rafmacalaba/data-use-mentions-extended huggingface dataset:
coverage in 9 of Mali’s 10 regions. The 10th region, Kidal, is not included due to insecurity in the zone. The sample size is large enough to estimate commune-level inequality and 90% of the communes in the data set are sampled more than twice, giving us a panel at the commune level over time. Within a commune, the choice of household is random, such that at the household level the data represents a repeated-cross section. Furthermore, the EMOP data are largely representative at the commune level.<sup>4</sup> Combined with the sampling strategy at the commune level, these data provide us with consistent data to estimate effects at the commune level including both urban and rural communes.<sup>5</sup> The commune-level IDP data come from the Displacement Tracking Matrix (DTM) provided by the IOM (IOM, n.d.). These data derive from approximately monthly surveys on the number of IDPs in host communities from 2014 to the present. With this data set, we are able to calculate the total yearly number of IDPs and households in each host community identified by the IOM, and also construct an indicator variable of IDP presence. While the data collection involves multiple layers of data validation, this data set will mostly reflect the number of IDPs recognized by regional authorities, NGO representatives, civil society organizations, who are potentially beneficiaries of humanitarian assistance, as the IOM relies on such local actors as major informants. Therefore, IDP households who temporarily live with relatives to escape violence without seeking assistance are unlikely to be counted in the data.<sup>6</sup> The ethnic diversity measure we use as an instrument in some specifications is calculated

> 4The average number of sampled households in the EMOP data is 15 households. There are, however, two communes out of the 370 communes surveyed in 2019 with only one household interviewed in each village. These villages are Didieni in the Djidieni arrondissement in the Koulikoro region, and Niantaga
We do not have panel data spanning 1993-2004.<sup>26</sup> Thus we cannot measure welfare changes over time for those who were farmers in 1993 but landless in 2004. Nor can we trace farmers who became landless and moved to urban areas (which is also impossible with most panels). Observed changes over time will reflect (in part) the changing internal composition of given socio-economic groups. For example, if relatively worse-off farmers sell their land this will put upward pressure on the poverty rate among the landless. Comparing a poverty measure for the landless over time, we cannot say how much is due to changing living conditions among the initially landless versus low living standards among those who become landless.

We do not have data on wealth, and we use consumption expenditure instead (the same consumption measure used in measuring poverty and inequality). We use a measure of household consumption per capita that is comparable across the four surveys, is expressed in real January 1998 prices and is appropriately deflated for spatial and monthly price variation.<sup>27</sup> Consumption includes the imputed rental value of housing and the use value of durable goods (see World Bank 1995, 2000; Glewwe, 2003, 2005). Figure 2 gives the frequency distribution of log consumption per person for 1993 and 2004 (these will help interpret later results); the upper panel is for rural Vietnam as a whole. (The lower panel is for the landless, which we return to.) We have no choice but to use current consumption, which will reflect impacts of the land reforms after 1993. (Ideally we would have pre-intervention consumption but this would require panel data.) We will comment on likely biases arising from this feature of the data.

The land concept we use is the amount of “cultivated land” in the annual, perennial, water-surface and forest categories. We define a household as landless if it has no land other

> 26 The first two surveys contain a subset of the
|9. Bathroom|0.385|0.486|0|1|
|10. Telephone|0.269|0.443|0|1|
|11. Total living area (square meters)|65.24|47.929|0|2000|
|12. Number of rooms|2.371|1.498|0|96|
|13. Dwelling Structure(separate house, no)|0.637|0.480|0|1|
|14. Ownership of dwelling|0.739|0.438|0|1|
|15. Rural household|0.430|0.495|0|1|

Each household asset for which the information was collected though the 1999 Azerbaijan Census was assigned a weight or factor score generated through principal components analysis. The principal components analysis (PCA) is a dimension reduction technique (Chatfield and Collins 1980). This multivariate statistical technique is used to examine the relationships between a set of correlated variables.

57
2:  Cross-country Evidence of Scale Economies in Posts</u>**

||**Average for Total Letters De**|**livered Band**|
|---|---|---|
||_<1,000,000_<br>_1,000,000 –_|_>100,000,000_|
||_100,000,000_||
|Letters/Employee|3,587<br>20,693|59,739|

_Source: Calculated from UPU and World Bank data, 2001_ We can further examine the impact of scale economies by looking at estimates based on the United States Postal Service (USPS) on the costs of a US-style service in lower volume environments with lower input costs. The USPS will differ dramatically in terms of automation, route topography, mix of mail, efficiency, and the nature of its customer base—and so the model is only an approximation (made less accurate by a weak proxy for input costs—that of GDP per capita). While Cohen et. al. show that it functions reasonably well in estimating features of rich country postal operations, it is very likely that the adjusted model will become increasingly inaccurate as a guide when looking at low-income postal markets (Cohen et. al. 2003a). It is also worth noting that UPU data suggests a fairly weak relationship between GDP per capita and total costs per letter.<sup>6</sup> With those caveats, Figure 2 shows what the Cohen study and World Bank data suggests the cost of delivering a letter under United States’ USO standards would be in countries with lower mail volumes. As the number of pieces per capita drop below 100 per year, costs rise dramatically

> 6 The result will be due at least in part to the facts that labor costs are lower in developing countries and that most developing countries offer a lower quality, extent, and scope of services (not delivery to
# <u>Inputs:</u>

1. Household Survey with consumption or income welfare aggregates

2. Project data/Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys

# <u>Outputs:</u>

1. Set of imputed welfare variables for project data/other survey for each household in the data

2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest

# <u>Models:</u>

1. Ordinary Least Squares (OLS)

2. Probit 3. Multiple Imputation (MI) Table 29. Model Specification

||**Variables**|
|---|---|
|**Demographic**|Share of children,share of adults,share of adults squared and share of old(omitted)|
|**Characteristics of head**|Age, gender,and level of education|
|**Interactions with urban dummy variable**|Level of education of the head,age of the head|
|**Geography**|Dummies for regions at NUTS 1 level(12 regions)|
|**Interactions with Geography**|Level of education of the head, age of the head interacted with regions at NUTS 1<br>level(12 regions)and urban-rural division|

60
where conflict in t−1 denotes conflict events only during the previous year relative to the second year of each specific wave and, in the case of food insecurity, the previous calendar year. This specification has its own drawback because we are omitting the peak year of the Boko Haram insurgency (2014) and therefore are omitting some of the conflict events.<sup>28</sup> The timing is illustrated in appendix F, figures F.1 and F.2.

Tables F.1 and F.2 present robustness checks for the results on food insecurity (tables 2 and 3) and consumption (tables 4 and 5), respectively. We can see that the results are robust to this alternative lag structure. The effects are even slightly stronger for food insecurity, which is a more volatile measure of well-being than consumption, and violence also seems to matter in this case, a result arising clearly from such shorter-term dynamics. The stronger effects on food insecurity suggest that households might have already recovered from the events that occurred further in the past, and their effect on food insecurity in period t is diminishing over time, perhaps suggesting that there is recovery from the initial shock when it comes to food insecurity and violent events. There might be an initial drop in welfare, as we find in our robustness check, but over time the household might start recovering. Overall, our main results remain unchanged with the alternative lag structure.

# 6.   Conclusions

In this paper, we have analyzed the relationship between victimization and household wellbeing by using panel data from Nigeria. We exploit the time and spatial variation of victimization to analyze these effects within a household fixed effects framework using up to six rounds of data. We find that victimization negatively affects household welfare, measured by consumption and food insecurity. We also find that property-related events are more detrimental to household welfare than are violent events. Both types of acts are perpetrated by different groups. Insurgents, bandits and criminals, and clashes between farmers and herders
Moreover, a factor that will prompt further consolidation in Russia is the increased minimum capital required for banks, introduced by the CBR in February 2009, of 180 million rubles (6 million US dollars). Existing banks have to comply with this limit by January 1, 2012.

Using data from Interfax as of 2008, Table 2 shows the percentage of banks that can comply with the new capital requirement. While a majority of large banks, government owned banks, and foreign banks seem to have capital in excess of 180 million rubles, approximately 30 percent of non-top 20 banks, and a similar percentage of domestic and business-oriented banks do not appear to be able to comply with this requirement. In the case of individual-oriented banks, the percentage of banks unable to meet the new capital requirement is almost 50 percent. Banks unable to meet the new requirement will need to merge with other banks, transform into a different type of a financial institution which does not require banking license, or would be declared insolvent and resolved accordingly. This will have a significant impact on the number of banks and potentially on bank competition.

# **4. Banking sector contestability**

A concentrated banking sector can remain competitive if it is contestable, that is if entry and exit into the system is easy and if bank regulations and supervision promote a level-playing field across all banks. This section describes the regulations and practices governing bank entry and exit in Russia and discusses the implementation of bank regulations and supervision in the country. Furthermore, we compare the situation in Russia to that in Brazil, China, and India.

Bank entry in Russia has been historically fairly easy.<sup>**7**</sup> In particular, during the early 1990s the central bank issued a very large number of banking licenses. Between 1992 and 1995,

> **7** The information in this paragraph comes from Chapter 4 of the _OECD Economic Surveys: Russian Federation, 2009._

9
may be a more immediate priority than land consolidation. This is particularly so as consolidation efforts -which may be desirable in specific situations or needed in the future- can be carried out cost-effectively and sustainably only if they complement existing markets rather than trying to substitute for them.

The paper is structured as follows. Section two places Albania in a regional context by describing the nature and challenges of its transition and by using LSMS data to illustrate salient characteristics of its rural economy. Section three introduces the key questions to be explored by discussing relevant methodologies and evidence from the literature. Section four provides evidence on levels of technical efficiency in the agricultural sector and underlying determinants, the extent, nature, and proximate causes of cropland not being used productively, and the extent to which markets for rental and sale help to transfer land to more productive producers. Section five concludes by drawing out relevant policy implications.

# **2. Data and context**

To place Albania in regional and global context, we review the circumstances that led to one of the most radical land redistributions in Eastern Europe‟s post-communist transformation and characterize the emerging farm structure and the challenges associated with it. Data from the 2005 LSMS then help to illustrate inter-regional differences in farm structure and productivity, income sources, operation of factor markets, and migration as well as plot fragmentation.

# **2.1 Albanian history of collectivization and reform**

At independence in 1912, Albania inherited a _latifundia_ system that had land ownership concentrated in few hands. The 1945 Agrarian Law eliminated private property and transferred land use rights to small farmers who were then organized in production cooperatives which often formed the basis for state farms. Private ownership of land, even for house plots, was eliminated with the nationalization of all land in 1976 (Cungu and Swinnen 1999). Agricultural policy thereafter focused on achieving food selfsufficiency. However, productivity declined, resulting in food shortages and a severe drop in welfare. In the late 1980s, reforms under the “new economic mechanism
2011a: 50) echoes the earlier ones in concluding that “the relationship between the quality of a country’s PFM systems and donors’ use of them is at best weak.” This conclusion is based on simple bivariate analyses of data on use of PFM systems from the PDMS, and the indicator of PFM quality specified in the Paris Declaration (from the World Bank’s Country Policy and Institutional Assessments, or CPIA).

Based on similar methods but using somewhat different data, IEG (2011) and Ellmers (2011) arrive at a similar conclusion. According to IEG (2011), there is no association between the World Bank’s use of PFM systems and their quality in low-income countries, as measured by countries’ scores from PEFA (Public Expenditure and Financial Accountability) assessments.<sup>1</sup> The analysis was limited to only 17 countries, however, because it used PEFA to measure quality of PFM systems instead of CPIA, which is available for many more countries. Ellmers (2011) concludes there is no correlation between donors’ use of country procurement systems (as measured by the 2007 PDMS) and quality of PFM systems as measured by the CPIA. However, that study is limited to only 9 countries in which Eurodad has also sponsored case studies of procurement systems. Ellmers (2011), IEG (2011) and OECD (2011a) all present simple bivariate correlations, each based on a single year of data.

Using data from all three rounds of the PDMS, Knack (2013) showed that a given donor’s use of country PFM systems was positively related to (1) its share of the total aid provided to a recipient, (2) public opinion in support of aid provision in the donor country, and (3) the trustworthiness of recipient country systems. In measuring the latter, however, Knack (2013) did not employ the CPIA indicator of PFM quality specified in the Paris Declaration. Rather, trustworthiness of country systems was
grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Overall, these results are highly consistent with Schultz, 2004; Behrman, Sengupta and Todd, 2005, where enrollment was measured by self-reported household data. With respect to gender, the results do not show significant differences by gender in the enrollment impacts, that is, program impacts on enrollment are similar for both genders. Note that both Schultz, 2004 and Behrman, Sengupta and Todd, 2005 demonstrate higher effects on enrollment at the secondary level for girls than boys in the randomized evaluation sample carried out in 506 communities in seven states in the initial years of this evaluation. Nevertheless, follow-up results of this sample 20
activity in conflict settings. The main innovation of the paper comes from the reliance on non-traditional sources of data with a high degree of spatial granularity. Thus, nighttime light intensity is used to measure economic activity at the aggregate level, mobile phone traffic from registered firms for the formal sector, and the land surface devoted to poppy cultivation for illicit activities.

The results suggest that the main impact of conflict is not as much on aggregate economic activity as it is on its composition. Conflict escalation is associated with a major drop in formal sector activity, an increase in its informal sector counterpart, and a surge in illicit activities. Therefore, assessments that focus on indicators giving a disproportionate weight to the formal sector tend to overestimate the impact of conflict on economic activity.

19
|DVA-in-Services|(10)|-0.13**<br>(0.060)<br>0.13<br>(0.097)<br>0.18<br>(0.19)<br>-0.24*<br>(0.14)<br>0.32<br>(0.24)|8,187<br>0.997|: _ti_<br>_xjt_.<br>Log DVA<br> country (_j_). The<br>tiguity data from<br>rter-exporter pair.|
|---|---|---|---|---|
|ity Proxies:|(9)|-0.16***<br>(0.059)<br>0.12<br>(0.094)<br>0.17<br>(0.19)|8,187<br>0.997|at time _t_<br> exporting <br>e, and con<br>d by impo|
|with Grav|(8)|-0.21***<br>(0.053)<br>0.045<br>(0.16)|8,187<br>0.997|exporter _j_<br>ry _x_ in the<br>y, languag<br>re clustere|
|Linear IV|(7)|-0.16***<br>(0.058)<br>0.090<br>(0.082)|8,187<br>0.997|y _x_ against <br>on in indust<br>tance, colon<br>rentheses) a|
|xies|(6)|-0.099<br>(0
)|(0.0666)|(0.0808)||(0.0420)**|(0.0419)**|(0.0498)***|
|IDN exports per worker *||||-0.157||||-0.0166||||-0.158|
|Share of hhlds with computer||||(0.136)||||(0.124)||||(0.0971)|
|Observations|195|195|195|195|195|195|195|195|195|195|195|195|
|R-squared|0.884|0.886|0.886|0.888|0.948|0.948|0.948|0.948|0.925|0.926|0.926|0.926|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

Table 15. Indonesian Exports, ICT use, and Aggregate Labor Market Indicators. Female workers. 2005-2010

|Dependent variable:|La|bor force p|articipation ra|te||Employ|ment rate|
sectors and the disturbance term _ωt_ +1 because this includes unexpected shocks to wages, we follow Artuc, Chaudhuri and McLaren (2010) and use past wage dierences as an instrument. Given the assumptions of the labor choice model, past wage dierences should be uncorrelated with shocks at _t_ + 1 . The key identication assumptions for this IV to work are that i) the idiosyncratic shocks _ε_ are iid shocks; ii) the state of the economy evolves as a Markov process. The Markov assumption is straightforward.<sup>19</sup>

# Assessment and Goodness of Fit

We end this section with an assessment of the estimation. First of all, note that the observed moments and simulated moments (at the estimated parameters) are well-matched by the SMM. This can be seen at the bottom of Table 2.

Figure A1 below establishes that the SMM objective function is well-behaved around the solution and that a minimum is achieved. To show this, we plot the SMM objective function as a function of one of the adjustment cost parameters while keeping the other four parameters constant at their solution value. In each panel, the dashed vertical line denotes the solution to the minimization problem.

Figure A2 provides more intuition about how the SMM identies these dierent parameters. Each panel shows the response of the dierence between the observed and the simulated moments (squared) to a dierent adjustment cost parameter. In panel A, we show that moment 2 (the correlation between investment and protability) identies the xed cost parameter _γ_ 1 . The quadratic cost parameter ( _γ_<sup>2</sup> ) and the irreversibility parameter ( _γ_<sup>3</sup> ) are both identied from the opposing forces of moment 1, the serial correlation in investment, and moment 3, the negative spike moment (panels B and C). The labor mobility cost across sectors ( _Ce_ ) is identied from the serial correlation between
35 holding constant either other institutional variables - checks - or informational variables, the probability of forbearance drops by approximately 16 percent.

Regression 3 demonstrates that greater independence of radio and TV stations {like the independence of newspapers, not reported) has a negative and significant impact on the probability that governments forbear from taking action against insolvent financial institutions. The availability of trade statistics (in regression 4) also has a significant effect on the decision to forbear. The greater explanatory power of information variables is consistent with the notion that fiscal transfers, but not forbearance, are easily observed by even generally uninformed voters.

Again, the role of information could be conditioned on the presence of competitive elections. This assumption can be examined for Table 3 regressions by once again splitting the samples according to levels of electoral competitiveness. Coefficient sizes and significance are much higher in the sub-sample of countries where elections are most competitive, consistent with the theory that the effect of incomplete voter information operates through an electoral channel. The marginal effect of radio and TV independence (evaluated at the mean of all variables) rises from ~ 3 percent in the 15 countries that exhibit lower levels of electoral competitiveness to - 41 percent in the 21 countries that exhibit the greatest electoral competitiveness. The change in the coefficient on newspaper independence is correspondingly large, from -5 percent to -64 percent. The coefficient on the trade statistics variable rises from ~15 percent in the sub-sample that includes only less competitive countries to -30 percent in the whole sample, including both less and more competitive countries.34 The regressions in Table 3 do not control for institutional variables. However, media independence and the publication of trade statistics are themselves outcomes of government 34 In the most competitive sub-sample, the trade statistics variable perfectly predicts forbearance and