rafmacalaba/datause-encoder-data huggingface dataset:
who borrow as firms under the program (Table 11, Panel B, Columns 1 and 2). These loans are distributed across firms of different risk categories. High-risk firms receive 7% of the guaranteed loans to formal firms, whereas low- and medium-low-risk firms receive 65% (Panel A, Column 3). That is, most of the loans go to safer firms. In fact, the actual distribution of credit under the program matches the weights that firms have in the economy according to sales (Figure 5). Therefore, the aggregate debt allocation across risk groups (including guaranteed and non-guaranteed debt) mentioned in the previous section is basically the same as the share that each risk group receives of the guaranteed credit reported in Table 11. For both total and guaranteed debt, larger, safer firms are the ones that receive the bulk of the credit, with their larger allocation given by their ex-ante weight in the economy according to sales.

One risk that pervades the program’s loan allocation is the loss from default of different tranches of the portfolio. This risk could be significant because the program targets firms smaller than the typically safe mega firms (even when some precautions are taken to exclude the riskiest firms in the country). To gauge the magnitude of this default risk and how it is distributed, we first estimate the default probability for 2020 for firms in each risk bin, defined by the different levels of coverage of the credit guarantee. To do so, we use the coefficients of the default risk model in Section 5 and plug in the 2019 information for the different regressors. This yields predicted default values for 2020 for firms that use the credit program across different categories. As expected, the predicted default probability declines monotonically with risk, going from 18.17% for high-risk firms to 2.05% for low-risk firms (Panel A, Column 4).<sup>27</sup> We then calculate a measure of total credit risk (i.e., expected loss) for each bin size by multiplying the dollar value of program loans by the predicted default probability. As a proportion of GDP, the total credit risk corresponds to an expected credit loss of 0.27% (Panel A, Column 8). The contribution to expected loss is similar across risk categories because the larger amounts granted to low-risk firms and their smaller default probability compensate across buckets. When considering all guaranteed loans to formal firms and natural persons, the expected credit loss is 0.44% of GDP (Panel B, Column 8), which corresponds to a 9.56% default probability of the guaranteed credit (0.44%/4.6% = 9.56%) (Panel B, Column 4). This estimate of expected loss seems large relative to the credit granted because it is 27A negligible fraction of firms do not have sales data and therefore cannot be classified by size. Likewise, a small fraction of firms do not have data to be classified in a risk category. To be conservative, we assume that firms with no sales or risk data (including naturals persons) have the default probability of the riskiest group of 23
**<u>Table 3.6. Irrigated area by irrigation source, 2001</u>**

||Area (hectare)|Share (%)|
|---|---|---|
|Total irrigated land|3,505,749|100|
|Well|1,316,709|37.6|
|Spring|352,403|10.0|
|Stream|1,003,856|28.6|
|Lake|67,666|1.9|
|Artificial lake|99,715|2.8|
|Dam|556,346|15.9|
|Othersources|109,052|3.1|

_Source: TurkSTAT (2004b)_

**<u>Table 3.7. Irrigated area by irrigation system, 2001</u>**

||Area (hectare)|Share (%)|
|---|---|---|
|Total|3,505,749|100|
|Flooding irrigation|2,865,356|84.1|
|Sprinkler irrigation|582,414|17.1|
|Dripirrigation|57,978|1.7|

_Source: TurkSTAT (2004b)_ To construct the regionalized SAM for the year 2003 (please see Table A.3 for a more aggregated version of the SAM for the year 2003), we combine various data from 2003 Agricultural Structure-Production, Price, Value Statistics (TurkSTAT, 2003); Telli (2004); 1998 Input Output Structure of the Turkish Economy (TurkSTAT, 2004a); 2003 foreign trade statistics by Undersecretariat of Foreign Trade (UFT Website); TurkSTAT 2002 Household consumption expenditures and income survey statistics (TurkSTAT Website), ARIP Quantitative Household Survey (QHS) commissioned by the Treasury and implemented by the G.G. Consulting et al. (conducted in 2002 and 2004), and the Turkish SAM for 2001 developed by GTAP. This regionalized SAM includes information on the regional level employment of labor and capital as well as intermediate input use by crop, along with water charge and irrigated versus rainfed land rents by crop. Employment of capital, labor and data on intermediate input use are also entered for activities at the national level in the SAM. Detailed income, consumption and saving information for the five-types of households (urban, and four rural) are included in the institutions block of the SAM. Also included in the institutions block are the data on government consumption, net tax receipts, and public savings, along with the WUAs’ accounts. Finally, import, export and tariff data concerning the EU-25 and the rest of the world constitute the 27
1991 and were largely unwanted by the Greek authorities. In fact, according to Baldwin-Edwards (2004), the Greek immigration policy since 1991 has been (and continue to be) fundamentally exclusionary, offering no realistic possibility of legal immigration for non-ethnic Greeks from the Balkan countries. The illegal status of Albanian immigrants contributed to the negative perceptions and even hostility of the host population. From their very first arrival in Greece, Albanian migrants were stigmatised as “criminals” by the Greek media and became stereotyped as “dangerous Albanians” (Karydis, 1992). Sociological studies report that, confronted with systematic discrimination, Albanian immigrants have adopted tactics to conceal or de-emphasise their Albanian or Muslim background, by assuming Greek names, being baptised, or pretending to be Christian in order to avoid prejudices (Hatziprokopiou, 2003; Pratsinakis, 2005). All these factors combined (cultural differences with natives, illegal status, negative stereotypes) may explain why Albanian immigrants had not experienced the same successful integration as the 1923 refugees.

# **3 Related literature and contribution**

## **3.1 Defining and Measuring Social Cohesion**

Despite its ubiquity in the literature, social cohesion remains a concept with no consistent definition. Social cohesion is variously described as the absence of social conflict (Durkheim, 1897), the “affective bond between citizens” (Chipkin and Ngqulunga, 2008) “local patterns of cooperation” (Fearon, Humphreys and Weinstein, 2009) and “the glue that bonds society together, promoting harmony, a sense of community, and a degree of commitment to promoting the common good” (Colletta et al., 2001). A contemporary definition that I will adopt for the rest of the paper is given by Kim, Sheely and Schmidt (2020): “a sense of shared purpose and trust among members of a given group or locality and the willingness of those group members to engage and cooperate with each other to survive and prosper”.

To operationalize this definition, this paper focuses on the presence or absence of major intergroup cleavages, which according to Chan, To and Chan (2006) and King, Samii and Snilstveit (2010) represents a key dimension of social cohesion, as strong divisions (whether by income, ethnicity, political party) can polarize the society and strain social solidarity. In the first part of the paper, I use different measures of refugees’ socio-economic integration and cultural assimilation to test whether the refugee inflow has caused the emergence of new cleavages (native vs. newcomer) within the Greek society as a whole. I notably use survey data on self-reported trust and selfperceived discrimination to measure the extent of social exclusion of the refugee population. I also use census data to measure educational and occupational inequalities between the two groups. In the second part of the paper, I focus on community-level measures of social cohesion, understood either as absence of societal conflict and division or, more positively, as civic participation. In partic7
||the public distribution system<br>and 0 otherwise.|||||
|---|---|---|---|---|---|
|Household males share|Share of working-age males<br>who are working(aged 15 and<br>over) in the household.|<br>0.177|0.113|0|1|
|Household females share|Share of working-age females<br>who are working(aged 15 and<br>over) in the household.|<br>0.029|0.078|0|1|
|Consumption per capita|Household’spatially deflated<br>monthly consumption per capita<br>indinars|<br>176,981|112,253|0|3,237,427|

# 3.2 Descriptive statistics

In the present analysis, we use the data gathered in the second round of the IHSES-II which is one of the most comprehensive surveys that provides information on household characteristics and living conditions in Iraq. The fieldwork was conducted between January 2012 and February 2013. The final data cover 25,146 households across 18 governorates of Iraq organized into 2,832 clusters for a total of 176,042 individuals. The respondent in each household is usually the head of the household or an authorized family member, although there are also questions on household members’ characteristics. One important feature of these data is the availability of information on household members’ migration history. It is this information that enables us to identify IDPs. These are people who have lived in another governorate – or their current governorate of residence but a different environment (rural or urban) – for an uninterrupted period of six months, and who have moved from that previous place for one of the following reasons:

- Forced displacement: Individuals who have been deported or forced out of their usual place of residence.

- Conventional armed conflict: People who lived close to battlefronts.

- Civil conflict: People who lived in areas where there is internal insecurity.

15
Policy Research Working Paper 8173

# **Abstract**

Railway transport generally has the advantage for large-volume, long-haul freight operations. Africa possesses significant railway assets. However, many rail lines are currently not operational because of the lack of maintenance. The paper recasts light on the impact of rail transportation on firm productivity, using micro data collected in Tanzania. To avoid the endogeneity problem, the instrumental variable technique is used to estimate the impact of rail transport. The paper shows that the overall impact of rail use on firm costs is significant despite that the rail unit rates are set lower when the shipping distance is longer.

Rail transport is a cost-effective option for firms. However, the study finds that firms’ inventory is costly. This is a disadvantage of using rail transport. Rail operations are unreliable, adding more inventory costs to firms. The implied elasticity of demand for transport services is estimated at −1.01 to −0.52, relatively high in absolute terms. This indicates the rail users’ sensitivity to prices as well as severity of modal competition against truck transportation. The study also finds that firm location matters to the decision to use rail services. Proximity to rail infrastructure is important for firms to take advantage of rail benefits.

This paper is a product of the Transport and ICT Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 3: Estimation results**

|**Selection equation parameters**|**Coefficient**|**Std. error**|
|---|---|---|
|Household size|0.103<sup>a</sup>|0.012|
|Age of household head|-0.007<sup>a</sup>|0.002|
|Education of household head|1.132<sup>a</sup>|0.212|
|Credit|0.336<sup>a</sup>|0.062|
|Extension|0.881<sup>a</sup>|0.059|
|Fertilizer distribution center|0.253<sup>a</sup>|0.061|
|Climate|0.002<sup>a</sup>|0.000|
|Price risk|-0.081<sup>b</sup>|0.039|
|Wealth|0.122<sup>b</sup>|0.059|
|Village Council membership|0.412<sup>a</sup>|0.085|
|**Selection-corrected regression parameters**|||
|Relative price|-14.272<sup>c</sup>|7.816|
|Use of organic fertilizer|9.916<sup>c</sup>|5.867|
|Area|-0.897<sup>a</sup>|0.159|
|Household size|0.021|1.131|
|Age of household head|0.078|0.215|
|Education of household head|24.532<sup>b</sup>|11.313|
|Wealth|9.736<sup>c</sup>|5.486|
|Weather|-14.246|9.399|
|Time effect|39.847<sup>a</sup>|7.210|
|**Error structure**|||
|Disturbance standard deviation, σ|0.008|0.641|
|Correlation between regression andprobit, �|-0.160<sup>a</sup>|0.043|

Note: The superscripts a, b, and c indicate significance at the 1, 5, and 10 percent level, respectively. The model was estimated using LIMDEP version 9.0.

18
Note that if _Z_ contains no elements, if _X_ 2 is set to the mean values for group 2, and μ<sup>ˆ</sup> 2 is set to zero, then _ˆ Y_ 2 corresponds to the standard estimate of what the mean of _Y_ 2 would be if group 2 members were treated like group 1 members, an estimate that is generally used to perform a Oaxaca-Blinder decomposition. Otherwise, this method should tend to bring the simulated earnings distribution for group 2 “closer” to the earnings distribution for group 1.

It is therefore of interest to see how much allowing the past to be changed, i.e., allowing educational attainment, number of children born, and occupational sector for group 2 to be determined by processes that are the same as group 1 faces, changes the current earnings outcome for group 2. If even this additional movement towards equalization of outcomes does not reduce earnings inequality significantly for the country as a whole, then it is difficult to make the case that earnings inequality is determined in any significant part by differences in treatment between the groups.

Our estimation of Z proceeds in three steps, as some actions are considered prior to others: we consider that first education is obtained, then children born, then (current) occupational sector entered. The first step in this process is to estimate education level for members of group 1 as a function of age, mother's level of schooling (when available),<sup>15</sup> and region of birth (when available; alternatively we proxy it using current geographic location). Then education is simulated for members of group 2 by using their values for age and mother's level of schooling in the education equation for group 1.

The second step, for women only, is to estimate the number of children for group 1 as a function of age, mother's level of schooling (when available), region, and education. Then number of children is simulated for members of group 2 by using their values for age, mother's

> 14 See Bourguignon, Ferreira, and Lustig (1998) for more exact details on how this methodology works. 15 For the Brazilian data, where mother’s level of schooling was available, we also carried out our process with an additional prior step, namely simulating mother’s level of schooling.  The results from this simulation are not substantially different from those reported below in the text for Brazil; the main difference is that even less of overall variance can be attributed to between-group variance once this additional leveling step is taken.

8
urban and rural areas. On the _x-axis_ , we have the monthly per capita household expenditure plotted against the cumulative distribution of population on the _y-axis_ . This figure provides a good illustration of first-order dominance. The cumulative frequency distribution of the population corresponding to the poverty incidence curve for rural households is uniformly above the cumulative frequency distribution of urban households for the period 2000 and the gap between the two curves is significant, suggesting that poverty is uniformly higher in rural areas (Ravallion [1992]).

**Figure 1: Poverty Incidence Curves for Distributions of Urban and Rural Per Capita Household Expenditure in LVL (2000)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30 Urban<br>20 Rural<br>10<br>0<br>2 52 102 152 204 283 509<br>Monthly per capita expenditure<br>Cumulative distribution of population<br><!-- End of picture text --> Source: Latvia Household Budget Survey (HBS 2000).

We also construct poverty incidence curves to assess the dynamics of welfare in urban areas (see Figure 2) and rural areas (see Figure 3). Similar to Figure 1, the cumulative distribution of the population appears on the _y-axis_ and monthly per capita household expenditures on the _x-axis_ . Note that for Figure 2, the poverty incidence curve corresponding to 1997 is uniformly above the curve corresponding to the year 2000. Moreover the gap between the two curves is significant over the support of the monthly household per capita expenditure range, suggesting that poverty is systematically lower in the latter year. However, a comparison of rural poverty incidence curves over time produced ambiguous results. Below the poverty line, the two curves intersect more than once. This reflects the relatively low improvement of living conditions in rural areas, and suggests that a number of poor households may have witnessed a deterioration of their welfare in the late 1990s, in spite of the general improvement during the recovery phase of the transition. By contrast, the rural poverty incidence curve for the latter year is everywhere dominated by that of the earlier reference period for _z_ > 32 , suggesting a larger increase in income at the higher end of the distribution in the latter year.

Surprisingly, in spite of the widening urban-rural income gaps— which may be a reflection of labor market segmentation where rural workers earn substantially less than urban counterparts for the same level of education and years of experience, the urban-rural poverty gap is not reflected in the outcome of the perception survey, which provides qualitative self13
parametric cross-entropy methods, but keeping it consistent with the United Nations’ population projections and levels of education observed in the household surveys.<sup>11</sup> To be consistent with the GIDD, LINKAGE is modified to adopt the former’s skilled-unskilled labor definition, whereby a skilled worker is anybody with more than nine years of education, and an unskilled worker is anybody with nine years of education, or less. This redefinition necessitates an adjustment of the GTAP data on value added by labor type in production, such that the number of workers of a given skill type in a given sector is consistent in the 2007 benchmark year across the two modeling frameworks.

# **3.2 Scenarios**

Using the simulation framework described above, seven alternative future scenarios are considered. The scenarios are designed so as to be able to identify the marginal impact of a given policy outcome as it interfaces with demographic change (Table 1). The policy outcomes are those related to the demographic dividend and include the maintenance and improvement of employment ratios, improvements in educational attainment and quality, and changes in savings behavior. To achieve this, first a “business-as-usual” scenario is considered to establish a baseline economic path till 2050 for the SACU economies. In the baseline, educational attainment rates are assumed to be constant, the total labor supply grows at the same rate as the working-age population (based on the same rate as the UN WPP’s medium fertility scenario), and employment ratios remain constant at 2015 values. The current account balances and investment shares track the forecasts of World Bank (2015b) till 2017 before converging to longrun values. Labor productivity is assumed to grow in a fashion that real GDP grows till 2017 following the World Bank (2015b), and following growth projections under the Shared-SocioEconomic Pathway 2 scenario of Dellink et al. (2015) from 2018 to 2050.

The alternative scenarios then deviate from this baseline scenario in a specific, but different, way. The differences in economic outcomes – such as real GDP per capita growth - are then compared against the outcomes in the baseline to determine the marginal impact of the demographic dividend enhancing policy outcome.

The first alternative scenario can be characterized as a _no demographic effects_ scenario. It differs from the baseline in the sense that the growing working-age population share, declining child dependency ratio, and rising aged dependency ratio do not lead to an acceleration of workers per capita or greater savings as shares of GDP.<sup>12</sup> Furthermore it assumes that the skill share of the labor supply is frozen at 2015 levels. So, the average skilling up of the labor force due to constant enrollment ratios and demographic change does not occur.

> 11 The GIDD model also projects the share of skilled and unskilled workers by using initial information on education available in the household surveys, following a methodology that assumes constant enrollment rate across age cohorts. 12 The total labor supply grows at the same rate as the total population.

12