rafmacalaba/data-use-mentions-extended huggingface dataset:
The two regressions reveal some notable differences. First, for manufacturing we find an impact from increases in foreign ownership of firm _i_ , where additional ownership increases employment growth (with no evidence that reductions in ownership lower growth). Second, we see that the impact from reductions in the horizontal spillover variable are attributable to manufacturing.<sup>31</sup> Conversely, the backward spillover variables are significant only for services. In addition, for services we now find significant impacts from both increases and decreases in the backward spillovers where, consistent with a mix of persistent learning and transitory leaning effects, the point estimate for entries is greater than for exits. It should be noted, however, that we cannot reject the null hypothesis for equality of these coefficients.<sup>32</sup> This result also lends some support to the notion that new entries may require time before their impacts are felt. If FDI in the service industry comes “on line” faster than that in manufacturing, something suggested by Davies and Desbordes (2016), then this may explain the difference in effects of new entries for manufacturing and services.

In columns (4) through (7), we focus on firm size, with (4) and (5) differentiating by employment (where small firms have less than 20 employees) and (6) and (7) differentiating by revenues (where small firms earn less than 80,000 Jordanian Dinar).<sup>33</sup> On the whole, we find significant spillover effects primarily for small firms, regardless of whether this is measured by employment or revenues. In particular, as in the full sample results, these seem to stem from horizontal and backward exits. One interpretation of these results is that, in order to benefit from FDI spillovers, a given firm must be sufficiently far from the technological frontier, i.e. that it has room to grow. Evidence for this is found in Chinese data by Hu et al
(4)|(5)|(6)|(7)|
|Protection|0.295|0.108|0.378|0.169|-0.007|0.013|
|Liberalization|0.295|0.108|0.264|0.273|0.007|0.056|
|Transparency|0.185|0.108|0.461|0.164|-0.141|0.064|
|Regulation|0.289|0.108|0.532|0.159|-0.128|0.055|
|Dispute Settlement|0.297|0.108|0.553|0.298|-0.095|0.100|
|Scope and Denitons|0.295|0.108|0.287|0.392|0.002|0.072|

Notes: This table reports estimates of the eects of a series of DTA investment provisions on FDI over the period 1990-2011. Each row in each panel corresponds to a separate econometric model. The results in panel A are based on the specication from column (3) of Table 2 after replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the six types of investment provisions from DCRTA. The results in panel B are based on the specication from column (4) of Table 2, where we also allow for dierential eects depending on the number of investment provisions (`depth') within each type. The dependent variable is always the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from columns (3) and (4) of Table 2, respectively. The
# **3 A New Regional SAM for Italy**

Here, some novelties concerning the cited works for Italy are proposed. In particular, the IRENCGE-DF model is built on a regional SAM with the following characteristics:

- 20 sectors at Nace Rev.2 level;

- 20 commodities by converting through a bridging matrix the 12 COICOP and 10 COFOG commodities included in the national SAM<sup>3</sup> ;

- 20 Italian regions at Nuts-II level<sup>4</sup> ;

- 2 factors: capital and labor at the national and regional levels, respectively;

- 4 agents: households, enterprises, central government, and local governments;

- 10 tax categories: Social Security Contributions (SSC) from employers; property tax (IMU); Personal Income Tax (PIT) at the national, regional, and municipal level; taxes on production; taxes on products; business tax (IRAP); Corporate Income Tax (IRES); taxes on commodities; value added tax (IVA); excises; taxes on imports.

At the current version, the Italian regional SAM refers to the year 2016. However, from a computational perspective, one of the key features of IRENCGE-DF is the ability to update the SAM automatically through a cross-entropy approach (Robinson and ElSaid (2000)) with a Higher posterior density (HPD) estimator (Britz (2020)). The SAM estimation module is separate from the model code and is run independently. Users can introduce new information (e.g., split any accounts in SAM, update macroeconomic totals or the whole of the macro SAM), and the code balances the SAM automatically. The code also allows for incorporating GTAP data to split the activities. Regionalization of the SAM is done in a separate process and is combined of two components: regionalization and balancing. The first component prepares the proto-SAM using regional data, while the latter balances it. The second component is the standard SAM balancing algorithm cited above (see Section
# **6. Summary and Conclusions**

Poverty has emerged as a significant problem in the transition economies. Although more widespread in the Former Soviet Union, much "transitional" poverty has proved to be difficult to eradicate even in Eastem Europe. The social protection systems of the transition countries have been inadequate to meet the challenges of transition, being both poorly targeted and costly. Although the open incidence of poverty increased everywhere during the transition period, distinctly different patterns of poverty emerged from the East European experience than from the Former Soviet Union. In general, poverty correlates are more- sharply defined _in Eastern_ Europe than in FSU, holding out the potential for better targeting in Eastern Europe.

In this paper, we undertook a comparative analysis of poverty in three East European countries and three FSU countries. We used the HEIDE data set, specially constructed for that purpose. The analysis consisted of three tasks: a profile of the incidence and depth of poverty using aggregate poverty indexes (Section 3); a multivariate analysis of the determinants of poverty (Section 4); and an empirical evaluation of the role of means testing and indicator-based targeting in poverty alleviation programs (Section 5).

We also raised a number of methodological issues. For the poverty profile, we used the well-known P-alpha class of poverty indexes, disaggregated along relevant socioeconomic and demographic dimensions. We opted, however, for a relative poverty line, rather than the more customary approach of absolute lines in cross-country research. In doing so, we put the comparability of the poverty profile ahead of the comparability of 99
0.38|0.43|-0.05|0.42|0.26|0.15|
|School attendance|0.36|0.36|-0.01|0.29|0.39|-0.10|
|Unemployment|0.14|0.17|-0.03|0.17|0.29|-0.12<sup>*</sup>|
|Legal identification|0.13|0.14|-0.01|0.18|0.21|-0.03|
|Early marriage|0.09|0.64|-0.56<sup>***</sup>|0.14|0.63|-0.50<sup>***</sup>|

Source: Authors’ calculations using the Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

Early marriage remains an important issue for both displaced and non-displaced females, as it displays the highest deprivation rate among women and exhibits the lowest deprivation rate among men. The results show a substantial gender gap in early marriage among the displaced population (43-percentage points) and hosts (35-percentage points). Among MPI poor households, the highest gender gap is also observed in early marriage, for both groups.

# **_5.3 Intrahousehold inequalities_**

Our final hypothesis investigates whether forcibly displaced households have higher rates of intrahousehold inequality than their non-displaced peers, and whether girls are more likely to be disadvantaged. We focus on four individual-level indicators – school attendance, primary school completion, unemployment, and legal identification. Displaced people experience multidimensional deprivations that result both from their current circumstances and the disadvantages they may have accumulated in their home country. For example, refugees fleeing long-term conflicts may not have completed 22
- Manna, G.C., 2010. Current status of industrial statistics in India: strengths and weaknesses. Economic and Political Weekly, pp.67-76.

- Moretti, E. 2010. “Local Labor Markets.” In Handbook of Labor Economics, Vol. 4, edited by V. Henderson and J. F. Thissse, 1237–1313. Amsterdam: Elsevier.

- MOSPI (Ministry of Statistics and Programme Implementation). 2013. Sixth Economic Census. New Delhi: Ministry of Statistics and Programme Implementatio <mark>n, Government of India.</mark> http://www.mospi.gov.in/sixth-economic-census

- Neumark, D., and H. Simpson. 2015. “Place-Based Policies.” In Handbook of Regional And Urban Economics, Vol. 5, edited by G. Duranton, V. Henderson, and W. Strange, 1197–287. Amsterdam: Elsevier.

- NSSO (National Sample Survey Office). 2012. National Sample Survey of India, 68th Round. Ministry of Statistics and Programme Implementation, New Delhi.

- <mark>ORGI (Office of the Registrar General and Census Commissioner). 2011a. Census of India</mark> _<mark>2011.</mark>_ <mark>New Delhi: Ministry of Home Affairs, Government of India. http://censusindia.gov.in/</mark>

<mark>______. 2011b.</mark> _<mark>Administrative Atlas</mark>_ <mark></mark> _<mark>2011.</mark>_ <mark>New Delhi: Ministry of Home Affairs, Government of India. http://censusindia.gov.in/2011census/maps/administrative_maps/admmaps2011.html.</mark>

- O’Sullivan, A. and Strange, W.C., 2018. The emergence of coagglomeration. Journal of Economic Geography, 18(
# **4 Research Design**

In order to test our hypotheses about the potential effects of IDPs on household and village level consumption and inequality we turn to an empirical estimation using data from 2011 - 2019, spanning the beginning, early, and middle periods of the current conflict in Mali. Our data provides fine-grained observations on households across more than three-quarters of the communes in Mali. In terms of methods, we take a broad-brush view of the appropriate methods, presenting different ways to measure the outcomes of interest-based on different assumptions. The point of this multi-method technique is to provide the most robust understanding of the outcomes across different issues of data quality and data generating processes. We first present the data, then the empirical strategy, the details of which are more fully outlined in the online appendix.

## **4.1 Data**

This work merges three main data sources on household consumption and IDPs to capture their effects on household and village consumption, poverty, and inequality. These are: a comprehensive household income and consumption data set (EMOP) collected by the same unit responsible for Mali’s Living Standard Measurement Survey (LSMS) data collection, data from the International Organization for Migration (IOM) on IDP movements, and data from the Demographic and Health Surveys (DHS). In addition, we access Malian census data and use conflict data from the Armed Conflict Location and Event Data Project (ACLED).

We use eight years (2011, 2013-2019) of Mali’s Enquˆete Modulaire et Permanente aupr`es des M ́enages (EMOP), a nationally representative household survey on employment, income, consumption, with indicators on demographics and well-being (Institut National de La Statistique, n.d.). This data set offers detailed documentation on household non-agricultural earnings and expenditures (consumption) which enable us to measure inequality and poverty. The EMOP data has 38,625 household observations in 607 of Mali’s 703 communes, with 14
on behalf of refugee beneficiaries.

# **III DATA**

# **III.A Data for Afghan Refugees in Tajikistan**

The primary dataset is a detailed census of Afghan refugees in Tajikistan carried out by the World Bank at the end of 2022, after the Taliban regained complete control of Afghanistan in 2021. The questionnaire administered to refugee families was comprehensive; it collected detailed information on the socio-economic background, migration history, labor market outcomes and current socio-economic situation of Afghan refugee household and their members, and some measures of socio-economic integration. The approximate duration of the interviews was about two hours. The sampling frame for the census is the administrative database of Afghan refugees registered with the United Nations Refugee Agency (UNHCR) in Tajikistan. All households recorded in the registry were interviewed, leading to a total of 1,958 Afghan refugee households and 9,763 individuals. Figure 2 displays the dates of arrival of the refugees in the census. As shown in the figure, the vast majority of migrants arrived to Tajikistan in 2021 when the Taliban gained complete control of Afghanistan.

We first use these data to describe the main socioeconomic characteristics of the population of Afghan refugees in Tajikistan and illustrate general patterns, as described in Appendix A. As shown in Figure A.1, the refugee population is disproportionately young and in working age. In terms of individual characteristics, the average age of refugees is approximately 25 years—with males being younger. Refugees have literacy rates close to 90 percent overall, but literacy is higher among males than females. Refugees completed 6 of formal education on with about 1 of difference between males years average, year and females (see Figure A.2).

Using this data, we also characterize the labor market situation of refugees before and after migrating to Tajikistan in Figure A.3. The data suggests that refugees are less in15
accounts, in particular how they could be used to set aside funds for the future (such as for school fees or agricultural inputs). The full script of the financial education session can be found in Appendix A.

The financial education session was deliberately provided to both treatment and control groups so that treatment effects could be attributed solely to the provision of the financial products, abstracting from the effect of financial education (for example, strategies for improved budgeting) implicitly provided during the product offer. For this reason, we can estimate neither

> 6 Tobacco is central to the Malawian economy, as it is the country’s main cash crop. About 70% of the country’s foreign exchange earnings come from tobacco sales, and a large share of the labor force works in tobacco and related industries. Despite its importance as a cash crop, the World Bank does not encourage its production (cf. OP4.76).

> 7 This number includes a number of “payroll” accounts opened in a previous season by OIBM and one of the tobacco buyer companies as a payment system for crop proceeds, and which do not actually allow for savings accumulation. Our baseline survey unfortunately did not properly distinguish between these two types of accounts.

6
||||
|Agglomeration premium: log employment||0.012|0.031|0.010|0.082+||0.006+++|
|density in district- or county-industry||(0.020)|(0.022)|(0.021)|(0.047)||(0.001)|
|Log share of local industry employment||-0.149+|-0.049|-0.088|0.015||0.007+++|
|in unorganized sector||(0.089)|(0.102)|(0.065)|(0.082)||(0.001)|
|Log share of local industry employment|||-0.301++|||||
|that is unpaid workers|||(0.140)|||||
|Observations|215,957|215,957|215,957|192,622|23,335|367,802|341,926|
|Adjusted R-squared|0.852|0.852|0.854|0.837|0.955|0.975|0.973|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Estimations consider simple production functions for manufacturing establishments in India and the United States, with country and time period indicated by column headers. Indian data are taken from Annual Survey of Industries and National Sample Statistics; U.S. data are from 1997 Census of Manufacturers. Metrics for Urbanization and Agglomeration Premiums are built upon the local density of manufacturing and own-industry employment, respectively. Metrics for shares of local activity in the unorganized sector use definitions shown in Table 1a; the U.S. estimations use the establishment size distribution to mimic the unorganized sector definitions for India. Estimations report
Lanka, some of the surveys do not specify a recall period at all when inquiring about employment. The majority of Sri Lanka’s HIESs have information about the usual employment status, while the LFS reports the weekly employment status.

Fourth, the dates when information was collected vary, even within the same type of instrument. For instance, in Afghanistan and India (NSS thin rounds), most of the analyzed surveys did not follow a constant data collection pattern. Another example is the Bangladesh LFS, for which data for the 2002-03 round was collected during a short span (less than six months) while data for the 2005-06 round was collected during a whole year (from October 2005 to September 2006). If there is seasonality in employment, figures from surveys with different data collection periods are not strictly comparable.

In some cases, the dates when information was collected allow generating quarterly employment data, in addition to annual estimates. Censuses are conducted over a very short period of time, and most of the household and labor force surveys record the month when a respondent was interviewed. In these cases, individual observations can be mapped to specific quarters. However, this approach is only used in the case of surveys in which interviews were spread across space and over time in a relatively even way.

Because of these four differences, the way we defined employment varied across countries and sources, as shown in Table 2. We provide even more detailed information in Table A2 in the Appendix, where we report the exact language used in the corresponding questionnaires.

6
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 61

# **Appendix 4. The Dynamic Interactive Vulnerability Assessment (DIVA) Model**

The Dynamic Interactive Vulnerability Assessment (DIVA) model (Hinkel et al., 2009) currently offers, to our knowledge, both the most detailed global scale representation of the coastal zone and the implications of sea-level rise and the most comprehensive and advanced representation of relevant processes at the global scale, including adaptation. It includes a global database which represents the world’s coasts (excluding Antarctica), using more than 12,000 linear segments with more than 100 parameters per segment. The DIVA model focuses on various aspects including coastal erosion impacts and adaptation (Hinkel et al., 2013), coastal wetland change (Spencer et al., 2016) and coastal flood impacts and adaptation (Hinkel et al., 2014). Here we will focus on flooding and use the same model as Hinkel et al (2014). The flood module can assess the impacts of increased coastal flooding on the population and coastal assets by comparing results obtained using various available data sources and adaptation strategies under a comprehensive sample of state-of-the art socioeconomic and sea-level rise scenarios. Flood risk is considered in terms of expected annual damage to assets, expected annual number of people flooded, and adaptation costs in terms of the dike and other defense investments and the additional maintenance costs to maintain the new defenses. The maintenance costs can be significant and are important to consider as it is important that such maintenance is included in long-term plans.

For adaptation, Hinkel et al. (2014) follow earlier studies, such as Hoozemans et al. (1983) and Nicholls (2004), and consider a common protection approach using dikes contrasting various adaptation strategies. One initial DIVA approach to adaptation was based on a demand for safety function (Yohe and Tol, 2002) which attempts to model human behavior based on empirical observation. However, the DIVA framework is flexible, and
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_18_ _Why Infrastructure Financing Facilities Often Fall Short of Their Objectives_ _46. Results._ The design phase of the facility has been a long drawn out process due to a lack of government coordination, the long lead time between the demand survey and the final implementation of the facilities and inconsistencies between developmental objectives being pursued by the government and commercial objectives being pursued by Bancoldex, the public banks in which the facility was to be housed.

_47. Lessons Learned._ The Colombian case study offers best practice in three important aspects: (i) employment of a detailed demand analysis that identifies impediments to private investment in infrastructure, (ii) provision of a limited number of instruments that are well targeted at specific non-commercial risks, and (iii) leverage of govemment funds. Nevertheless, it also highlights some of the problems governments face when setting up these facilities.

48. The concept of the facility and the design of the instruments were based on a detailed demand analysis, which helped to initially identify obstacles to private investment in infrastructure. However, the survey was not of sufficient detail to provide specific information as to the underlying factors that are constraining private capital flows to that sector. For example construction risk which was identified as a key impediment for private investment in toll roads. Yet the information gathered in the survey was of insufficient detail to identify specifically what part of the construction risk was the constraining factor. And the facilities was to offer a limited number of instruments targeted at specific risks-financial market interruption-and aimed at achieving a better risk allocation between the private and public sector and allowing the project company to insure itself against those aspects of the refinancing risks it has little control over. By the same token, as the exercise of the refinancing option is linked to good performance, the facility is insulated against such risk over which they have limited control, commercial risks. The limited number of instruments in combination with the limited number of sectors targeted also mitigate demands on the organizational structure and on internal technical capacity and make the management of
port area. Further, a new container port is also being planned for a 2,400 hectare site, at Barra do Dande, north of Luanda, which is in the process of being cleared of land mines (Nathan 2010).

**Table 11. Benchmarking port performance**

|**Performance**|Cape Town|Durban|East London|Port Elizabeth|Walvis Bay|Luanda|Beira|Maputo|Mombasa|Dar es Salaam|Tema|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Container dwell time—average (days)|6|4|7|6|8|12|20|22|5|7|25|
|Truck time for receipt and delivery of cargo (hours)|5|5|2|5|3|14|7|4|5|5|8|
|Container crane productivity (container per hour)|18|15|8|15||7|10|11|10|20|13|
|**Prices**||||||||||||
|Container-cargo-handling charge ($ per TEU)|258|258|258|258|110|320|125|155|68|275|168|
|General-cargo-handling charge ($ per tonne)||8|8|8|15|9|7|6|7|14|10|

_Source:_ AICD Ports Database 2008. _Note:_ TEU = 20-foot equivalent unit.

25