rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1.0 Introduction

For researchers estimating the effect of inequality on growth, China would seem a promising “laboratory.” Since the start of economic reform in the early 1980s, it experienced plenty of both: Per capita income has grown nearly 8 percent annually, while the Gini coefficient rose from 0.28 to 0.39 (Ravallion and Chen, 2007). There was also significant within-China variation in growth and inequality at the local level (Benjamin, Brandt, and Giles 2005). Experience with cross-country aggregate data, however, demonstrates that estimating a robust correlation, let alone a causal relationship between inequality and growth, faces major empirical challenges, some of which stem from less than ideal data.<sup>1</sup> Better data alone, however, cannot solve the causality problem. Kuznets (1955) suggests the opposite chain of causality, from growth to changes in the income distribution; and almost certainly, unobserved heterogeneity is a potential factor, with inequality reflecting other factors that drive growth. Finally, even if we can estimate a “reduced form” effect of inequality on growth, it may still be impossible to identify the channels through which it matters.

In this paper we use the post-reform experience of rural China to determine whether local inequality impeded the growth of household incomes. We are able to address some of the methodological problems that plague cross-country data. By using a repeated, consistently applied household survey we avoid some of the measurement problems endemic in the cross-country setting. At least compared to international variation, the relative similarity of local institutions across villages in China also permits a cleaner isolation of the impact of inequality from other unobserved factors. At the same time, there are sufficient spatial differences in institutions and inequality that exploring the experiences of rural households scattered across villages can inform us about the potential channels by which inequality affects income growth. In particular, we can distinguish between two broad classes of explanations that have been offered as to why inequality affects growth: imperfect factor markets
<mark>Jordà, Ò., Schularick, M. and Taylor, A.M., 2011. Financial crises, credit booms, and external imbalances: 140 years of lessons.</mark> _<mark>IMF Economic Review</mark>_ <mark>,</mark> _<mark>59</mark>_ <mark>(2), pp.340-378.</mark> <mark>Lown, C., & Morgan, D. P. (2006). The credit cycle and the business cycle: new findings using the loan officer opinion survey.</mark> _<mark>Journal of Money, Credit and Banking</mark>_ <mark>, 1575-1597.</mark> <mark>Lown, C., & Morgan, D. P. (2001). The credit cycle and the business cycle: new findings using the survey of senior loan officers.</mark> _<mark>Federal Reserve Bank of New York</mark>_ <mark>Lown, C. S., & Morgan, D. P. (2002). Credit effects in the monetary mechanism.</mark> _<mark>Federal Reserve Bank of New York Economic Policy Review</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(1), 217-235.</mark> <mark>Minsky Human, P., 1986. Stabilizing an unstable economy, New Haven, CN: Yale University Press.</mark> <mark>Rünstler, G., 2002. The information content of real-time output gap estimates: an application to the euro area.</mark> <mark>Schüler, Y.S., Hiebert, P. and Peltonen, T.A., 2015. Characterising the financial cycle: a multivariate and time-varying approach.<
Lanjouw, and Neri (2001), 20 percent of all households had a household head employed in agriculture and these households contributed 34 percent to overall poverty in 1996. Changes in poverty, not only its levels, also differ across regions, rural and urban areas, and activities. Verner’s (2004) PNAD (Pesquisa Nacional por Amostra de Domicílios)based figures suggest that the poverty headcount in the Northeast declined from almost 60 percent in 1990 to 42.3 percent in 2001, whereas poverty in Brazil’s most populous state São Paulo rose slightly from 8.6 to 9.4 percent during the same period. For urban areas, Ferreira and Paes de Barros (2005) show that extreme poverty increased between 1976 and 1996. According to Paes de Barros (2004) however, the poverty incidence in rural areas in general and among households engaged in agricultural activities, in particular, declined from levels of about 60 percent to around 50 percent between 1992 and 2001.

One important factor for understanding these developments is the structural change in Brazilian agriculture in the 1980s and 1990s. In their assessment of the impact of sectorspecific as well as economy-wide reforms on Brazilian agriculture, Helfand and Rezende (2004) conclude that agriculture became one of the most dynamic sectors in the Brazilian economy. Between 1980 and 1998 real GDP grew by about 40 percent and real agricultural output by about 70 percent. In many sub-sectors, yields increased significantly and more harvested area was dedicated to exportables, in particular soybeans and sugarcane. Agriculture benefited from favorable macroeconomic environment and trade reforms that led to less industrial protection and the elimination of taxes and quantitative restrictions on agricultural exports. In addition, specific agricultural reforms – in particular a reform of agricultural credit and price support policies; an agrarian reform program, including a land reform; and, finally, the deregulation of domestic markets for agricultural goods – were important drivers of the observed agricultural performance.<sup>5</sup> However, the increase in agricultural productivity was accompanied by
shocks experienced by households over the preceding 12 months and the type of coping mechanisms that were adopted.

Several patterns emerge. While households in Bangladesh faced a wide variety of shocks in 2008-09, we find that health related shocks dominate, followed by climatic shocks. More importantly, a large number of households said they were unable to cope with climatic shocks presumably due to their covariate nature, while they were better able to handle health issues. Moreover the poor are least able to cope with shocks though surprisingly the non-poor appear to face a larger incidence of shocks.

The paper is organized as follows. Section 2 provides details on the data used for the paper and Section 3 provides a descriptive analysis of the type of shocks faced by households in Bangladesh and the main coping strategies. Section 4 discusses results from a multivariate analysis assessing the factors associated with a household being able to cope with shocks. Section 5 concludes.

# **2. Data and household characteristics**

In this paper, we use a nationally-representative survey of 2000 households. The data were collected by the Power and Participation Research Center (PPRC), a Dhaka based research institute. The sampling frame for this study uses the Integrated Multi-Purpose Sample (IMPS) design of the Bangladesh Bureau of Statistics. The IMPS was updated on the basis of the Population Census data of 2001 and now consists of 1000 Primary Sampling Units (PSUs) across the country of which 640 PSUs are rural and 360 urban with the urban further sub-divided into other urban and statistical metropolitan area (SMA). Each PSU consists of 200 households. A total of 16 strata were formed: 1 rural strata from each of the 6 administrative divisions of the country, 1 urban strata from each of the 6 administrative divisions, and 1 SMA (statistical metropolitan area) strata from each of the 4 divisions having SMA (Barisal and Sylhet have no SMAs).

The survey was implemented in 100 PSUs within the 16 strata described above. This was done on the principle of probability proportionate to size
time across all vaccines, given the time the contract was signed, and the income level of the country signing the contract.

Table 2: Regressions of delivery data on purchase contract date and fixed effects

|VARIABLES|(1)<br>Month of first<br>delivery|(2)<br>Month of first<br>delivery|
|---|---|---|
|Month contract is signed|0.432***<br>(0.0759)|-0.115<br>(0.137)|
|Month signed X Contract signed in 2021||0.823***<br>(0.168)|
|Low-income or lower-middle-income (=1)|1.447***<br>(0.461)|1.450***<br>(0.462)|
|Upper-middle income (=1)|1.159***<br>(0.351)|1.449***<br>(0.349)|
|Contract signed in 2021 (=1)||-10.49***<br>(2.110)|
|Constant|10.55***<br>(0.706)|15.68***<br>(1.191)|
|R-squared|0.431|0.515|
|Numberofobservations|367|367|
|Vaccine candidatefixed effects|Yes|Yes|

Source: Authors’ calculations based on the IMF-WHO Vaccine Supply Tracker and the UNICEF Vaccine Market Dashboard Notes: Months are measured in months since January 1, 2020. Data include contracts for which there were partial or full deliveries, 77 percent of contracts by HICs, 83 percent by UMICs, 84 percent by LMICs, and 50 percent by LICs. The fixed effects for a highincome country and the AstraZeneca/Oxford vaccine
of the commercial banks was US$4.9 billion in December 1993, and declined to US$ 1.1 billion in June 1994. The same figure for end 1993 is noted in OECD Economic Survey on Turkey, 1995. Short term debt instruments were not issued by the Treasury in the last two months of 1993, and in the beginning of 1994, the stock market was in a steady decline, (see Figure 16) and the TL was losing its value sharply against foreign currencies. The banks rushed to the foreign exchange market to close their positions in foreign exchange, the Central Bank was defending the parity by selling foreign currency at rates below the market rate, as a result of which the Central Bank lost half of its reserves while the commercial banks closed their short positions. The fall in foreign liabilities of the depos:it banks from early 1994 on is seen in Figure 15. At the same time, there were large withdrawals of TL and foreign currency denominated deposits from the system. Three small commercial banks (with share in total deposits less than one percent) were placed in to receivership in April 1994. Following that, 100% of bank deposits were insured by the Central Bank at the end of April 1994. Although the amount of reserves were at an all time low, this seems to have helped to stop the withdrawals. This insurance on deposits has not been removed to date, signifying the fragility of the system.

In April 5, about a week after the local elections, the government announced a stabilization programme. This involved price increases of 70 to 100 percent on SEE goods, and a freeze thereafter up to 6 months. Public sector wages would be freezed, the planned consolidated government deficit was halved for 1994. This deficit reduction would be achieved mainly through one-time taxes on the net assets of firms, wealth and corporate taxes, also by the real decline in the wage bill, and a cutting down on public investment. Reserve and liquidity requirement rules were revised in
annum), mostly due to the rising relative cost of health care.<sup>1</sup> In contrast, they do not find systematic differences between the inflation rates faced by rich and poor households, nor between those of urban and rural households.<sup>2</sup> Inflation inequality has also attracted some attention outside the U.S. For the U.K., Crawford and Smith (2002) find that poor households experienced significantly lower average rates of inflation than rich households over 1976-2000. For the case of Argentina, which experienced large fluctuations in inflation over 1989-1998, Lodola et al. (2000) find that on the whole the poor suffered lower inflation than the rich in high-inflation years, and the opposite happened in low-inflation years. For Taiwan, Lieu, Chang and Chang (2001) conclude that in 1991-96 inflation was persistently higher for low-income and urban households.

In Hong Kong, the Census and Statistics Department actually computes three consumer price indices by income bracket (in addition to the standard CPI).<sup>3</sup> In the year 2000, for example, the inflation rates of the three group-specific indices were, respectively -2.8, -3.8, and -4.5 percent, while the overall CPI fell by 3.7 percent. Thus, the differences in inflation rates across income groups were fairly substantial, given the relatively low levels of overall inflation.<sup>4</sup> Few papers have focused explicitly on the distributional consequences of inflation heterogeneity. Ruiz-Castillo _et al_ (2002) use Spanish data to explore the distributional implications of adjusting household-specific price indices for quality-change bias. In turn, Crawford and Smith (2002) reassess the trends in UK inequality over 1975-2000 using household-specific CPIs, but find that the results are highly sensitive to the choice of base year for the indices.

It is worth noting that these issues are closely related to, but conceptually different from, the question of whether inflation
, 2007).

Using these estimates, the quasi-innovations to the present value of income growth can be constructed in a straightforward manner using income growth forecasts computed recursively with (19).

# **3.3 Data**

The empirical sample is dictated by data availability. We work with the 50 largest countries in the world in terms of their respective GDP (in U.S. dollars in the year 2005) for which complete annual data on consumption and income, as well as measures of de jure and de facto financial openness (as described below) could be assembled. Table B.1 in the appendix lists the countries included in the analysis. Taken together, they account for almost 90 percent of world GDP in the year 2005.

Data on real GDP growth, aggregate consumption growth, and total population are taken from the Penn World Tables 7.1 (PWT 7.1). Along the time dimension, our sample runs from 1970 to 2010. We use the 1970-71 averages to construct the initial shares **W0** and population **H** .<sup>18</sup> The regression sample therefore runs from 1972 to 2010, and comprises 1,950 country-year observations. As explained in Section 2, for the calculation of the quasi-innovation to the discounted present value of income growth, we set ρ = 0.97.

For our analysis of the covariates of risk sharing, we measure countries’ international financial integration using data on financial openness drawn from the Chinn-Ito dataset, updated to 2012, as well as international asset and liability positions from the Lane and 18Estimation results change very little if we use instead the averages over 1970-74 or the year 1970 only.

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of trade with government. Because the dataset used for this study is crosssectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts.

Further, citizens may not be attributing goods and services to the government. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to various non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services.

Each of the indicators of perceptions of government performance is significant at the p _<_ 0.05 level. Food security is positively associated with a willingness to defer to the tax department. The difference in the predicted probability of deferring to the tax department for respondents who have never experienced food insecurity and for responden fts who have experienced food security at least once within the past year is 4 percentage points. A disinclination to pay taxes on the part of the hungry may reflect an inability to pay. It may also reflect a belief that government is not fulfilling its end of the fiscal contract. Satisfaction with government’s maintenance of water and sewage corresponds to a percentage point increase of 3.4 in the predicted probability that one will defer to the tax department. Similarly, satisfaction with government efforts to maintain roads and bridges corresponds to a percentage point increase of 3.0 in the predicted probability that one will defer to the tax department I find support for the hypothesis that citizens’ assessment of administrative competence is positively related to their acceptance of government’
about 40% of each types of plots receiving an average of 50 kg/ha. In line with factor equalization as presented in table 2, use of oxen for land preparation is less unequal although, with a difference of 4 oxen-pair days/ha, it is still higher on owned than leased plots.

Although one might expect that, to reduce the scope for shirking, landlords might require tenants to grow less labor intensive crops on leased plots descriptive statistics suggest otherwise; _teff_ , the traditional labor intensive staple is grown on 36% of leased vs. 29% of own plots. Multivariate analysis will be required to assess which of these effects can be attributed to share tenancy. Before conducting such analysis, we report descriptive statistics for matched tenant-landlord pairs that can give an indication of whether rental markets transfer land from less to more able and productive farmers, thus providing some initial evidence on potential productivity impacts of land rental contracts.<sup>12</sup> Comparing input and output quantities, plot characteristics, and crop choice by operator-cum-landlords and their rental partners who also cultivate their own land (i.e., owner-cum-tenants) in table 4 points to some interesting observations. The value of crop output per hectare differs markedly between the two categories with average productivity on plots operated by owner-cum-tenants 20% higher than on those cultivated by landlord farmers. The top panel suggests that this difference could be partly explained by observed input applications: with the exception of manure and use (but not the amount) of hired labor, the intensity with which family labor, oxen power, and chemical fertilizer are used is higher on plots cultivated by operator-cum-tenants compared to their rental partners. This could imply higher productivity by tenants compared to their partners who cultivate some of their land but econometric analysis is needed to confirm this and explore sources that could underlie such differences.

# **3. Conceptual framework and empirical strategy**

Use of partner data allows us to take a three-pronged approach to assess productivity implications of land rental,
The experimental groups were compared to a control group where respondents were first shown the statement: “There are many popular search engines in the world. Search engines are used every day by over 1 billion people worldwide.” and then were asked “Many people say they are annoyed by all the advertising on search engines. Does this apply to you?” with the answer options of yes and no available.<sup>1</sup> These treatments allow us to compare the effectiveness of a standard descriptive norms treatment where the reference group is relevant to the citizen, with treatments of the descriptive behavior of salient outgroups.

In this study we are interested in two outcomes related to tax morale: 1) a survey measure of tax morale “If a taxpayer does not report all of his income in order to pay less income taxes do you feel it is: Not wrong, A bit wrong, Wrong, and Seriously wrong” and 2) a survey measure of how much respondents would fine tax cheats: “If a taxpayer does not report all of their income in order to pay less income taxes, what percentage of their income should they pay as a penalty? None (0%), 1-10%, 11-20%, More than 20%.”

# Riwi survey platform

We conducted the survey using the Riwi survey platform between 2017-06-21 and 2017-06-29. Riwi recruits respondents by presenting the survey to people who accidentally type an incorrect URL into their browser. Normally these websites will display advertisements to people but Riwi buys temporary space on these pages to display the survey instead. Respondents are first asked their age and gender (with respondents below the age threshold removed from the survey) and their location is determined based on their IP address. Because Riwi respondents are not expecting to take a survey, Riwi surveys have high attrition rates as the survey goes on. As a result, we take care to check for differential attrition and covariate imbalance (see appendix). We found no evidence of either problem in
measured in practice remains unclear.

Closely related, the second goal concerns ending hunger and malnutrition. Objective measures of hunger rely on caloric intake which may not be strongly correlated with nutrition. Therefore, Deaton (2010, pg. 41) recommends measuring anthropometrics, but highlights a key data limitation.

The obvious alternative is to use the anthropometric measures directly. Here there has been enormous progress, through the spread of the Demographic and Health Surveys...These surveys are as close to a gold standard as we are going to get in this area, although the irregularity of the DHS surveys makes it difficult to use them for monitoring, for example for assessing the effects of the food price crisis on the heights and weights of children.

Our study may be viewed as an attempt to address this limitation by combining existing Demographic and Health Survey (DHS) data with remote-sensed information. The

> 7https://www.un.org/sustainabledevelopment/sustainable-development-goals/

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classes a rate of 69.5 and 3 classes achieving a rate of 78.8% it was determined that a maximum of 3 classes could be reasonably extracted. With some manual cleanup, all 15 African cities reached an accuracy level above 85%.

Figure 18 below shows Maputo classified into 3 classes (Commercial/industrial, Residential, and Un‐built land) in 2006 and then again in 2012. These land cover maps do not merely show the urban expansion, but they also provide insight into the changes of the land cover composition (i.e. what type of land has been expanding or where has land cover remained constant. In Maputo, a noticeable amount of growth appears to be occurring the north and to the west of the city with new residential land as well as the industrial parks being built. In contrast, the downtown area near the port, remains quite unchanged. It should be noted that when conducting change detection analysis using satellite imagery, there will always be an element of uncertainty introduced by the image as well as the classification process. For example, in Maputo, the scenes were taken during different months and at different angles, the classifier was then tailored to the pixel values in each scene. As a result, buildings in the first scene might appear to have higher values than buildings in the later. The classifier must therefore adjust and identify buildings in the first scene using different criteria than the buildings in the second scene. While this source of error should be recognized, it does not invalidate the value of classifying layers and inspecting their change over time as an exploratory tool.

Figure 18 – Maputo Land cover classification using SPOT5 <!-- Start of picture text --> New growth<br>New growth<br>2012 2006<br>Source: Author’s classification of SPOT5<br><!-- End of picture text --> Although the goal of classifying SPOT5 imagery was to explore how well coarser imagery could be used to