rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 5. Quartiles of predicted probability of formality by model and data source, informal firms**

|**Predicted probability of formality:**|**<25%**|**25% to <50%**|**50% to <75%**|**75%+**|
|---|---|---|---|---|
||**Model**|**1**|||
|**ELMPS 2012 in est.**|5.8|26.2|62.4|5.5|
|**ELMPS 2018 in est.**|6.8|42.6|46.7|3.8|
|**Economic Census**|48.4|39.8|11.0|0.8|
|**Establishment Census**|30.9|51.7|16.1|1.4|
||**Model**|**2**|||
|**Economic Census**|50.2|39.0|9.9|1.0|
|**ELMPS 2012 in est.**|19.7|37.0|30.6|12.7|
|**ELMPS 2018 in est.**|17.3|44.1|30.1|8.5|
||**Model**|**3**|||
|**ELMPS 2012 in est.**|27.2|36.5|23.2|13.0|
|**ELMPS 2018 in est.**|30.1|37.8|23.3|8.9|

Source: predictions based on models in Table 2, Table
|X|

Notes: In all columns the outcome is the average number of monthly fires in a district averaged over the years 2003-2005. In column 1 each row is a seperate regression. In column 2 all covariates listed down the left are included in the same regression. In all cases the specification is a fixed effects poisson regression. All independent variables are measured as Z scores so that units are comparable. Per Capita GDP (Ag) reports the average per capita value of (agricultural) GDP in the district in from 2003 to 2005 measured in Lakh |. Average Area Planted in Wheat, Rice, and Sugarcane measures the district average area in each crop from 2003-2005 in 1,000s of hectares as reported in the ICRISAT Meso data (Rao et al., 2012). Total area reports the total area in all other crops also in 000s of HA from the same data. Combines reports the number (in 000s) of self-propelled combines in the district as recorded in the 2006 Agricultural Input Survey. The Share of Holdings in each size class reports the share of acreage in the district in holdings within each size class in 2005 as reported in the ICRISAT Meso data. All columns include state fixed effects. Standard errors clustered at the state level are reported in parentheses.

37
welfare by the household income or expenditure recorded in surveys (similar to Milanovic’s earlier work) and do not distribute total income from national accounts like others have done (for a discussion of one such method, see Piketty et al 2018). An advantage of our approach is that our inequality measures are consistent with the global poverty measures produced by the World Bank.

Due to the paucity of household survey data for 2020, despite our best efforts of using alternative data sources, the estimate of inequality and poverty in 2020 must be viewed with a higher level of uncertainty than usual. Even among the countries that conducted traditional household surveys, many were forced to modify the survey design, change the mode of the survey – from face-to-face interviews to phone interviews – or capture one-time transfer payments traditionally not accounted for in household surveys. Neither China nor India, which always play a large role in driving global results, have household survey data for 2020 available yet, further exacerbating uncertainty. The increases in both global inequality and extreme poverty are driven by India and the choice of methodology for data in India plays a large role in the variance of the estimate. Yet, it is important to underscore that the qualitative results are robust to all available data options for India.

The rest of the paper is organized as follows. Section 1 outlines some of the possible mechanisms that could influence the impact of COVID-19 on poverty and inequality. Section 2 explains the data sources we use. Section 3 details our methodology to recover distributions for 2020. Section 4 covers our main results. Section 5 contains some robustness checks. Section 6 concludes.

# **1. Potential mechanisms**

The pandemic, its economic consequences, and the policy responses to these consequences have impacted inequality and poverty across and within countries in a variety of ways. Several channels work in opposite directions, and hence ex-ante the impact of the pandemic on countrylevel or global inequality and poverty is not clear.

Firstly, the virus itself may have impacted poverty. When
doubling farm size increases price by 10%, 11% and 13% at the 25<sup>th</sup> percentile (24 ha), median (452 ha) and 75<sup>th</sup> percentile (721 ha)), plausibly passing on some of the advantages it enjoys in terms of access to markets for capital or output. At the same time, results in column 5 suggest that buyers who already use some village land benefit from a discount the magnitude of which is increasing with the share of village area used: farms that operate less than one-third, between a third and a half, or more than half of village land, respectively, would, based on regression results, enjoy discounts of 19%, 23% or 25% relative to one that has no operational presence in the village. F-tests rejects equality of estimated coefficients for buyers with less and more than 33% and 50% of village land in columns 5 and 6, respectively, suggesting study of market power in sales markets, ideally by using all registry data to ensure robust coefficient estimates, is warranted.

# **5. Conclusion and policy implications**

Our analysis shows that administrative data on prices and parcel characteristics that are routinely generated but often not systematically captured by land registries offer considerable promise to improve analysis of relatively rare events such as land sales or leases. Regulations to ensure such data are captured and reported without bias and can be linked to relevant spatial and textual attributes thus contribute to an important public good that is particularly relevant in settings undergoing structural transformation or being exposed to shocks that may make traditional modes of data collection more difficult.

Three findings from the analysis are notable: First, land sales market opening increased competition, generating opportunities for landowners to benefit from higher rental prices. The fact that a quarter of 20
TCD TCD<br>NER NER<br>7 8 9 10 11 0 20 40 60 80<br>LOG GDP per capita, PPP (current international $) WDI 2019 Mortality rate, infant (per 1,000 live births) WDI 2018<br>Source: World Bank Source: World Bank<br>1 1<br>.8 .8<br>.6 .6<br>Adequate Housing Index .4 Adequate Housing Index .4<br>.2 .2<br><!-- End of picture text -->

**Note** :  Relationship between the _Adequate Housing Index_ and GDP per capita (current international $) as well as child mortality per 1,000 live births. The correlation coefficient for _AHI_ and GDP is .90; the correlation coefficient for _AHI_ and infant mortality is -.83. (Source: Authors’ calculation based on countries’ latest available household consumption and expenditure survey, WDI 2018 / 2019 (GDP per capita, PPP) and WDI 2018 (Mortality rate, infant)).

In presenting national _AHI_ figures, this paper relies heavily on the national representation of HCESs. As informal settlements or slum dwellers – HHs more likely to live in inadequate housing – might be inadvertently omitted from or underrepresented in these surveys (e.g., Carr-Hill 2013; Lucci et al. 2018), we assess the correlation between slum population and _AHI_ (Figure 3). The correlation is strong and negative, providing suggestive evidence that at least at aggregate level, under-representation of slum population does not affect the cross-country interpretation of _AHI_ .
Meza, 2002; Åstebro, 2003; Coelho, de Meza and Reyniers, 2004). Conversely, Caliendo, Fossen and Kritikos (2010) shows that a lower risk aversion is not connected at all with entrepreneurial survival.

If one takes into account the (often dominant) psychological attitudes discussed above (especially a desire to be independent, a desperate search for autonomy caused by frustration in the previous job, a fear of becoming unemployed), entry mistakes and excess entry can be further justified. In fact, the observed occurrence of these entry mistakes suggests an attitude which can be defined as a ‘try and see’ bet. In this view, new founders, mainly driven by a personal search for autonomy and job satisfaction, ‘visit’ a sectoral niche searching for business chances; later, they find out whether their entry decision was right or wrong and may decide to exit. Accordingly, market churning, turbulence and early failure, observed at a more aggregate level of analysis, see Section 2, emerge as normal and expected features of industrial dynamics.

These findings lead to the conclusion that several heterogeneous entry processes are simultaneously at play in the economy and that ‘opportunity entrepreneurs’, those bringing about innovation and economic growth, should be distinguished from ‘revolving door’ start-ups doomed to early failure and generating only precarious and temporary jobs.

Obviously enough, this distinction is _a fortiori_ crucial when we focus on the DCs, where ‘entrepreneurship’ and ‘self-employment’ often generate informal and very transient activities not so very different from ‘disguised unemployment’. In the following section an attempt will be made to correlate entrepreneurial characteristics with new firms’ actual potentials in terms of economic growth and job creation; special attention will be devoted to evidence from the DCs, when available.

# **4.** **<u>The post-entry performances of entrepreneurial firms</u>**

Since entrepreneurs are driven by both progressive and regressive determinants and are intrinsically heterogeneous, the post-entry performance of newborn
local leaders, etc.—together manage (or fail to manage) different types of conflict in different settings. Using a version of case-based process tracing (George and Bennett 2005, Varshney 2002), conflict pathways describe discrete stages in the evolution of conflict including conflict triggers and factors or mechanisms that sustain conflict, allow it either to escalate, stagnate, or move toward resolution. By following cases of everyday conflict, the factors that transform underlying tensions into different outcomes—violence or peace—become evident. In essence, the cases work backwards from an outcome by asking what led to what. Importantly, they were selected in a way that controls for some of the traditional weaknesses of qualitative approaches<sup>33</sup> while capturing each stage of a conflict, attempts at its resolution, and events that linked different stages. Each case study includes a summary of the case, its ”pre-history,” evolution, attempts at resolution, impacts, and aftermath.

**Figure 1 District and Sub-district Qualitative Sampling Frame**<sup>**34**</sup>

<!-- Start of picture text --> Nusa Tengarra Timor<br>East Java<br>(NTT)<br>High Capacity Low Capacity High Capacity Low Capacity<br>(Ponorogo) (Pamekasan) (Sikka) (Mangarrai)<br>KDP Non- KDP Non- KDP Non- KDP Non-<br>KDP KDP KDP KDP<br><!-- End of picture text --> Collection of the qualitative data used to construct these cases occurred in three stages and used a quantitatively oriented sampling frame in order to construct a plausible counterfactual. Propensity score matching<sup>35</sup> was used to “match” villages that received KDP with those that would have been likely to receive it but did not, further verified by qualitative interviews to

> 33 See Barron, et al (2004) for a more detailed description of
) data set by Muis et al. 2016. As it uses a digital terrain model that does not account for common errors such as stripe noise and tree heights (unlike for instance DTM MERIT), this map is expected to provide a lower bound or conservative estimate of global coastal flood risk. In particular, resulting from coarse resolution in modeling elevation, bathymetry and meteorological forcing, the data set underestimates extreme sea levels. In addition, the largest tropical cyclone-induced surges are not all included in the available observations after which storm surges were modeled due to the limited period of the available records (Muis et al. 2016). However, no global coastal flood maps are readily available at this point to address these shortcomings (specifically, no flood maps that offer different return periods based on modeled storm surges).

**Interpreting return periods: classifying the probability and intensity of natural shocks:** Natural hazard data typically distinguish _return periods_ to describe the spatial distribution and intensity of natural shocks. A return period describes how much time is expected to pass before a natural shock of the same intensity occurs again. Using historical data and based on the statistical frequency of a shock of a certain intensity, it describes the probability of such an event.

Figure 1 illustrates the extent of floods with two return periods: one with a 25-year return period (or a 1- in-25-year flood) and one with a 100-year return period (or a 1-in-100-year flood). The 1-in-25-year flood has a 1/25 or 0.04 annual probability of occurring. In other words, each year there is a 4 percent chance of such an event occurring, regardless of when the last such event took place. The probabilistic nature of return periods means that there is a 63.9 percent probability that a flood of at least this intensity will occur once within a 25-year period. But this also leaves the possibility for this event to not occur at all, or to occur several times. In comparison, a 1-in-100-year flood is a
interpretation, FONCODES then standardized the index by dividing all values by the lowest value. The resulting index ranges from a value of 1 to 36.38.

# _Imputed poverty and income_

In Peru, there are no survey-based estimates of income or expenditures at a level more disaggregated than the department: for example, household surveys conducted by INEI, which generally have sample sizes of 15,000 to 20,000 households, can only be used to compare income i This procedure had the unintended consequence that the greatest weight was given to those indicators with the greatest variance. Thus, while the intended weights were 50% for the rate of chronic malnutrition, and 7.14% each for the seven other measures, the actual weights in the index turned out to be 15.3% for the rate of chronic malnutrition, and 3.4%, 2.2%, 3.0%, 38.3%, 8.8%, 7.4%, and 21.6% for the measures of illiteracy, school attendance, overcrowding, inadequate roofing, and access to water, sewerage and electricity, respectively (World Bank 1996, p. 7).

4
|
|Panel C: Newly admitted in public IHEs in _thou_<br>|_sands_<br>||||
|Female<br>14.64|(3.03)|||19%|
|Male<br>41.39|(1.35)|||29%|
|Total<br>56.04|(3.37)|26%||25%|
|Panel D: Newly admitted in private IHEs in _tho_|_usands_<br>||||
|Female<br>9.23|(1.83)|||12%|
|Male<br>35.99|(5.65)|||25%|
|Total<br>45.22|(7.47)|20%||20%|
|Panel E: Average score for Kankor applicants (0|-360)<br>||||
|Female<br>186.81|(44.66)||||
|Male<br>193.23|(48.43)||||
|Total<br>189.41|(50.44)|0.97|||
|Panel F: Average score for admitted students in|public IHE|(0-360)|||
|Female<br>235.66|(47.04)||||
|Male<br>236.47|(46.05)||||
|Total<br>236.26|(46.31)|0.99|||

Note: the raw data is compiled from multiple sources, mainly Afghanistan yearly statistics books published by the National Statistics and Information Authority (NSIA), Ministry of Education (MoE), and the Kankor results for 2013–2018.

# **3.2 Gender quota**

The gender quota for admission to
, significant at the 5% level<sup>24</sup> ). The correlation between women’s and children’s nutritional status is weaker for stunting (r=0.14).<sup>25</sup> This is what we would expect if a woman being underweight and her children being wasted are caused by similar short-term shocks, while stunting is a more long-term condition.

> 19 Unfortunately, we are unable to exclude pregnant women for Tanzania’s consumption survey, as it did not ask women whether they were pregnant.

> 20 We also dropped observations with missing values for any variables used in the paper, such that sample sizes are consistently the same and comparable throughout the paper. However, we tested the effect of relaxing this constraint and found that it makes negligible difference to the results.

> 21 These variables are already constructed in the DHSs. For the consumption surveys, we use the Stata command zscore06 to convert height and weight values into a standardized value.

> 22 See, for example, Walker et al. (2007) and Hoddinott et al. (2008).

> 23 There are some discrepancies in the means between the two data sets, much of which is likely to do with the timing of the surveys, although differences in sample selection and measurement may also be contributing. 24 For prob.= 0.05, the critical value of the correlation coefficient is 0.306.

> 25 This weak correlation between wasting and stunting is not surprising (Victora 1992). Although there is some evidence that wasting in early childhood can cause subsequent stunting (Richard et al. 2012), the fact that stunting is a longer-term condition while wasting tends to be more transient points to different causative factors.

12
a high correlation between income and fiscal capacity, a transfer system like the one suggested above should also have strong redistributive effects on per capita income.

T he following regression is conducted to test the hypothesis that a transfer system equalizes per capita income across provinces: PFCTi = ao + a, PCGDPi where PC'Ti is the per capita transfer to province i, and PCGDPi is the per capita GDP of province i. If a, is negative and statistically significant, it means that the system has a significant equalization effect.

When per capita transfers are calculated using need figures unadjusted for cost differentials, a, is significantly negative. At the same time, the R-square is 0.55, implying that 55 percent of the variations of the transfers across provinces serves the purpose of "equalization." From Figure 1, one can see a strong negative correlation between the two indices, indicating a significant redistributive effect of the proposed transfer slystem. The regression results are as follows: Regression lll: PCTi = 544.26 - 91.26 PCGDP, (2.55) (-14.51) R.-square = 0.59, No. of observations = 30, Degrees of freedom =28.

When per capita transfers are calculated using needs figures adjusted for cost differentials, a, is also significantly negative. The R-square is 0.42. The two variables are plotted in Figure 2, which shows a slightly weaker correlation between the two indices than that in Figure 1. The regression results are as follows:

# Regression IV: PCTi = 518.77 - 83.93 PCGDP (1.93) (-4.54)

R-square = 0.42, No. of observations = 30, Degrees of freedom =28.

For comparison purposes, I used the actual transfer figures in 1994 to run the same regression. The resulting a, is statistically insignificant and the R-square is only 0.0002, showing not even a slight correlation between per capita transfers and per capita
# **1. Introduction**

The Syrian crisis has caused one of the largest episodes of forced displacement since World War II and some of the densest refugee-hosting situations in modern history. The Syrian Arab Republic’s immediate neighbors host the bulk of Syrian refugees: Turkey, Lebanon, and Jordan rank in the top five countries globally for the number of refugees hosted. According to United Nations High Commissioner for Refugees (UNHCR) data, in 2018, Turkey hosted 3.5 million Syrian refugees, Lebanon 0.94 million, and Jordan 0.68 million.<sup>4</sup> This paper characterizes service delivery in the context of a rapid influx of displaced populations in three different contexts: the arrival of Syrian refugees in Jordan and Iraq, and the influx of Syrian refugees and internally displaced Iraqis to the Kurdistan region of northern Iraq (KRI). In assessing service delivery, the paper includes measures both of access to services and perceived service quality. It encompasses infrastructure services, such as electricity and garbage disposal, and social services, such as health and education. The analysis is primarily based on quantitative data from the Syrian Refugees and Host Communities Survey (SRHCS), conducted in 2015–16. The SRHCS attempted a comparable and comparative analysis of forced displacement across countries with different policy regimes governing refugees (and internally displaced people). The goal was to evaluate the host countries’ main concerns about hosting refugees, and different strategies adopted to address the refugee (and more broadly forcibly displaced) influx.

In the three contexts considered in this paper, host county service delivery systems have played a key role in addressing the needs of Syrian refugees, especially those living outside camps. Education has largely been made available through the public schooling system, often with innovations like double-shift instruction, while health services have been made available through primary health care centers run by ministries of health, as well as through non-governmental organizations. Infrastructure services – electricity, sanitation, water supply – are by and large
household is involved in planting any commercial crops<sup>9</sup> that would also be considered a household.

Table 1 displays the percentage of households involved in the different income generating activities, and how these activities are combined. There are four main activities which we consider: non-farm enterprises (NFE), non-farm wage labor, farm wage labor (outside one’s household’s farm), and commercial crop production. A number of preliminary findings are worth noting. First, the majority of Nigerian farming households diversify according to the aforementioned classification. On average, depending on the GHS-Panel survey round and location, 60-65 percent of households have a portfolio that is not solely based on food crop farming. Second, NFE diversification activities constitute the lion’s share for all non-farm diversification activities. Diversification by pursuing commercial crop production (which is more prominent in the South than in the North) follows after NFE activities. The other two options are quite marginal. Households also combine more than one activity. The most common combination is commercial crop production and NFE ownership.

Table 1 also illustrates a sharp variation in the number of diversifying households between the two survey rounds; Northern households in particular increased their participation in NFE activities by close to 10 percent. A plausible explanation for this increase is the climatic shock faced by Nigeria in 2011, (Figure 1). The country, in particualr in the Northern area, faced severe droughts that started just after the Post-Harvest 2011 survey data were collected and lasted until a few months before the new survey round (Post Planting 2012) started (Bjerge and Fisker, 2016). The increase in diversification observed can be considered a a response to these unexpected climatic events.

The welfare measure chosen for the analysis is expenditures per capita. We expect diversified households to have higher per capita expenditures. The reason for this is that these households, as discussed above, should have lower income volatility, and should be less susceptible to shocks. Table 3 presents summary statistics by