rafmacalaba/datause-encoder-data huggingface dataset:
the country. We aggregate data at the city-month level. By limiting crime reports to Venezuelans, we attempt to capture the effect of the regularization on the propensity of Venezuelan migrants to report crimes.

2. _Crimes perpetrated by migrants._ To examine changes in crime perpetrated by migrants we use data from the _Fiscal ́ıa General de la Naci ́on_ , Colombia’s Office of the Attorney General. The data set includes all criminal cases in Colombia that are in trial or have yielded convictions, including information on the year the crime occurred, the type of crime, and the gender, age, and nationality of the indicted individual between 2017 and 2021.<sup>9</sup> In the data, 97.11 percent of crimes (178 _,_ 483) were committed by Colombian nationals and 2.8 percent (5 _,_ 312) were committed by Venezuelan migrants.

3. _Registro Administrativo de Migrantes Venezolanos (RAMV)_ . We use administrative data sourced from the RAMV survey to compute the number of people eligible for the PEP migratory status and the actual number of individuals who applied for (and received) the PEP migratory status. Using individual records, we compute the allocated time window each individual had to register in the PEP (based on the registration number), which we use for our identification strategy.

4. _Municipality Controls_ . We control for a rich set of baseline municipal covariates including night light density, variables related to internal conflict, GDP municipal composition, distance to the Venezuelan border, and number of Venezuelans with previous rollouts of PEP migratory status (particularly the PEP1 and PEP2), as well as proxies for government activity. Administrative information at the municipal level comes from the CEDE municipal panel, the Ministry of Defense, the National Planning Department, and DANE (Colombia’s national statistical agency). Night

> 9The original data set had more information that we do not use in our effort to measure actual crimes. Thus, we exclude cases that were found not guilty, were in pretrial investigation (i.e., not enough evidence had been compiled to go to trial), were closed by the prosecutor, or suffered “procedural rupture” (according to Article 53 of the Code of Criminal Procedure).

11
Although the policy responses varied across the region (see Table 1), countries primarily restrained real wage growth in the public sector by freezing hiring and nominal wages. Some countries, such as Bosnia, Latvia, Lithuania, and Romania, implemented cuts in nominal wages, mostly by cutting or suspending variable pay components, such as allowances. In addition, some countries curtailed employment, mostly relying on attrition in combination with general hiring freezes. Only a few countries, such as Latvia, which cut 25% of the general government workforce, implemented deeper government restructurings and right-sizing exercises. On the opposite side, Tajikistan, Kosovo, Kyrgyz Republic, Azerbaijan, Turkey, Montenegro, and Slovenia experienced strong expansions of the wage bill, partly in response to countercyclical policies and more favorable economic conditions.

**Table 1. Wage Bill Related Policy Responses to the Financial Crisis (2008-10)**

|**Nominal Wage**<br>**Freeze**|**Nominal Wage**<br>**Cuts**|**Hiring**<br>**Freeze**|**Retrenchment**<br>**/Rationalization**|
|---|---|---|---|
|Belarus|Bosnia|Croatia|Bulgaria|
|Croatia|Latvia|Georgia|Hungary|
|Georgia|Lithuania|Bulgaria|Latvia|
|Hungary|Romania|Hungary|Moldova|
|Macedonia|Serbia|Latvia|Serbia|
|Montenegro||Macedonia|Slovakia|
|Russia||Moldova|Romania|
|Slovakia||Montenegro||
|Ukraine||Ukraine||

Source: Country Sources, World Bank Staff.

However, high wage bill spending does not necessarily imply unsustainable public finances. The notion that large public sectors imply fiscal profligacy is not supported by empirical evidence. For example, Estonia and Lithuania have high levels of public employment yet do not experience large or recurrent deficits. Simple cross-country correlations between the share of wage bill expenditures and the overall government deficit (both measured in relation to GDP) show that there is a weak, but insignificant relationship between wage bill spending and fiscal balances (see Figure 4).

6
20 Table 7

|PROPORTION<br>OF|NEW LABORMARKET EN|TRANTS AMONG<br>UNEM|PLOYED<br>1983-1991 ^|
|---|---|---|---|
|YEAR|UNEMPLOYED|WITH WORK<br>EXPERIENCE|NEVER WORKED<br>I|
|1983|100.00|70.79|29.21|
|1984|100.00|67.75|32.25|
|1985|100.00|73.18|26.82|
|1986|100.00|77.22|22.78|
|1987|100.00|75.97|24.03|
|1988|100.00|73.78|26.22|
|1989|100.00|76.79|23.21|
|1990|100.00|78.65|21.35|
|JANh-OCT|100.00|81.40|18.60|

_SuAvera e_ 16 _aCtf_ wtnrban areas Sou.rce: 'TPb~' r~esL-Lts fromn National Urban Emptoymnent Surveys 1983-1991, INEGI
Log linearizing around the non stochastic steady state, intertemporal household utility can be written (to a ...rst order approximation) as: where v is the steady state value of utility, xb = log xt � log x denote log deviation from the steady state. In obtaining this result we have used the FOCs of the household maximization problem; for time t these consist of: plus the two trasversality conditions, one for bonds and one for capital. Equation (5) is an immediate consequence of the envelope theorem and expresses normalized utility as a function of the variables that the consumer takes as exogenous or predetermined, e.g. prices and the initial stock of capital and bonds. Using the log linear approximation of the budget constraint around the steady state: we can re-write equation (5) in terms of quantities: Equation (11) says that intertemporal utility (in log deviation from the steady state) re‡ects the expected present discounted value of terms that represent the sum of the components of ...nal demand (in log deviation from the steady state), weighted by their steady state contribution to demand, minus primary inputs (in log deviation from the steady state) times their respective steady state total remuneration. In addition, intertemporal utility depends upon the initial level of the 8
**Table 2. Standardized Height-for-Age and Incidence of Stunting: Children between 6 and 60 months in 1997 & 2000**

||1997|2000|Diff|
|---|---|---|---|
|**_6-12 Months_**||||
|Mean|-0.96|-0.90|0.06|
||(0.12)|(0.07)|(0.13)|
|Stunted (%)|20.29|16.67|-3.62|
||(0.03)|(0.02)|(0.03)|
|Observations|207|318||
|**_12-24 Months_**||||
|Mean|-1.66|-1.62|0.04|
||(0.06)|(0.05)|(0.08)|
|Stunted (%)|36.32|28.05|-8.28**|
||(0.02)|(0.02)|(0.03)|
|Observations|468|574||
|**_24-60 months_**||||
|Mean|-1.60|-1.56|0.04|
||(0.03)|(0.03)|(0.04)|
|Stunted (%)|<br>35.12|<br>32.33|<br>-2.79*|
||(0.01)|(0.01)|(0.02)|
|Observations|1495|1726||

Notes: Calculated from IFLS data. Standard errors are in parentheses. (*) is significant at 10%; (**) is significant at 5%; (***) is significant at 1%.

28
Laiglesia (2012) also note that by making the relative poverty line a function of the median and not the mean, poverty lines will differ across countries with the same level of consumption per capita if the overall distribution of consumption differs across the countries. The proposed SPL also has relevance to SDG target 10.2 aimed at “social, economic and political inclusion of all,” the indicator for which is the proportion of people living below 50 percent of median income. While the focus of this SDG is on reducing inequality and improving inclusion, and it is useful to recognize that it overlaps well with the idea of monitoring societal poverty. As countries grow, societal poverty provides information on the extent to which the poor share in the growth.

Methodologically, the approach followed in this paper to parameterize a global relative poverty line is similar to how others have approached this problem. The analysis in this paper though is based on a much larger data set of national poverty lines and this has allowed us to more precisely estimate the shape of the fitted relative line. One key finding of our estimates is that national poverty lines increase in value as countries get richer over the entire observed range of countries. We do not observe a flat-line relationship for the poorest of countries, in contrast to much of the limited literature on this point. Our parameterization of the SPL also includes a positive intercept, which results in our SPL being an example of a weakly relative poverty line. Another way of interpreting the positive intercept is that it brings a fixed component to the relative poverty line, ensuring that the elasticity of the poverty line to growth is always less than one but increasing with income.

This last point also addresses a recommendation in World Bank (2017, recommendation 16) that the World Bank should monitor a measure of global societal poverty that takes into account a country’s standard of living and that combines both fixed and relative elements in one line. The SPL proposed in this paper fills this gap in the effort to monitor global poverty.

30
To mitigate the impact of these factors, several methods are used. First, researchers, starting with pioneering work on Indonesia by Alderman and Garcia (1993), attempt to model the observed changes in consumption. They also construct transition matrices by quintile using predicted consumption rather than actual consumption (e.g. Woorland and Klasen or Hyat). These estimated transition matrices put a bound on possible measurement error. Second, some researchers use long panels to distinguish between persistent shocks to income or consumption and transitory ones (Friedman, 1957). For longer panels expanding more than two rounds, it is typical to average several periods and work with the resulting moving averages. By taking an average over a period of time, measurement error and transitory shocks will be partially averaged out (Shorrocks, 1978). Due to availability of just two rounds in Egypt panel we limit ourselves to the first approach.

# **III. Data and methodology**

Living standards in Egypt are monitored with the high-quality large household survey: Household Income, Expenditures and Consumption Surveys (HIECS). Conducted every five years since 1995, these surveys have been the main (and the only official) source for poverty and inequality data in Egypt. In late 2007, faced with multiple policy demands arising from the social policy agenda, the authorities decided to make the data collection more frequent. Since the preparation of a new large survey takes time (including the need to update the sampling frame following the new 2006 census), and the survey itself takes 12 months to collect all data, the decision to proceed without delays resulted in a design requiring less preparation: it was decided to re-visit in 2008 the households interviewed in the February during HIECS 2004/05, applying the same questionnaire. International experts contacted by CAPMAS (Kalton) confirmed the feasibility of such an approach. CAPMAS conducted the field work, revisiting all addresses, completing the survey and matching the new sample to the 2004/05 data. The Household Income, Expenditure and Consumption Panel Survey (HIECPS) 2005-2008 follows the same households over time and allows an unprecedented comparability in the analysis of living standards.

**_The Sample:_** The data used in this analysis as panel (for 2005 and 2008) are based on a one-month subsample of the household from the full HIECPS 2004/05 interviewed in February 2005 and 2008 (Figure 1).  The sample of HIECS 2004/05 was based on the 1996 Population Census's updated sample frames of 1,200 area sampling units (PSUs) distributed between urban and rural areas of all governorates. The area sample consists of a number of neighbouring census blocks containing 1,500 households. The

between the first and the second rolls (the die is fair), there is a correlation between the initial outcome and the change in outcome.

6
Figure 5: Evolution of Weighted Firm Degree by 2014 Percentile <!-- Start of picture text --> 125 80<br>100<br>60<br>75<br>40<br>50<br>20<br>25<br>0 0<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>2014 Percentile: <25th 25-50th 50-75th 75-90th >90th 2014 Percentile: <25th 25-50th 50-75th 75-90th >90th<br>firm average by 2014 percentile firm median by 2014 percentile<br>Supplier and customer links (wgt. degree) Supplier and customer links (wgt. degree)<br><!-- End of picture text -->

**Note:** The graph tracks the average (left panel) and median (right panel) number of links for firms in the bottom 25th percentile, the 25th-50th percentile, the 50th-75th percentile, the 75th to 90th percentile, and above the 90th percentile of the weighted degree distribution (i.e. the firm link count) in 2014 over time, focusing on the balanced sample of actively covered agribusiness firms.

Our findings also indicate signs of strategic diversification and concentration. Within narrowly defined industries, agribusiness firms maintain a stable number of relationships and do not exhibit a tendency to diversify these specific relationships (see top and bottom left panel, Figure 6. Instead, these firms demonstrate a strategic pattern of maintaining a constant number of firms per partner-industry, for both customer and supplier connections. This observation suggests a preference for deepening existing relationships rather than expanding their network within the immediate industry sectors of their partner firms.

Conversely, there is a clear trend toward industry diversification, as evidenced by the increasing number of distinct industries to which firms maintain supplier or customer relationships. This trend is illustrated in the left panels of Figure 6, which show a steady rise in the variety of industries connected to these firms over time, both for the full sample of actively covered agribusiness (upper left panel) and the balanced subset of firms (bottom left panel).

Further insights from the balanced sample of agribusinesses, as shown in the right bottom panel of Figure 6, indicate a tendency to increase the number of partner countries and This the efforts to their firms per partner country. finding highlights firms’ expand international footprint, despite some fluctuations in the share of firms with international 21
**A comparison of the distributions of "true" income based on EU-SILC and the imputed tax admin income reflects the underreporting of income at lower income quintiles and the underreporting of top incomes in survey data. At the median, the gap between “true” and “reported” income is 9%; thus, tax compliance is 91%.** The mean value of income imputed tax income is higher than that of survey income. This results from a significant underreporting of top income in EU-SILC data. If we censor income distribution at the 99<sup>th</sup> percentile of the distribution of labor income in EU-SILC data, the mean imputed tax income is lower than survey income. The gap between mean “reported” and “true” mean income is 6%.<sup>12</sup> our Due to challenges arising from the underreporting of top incomes in EU-SILC data,<sup>13</sup> preference is to assess tax compliance by comparing median incomes. This approach mitigates complications associated with data discrepancies and provides a clearer perspective on tax compliance levels.

**We present the income distributions in the (original) EU-SILC (gross labor income) and imputed tax income in the EU-SILC in Figure 1** . We censured the distributions at the 99th percentile to keep the graphs readable. The rounding of income is an obvious problem in the EU-SILC data. Many respondents do not provide the exact income value, only a rounded value. When comparing the distribution of income observed in EU-SILC with the imputed tax income, we observe a spike in values corresponding to the minimum wage (the annual value of minimum wage for the employees without higher education is 5,531 EUR, the annual value of minimum wage for the employees in the construction sector is 7,441 EUR). However, the imputed tax income data spike is significantly higher than that observed in EU-SILC data. The spike in the value of minimum wage may be seen as evidence that the minimum wage is effective in providing a floor constraining the value of wages. Without the minimum wage, the underreporting of income will probably be higher (minimum wage establishes a minimum value of

12 After applying censoring at income equal to €29,678, the mean survey income becomes €10,863 and the mean imputed tax income (tax data imputed into the survey) is €10,191.

13 The value of income at P90 in tax data is 19% higher than in the survey, and at P95, the income reported in tax data is 28% higher. At P99, income in tax data is nearly twice as high as in survey data (see Table 3).

19
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2. LONG DWELL TIME IN THE PORT OF DOUALA: FACTS AND FIGURES

# **The various perceptions of long cargo dwell time in the port of Douala**

Container dwell time is one of the many performance indicators to assess port performance. As compared to standard indicators such as ship turnaround time or productivity indicators it is however not yet widely used for global benchmarking purposes. It is therefore challenging to define standard limits above which dwell time would be considered too long in any given port. Sector experts tend to agree however on a 3 to 4 days representative mean value.<sup>**4**</sup> From a national perspective, the issue of dwell time has been specifically identified as a major hindrance to Cameroon economic development for a long time. In November 1997 a dwell time target of 7 days for container imports has been officially defined<sup>5</sup> . This objective has however not been adopted by all port stakeholders to date notably because it did not take into account shippers and C&F behaviors, as we will demonstrate later.

On the other hand gateways ports are not only gateways, they are also a place of interaction between a number of agents: port operators, public administration and authorities, brokers or intermediaries and shippers. Each of this group has a specific use of the port that conditions its perception of the long dwell time issue.

For the terminal operating company (TOC) - which is called Douala International Terminal (DIT) in Douala - there is a direct relationship between distribution of dwell times and terminal occupancy. It therefore needs to precisely evaluate a standard dwell time beyond which the performance of the terminal is negatively affected. This standard is the free time period defined “ _as the period during which a container can reside in the container yard without being assessed a demurrage fee_ ” (Huynh, 2006). According UNCTAD it should correspond from a user perspective to the “ _sufficient time to allow efficient importers to clear their cargo”_ (UNCTAD, 1995). But in practice the port authority and the TOC define this free time according capacity constraints, profit maximization, container traffic patterns or other consideration (for example differentiation between transit and domestic goods), and they tend to reduce it for example when facing high congestion patterns. Free time in DIT has been set at **11 days** since the signature of the concession contract in 2005, a somewhat long free time given congestion patterns in the port.

> 4 See for example, Benchmarking of container terminals, Gordon Rankine, Container Port Conference – Rotterdam, February 2003 at http://www.beckettrankine.com/downloads/BCT.PDF

> 5 Roundtable on the Douala port reform, Douala, November 1997

6
# **Table 5:  Educational Characteristics of Net Gainers**

||**Primary: Te**|**rminal Effici**<br>**(%)**|**ency rate**|**Lower Sec**|**ondary: gros**<br>**rate (%)**|**s enrollment**|
|---|---|---|---|---|---|---|
||**Big**<br>**Losers**|**Medium**|**High**<br>**gainers**|**Big**<br>**Losers**|**Medium**|**High gains**|
|Formula I|90.0|77.6|80|84.6|69.4|69.2|
|Formula II|92.3|80.1|77.3|91|70.3|69.1|
|Formula III|86.7|84.6|73.6|88.4|71.2|68.1|

Two observations should be made at this point. First, even with the changes introduced by the government in the way Federal transfers are allocated, the need for improving the current allocation formula remains an important issue. The reason for this is that because the new methodology uses mainly an “inertial formula” (that is, states get at least the same amount of allocation as the previous year’s, adjusted for the rate of inflation), past inequities tend to persist. Moreover, giving States budgetary increments based on the number of new schools and teachers needed tends to penalize more effective resource users and, hence, undermine efficiency. The second observation is that at present individual States have yet to confront the issue of intrastate allocation. Application of the above methodologies to this issue can be useful.

# **III.- Conclusions**

The above formulae can be criticized in that while they clearly provide incentives for increased enrollment, they do not similarly reward quality of education. Hence, it might be argued that student learning might be undermined as a consequence. The size of this effect is not known empirically. Such concern, however, should not prevent the use of the formulae just because it is not perfect. The formulae should be compared with the existing practice which neither rewards enrollment nor learning outcomes.

In theory, such concern can be dealt with by adding a third component to the calculation of the budget. This component would fund rewards to states according to student learning improvements gained during some specified time period. Even with this type of incentive system, there is concern too that it would be unfair to states with relatively more disadvantaged children.

10