rafmacalaba/data-use-mentions-extended huggingface dataset:
Market house price data can often be obtained more easily and, most importantly, tend to be available in the public domain, in contrast to tax administration data which are subject to important confidentiality concerns and require cooperation from governments. Also, house sellers have no incentive to understate the value of their homes, in contrast to the income they report on their tax returns.

Using house prices as an alternative to income tax records demands two methodological innovations to the study of top incomes. Firstly, we will not be observing actual household income or expenditure (as is the case with tax record data), but rather a predictor of income. Secondly, a database with house price listings is generally not obtained using a particular sampling design. Therefore, the data are not guaranteed to provide a nationally representative sample, they will arguably be biased towards large urban centers. We will propose workable solutions to both these challenges that will hopefully contribute to a wider use of this approach. Note that the methodology is not restricted to the use of house prices, it can be applied to any database containing predictors of top incomes.

We illustrate our approach with an empirical application to Egypt which provides a good testing ground for our method. In addition to being a major Arab country, inequality in Egypt is of considerable interest not least because it has been cited as one of the factors behind the Egyptian revolution (Hlasny and Verme, 2013). Estimates of inequality based on household surveys suggest that inequality is low in Egypt and that it has declined in the last decade to a Gini of around 31 in 2009. Using house prices to capture top incomes we find that inequality may be significantly underestimated in Egypt. The Gini for urban Egypt in 2009 is estimated at 47 _._ 0 compared to a survey-only estimate of 36 _._ 4. Our results are in contrast with other studies using different methods of adjusting for top incomes in Egypt (Hlasny and Verme, 2013), which report a more modest effect.<sup>
or urban center vary from 20 to 21 percent, with an overall average of 21 percent (median 20 percent). The equality test confirms equality for the unit transaction costs and rejects equality for shares (both with the exception of the rural north, possibly because of the number of observations).

We attribute the rejection of equality for shares to the difference in the type of data used for this exercise. Survey data on purchase and sale prices reported by traders are (and should be) well below market prices. The available evidence indicates that market prices, on average, are 9 to 38 percent above the sales prices reported by traders. In addition, the trader data refer to a single completed trade transaction. Such documented transactions are likely to bias transaction costs upward. The observed differences between purchase and sales prices in the survey data also contain a component of trader profit that is not well captured in the estimations. Conversely, monthly aggregate district market prices are averages computed over one month and a range of transactions among a range of traders and other market participants. The process of aggregation 22
Policy Research Working Paper 6016

# **Abstract**

The crash of global financial markets in 2008 caused a ripple effect on economic demand and growth worldwide. Export-oriented economies were hit particularly hard, and many governments stepped in quickly with broad-ranging stimulus programs to lessen the effects on households of rising unemployment and falling income. To better understand the role that stimulus policy might play in softening the effects of these shocks, this paper examines recent nationallyrepresentative data from Thailand, an export-dependent economy where a large-scale stimulus program was introduced in 2009. Using monthly data spanning 2006– 2010, the paper uses sub-province-level community panel data to examine the effects of major components of the stimulus on household consumption, income, borrowing, and debt repaid. To address simultaneity of changes in government spending and household outcomes, the analysis estimates a dynamic panel regression, instrumenting the stimulus effect with second-order lagged outcome variables, and estimating the model using the Generalized Method of Moments. The results suggest that household participation in these programs helped smooth consumption. This increase in monthly consumption was not supported from household receipts from the government stimulus, but more likely through a reallocation of consumption and savings that included greater debt repayment. The paper typically finds stronger effects in urban compared with rural areas.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed
Countries face two types of constraints to mobilize revenues. One is the actual ability to raise taxes, which includes both whether resources are available to tax–the arithmetics of resource mobilization–as well as the capacity to collect those taxes. The second type of constraint refers to the notion that low fiscal revenues are often the result of a political equilibrium, where the incentives for stakeholders are aligned in such a way that there is a tendency to revert to such equilibrium. We refer to this as the politics of domestic resource mobilization. In this way, some countries may not have the resources to finance their own development; while the political equilibria in others may be such that, even though resources exist, it is unlikely that reforms will be passed to mobilize them towards reducing poverty.

This paper puts forth an accounting exercise to evaluate the feasibility to eradicate poverty using domestic resources–the arithmetic capacity–by mere redistribution. To assess the capacity of countries to eliminate poverty through redistribution we propose a new tool: an index of poverty eradication capacity. Our poverty eradication policy considers a set of transfers from those above the poverty (or affluence) line to those below the poverty line, aimed at filling up the shortfalls of poor individuals, without making anybody else poor and avoiding re-ranking.

Additionally, we propose a tool to estimate the concentration of political influence starting from the distribution of income in the society. The paper argues that the concentration of resources among few individuals can limit the feasibility of effective fiscal policies to eradicate poverty, both in terms of obtaining revenues as well as regarding social spending. We consider that richer individuals, who would lose out from redistributive reforms, would oppose their implementation; and that the strength of their political opposition is proportional to their income. To outweigh this opposition from the top, poorer individuals (the potential winners from redistributive policies) would need to form a coalition. Countries where political influence is more concentrated would need to mobilize larger coalitions of individuals to counter-balance the political influence in the hands
ensuing restriction in bank lending.

Regarding the first question, some authors provide evidence that banks change the composition of their assets away from high risk-weighted assets when capital requirements are binding for them. Studies on this issue have mostly focused on US banks: e.g. Dahl and Shrieves (1990), Aggarwal and Jacques (1997), and Jacques and Nigro (1997) all offer evidence for US banks only. Some other contributions have tried to assess whether negative shocks to capital induced Japanese banks to restrict lending. Considering that a large part of Japanese banks' Tier 2 capital consists of unrealized capital gains on equity investments, Kim and Moreno (1994) - on aggregate data - and Ito and Sasaki (1998) - on individual bank data - provide evidence that negative shocks to capital stemming from stock market plunges lead to significant declines in lending. Peek and Rosengren (1997) show that negative shocks to capital resulting from declines in the Tokyo stock market translate into curtailing loans in the US by Japanese banks and subsidiaries.

The second issue - whether the stiffening of capital adequacy can lead to a - contraction in banks' supply of loans has been debated at length in trying to explain the early 1990s US recession. By historical standard such recession was rather mild and yet recovery was extremely slow. Various authors have suggested that the reason for such extremely tardy recovery has to be found in the capital crunch, a particular type of credit crunch - i.e. a retrenchment in banks' loan supply - precipitated by the inception in the US of the Basel Committee capital adequacy standards. The consequent failure of the 4 For general discussion, see Dewatripont and Tirole (1994); Freixas and Rochet (1997).

3
teachers believe, _the relationships index_ which measures teacher beliefs about their relationships with students, colleagues and the head teacher is important.<sup>32</sup> Analysis of the Post-CIF and Post-LASSO regressions show that in a more parsimonious model, offering extra help to lagging students (teacher practice) and the Relationships index (teacher beliefs) are statistically significant at the 1% level. Moving from the 25<sup>th</sup> to the 75<sup>th</sup> percentile in terms of teachers offering extra help to lagging students, is linked to student learning gains of 0.19 SD. Similarly, going from the teacher at the 25<sup>th</sup> percentile to the 75<sup>th</sup> percentile on the Relationships Index is associated with an increase in student learning gains of 0.14 SD.

# **5. Conclusion**

Research on teacher effectiveness has struggled to identify observable teacher characteristics that can help explain variation in student performance. In this study, we apply machine learning methods to this problem. Using matched student-teacher data for Grade 2 and 3 students from across 392 schools in Tanzania, we use two ML approaches, Conditional Inference Forest (CIF) and LASSO, to predict student learning gains.

We find that ML approaches outperform the standard OLS model by 14-24 percent in out-of-sample predictions. Further, even though both CIF and LASSO take different model-building approaches, they produce largely consistent results. As expected, student baseline scores are the most predictive of student learning gains, signaling mean reversion in the data.

Our key finding is that specific elements of what teachers know (teacher preparation); what teachers do (teacher practice); and what teachers believe (teacher beliefs) are more strongly predictive of student learning gains than other teacher, student, and school factors, especially in Math. For Math, CIF results show that the teacher practice of providing written feedback on homework/tests and reviewing key concepts at
drugs.

# **_3. EMPIRICAL STRATEGY_**

Can the evolution of violence described in the previous section be partly explained by the patterns of youth out of school and out of work in Mexico? Is this relationship significant regardless of the period of analysis? Or is it only relevant during the escalation of violence between 2007 and 2012 and, in particular, in the northern states which were hit disproportionally both by the economic crisis and the violence related to DTOs? To answer these questions, we construct panel data from the 31 Mexican states plus the Federal District as the unit of observation during the period 1995 to 2013. In particular, we estimate the following relationship: where ��� is a variable measuring the levels of violence in state “ _i_ ” at time “ _t_ ”; ��������� is the proportion of youth aged 15 to 24 who are not studying and not working; and ��� is a matrix of state-level controls. The parameter of interest is β, which captures the conditional correlation between the proportion of ninis and the incidence of violence. Equation (1) controls for all state characteristics that are constant over time α� **,** and time effects common to all states μ�. Finally, ε�� is an idiosyncratic term following a normal distribution with zero mean and known variance. To try to answer the questions posed above, several variations of equation (1), including different controls, interaction terms, and the use of different types of ninis, are estimated and presented in the results section.

The controls include variables that have been identified by the literature as having a significant effect on crime and violence.<sup>9</sup> However, the controls included in the empirical estimation of equation (1) capture only elements related to the expected relative benefit

> 9 For a recent comprehensive study on the determinants of crime and violence in Latin America, see Chioda (2015).

12
agricultural extension service. Promotion was administered by sub-Assistant Agricultural Officers, whom we refer to as extension agents. Each extension agent works in a single sub-district ( _Union_ ), spanning on average 9 villages. To limit the potential for our demonstration interventions to displace extension agents’ attention away from farmers in control and non-experimental villages, our experimental sample consists of 2 randomly selected villages in 55 Unions of the Barisal district, yielding a study sample of 110 villages across the district.<sup>4</sup> One of the primary activities for extension agents under IAPP was to form farmer field groups of approximately 25 farmers in each of the project village. These farmers were self-designated during a village meeting with the extension agent; the only requirement was for these farmers to manifest a high level of interest in learning about new agricultural technologies and attend regular group meetings. The farmer field groups discussed a range of agricultural practices and technologies, including the adoption and properties of the new flood-saline-resilient seeds demonstrated in our experiment. They met monthly, on average, during the dry season.

# **2.2 Dry season agriculture in Barisal**

Agriculture comprises two main growing seasons in Barisal: the monsoon season ( _kharif_ season) spans roughly from April to October, while the dry season ( _rabi_ season) runs roughly from November to March. Rice is the traditional staple crop in Bangladesh, and is well-suited to cultivation in the kharif season. While productive, rice cultivation in the rabi season ( _boro_ rice) requires heavy irrigation which is primarily provided by groundwater.<sup>5</sup> Heavy reliance on groundwater irrigation poses economic and environmental challenges: at the time of this study, heavy energy subsidies were in place to ensure the profitability of boro rice cultivation, and associated groundwater extraction was drawing down the water table.

We present basic descriptive statistics on dry season production of pulses and boro rice in Barisal in Appendix Table A5; our household survey data used to construct this table is described in Section 3.
# **6 Discussion and conclusion**

This study presents a new analysis of internationally comparable urban and rural poverty statistics, utilizing a novel dataset created for 16 SSA countries. By integrating consistent urban delineation approaches into the framework of global poverty measurement, we provide valuable insights into the spatial dimensions of poverty.

Primarily focusing on 16 SSA countries, our analysis highlights the urgent need to address urban poverty in order to accelerate global poverty reduction efforts. Based on internationally consistent urban and poverty measures, we find that urban poverty rates are consistently lower than rural poverty rates in all the studied countries. Notably, poverty incidence in dense urban areas is particularly low, while poverty rates in low-density urban areas are closer to those in rural areas. The patterns hold even when accounting for country fixed effects and controlling for observed household characteristics. The use of the DOU and DB approaches, instead of relying solely on each country’s official urban definition, reveals higher urban poverty rates in most countries. However, the choice between the DOU or DB approaches does not significantly impact the results. In terms of the spatial distribution of poverty, our findings challenge previous assumptions by revealing that a larger share of the poor population resides in urban areas than previously thought. While the incidence of poverty may be lower in urban areas, the sheer number of people living in these areas compensates for this, resulting in a significant concentration of poverty. In many of the studied countries, more than half of the poor populations live in DOU/DB urban areas, with dense urban areas accommodating a substantial share of these poor populations. Furthermore, our analysis demonstrates that the choice between the DOU and DB approaches has a critical impact on the spatial distribution of poverty. The DB approach, which is specific to each country and captures local variations in population density, better reflects the concentration of poverty in urban areas. This is particularly evident in countries with either high or low average population density.

From a methodological perspective, our study carries important implications.
: IMF Balance of Payments Statistics; Note: -The “Business Services” category includes Total Services minus Transportation, Travel and Government Services. Alternatively, Business Services consist of: Communication, Construction, Insurance, Financial, Computer & info, Other business, Personal, cultural and recreational services, as well as Royalties and License fees.

The resulting occupational shifts in importing countries and the implied adjustment costs have, however, raised the specter of protectionism. This specter may never materialize, because of the technical infeasibility of protection or the triumph of good sense. But it would still be wise to preempt protectionism, to ensure that any adjustment pressures are dealt with through desirable domestic assistance rather than inefficient barriers to trade. And if the GATS cannot secure existing openness, then what hope is there of eliminating entrenched restrictions? Hence:

- Goal 1: _WTO Members would lock-in the current openness of cross-border trade for a wide range of services.  Certain services, such as those that involve the mobility of capital, would be exempt._

6
bias.<sup>20</sup> This misspecification is manifested as failure of the linearity assumption and the magnitude of this bias depends on the form of the covariates (see Figure 5 and appendix). Additionally, it will vary for each type of indicator. For example, when estimating mean income in a model without log transformation, linearity is preserved when aggregating and thus the approach is expected to perform well.<sup>21</sup> However, the empirical MSEs for the unit-context models in most areas outperform those from ELL (see Table 1 for average results). In fact, under scenario 1 (Figure 2) in some areas the MSE for the unit-context models are only slightly worse than those of the _CEBa_ and _CEBac_ . Under scenario 2, where the area effects have a higher variance relative to that of the cluster effect, which is likely the case in real world scenarios, the empirical MSE for the unit-context models show considerable variability for both fitting models. The results suggest the method could outperform direct estimates as well as misspecified models with random effects for the area level only, in terms of MSE, and may be an alternative under scenarios where census and survey data are not aligned. However, bias in these unit-context models may be a considerable concern. As one can see in the results from Table 1 the unit context method yields FGT0 estimators with an average absolute bias that is 56 times larger than the bias of the CensusEB estimators, and almost 5 times larger than ELL’s average absolute bias.<sup>22</sup> In the sections that follow this is further explored.

# **4 Design-based simulation**

In this section we present a design-based simulation experiment based on the Mexican Intra Censal Survey of 2015 (Encuesta Intracensal). The purpose of the simulation is to observe performance of the different methods under more realistic scenarios. The survey is carried out by the Mexican National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia -
23 observations with zero unearned income.<sup>35</sup>

# **6 Conclusion**

Behavioral economists are calling attention to consumption phenomena that violate the income fungibility assumption, which has underpinned most micro-economic modeling and policy advice. This paper draws attention to one such phenomenon, the link between spending behaviors and the effort dispensed in earning income. This has received little attention in the literature so far, despite wide recognition of its practical relevance as reflected in the recurrence of the saying “Easy come, Easy go” across the world’s cultures and languages. If true, this would suggest that incomes are categorized in different mental accounts based the efforts involved in acquiring it, from which people then spend differently.

This is explored here empirically by examining whether the marginal propensity to consume from earned and unearned income differs across more and less basic consumption categories (e.g. staples versus non-staples food, clothing, tobacco, alcohol). To do so, it is people’s behavior in their natural environment that is examined here, unlike in most other studies, which rely on controlled experimental settings. In particular, the MPCs from earned and unearned income are estimated and compared across a series of (mainly nondurable) consumption categories using OLS and household fixed effect estimators applied to household panel data from four different rural, low income settings in two countries, Tanzania and China.

The results suggest that people are more likely to spend unearned income on clothing, alcohol and tobacco, transportation and communication, as well as gifts, while they are somewhat more likely to spend earned income on staple food and invest it in education. No systematic differences were revealed in spending from earned and unearned income sources on health and housing maintenance/appliances. While follow up investigation confirmed that gender preferences affect spending patterns, a larger inclination to spend more from unearned income on less basic goods than from earned income was also observed when controlling for the gender of the income recipient. This suggests that unobserved intra-household dynamics are