rafmacalaba/datause-encoder-data huggingface dataset:
113 marginal relatives from the rural areas to the town or (2) induced the worker to inc-ease the number of people supported in the extended family.

After excluding young workers living with ti,eir fathers and not in charge of a family, Pfeffermann (1968, p. 2W9) found, that the coefficient of correlation between total wage compensation and the number of people maintained by the worker was 0.319. That Is to say for every 1 percent increase in monthly earnings, the typical industrial worker would support 0.3 percent additional people. The relationship existed but was not very strong, as shown FIGURE 8.1 <!-- Start of picture text --> Se - hRdBeme<br>nd the Exanded Family, 1963<br>Number of<br>dependents<br>10-<br>10.<br>up to 11- 15- 19- 27- 3- over<br>10,000 14,999 18,99 22,999 26,999 30,999 39,999 39,999<br>Source: Pfeffennann (1968, p. 220)<br><!-- End of picture text --> in Pfeffermann's diagram (reproduced as Figure 8.1) plotting income against number of dependents.

1. Pfeftennann claims there is no evidence that the flow of money transferred by industrial workers to relatives in the country increases with income. If anything, the evidence suggests that younger workers tend to send larger amounts of money homne than do older oneL
The presence of onshore oil in many parts of the Niger Delta raises the financial viability of rebellion. A lack of government presence; longer distance from state capital; and lack of infrastructure, especially access roads; also reduces the risk of participation. However, higher educational attainment, higher income level from present employment, asset possession and/or asset-immobility, marital commitment, enrolment in a formal educational institution or on-going vocational training (apprenticeship), increases the individual’s opportunity cost of participation. Also, while larger oil endowment offers larger potential benefits from rebellion, it raises the prize to be won by both sides in a conflict, and thus tends to increase the intensity of conflict. In particular, larger oil endowment may increase the risk of participation through the “ _pre-emptive repression effect_ ”, whereby the government acts strategically to protect its control of a resource by using terror against the population (Ross, 2004a).

# **4.4 Estimation Procedure and Results**

# **_Estimating the Propensity to Armed Struggle_**

Table 5 presents the estimated coefficients of the explanatory variables and the associated z statistic for the _Logit model_ on the propensity to armed rebellion and the predicted probability of participation. The diagnostic tests show that the model fits fairly well and we can reject the hypothesis that none of the regressors determine the propensity toward armed conflict. About 36.18 percent of the sampled population revealed a propensity to take up arms against the state. This implies the existence of a potential rebel army size of 1, 262, 271 (or 24.19 percent of the overall male population) in the three states that constitute the heart of the Niger Delta<sup>32</sup> .

An examination of the results reveals five individual-level and four community-level characteristics that determine the propensity to armed conflict in the Niger Delta. At the individual level, the combination of a low income, low formal educational attainment, lack of marital bond, and lack of asset (or possession of highly mobile asset), reduces the propensity to armed conflict. (This agrees with predictions of the theoretical model developed in section 3).

> 32 The figure is derived as follows.  I estimated the population for the three states based on figures for 1998 provided by the  Federal Office of Statistics (FOS), Nigeria, using the formula, Pt = Poe<sup>rt</sup> , with r = 0.0283. With information provided on the sex and age distributions of the population, I was able to estimate the male population size for each age group in my sample.  Next, I found the percentage of the population in each age group in the sample that indicated a propensity to armed struggle and multiplied this by the projected population for the group. The final figure was derived by summing the figures for the various groups in the three states.

Since Bayelsa State came into existence after 1998, and was carved out of the other two, figures provided for Delta and Rivers States were used to estimate the population of the three states.

20
# I. The characteristics of the poor, the food poor and the malnourished

In this section we look at different groups of vulnerable households and we compare them across common dimensions such as: household characteristics, location characteristics, activity, assets owned, welfare, and transfers received. We compare the general Malian population to (1) the poor, (2) the food poor and (3) malnourished (households with at least a ‘stunted’ child; stunting is also referred to as chronically malnourished).

For the analysis of malnourishment, we focus on the subsample of households for which information on children’s nutritional outcomes is available. These are all the households with at least one child under the age of 5 years old. Using population weights, this subsample is representative for about 937,400 households or 10.8 million people (the total population of Mali was 14.5 million in 2010).

Overall, the prevalence of malnutrition in Mali is high. This counts especially for chronic malnutrition. 44 percent of households have at least one child that is chronically malnourished. This prevalence of malnutrition is even higher among the poor (59%) and among the food-poor (66%).<sup>v</sup> In 2010, the proportion of people in Mali who were food poor (lived in a household whose consumption per person was below the food poverty line) was 22 percent (or some 3.1 million people).

**Table 1:  Prevalence of malnutrition among the whole population, the poor and the food poor (%)**

|**Households with at least:**|**Whole population **|**Poor **|**Food poor **|
|---|---|---|---|
|one wasted child|18|23|25|
|one stunted child|44|59|66|
|Both|11|17|20|

**_Source_** _: Authors’ calculations using ELIM –MICS 2010_

Food poverty is positively associated with malnutrition. The graph plots in Annex 1 show that there is a positive association between food poverty and malnutrition at the district level in Mali. This relation is strongest for chronic malnutrition. The correspondence is however not perfect, and we find households who are food poor but not malnourished (upper-left part of the graph plots) as well as households who are not food-poor but who are malnourished (lower-right part of the plots). Furthermore, it is noteworthy that while food poverty affects malnutrition outcomes by restricting access to food resources, lack of access is not the unique channel to malnutrition. A proper utilization of the resources, as well as other environmental factors such as sanitation and access to health services, also influence nutritional outcomes.

Looking at the intersection between food poverty and chronic malnutrition, we find that 12 percent are both malnourished and food poor. Six percent are food poor but not chronically malnourished and 32 percent are malnourished but not food poor at the time of the survey. The remaining 50 person are neither food poor nor malnourished.

4
Then we compute squared yearly deviations of ranks from their medians for temperature and rainfall in each grid square; calculate sums of squared deviations (SSD) for each variable; and identify the year with lowest SSD as the benchmark year. By this “twostage least-squares” criterion, the temperature and rainfall for the benchmark year provide the best bivariate representation for the 21-year dataset in each grid square. The process generates nine benchmark annual datasets – CRU and eight GCMs – for each of the 372 grid squares.

# **2.3  Generation of benchmarked annual forecast ratios for the GCMs**

For each GCM, this entails dividing each observation in each future year by the GCMs counterpart observation in its benchmark dataset (most representative temperature/rainfall among the 21 years in the period 1980-2000). The resulting dataset contains 297,600 ratios (8 GCMs, 372 grid squares, 100 years).

# **2.4  Computation of GCM-based forecasts from actual historical data**

For each GCM, this entails multiplying each yearly ratio (from 2.3 above) in each future year by the counterpart observation in the CRU benchmark dataset (from the most representative temperature/rainfall combination, derived from CRU data for 1980-2000). This procedure serves two goals. It retains the relative changes incorporated in future GCM estimates, and it applies these relative changes to the actual point of historical departure: the benchmark series from the CRU data. This translation step is necessary because the GCMs reflect consistent, global-scale computations but are not designed to accurately reproduce local climate regimes. That is why we have developed R<sup>2</sup> -based “goodness-of-fit” scores in the first stage of this process.

5
||**(0.207)**|**(0.194)**|
|---|---|---|
|_Marketing_|||
|_Practices_|||
|Implementation of analysis of marketing trends|0.485**|**0.867*****|
||(0.227)|**(0.196)**|
|Implementation of analysis of marketing risks|0.630***|**0.898*****|
||(0.230)|**(0.226)**|
|Alignment of marketing and sales plan with business strategy|0.663***|**0.825*****|
||(0.216)|**(0.227)**|
|Monitoring of sale behavior and trends|0.719***|**0.901*****|
||(0.209)|**(0.224)**|
|_Production Practices_|||
|Implementation of strategic goals between plant manager and supervisor|0.616***<br>(0.176)|**0.966*****<br>**(0.173)**|
|Monthly monitoring of strategic goals between plant manager and supervisor|0.686***|**0.895*****|
||(0.215)|**(0.207)**|
|Strategic goals and roles clear to each worker|0.670***|**0.896*****|
||(0.166)|**(0.162)**|
|Each worker has improvement goals|0.562***|**0.892*****|
||(0.188)|**(0.170)**|
|Bottlenecks are identified and managed|0.514***|**0.842*****|
||(0.179)|**(0.194)**|
|Monthly measurement of plant KPIs|**0.822*****|**0.857*****|
||**(0.193)**|**(0.200)**|
|Weekly or bi‐weekly management of KPIs|**0.851*****|0.650***|
||**(0.227)**|(0.212)|
|Improvement programs for KPIs developed|**0.927*****|**0.989*****|
||**(0.223)**|**(0.220)**|
|Culture of visual management with graphs of machine performance|**0.810*****|0.515**|
||**(0.210)**|(0.212)|
|Supervisors and workers manage improvement plans for quality anomalies|**0.802*****|**0.944*****|
||**(0.187)**|**(0.215)**|
|Notes: robust standard errors in parentheses, clustered at the firm level. *** deno|tes significanc|e at the|
|1 percent level. Coefficients are treatment effects post‐intervention, and control f<br>randomization strata, and|or time effects,||

# Appendix 6: Robustness of Management Improvements to Sample Attrition

Table A6.1 shows the availability of our management score data by time period and measure. The greatest data availability is for the Anexo K measure, but this still suffers from attrition, while the WMS and MOPS data are available for subsets of the same only.

57
are available at all. Unfortunately, typically little if any effort is devoted to estimating, let alone reporting, the substantial margins of error in any other governance and/or investment climate indicators – objective or subjective, aggregate or individual. A key advantage of our measures of governance is that we are explicit about the accompanying margins of error, whereas in most other cases they are often left implicit or ignored altogether.

Despite these margins of error, our aggregate indicators are sufficiently informative that many cross-country comparisons of governance can result in statistically (and practically) significant differences. In comparing governance levels across countries, for example, we document that over 60 percent of all cross-country pairwise comparisons using the WGI for 2006 result in statistically significant differences at the 90 percent significance level, and nearly 75 percent of comparisons are significant at the less stringent 75 percent significance level. In assessing trends over time, we find that nearly 30 percent of countries experience significant changes over the period 1998-2006 in at least one of the six indicators (roughly evenly divided between significant improvements and deteriorations). This highlights the fact that governance can and does change even over relatively short periods such as a decade. This should both provide encouragement to reformers seeking to improve governance, as well as warn against complacency in other cases as sharp deteriorations in governance are possible.

The aggregate indicators that we report are a useful way of summarizing the very large amount of information embodied in all of our underlying data sources. The specific aggregation procedure we use also allows us to calculate explicit margins of error to capture the inherent uncertainties in measuring governance. At the same time, we recognize that for many purposes the information in each of our individual underlying data sources can be of interest to users. In many cases these provide highly specific and disaggregated information about particular dimensions of governance that are also of interest for monitoring and diagnostic purposes. For this reason we began last year, and continue this year, the practice of reporting on our website country scores on all of the individual indicators underlying our aggregate governance indicators. We report these disaggregated underlying indicators for the entire time period covered by the aggregate indicators, from 1996 to 2006.

2
# **Why Do People Move? A Data-Driven Approach to Identifying and Predicting Gender-Specific**

# **Aspirations to Migrate**<sup>*****</sup>

Daniel Halim World Bank Suneha Seetahul World Bank

**_Keywords_** : Migration, gender, machine-learning

**_JEL codes_** : F22, J16

* We thank Andrew Mason, Elizaveta Perova, Tobias Pfutze, Woubet Kassa, Emcet Oktay Tas, Jessica Ludwig, and seminar participants at the World Bank East Asia & Pacific Chief Economist’s Office Brown Bag and Africa Fellowship BBL for many helpful discussions and suggestions. All errors remain our own.

Data was collected by World Bank East Asia & Pacific Gender Innovation Lab and was funded through the Umbrella Facility for Gender Equality in partnership with the Australian Department of Foreign Affairs and Trade. The Umbrella Facility for Gender Equality (UFGE) is a multi-donor trust fund administered by the World Bank to advance gender equality and women’s empowerment through experimentation and knowledge creation to help governments and the private sector focus policy and programs on scalable solutions with sustainable outcomes. The UFGE is supported with generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, Latvia, the Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States, and the Bill and Melinda Gates Foundation.

The findings, interpretations, and conclusions expressed in this report do not necessarily reflect the views of the Board of Executive Directors of the World Bank or the governments they represent.

Contact information: World Bank Group, 1818 H Street, NW, Washington, DC 20433. Email: <u>dhalim@worldbank.org; sseetahul@worldbank.org.</u>
independence. At the same time, despite substantial progress, it would be useful to further increase transparency and accountability in the banking supervision procedures of the Bank of Tanzania, as well as the development of a risk based approach to banking supervision.

# **Insurance**

Although the structure of the non-life insurance market is competitive (but not the life insurance market), the insurance sector is not well developed. Current regulations impede the growth of the sector. These rules include restricting foreign ownership to two-thirds of capital, requiring insurers to be locally incorporated entities, not allowing foreign branches or wholly owned subsidiaries of foreign insurers, not allowing cooperative and mutual insurance companies and limits that make it less attractive for banks to participate in the distribution of insurance.

There are no foreign reinsurance companies operating in Tanzania. This is partly due to the fact that reinsurance arrangements have, at their core, compulsory reinsurance requirements to TanRe, a company indirectly owned by the government. Moreover, reinsurance companies are prohibited to operate as either branches or as wholly owned subsidiaries of foreign domiciled reinsurers. Reinsuring externally, and insuring for that matter, also requires approval.

## **V. Estimation of the Tariff Equivalence of the Regulatory Barriers**

Estimates of the ad valorem equivalents of the regulatory barriers in services are key to the results. In order to make these estimates, we first need to assess the regulatory environment in the services sectors in our model. We commissioned a 54 page survey of the regulatory regimes in key Tanzanian business services sectors, namely, insurance, banking, fixed line and 23
**Figure 6:** The red-square

<!-- Start of picture text --> PRY ECU<br>4 PAN GTM<br>BGD IDN ALB<br>KEN<br>THA CMR<br>MEX DOM<br>RUS<br>VNM PHL BGR MUS CRI MDAMNEBIHCOL ZAFTUN HND<br>2 DMA UKR IND JOR LCA BLZ BLR BRA TJK GEO SEN<br>GUY ARG AZE AGO<br>GHA NGA KAZ GAB<br>COG SWZ UZB MYS JAM<br>MRT MAR LSO DJI ARM TZA<br>0 BWA BOL ROU CHN SLV PAKNIC LBNSLB ZMB<br>VUT PER EGY<br>SRB<br>SDN<br>MMR<br>RWA<br>−2<br>TUR<br>IRN<br>−4 DZA<br>−4 0 4<br>Exposure to debt risk<br>Resilience<br><!-- End of picture text --> 16 percent accounts for upcoming redemptions. The rest is split between the remaining variables.

# **3.2 The Default Index**

## **Proxying government ability**

The fiscal solvency constraint states that if debt is larger than the sum of the discounted future primary balances, then a country defaults. This gives rise to a debt stabilizing primary balance, or the deficit required to keep debt equal to the previous period. One can use this measure and compare it to a country’s historical efforts (measured as past primary balances). If the required primary deficit is larger than the historical deficits, then the likelihood that a country will default increases. In most DSAs this information is captured through a ”fan chart” that summarizes the possible paths of debt as an indication of sustainability. If debt is rising (or if its colored fans are expanding upwards) then there is a chance that debt will not be kept fixed at a certain level - in this case the risk of default increases (and policy changes may not be sufficient to cover upcoming interest repayments). The fan chart is constructed by drawing random values from a joint distribution (usually real interest rates and GDP growth from a macroeconomic model) and constructing the subsequent debt paths.

One can invert the formulas and proxy it by calculating the probability distribution function of primary balances and contrast it to the required primary balance to stabilize debt. This is not a perfect proxy, but yields an estimate of a country’s default probability associated with ability (i.e., its past actions speak to its resolve to keep debt stable). A large 11
inefficient load allocation combined with short route distances could also play a role. Central American countries are geographically small and many routes are less than 150 kilometers long (See Table 10). However, with the exception of Panama, a majority of firms’ main service routes are long haul routes. Moreover, short distances do not explain the lack of efficient truck dispatch systems. Other time-related factors – in particular, low travel speeds and high wait times – can also reduce vehicle utilization. Travel speeds in the region average 71 kilometers per hour, with substantially lower speeds on national routes to or from national cities or to ports, where traffic congestion is a major issue (See Figure 8).<sup>19</sup>

**TABLE 10: PERCENTAGE OF ALL MAIN ROUTES** **<u>SERVED BY LENGTH OF ROUTE</u>**

||Short-haul (less than 150<br>kilometer National Routes)|Long-haul (National)|Long-Haul<br>(International)|
|---|---|---|---|
|Costa Rica|**6.7**|**17.5**|**75.8**|
|El Salvador|**14.0**|**1.7**|**84.3**|
|Guatemala|**11.2**|**16.3**|**72.5**|
|Honduras|**21.5**|**35.5**|**43.0**|
|Nicaragua|**12.0**|**33.3**|**54.7**|
|Panama|**70.8**|**14.4**|**14.8**|
|Total|**22.7**|**20.3**|**57.0**|

Note: Data are based on listing of main routes from survey and are not weighted by intensity of service.

Wait times are also substantial (see Figure 9), and are highest for Honduras and Panama. Although wait times are generally considered a problem at overland border crossings, by kilometer traveled they are higher for national routes – 3 minutes per kilometer relative to 1.8 minutes on international routes. Wait times on domestic routes are typically due to turn around and wait time at sea ports. In addition, for security reasons trucks may be forced to wait overnight to avoid night travel.

Nonetheless, low utilization and cannot be fully explained by low speeds and wait times. In the year of the survey, each truck was occupied – either with or without load, waiting, in traffic, or moving – on average 1,652 hours, or 18 percent of the year. Moreover, as shown in Figure 10, time utilization varies, and its distribution suggests that only a minority of trucks were fully occupied in the year of the survey. Thus for many, there

**FIGURE 8:  SPEED ON NATIONAL AND INTERNATIONAL ROUTES**

<!-- Start of picture text --> 80<br>78<br>76<br>74<br>72<br>70<br>68 national<br>66<br>international<br>64<br>62<br>60<br>km/hr<br><!-- End of picture text --> remains significant scope to increase served kilometers, even with current speeds and wait times. Similarly, whereas some empty backhaul is normal given geographic imbalances in merchandise flows, it 19Of course, slower speeds overall might be socially optimal, since trucks travelling at faster speeds carry risks for other motorists. However, this is unlikely in cases where slow speeds are due to traffic congestion or queuing.

14