rafmacalaba/data-use-mentions-extended huggingface dataset:
# _(ii) Real-time macroeconomic uncertainty_

<mark>Real business cycle data is permanently revised as new information becomes available with some lag which undermines the ability to identify economic activity fluctuations and to provide timely countercyclical support. P</mark> apaioannou et al. (2013) find that elevated macroeconomic volatility and market-risk uncertainty can make it difficult for investors to distinguish temporary price fluctuations from more fundamental changes in risk. Macroeconomic monitoring informs risk assessments by measuring expected losses, and Eicher et al. (2019) find that the poor quality of high-frequency data in many low-income countries can diminish the ability to grasp the timing of downturn and upturn cycles. In addition, frequent statistical revisions negatively affect the ability of analysts to separate cyclical fluctuations from secular macroeconomic trends, complicating efforts to precisely date expansions and recessions. In turn, the absence of precise information on the state of the business cycle in client countries may undermine efforts to implement a countercyclical investment strategy.

# _(iii) Mechanics of co-financing, sponsor and staff incentives_

Disbursement may be delayed due to pending completion of the financing plan (that is, the cofinancing partners transmit their cyclicality to IFC disbursement due to covenants which require other financing to be in place). Debt is more likely to be delayed by inability of sponsors to put in equity (or raise it from others) in a downturn. Equity may be deferred in a downturn because it is harder to agree on a valuation – sponsors may not prefer to take equity at what they perceive as temporarily lowered equity prices.

As internal investment targets are set only for commitments not for disbursements, staff incentives can be another potential factor behind commitment countercyclicality and disbursement procyclicality. Indeed, targets on commitments tend to favor a countercyclical pattern for commitments as they push staff towards continuing to meet commitment targets during downturns, as has been the case in FY21. However, in the absence of targets for disbursements, incentives to materialize commitments and accelerate disbursements in downturns are weaker, therefore, the cyclical pattern of disbursements is
**|35|116|308|17,017|64|
|**2013**|40|177|451|14,949|95|
|**2014**|39|225|481|15,507|112|
|**2015**|47|377|2,822|16,905|137|
|**2016**|61|301|885|20,143|100|
|**2017**|60|108|383|19,254|37|

Source: Own elaboration based on data collected by the World Bank.

<!-- Start of picture text --> COP/kWh<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Maximum price Minimum price Average price Yearly average price<br><!-- End of picture text --> Figure 6-5 Evolution of monthly pool price (hourly minimum, average and maximum) in Colombia.

Source: Analysis based on data collected by the World Bank.

40
We establish the extent of industrial and occupational segregation by using the Duncan index (Duncan and Duncan, 1955), defined as: where m� is the percentage of males in total males in category _i_ and w� is the percentage of females in total females in category _i_ . The value of the index varies from 0 to 1. The value of 0 implies that there is no segregation by category _i_ , and the value of 1 indicates complete segregation. The value of the index increases with the level of disaggregation. For example, Anker (1998) reports that the value of the Duncan index in France in 1990 was 0.393 when using single-digit standard industrial classification (SIC) categories for occupation and 0.607 when using three-digit SIC categories. In this analysis, we present the values for industries and occupations based on five sets of categories: public and private sector; 13 industry categories; 4 skill categories, 9 single-digit ISCO-88 categories; and 299 ISCO-88 four-digit occupation categories.

Table 2. Duncan index in Georgia.

||||Skill<br>(4|Occupation<br>(9<br>single-<br>digit ISCO-<br>88<br>categories)|Occupation<br>(299<br>categories,<br>four-digit<br>ISCO-88|
|---|---|---|---|---|---|
|year|public|industry|categories)||categories)|
|2004|0.2137|0.4489|0.3229|0.3763|0.7007|
|2005|0.1859|0.4458|0.3329|0.3737|0.6697|
|2006|0.1964|0.4987|0.3581|0.3897|0.7044|
|2007|0.2162|0.5176|0.3637|0.3903|0.
_Table A2.3: Impacts on S/E Skills Index and GPAs in Short-Run, Balanced Sample (Z-Scores)_

|||Gender||Ethnicity||||Academic Year||Pre-Treatm|ent Achievem|ent|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Average|Male|Female|<br>Macedonian|Albanian|Roma|Other|Sixth Grade|Seventh Grade|Tercile 1|Tercile 2|Tercile 3|
||<br>(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|_Panel A: S/E Ski_|_lls Index_||||||||||||
|Treatment 1|0.060***|0.034|0.087***|0.069**|0.029|0.225|0.104|0.057*|0.121***|-0.007|0.047|0.122***|
|“Student Self-<br>Learning”|_(0.023)_|_(0.030)_|_(0.026)_|_(0.027)_|_(0.054)_|_(0.182)_|_(0.104)_|_(0.031)_|_(0.029)_|_(0.033)_|_(0.033)_|_(0.030)_|
|
population, has a comparable absolute number of inadequate housing units (1.32 million), which amounts to 0.10 inadequate housing units per capita. Sub-Saharan African countries in the sample, which constitute about 95 percent of Sub-Saharan Africa’s population, have a cumulative stock deficit of about 97 million units. Hence, 37 percent of the housing deficit in the sample is accounted for by Sub-Saharan African countries.

An important consideration for the stock housing deficit is understanding the distinction between deficits arising among HHs whose housing units can be upgraded through the provision of infrastructure (e.g., water or sanitation services) or which can be refurbished or repaired (e.g., upgrading building material; better roofing etc.) from those HHs that may need an entirely new housing unit to overcome inadequacies. Although both aspects on inadequacy contribute to the housing deficit, they propose entirely different implications for policy makers and other stakeholders. Our data does not allow us to fully distinguish these two aspects contributing to the overall deficit. For example, consistent data on co-habitation and the number of households sharing the same dwelling are absent in HCESs. Nevertheless, to better understand the scale of the proportion of the deficit requiring new building construction, we assess the prevalence of severe over-crowdedness along the dimension of adequacy of living space in the AHI. In doing so, we assume that this dimension is most closely linked with co-habitation, and given data challenges,
areas with high unemployment as well as a higher share of right-wing voters.

These results hold several important lessons for policy. First, the German case shows that a large and sudden increase in the number of asylum seekers had only small effects on many dimensions of social cohesion – at least in the short run. Our results counter an often-expressed fear – and common argument against hosting refugees – that immigration undermines the foundations of societies in the host countries. This insight, in turn, holds lessons for the design of refugee policies. A fairer distribution of refugees across countries would require rich countries to host more refugees. Our results suggests that this can be done without major impacts on social cohesion.

A second lesson is about the differential regional impact of refugee inflows. In some regions – predominantly those with a strong regional economy and voters that are open to the idea of hosting refugees – the effect on anti-immigrant violence is non-existent. In other areas – often those with high unemployment and a large share of right-wing votes – we see a strong effect on anti-immigrant violence. This information can be useful for the design of assignment and integration policies. It is imperative to adopt policies that foster inclusiveness and empathy with immigrants, and to target areas that are prone to anti-immigrant violence.

A third lesson is about defining and measuring social cohesion. Social cohesion is an important characteristic of a thriving society, but it is neither straightforward to measure nor to define. Our results highlight the importance of considering multiple data sources as well as multiple dimensions of social cohesion in order to get a nuanced picture of the consequences of refugee inflows. If we only relied on survey data, we would conclude that the local presence had no effect on social cohesion. However, when we look at anti-immigrant violence – an extreme expression of undermined social cohesion – we see effects that should be a concern for policymakers.

# **2 Migration to Germany: Historical Context**

Our goal is to document the social effects of the immigration wave in 2015/16,
# 3. Anatomy of the Drop

## **3.1 Age Group and Female Labor Force Participation (FLFP)**

Looking at the drop in FLFP across age groups during the period 2004-05 to 2011-12, we find that approximately 53 percent of the 19.16 million drop had occurred solely within the younger cohort, among the age group of 15 to 24 years old (Table 6). Among the rest of the population, 32 percent of this drop occurred within the age group of 25-34 years, and about 15.6 percent in the age group of 35 years and above.

**Table 6: Distribution of Drop in FLFP Across Age Group During 2004-05 to 2011-12**

|Age group|Rural|Urban|Total|
|---|---|---|---|
|Between 15 and 24 years|(9,364,994)|(790,695)|(10,155,689)|
|Between 25 and 34 years|(7,362,792)|1,226,678|(6,136,114)|
|Between 35 and 44 years|(2,790,808)|525,240|(2,265,568)|
|Between 45 and 65 years|(1,189,800)|855,174|(334,627)|
|Above 65 years|(220,181)|(47,982)|(268,162)|
|Total|(20,928,575)|1,768,415|(19,160,160)|

Source: Authors’ own estimate based on NSSO and census data.

The FLFP rate across different age groups displays an inverted U shape for all years, as expected, in both rural and urban areas (Figure 1). The FLFP rate peaked in the age group of 35 to 44 years. In terms of change, between 1993-94 and
change in well-being between two nationally representative surveys conducted during the last resource boom. Specifically, we estimate the change in monetary poverty and access to several essential services using the 2009 Household Income and Expenditure Survey (HIES) and the 2016-2018 DHS. Figure 1 reports both the GDP per capita in current US $ over this time period and the real per capita GDP growth rates in each year.<sup>5</sup> The figure illustrates that per capita GDP in constant US $ increased by over 30 percent between 2010 and 2017 and that the average annual growth in real per capita GDP was 3.3 percentage points. And in a subset

> 5The GDP figures are obtained from the World Development Indicators; and the real per capita growth rates are obtained from the World Bank’s 2023 Macroeconomic and Poverty Outlook.

6
statistics tabulations ( _Defunciones no fetales_ ) were compiled by DANE. 2019 and 2020 tabulations are preliminary.

Historic (2015-2019) Mexico vital statistics microdata are those compiled ( _Defunciones registradas, mortalidad general_ ) by INEGI, 2020-21 deaths microdata based on death certificates compiled by RENAPO and accessed via the government of Mexico’s open data <u>platform.</u> 17
**Table 7.  Spread Decomposition by Size Quartile, Ranking Based on 2007 Deposits**

||Smallest|||Largest|
|---|---|---|---|---|
||0-25<sup>th</sup><br>Percentile|26-50<sup>th</sup><br>Percentile|51-75<sup>th</sup><br>Percentile|76-100<sup>th</sup><br>Percentile|
|Average Lending Rate|28.97|14.21|18.91|10.46|
|Average Deposit Rate|4.23|3.79|4.63|2.16|
|Spread|24.74|10.42|14.28|8.30|
|Overhead Costs|7.65|4.15|3.96|2.38|
|Loan-loss Provisions|1.33|1.26|1.19|0.45|
|Reserve Requirement|0.47|0.42|0.51|0.24|
|Taxes|4.59|1.38|2.58|1.57|
|Profit Margin|10.70|3.22|6.03|3.66|

Source: Own calculations, based on CBK data. Note: Cooperative, Consolidated, and KCB, and NBK are excluded from these calculations.

The pattern in Table 7 suggests that the largest private banks are more efficient, as reflected in their overhead costs, and more stable, as reflected in their loan-loss provisions. For these reasons, proposals to increase minimum capital requirements and thereby consolidate the sector by creating larger banks are being explored. Section 6 discusses this issue in greater detail.

While the size of private banks appears to have an effect on the efficiency of financial intermediation, bank ownership plays at least as important a role
Table A3: Robustness to imputing missing income responses by community mean

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.:Growth inper capita income(1|996-2011)||||
|Homeland (=1)|2.162***|2.300***|1.852***|1.472*|
||[0.567]|[0.577]|[0.563]|[0.753]|
|Share Employed mining1996|-0.523***|-0.354*|-0.779***|-0.198|
||[0.179]|[0.212]|[0.235]|[0.216]|
|Homeland*Share Employed mining1996|0.975***|0.897***|1.189***|0.621***|
||[0.257]|[0.289]|[0.263]|[0.227]|
|Observations|9,583|9,583|9,583|9,558|
|R-squared|0.585|0.593|0.631|0.716|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference
assessments of cost-of-living differences.

2.2 Experimental Design: Measuring the Responsiveness of Migration Intentions to Alternate Scenarios of Stabilization We attempt to measure the degree to which migration intentions respond to signals of political or economic stabilization through a randomized survey experiment. We introduce hypothetical scenarios of political and economic stabilization in the opening question described in the preceding sub-section to measure the willingness to migrate. The sample is randomly assigned to two treatment arms representing political and economic stabilization, respectively, or a control group that provides no signal of stabilization. Political stabilization is signaled by prefacing the introductory question with a brief description of positive political developments such that the question would read as, _“Imagine a situation:_ **_The political situation in Myanmar improves, violent conflict reduces substantially, and a political agreement is reached_** _. You are offered a job in a country of your choice which involves the same type of work that you currently do, and which pays you what your currently earn. Your employer at the new job will also help you with relocation, including any travel costs and visas. Will you take the job?”_ subsequent iterative questions follow and the three outcomes of interest are measured in the same way as described earlier.

Similarly, economic stabilization is signaled by prefacing the introductory question as, “ _Imagine a situation:_ **_The economic situation in Myanmar improves, growth picks up and prices stabilize._** _You are offered a job in a country of your choice which involves the same type of work that you currently do, and which pays you what your currently earn. Your employer at the new job will also help you with relocation, including any travel costs and visas. Will you take the job?”_ We then assess whether migration intentions vary with the prospect of political or economic stabilization by comparing migration outcome variables for these two scenarios with the control arm, i.e., the neutral framing of the question as described in Section 3. that does not make any
The estimated relationship between exports and employment at the local level is the result of decisions of individual firms. Firms might react to an exogenous increase in export demand in three different ways. Existing non-exporters could become exporters (the extensive margin), existing exporters might increase labor demand (the intensive margin), or existing exporters might increase exports by exporting products previously designated for the domestic market (having no net effect on labor-market outcomes). Additionally, exports might affect FLFP in similar ways. Female-intensive firms could either enter or exit the export market or increase production at different rates than male-intensive firms. Twosector trade models with heterogeneous firms and differences in factor intensity across sectors (Bernard et al. 2011) predict that rising export demand in one sector will increase demand for the workers intensively employed in that sector throughout the economy. While most manufacturing exports tend to be male-intensive, rising (falling) exports from female-intensive sectors could increase (reduce) demand for women and expand (reduce) female employment.

Simple models that incorporate firm heterogeneity and exports suggest that changes in employment concentrate along the extensive margin as new firms enter export markets. In this section we illustrate this result and then draw upon the rich firm-level data to complement our analysis. To illustrate these firm dynamics, we draw upon the simplified general equilibrium model by Melitz (2003) used in Berg et al. (2022). This two-sector model starts with a “reserve” sector _a_ , in which firms are homogeneous and produce using only labor with decreasing returns-to-scale technology: (4) aa The parameters and _λ_ characterize the labor demand function, and firms are assumed to ll = ff(κκ, λλ, ww) (4) The parameters and _λ_ characterize the labor demand function, and firms are assumed to be small and take the market wage as given. κκ The other sector is an “exporting” sector. In the exporting sector, firms are heterogeneous and differentiated by a firm-specific productivity parameter _φ._ They decide whether to shut down, produce
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 47 interest and of the auxiliary variables for all areas, _{_ ( _zdi,_ **x** _di_ ); _i ∈ sd, d_ = 1 _, . . . , D}_ , as well as a census with the values of the same auxiliary variables for all the population units, _{_ **x** _di_ ; _i_ = 1 _, . . . , Nd, d_ = 1 _, . . . , D}_ . The original EB estimator proposed by Molina and Rao (2010) additionally requires identifying the units in the census that are also in the sample for each area, a process that was not needed in the ELL approach. Linking the survey and census data files for each area is not always feasible in practice. Actually, the original EB method assumes that, for each area _d_ , the survey sample _sd_ is contained in the set of census units, which is regarded as _Ud_ . In practice, these two data files might come from different time instants, and the census set of units does not necessarily include the set of survey units. Hence, **y** _ds_ is not necessarily a sub-vector of **y** _d_ , _d_ = 1 _. . . , D_ .

Correa, Molina, and Rao (2012) proposed the _Census_ EB (CEB) predictor, which does not require identifying the survey sample units in the census file and delivers practically the same point estimate as EB, if the sampling fraction _nd/Nd_ is small. The CEB predictor of _δd_ = _hd_ ( **y** _d_ ) is the EB predictor of _δd_ = _hd_ ( **y** _d_ ), assuming that the augmented vectors **y** _da_ = ( **y** _ds,_ **y** _d_ )<sup>_′_</sup> , composed by the survey and
# FIGURE 3 Evolution of potential growth

## A. Potential growth

<!-- Start of picture text --> Percent<br>Potential growth<br>8<br>Actual growth<br>6<br>2000-2021 potential growth<br>4<br>2<br>0<br>World AEs EMDEs<br>C. Per capita potential growth<br>Percent<br>6 Potential growth<br>Actual growth<br>4<br>2000-2021 potential growth<br>2<br>0<br>World AEs EMDEs<br>2000-10 2011-21 2000-10 2011-21 2000-10 2011-21<br>2000-10 2011-21 2000-10 2011-21 2000-10 2011-21<br><!-- End of picture text --> B. Share of economies and GDP with potential growth below 2000-10 average, 2011-21 <!-- Start of picture text --> Percent<br>100<br>80<br>60<br>40<br>20<br>0<br>World Advanced EMDEs<br>economies<br><!-- End of picture text -->

- D. Global potential growth

<!-- Start of picture text --> Percent 2000-10 2011-21<br>5<br>4<br>3<br>2<br>1<br>0<br>PF MVF UVF For. UCM<br><!-- End of picture text --> Sources: World Bank, UN population statistics.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies. A.B.C. Based on potential growth derived using production function approach. GDP-weighted average. Sample includes 30 advanced and 53 emerging market and developing economies.

B. Number of economies and their share of global or group GDP with potential growth in each period below its 2000-10 average. Horizontal line indicates 50 percent. Unbalanced sample of 30 advanced economies and 53 EMDEs for
Learning from Developing Country Power Market Experiences: The Case of Colombia <!-- Start of picture text --> COP/kWh<br>200 35%<br>180 30%<br>30%<br>160 25%<br>24%<br>140 23% 22% 22% 25%<br>21%<br>120 19%<br>17% 18% 20%<br>100 15%<br>80 13% 12% 15%<br>60 10%<br>40<br>5%<br>20<br>0 0%<br>1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Average regulated contracts price<br>Average liberalized contracts price<br>Price premium of regulated over liberalized contracts (yearly)<br><!-- End of picture text -->

# Figure 6-3 Average price of contracts in the regulated and liberalized markets.

Source: Own elaboration based on data collected by the World Bank.

38
However, it is likely that the amount of such transfers will increase following the realization of shocks. For instance, in the Philippines, households with overseas migrants received higher remittances in the aftermath of floods (Yang and Choi, 2007). In this study, we improve on previous welfare impact studies by excluding food aid or in-kind and cash transfers received by households from the consumption per capita.

Various definitions and theoretical frameworks of vulnerability to poverty have been suggested in the literature (Calvo and Dercon, 2013; Hoddinott and Quisumbing, 2003; Pritchett, Suryahadi, and Sumarto, 2000). Even though there is no consensus as to which one is a superior approach, they all rely on the estimation of expected consumption per capita and variance of consumption per capita. Some vulnerability studies use panel data to assess insurance of consumption in the aftermath of shocks (Townsend 1994; Udry 1995). The main challenge with adopting this approach is the lack of nationally representative panel data, which is rare in developing countries where vulnerability and resilience matter the most. Indeed, most nationally representative data sets are cross-sectional, and some vulnerability studies utilize these rich data sets. For example, Chaudhuri (2003) interprets the residuals from a regression of consumption per capita on household characteristics as the variance of welfare due to shocks. The distribution of consumption is then assumed to be lognormal with variance being the one that was imputed to the shocks (Chaudhuri, 2003). An alternative to rigorously used cross-sectional data is to build a pseudo-panel before conducting the vulnerability estimation (Christiaensen and Subbarao, 2004). Other authors have designed methodologies bridging the gap between these two approaches. On the one hand, Gunther and Harttgen (2009) improve on Chaudhuri’s method by using a multilevel model that decomposes the error term into idiosyncratic and community shocks. This method relies on interpreting the residuals and may, therefore, be particularly sensitive to measurement errors (Gunther and Harttgen, 2009). On the other, Hill and Porter (2017) include