries). Dwell time lapses therefore between containership arrival and exit of the truck or train from the port area.

3
Table 9: Domestic Violence and Job Quality

|Dependent Variable|Log(wage)|Log(wage)|Positive<br>Relationship with<br>Management|Positive<br>Relationship with<br>Management|
|---|---|---|---|---|
|Estimation Method|OLS|OLS|Ordered Probit|Ordered Probit|
||(1)|(2)|(3)|(4)|
|Ever Been Beaten|-0.124*|0.064|0.137|-0.071|
||[0.062]|[0.272]|[0.219]|[0.794]|
|Age First Marriage|0.013||-0.023||
||[0.008]||[0.027]||
|Age First Marriage X Ever Been Beaten|-0.006||0.001||
||[0.017]||[0.049]||
|Education||0.011||0.000|
|||[0.007]||[0.025]|
|Education X Ever Been Beaten||0.032***||-0.043|
|||[0.011]||[0.039]|
|Observations|467|467|359|359|

_Standard errors in brackets, clustered at the level of the village. *** p<0.01, ** p<0.05, * p<0.1. Relationship with Management: 1 = excellent; 2 = good; 3 = okay; 4 = bad; 5 = very bad_
- Leroy, Jef L., Marie Ruel, and Ellen Verhofstadt. 2009. “The Impact of Conditional Cash Transfer Programmes on Child Nutrition: A Review of Evidence Using a Programme Theory Framework.” _Journal of Development Effectiveness_ 1 (2) (June 11): 103–129. doi:10.1080/19439340902924043.

Macours, Karen, Norbert Schady, and Renos Vakis. 2012. “Cash Transfers, Behavioral Changes, and the Cognitive Development of Young Children: Evidence from a Randomized Experiment.” _American Economic Journal: Applied Economics_ : 247–273.

- Maluccio, John A., and Rafael Flores. 2005. “Impact Evaluation of a Conditional Cash Transfer Program”. Washington, DC.

Mensch, Barbara S., Paul C. Hewett, Richard Gregory, and Stephane Helleringer. 2008. “Sexual Behavior and STI/HIV Status among Adolescents in Rural Malawi: An Evaluation of the Effect of Interview Mode on Reporting.” _Studies in Family Planning_ 39 (4) (December): 321–34.

- Miller, Candace, Maxton Tsoka, and Kathryn Reichert. 2008. “Impact Evaluation Report External Evaluation of the Mchinji Social Cash Transfer Pilot”. Boston, USA, and Zomba, Malawi.

- Mohiddin, Lili, Manohar Sharma, and Anette Haller. 2007. “Comparing Cash and Food Transfers: Findings from a Pilot Project in Sri Lanka”. London, UK, Washington DC, USA, and Rome, Italy.

- Moore, Charity. 2009. “Nicaragua’s Red de Protección Social: An Exemplary but Short-Lived Conditional Cash Transfer Programme.”

Musgrave, Richard. 1959. _The Theory of Public Finance: A Study in Public Economy_ . McGraw-Hill.

Onyango-Ouma, W., and Fiona Samuels. 2012. “Transforming Cash Transfers: Beneficiary and Community Perspectives on the Cash Transfer for Orphans and Vulnerable Children Programme in Kenya”. London, UK, and Nairobi, Kenya.

- Perova, Elizaveta. 2011. “Three Essays on Intended and Not Intended Impacts of Conditional Cash Transfers”. University of California, Berkeley.

- Phiri, Swedi. 2012. “A Comparative Assessment of the Impact of Unconditional Cash Transfers for Urban Vulnerable Households Headed by Elderly and Non-Elderly Women in Mucheke Ward”. University of Zimbabwe.

Plummer, M. L., D. A Ross, D. Wight, J. Changalucha, G. Mshana, J. Wamoyi, J. Todd, et al. 2004. “‘A Bit More Truthful’: The Validity of Adolescent Sexual Behaviour Data Collected in Rural Northern Tanzania Using Five Methods.” _Sexually Transmitted Infections_ 80 (II) (December): ii49–56. doi:10.1136/sti.2004.011924.

- Ranganathan, Meghna, and Mylene Lagarde. 2012. “Promoting Healthy Behaviours and Improving Health Outcomes in Low and Middle Income Countries: A Review of the Impact of Conditional Cash Transfer Programmes.” _Preventive Medicine_ (November): S95–S105. doi:10.1016/j.ypmed.2011.11.015.

19
Table A3: Schools in _Mais Educação_ in the period 2008–2011 with missing information

||Panel|A: Fifth-grade schools||Schools in<br>Panel|B: Ninth-grade sch|ools|
|---|---|---|---|---|---|---|
||Schools in_Mais_||||Schools in_Mais_||
|Variable|<br>_Educação_with<br>complete<br>information|Schools in_Mais_<br>_Educação_with<br>missing information|Difference<br>(1)−(2)|_Mais_<br>_Educação_<br>with complete<br>information|<br>_Educação_with<br>missing<br>information|Difference<br>(4)−(5)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Portuguese grade|164|163|1.0|222|218|4.3***|
|Mathematics grade|180|179|0.8|230|227|3.2***|
|Dropout rate|5.9|8.4|-2.5***|12.0|16.2|-4.2***|
|∆ Portuguese grade|1.0|-1.0|2.0*|5.0|3.4|1.5|
|∆ Mathematics grade|10.5|6.9|3.6***|1.6|1.1|0.6|
|IDEB Index|3.5|3.3|0.2***|2.9|2.6|0.2***|
|∆ IDEB|0.4|0.3|0.0|0.3|0.2|0.2***|
|Municipal population|1,180,426|1,051,670|128,756*|1,364,439|1,290,785|73,654|
|Students' enrollments|924|953|-28.8|1159|1090|68.6*|
|IDHM|0.8|0.7|0.0***|0.76|0.75|0.0***|
|Urban|1.0|1.0|0.0**|1.0|1.0|0.0|
|Basic sanitation|1.0|1.0|0.0|1.0|1.0|0.0|
|Library|0.8|0.8|0.0|0.9|0.8|0.1*|
|Sports court|0.5|0.6|-0.1|0.7|0.7|0.0|
|Number of classrooms|13.0|13.0|0.1|14.3|14.2|0.1|
|Number of computers|0.9|0.9|0.0|1.0|0.9|0.0*|
|Science lab|0.1|0.2|0.0|0.3|0.2|0.1|
|Computer lab|0.5|0.5|0.0|0.6|0.6|0.0|
|Teachers room|0.9|0.9|0.0|1.0|0.9|0.0|
|DVD|0.9|0.9|0.0|0.9|0.9|0.0|
|Overhead projector|0.7|0.8|0.0|0.9|0.9|0.0|
|Number of employees|65.3|66.7|-1.4|76.2|75.8|0.4|
|School meals|0.9|0.9|0.0|0.9|0.9|0.0|
|Number of classrooms used|13.6|12.7|0.9|14.4|14.4|0.0|
|Morning lessons|0.5|0.5|0.0|0.51|0.51|0.0|
|Child care|0.0|0.0|0.0***|0.0|0.0|0.0|
|Kindergarten|0.4|0.2|0.2***|0.2|0.3|-0.1**|
|State schools|0.3|0.7|-0.4***|0.6|0.5|0.1*|
|High school|0.1|0.4|-0.3***|0.4|0.3|0.1|
|% of classes with full-time edu|0.0|0.0|0.0|0.0|0.0|0.0|
|Duration of school day|4.3|4.3|0.0|4.3|4.3|0.0|
|N|611|195||555|196||

_Sources: Prova Brasil,_ School Census, and INEP.

_Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2008 and remained until 2011 that have information for all variables in 2005, 2007, 2009, and 2011 (columns (1) and (4)) and for the schools that joined _Mais Educação_ in 2008 and remained until 2011 but have missing information (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
44 and comparability. In the 2018 HEFPI database, we rely on several data harmonization efforts (see Table 6):

- <u>LIS database. The LIS acquires data sets with income, wealth, employment, and</u> demographic data from many high- and middle-income countries, harmonizes them to enable cross-national comparisons, and makes them publicly available in two databases, the LIS database and the Luxembourg Wealth Study database. For the 2018 HEFPI data set, we use all available datapoints from the LIS database.

- <u>World Bank regional harmonized databases. Regional teams at the World Bank are also</u> working to produce adaptations of raw country level survey data sets using regionally harmonized definitions and aggregation methods (e.g. the ECAPOV data sets for Europe and Central Asia, the EAPPOV data set for East Asia and Pacific, the SHIP for Sub-Saharan Africa, the MNAPOV for Middle East and North Africa, the SEDLAC for Latin America and the Caribbean, and the SARMD for South Asia). These harmonized data sets are used for the global monitoring of poverty by the World Bank. We also use these harmonized data sets whenever possible.

# _Supplemental indicators used in computing absolute impoverishment_

When we measure impoverishment using the absolute $1.90-a-day and $3.20-a-day poverty lines in 2011 PPP dollars, we have to take into account that the survey values are in local currency units (LCUs), and that they refer to the year of the survey which is not necessarily 2011, the year to which the PPP conversion factors that we use refer. The PPP conversion factor tells us the number of LCUs that would have been needed in 2011 to buy the same amounts of goods and services in the country as one US dollar would have bought in the USA in 2011. Multiplying the conversion factor by 1.9 or 3.2 gives us the amount of LCUs that would have been needed in 2011 to buy the same amount of goods and services in the country as US$1.90 or US$3.20 would have bought in the USA. This is the poverty line (per day) in the country in question in 2011. We then need to take into
and subsidies to farmers, the viability definition became almost a self-fulfilling prophecy, because under the Agricultural Credit Act all farms below the viable size were excluded from assistance. Moreover, under the Subdivision of Agricultural Land Act of 1970, it is not possible to subdivide an existing title deed without ministerial approval. Permission is granted only with proof that a reasonable net farm income can be obtained with "average" management. The subjectivity of this requirement, together with the lending criteria of the official funding agencies, precludes systematic empirical analysis of small farms in South Africa. Yet, it is interesting to observe that despite the lack of assistance for small farmers, official records of deed transfers show that the prices of small parcels of land increased more rapidly than the prices of large parcels since the 1960s.

Ironically, the benchmark for determining farm viability -farm size-- has changed over time; during the 1960s and 1970s, expansion and mechanization were considered the solution to remain competitive with non-farm incomes. However, in the 1980s, the high debt loads from capital and land purchases reduced farm profitability and decreased returns to capital-intensive investment. Thus many farms once thought to be viable by the criteria set in the 1970s were exposed as not viable in the financial crisis of the 1980s.

Farmers themselves seem to view consolidation of farms as a rational economic reaction capturing economies of scale. For instance, Moll (1988) reports that of 55 farmers surveyed in Bredasdorp and Malmesbury regions who had bought land during the previous decade, _35_ (or 64%/o) indicated that they had done so partly to take advantage of size economies.

De Klerk (1991) attributes the process of farm consolidation to technical change, _viz._ mechanization. Consolidation has generally also caused a reduction in farm employment, because the new mechanized farm did not need to employ of the workers from the more labor-intensive smaller farms that were acquired. While seasonal workers bore the brunt of mechanization, permanent workers were most directly affected by consolidation (De Klerk, 1985).

Sartorius von Bach, Van Zyl, and Koch (1992) constructed an index of managerial ability based on indicators such as budgeting and the keeping of records and found it to be highly correlated with both farm size and total farm income. By evaluating Cobb-Douglas production function coefficients with the managerial ability index included as an input, the authors found significantly increasing returns to scale among 34 farmers in Vaalharts Irrigation Area. When managerial ability is excluded from the regression, however, results indicate constant returns to size. These results are confirmed by Van Schalkwyk, Van Zyl and Sartorius von Bach (1993) using non-parametric procedures to analyze the same sample and adjusting 12
Figure 6. Kernel density graphs: time of sleep for men and women <!-- Start of picture text --> (6a) Recall versus smartphone: women (6b) Recall versus smartphone: men<br><!-- End of picture text --> <u>Notes:</u> (1) The estimates pool the Day 5, 8 and 10 samples for each treatment arm. K-S p-values for differences across treatment arms in (6a) and (6b): 0.000.

Therefore, the smartphone arm provides greater variation in intraday reporting and could potentially provide an easier way to avoid issues around understanding time in the day – given how early people are reporting sleep time. If time use statistics have typically been analyzed on a day-level aggregation, however, a natural question arises as to why we should potentially care about intraday time allocation. In addition to our understanding of how men and women organize their time, the distinctions we observed across treatment arms of reporting during morning versus evening hours also have important implications for difficult-to-answer questions around infrastructural interventions affecting time use. This includes, for example, the role of household electrification in supporting time allocation in different productive areas during evening and nighttime hours (see, for example, Agénor and Agénor, 2023; Ribeiro et al., 2021; van de Walle, et. al., 2017). Taking electricity and access to energy sources more broadly, high-quality intraday data on time allocation can help us better understand the role of energy infrastructure on individuals’ economic and other intrahousehold decision making. As an example, Appendix Table A6 presents OLS regressions showing that for women in the smartphone arm in particular, electricity access (as opposed to no access, or household lighting through battery-operated torches) are significantly associated with time allocation in nonfarm employment and leisure during the evening/nighttime, but with no effects for women in the recall sample.<sup>21</sup> The choice of survey approach to time use data collection can therefore provide very different perspectives in understanding how time allocation is linked to energy infrastructure and transport.<sup>22</sup>

> 21 The regressions control for the same right hand side variables as in Equation (1), and examine effects separately by treatment arm, men and women, as well as employed men and women — given the findings in Figure 5 and Table 9 regarding agriculture and nonfarm employment.

> 22 In separate estimations, we also found fewer effects of electricity or motorized transport on time allocation in the morning hours across both treatment arms, supporting the relevance of the intraday distinction.

23
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