rafmacalaba/data-use-mentions-extended huggingface dataset:
from undertaking detailed subjective welfare dynamics by country before the Arab Spring and limited our ability to compare systematically the welfare dynamics with subjective and objective indicators. Furthermore, cross-country comparisons based on objective welfare dynamics metrics should also be interpreted with caution because the available household survey data were collected during different but overlapping time periods.

Several lessons emerge from this work. First, analysis of welfare dynamics using household surveys’ expenditure data do not always align with that based on subjective well-being data, as is the case in Yemen and Egypt during the pre-Arab Spring period. The results are qualitatively different for Syria and Tunisia where downward mobility is stronger than upward mobility, according to subjective well-being data, but the reverse was the case according to monetary welfare indicators. Thus, our study, can provide a potentially useful framework of analysis for future work in different country contexts, including in terms of defining the middle class.

Second, welfare dynamics based on expenditure measures indicate mixed trends. Slightly more than half of the poor in the first half of the 2000s were able to move out of poverty by the end of the decade. However, chronic poverty was also high, accounting for around 50 percent of total poverty in the region. Upward mobility was particularly strong in Tunisia and Syria, but downward mobility was most pronounced in Yemen and Egypt. Although the poverty rate in Syria declined dramatically, chronic poverty was still alarmingly high at the end of the 2000s.

Third, the subjective welfare dynamics suggest negative developments in most countries during the Arab Spring transitions. The share of dissatisfied people increased while those of the happier groups declined in almost all countries. This trends is particularly pronounced in the Arab Spring countries. Only Morocco shows positive dynamics with the size of the unhappy population declining by more than a quarter.

24
/09 1/10 1/11 -200<br>Total loans I.00 I.02 I.04 I.06 I.08 I.10<br>Non-financial corporations Total loans Nonfinancial corporations Households<br>Households<br>Source: CNB Source: CNB<br><!-- End of picture text --> From the beginning of 2002 until the middle of 2008, credit to the household sector grew by 30 percent or slightly more on a year-over-year basis (Chart 3), albeit initially from a very low base (Chart 4 ). Credit to the nonfinancial corporate sector exhibited a much slower dynamic, but between the beginning of 2006 and the middle 2008 it also rose rather fast (15–20 percent). Unlike the household credit, the nonfinancial corporate credit started from a much higher base, even though its absolute level at the beginning of 2011 was roughly the same as at the beginning of 2000.

Despite comparatively strong credit growth observed in from 2003 to 2007, the stock of bank loans relative to income in the Czech Republic was still relatively low at the end of 2007.<sup>2</sup> This applies not only to a comparison with the advanced euro area economies but also with the most economies from the Central and Eastern European (CEE) region. Chart 5 reveals that in

> 2 One limitation of this comparison is that it is based solely on data on domestic bank loans. This indicator may understate total private credit a bit, as it neglects loans provided by nonbank financial intermediaries and loans provided directly from abroad.

4
subsidy) and an export subsidy (or tax).<sup>20</sup> These measures, in turn, reinforce the original shock to world prices. The data used for quantifying the extent of trade policy interventions are taken primarily from the Ag-Incentives Consortium database reflecting changes in domestic and world prices for 57 countries and 68 agricultural and food commodities during 2005-2015.<sup>21</sup> Where data from the Ag-Incentives database were unavailable, alternative data were used from FAOSTAT, GIEWS and Fewsnet. Overall, this analysis covers 24 major food producing and consuming countries, using data on household income sources and spending patterns from 2011. Of these, 18 are EMDEs and 6 are LICs.

**Impact of policy interventions on global prices.** During the food price spike of 2010-11, world prices of maize, wheat and rice rose by 44, 39, and 6 percent, respectively, but domestic prices considerably less (Figure 4). Model results suggest that the combined action of government policies amplified global wheat and maize price increases, accounting for about 40 percent of the increase in world price of wheat and one-quarter of the increase in the price of maize. In contrast, combined policy action reduced the rice price surge compared to a non-action scenario.<sup>22</sup>

_Rice._ Some countries (e.g., Bangladesh, Nepal, Panama, Tanzania and Zambia) reduced trade barriers to partially offset the rise in world rice prices. However, important net rice exporters such as India, Pakistan, and Yemen implemented policy interventions that, ultimately, raised domestic rice prices more than the increase in world prices. In India, the world’s second-largest rice producer, quantitative restrictions imposed in 2007 initially prevented domestic price increases. However, the subsequent abolition of export quotas in September 2011 resulted in a surge in exports and a rise in domestic prices. In Pakistan, summer that affected of the land area and heavy flooding one-fifth country’s inflicted extensive
1.3 model 15

section). FDI Procedures measure the number of legal steps required before and after incorporation to start a wholly foreign-owned business. I construct the FDI Arbitration index by averaging indicators measuring i) the Ease of Arbitration Process and ii) the Extent of Judicial Assistance in resolving commercial disputes from the IAB data set. The Ease of Arbitration Process assesses whether there are restrictions on what the conflicting parties can or cannot do to resolve their dispute. The Extent of Judicial Assistance measures the role of domestic courts in assisting the process of arbitration and enforcing awards.

To control for the _general_ quality of institutions, I compute a composite indicator of five WGI developed by Kaufmann et al. (2010) with weights derived from Principal Components Analysis (PCA). The WGI variables are: i) the rule of law; ii) control of corruption; iii) regulatory quality; iv) political stability; and v) governmental effectiveness. Of the six WGI indicators, the one that I exclude is “voice and accountability” which is argued to capture citizens’ participation in selecting their government, as well as freedom of expression, freedom of association, and<sup>17</sup> The control for a general quality of institutions as measured by the WGI variables is important because I want to see whether FDI-specific regulations matter for FDI _over_ and _beyond_ the general quality of institutions. In other words, after controlling for the general quality of institutions, do FDI-specific provisions add any value?

Market size of the host country, proxied by GDP, is one of the most important determinants of horizontal FDI. Motives for vertical FDI are captured by GDP per capita (which proxies for average wage and the quality of infrastructure). Tariff

> 17 I concur with Thomas (2006) who argues that the concept of voice first articulated by Hirschman (1970) is not synonymous with accountability, freedom to select government, or other political freedoms. Nor is there a well-documented relationship between them. Overall, in terms of
Finally, sensitivity analyses demonstrate that the findings regarding transfer rights are robust to the implementation of hierarchical clustering with (i) simpler categorization (holding versus not holding) of rights variables and (ii) an expanded sample that includes individuals with land use rights, together with landowners. The inclusion of individuals with land use rights in the analysis sample is particularly important in Ethiopia, where 19 percent of the individuals within the cluster with mostly exclusive transfer rights are those with land use rights (constituting 50 percent of the sample of individuals with land use rights). Therefore, research that focuses only on self-reported landowners would miss out on a large portion of individuals who would otherwise hold transfer rights over their land.

Overall, our findings provide a more nuanced perspective on land ownership and rights among men and women across different countries in Sub-Saharan Africa. The analysis showcases the utility of machine learning techniques that can be applied to high-dimensional, intra-household, individual-disaggregated survey data on asset ownership to (a) identify the subset of rights that is essential to the conceptualization of land ownership under customary tenure arrangements and that is consistent across Ethiopia, Malawi and Tanzania, and (b) allow for a cross-country-comparable approach to profiling landowners across contexts with significant variation in institutions and norms governing land ownership.

31
726)|
|<br>Y:1 N:0|||||||||
|No. of Workers (logs)*WBL Index||||||||-3.368*<br>(1.732)|
|No. of Workers (logs)||0.533**|0.699***|0.743***|0.903***|0.902***|0.867|2.887**|
|||(0.243)|(0.257)|(0.280)|(0.291)|(0.291)|(0.655)|(1.271)|
|GDP per capita (logs)||13.906***|14.101***|15.962***|15.592***|5.876|4.688|4.791|
|||(5.151)|(5.346)|(5.348)|(5.748)|(5.710)|(5.768)|(5.740)|
|Herfindahl-Hirschman Index|||<br>-6.321*|<br>-4.724|<br>-8.467**|<br>-5.197|<br>-5.364|<br>-5.022|
||||(3.499)|(3.528)|(4.117)|(3.662)|(3.714)|(3.709)|
|Age of Firm (logs)|||<br>-0.522|<br>-0.914***|<br>-0.689*|<
necessary. Furthermore, we do not have evidence on the trading behavior of funds with different investment mandates. We thus have no strong prior about the impact of liquidity across mutual fund mandates.

A preference for low transaction cost assets might reflect other factors than informational frictions. If institutions are large relative to foreign stocks, they might prefer firms with low transaction costs. For example, Perold and Salomon (1991) and Chen, Hing, Huang, and Kubik (2004) argue that a large asset base might erode performance because of trading costs associated with price impact. In this case, firm size would not be capturing any effects related to information asymmetries. Moreover, because global funds tend to be larger than specialized funds, as reported in Table 1, transaction costs, particularly a price impact measure, should have a greater negative effect on the portfolio choices of global funds than on the specialized funds.

Lastly, we interpret the residual effect of firm size on mutual fund portfolio choices as evidence in favor of informational frictions, given that only a particular aspect of it might be captured by the liquidity and transaction costs controls. For example, these controls should not be associated with different costs of processing information between global and specialized funds. Hence, we test whether these residual effects are significantly different across mutual fund mandates.

To control for price impact, we use a country-level transaction cost variable measuring price impact from Elkins/McSherry. This price impact variable is calculated by comparing trades 30
these important trade costs are due to trade policy or to something else. Anderson and van Wincoop (2004), through a combination of direct observation and inferences from a gravity trade model, quantify the representative border-related trade costs for an industrialized country at 44 percent ad valorem and the representative transportation costs at 21 percent. In contrast, Hummels (2007, p. 136) suggests that the importance of policy barriers has been completely eclipsed by real transportation costs: “For the median individual shipment in U.S. imports in 2004, exporters paid $9 in transportation costs for every $1 they paid in tariff duties.” Although we are not be able to quantify the relative contributions of different forms of trade costs, we do hope to illustrate why many summary measures of trade policy are not appropriate for understanding the full economic importance of policy barriers to trade. Three simple examples help to illustrate the point. First, the real world consists of significant non-tariff policy instruments; these include a number of border policies that, while _technically_ applied as a tariff, are typically not captured in the reported data of ‘headline’ measures of tariffs. Second, trade policy is commonly applied in a non-uniform manner across trading partners. Third, applied trade policy can vary considerably over time, especially in response to the business cycle, movements in the real exchange rate, or due to trade volume shocks; nevertheless, these policy changes are not made through applied ad valorem import tariffs, but via some other, less transparent, policy tool.

In the coming sections, we document that policy barriers to trade still exist; they vary considerably across products, trading partners and time; they take many forms; and they arise under legal frameworks (established by international trade agreements) that can result in subtle differences in both the frequency of their use and of their trade-reducing potential.

Section 2 begins by analyzing the focal policy of the trading system - the import tariff. This section examines different dimensions of tariff data for our sample of
explicit development programs – using Brazilian data to implement the initial stages of the analysis, and plotting a course for continued research.

# **_Regional Disparities in Brazil_**

Brazil has a long history of regional disparities. The Brazilian Northeast has historically been the poorest region in the country, with regional per capita incomes being about one half of those in the prosperous Southeast (Lall and Shalizi, 2003). Figure 1 describes the 25<sup>th</sup> , 50<sup>th</sup> , and 75<sup>th</sup> percentiles of the Brazilian monthly income distribution by state based on 1991 demographic census data. The highest median per capita household income ($79.93 per month in Goias) is nearly four times larger than that of the lowest median per capita household income state ($20.49 per month in Maranhao). Eight states report median per capita household incomes of less than $1 per day, seven of which are located in the country’s Northeast region. Of the ten poorest states in the country, eight are in the Northeast, and two in the North region (Azzoni et. al, 2002). These large per capita income differences across regions are, moreover, surprisingly stable over very long periods. Per capita income in the Southeast was 2.9 times that of the Northeast in 1939 and 2.8 times in 1992 (World Bank, 1998).

Beyond purely economic indicators, social indicators in the Northeast region are also considerably worse than the national average. The illiteracy rate is at least three times higher than that of São Paulo, the child mortality rate is twice as large as that of the Southeast (54.5 per thousand in the Northeast compared to 26.3 per thousand in the Southeast), life expectancy four years shorter, and income inequality is much worse (Ferreira, 2004). Inequality, measured by the Theil coefficient is 0.80 for Ceará, Bahia and
Beyond agricultural production, demand is also expected to increase for land to provide green energy or environmental services such as carbon storage. An early program subsidizing afforestation in Chile in <mark>19862011 seems to have realized neither economic nor environmental benefits-it triggered a</mark> decline in rural population and increased unemployment (Jordán & Heilmayr 2022), reducing <mark>biodiversity without higher carbon storage in above-ground biomass (Heilmayr</mark> _<mark>et al.</mark>_ <mark>2020), suggesting that careful design and possibly a combination of such programs with safeguards on converting natural ecosystems will have benefits.</mark> <mark>Programs to provide environmental services can also increase l</mark> and demand and, if rights are ill-defined, dispossess current users (Kansanga & Luginaah 2019). Evaluations of PES schemes (Börner _et al._ 2017) suggest their performance can be maximized via (i) additionality, i.e., PES target areas that, without support, are at high risk of irreversible land use change; (ii) leakage is avoided, i.e., better conservation outcomes in target areas are not offset by worse ones in non-target areas instead; and (iii) effects are sustained over time (Chabe-Ferret & Voia 2021). While internal enforcement is a necessary precondition for conservation (van der Zon _et al._ 2023), it can be reinforced by external monitoring using remote sensing data.

Allocating PES subsidies via competitive auction can help reveal private information including on strategic complementarities between neighboring parcels (Lewis & Polasky 2018). Auctions have been implemented successfully in settings such as Malawi (Ajayi _et al._ 2012) where they increased effectiveness and reduced cost by about 30% (Jack 2013). PES also achieved temporary reductions in deforestation in Uganda (Jayachandran _et al._ 2016) that were economically viable at a cost of carbon of $39/t. If the size of incentives exceeds the opportunity
While our results imply that application of diaries may provide opportunities to complement other survey methods, they also point to ways in which training and/or supervision could help improve and thus yield greater consistency between the results from recall-based surveys and diaries, especially regarding the elimination of what appeared like worryingly high levels of discrepancies in reporting of any output from a given crop. For diaries, where no training was provided at all, marginal returns to minimum efforts in this respect to ensure consistency and alert enumerators or monitors of potential pitfalls appear to be particularly high.<sup>18</sup> Given the level of performance achieved even in the absence of any such effort, this could provide scope to add some elements to the diary, e.g. the identity of the person reporting and the plot from which output was obtained, to improve the quality of information and the scope for research. These could include the. Some of these lessons are applied and others tested in an ongoing multi-year survey program by the Uganda Bureau of Statistics in collaboration with the World Bank.

# **5. Conclusion**

This paper started out from the observation that, although a number of factors have made multi-purpose household surveys the workhorse for collection of socio-economic household-level data of many types, the quality of implementation matters and the scope they offer to collect good agricultural information on recurring small events such as the collection of small and seasonally varying amounts of continuously harvested crops often for home consumption, may well be improved. Three results stand out: First, the fact that, even in a country with a long tradition of conducting high quality surveys and a statistical agency that is generally considered to be among the better ones in Africa, data on agricultural output are subject to very large variation suggests that even relatively simple measures (e.g. training, quality audits, consistent coding) to improve data quality could have significant impact on improving the basis of data for decision-making. This points towards the need for a concerted effort to explore
|0.085|0.089|
|R2 (ANCOVA)|0.218|0.218|0.312|0.308|0.173|0.173|0.127|0.127|
|N (Post)|1,042|969|1,107|970|1,042|969|1,009|937|
|N(ANCOVA)|969|969|1,107|970|969|969|913|913|

_Note:_ ***p < .01, **p < .05, *p < .1. Formalization, banking, and bookkeeping are dummies, and each equals one if the firm is formalized, owns a financial account, or keeps books on sales and purchases. Innovation is an index, that is, the first component from a principal component analysis applied on 15 dummies each taking the value 1 if the firm introduced either of the 15 listed innovations. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head, and whether a member in the owner household
Bank 2020b), financial benchmarks in the World Bank’s Global Financial Development Database (Čihák et al. 2012), IMF’s Index of Financial Development (Katsiaryna 2016), or the recently developed Fintech Activity Index (World Bank 2021).

Despite their intuitive interpretation, equal weighting schemes are also criticized for their normative assumptions and subjective judgements, oversimplification, and missing the point of differentiating between essential and non-essential dimensions (Greco et al. 2018; OECD 2008; Paruolo et al. 2013). However, as we discuss below, other aggregation methods also have drawbacks and we consider the assumption of equal weighting to be least problematic given the lack of information in the literature and priors that could guide alternate weighting schemes.

If propositions of many multilateral institutions such as the UN on how to estimate the housing deficit are taken in absolute terms (i.e., a housing unit can only be adequate if all adequacy dimensions are present at the same time), we obtain what we call the _strict Adequate Housing Index (sAHI_ ). The _sAHI_ aggregates the adequacy dimensions based on the most lagging dimension and can be represented as a non-linear function: sAHIi,c = min{a1i, a2i ... a7i} (3) This strict _AHI_ considers an HH to live in inadequate housing if there is a deficiency on _any_ of the seven dimensions. With this method, the most lagging dimension in terms of adequacy drives how many housing units are currently in need for adequate housing. Structural deficiencies which need to be addressed at regulatory level (e.g., improving tenure security through digitization of tenure records, etc.) drive the _sAHI_ . While it has important implications for the realization of the Agenda 2030, the _sAHI_ does not reflect the intensity of the inadequacy of housing (i.e., how many dimensions are deficient) and therefore might be too simplistic for practical applicability. Results reported in the remainder of this paper are, therefore, based on the _AHI_ – if
and 5+ years after the reform. This entry rate is statistically significant for the 2-4 year period and falls just short of conventional significance levels for 5+ years. The lower panel shows no response for male-owned entrants other than a dip in pre-period. While our three data surveys do not have sufficient power to estimate year-by-year interactions with state-level clustering of standard errors, these estimates

> 16 The share of male employees in male-owned firms is stable at 97% during the period. This extreme and growing segregation of the employment bases by the gender of business owner is a particularly important topic for future research.

21