rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

COVID-19 is adversely affecting the lifestyle, health, and livelihoods of billions of people around the world. Early forecasts and simulations show that, besides grave health consequences, the pandemic will contract global economic growth, increase global poverty and unemployment (ILO, 2020; Mahler et al., 2020; Vos et al., 2020; Sumner et al., 2020;). However, estimating the overall health and economic costs of COVID-19 is challenging for various reasons. First, the sheer magnitude of the pandemic in terms of confirmed cases and fatalities, the speed at which the virus has been spreading, and the number of economies it has impacted are unprecedented since the Spanish Flu pandemic in 1918. Second, while various government measures to contain the virus and limit economic damages are still evolving, there is limited understanding of how economic agents behave under such a large-scale pandemic, further complicating the mechanisms through which COVID-19 impacts economies across the world. Third, data on standard economic indicators for the real sectors, such as earnings, consumption, profits, and losses, are not yet available for much of the developing world to assess the impacts globally. Estimating the economic consequences of COVID-19, therefore, requires new sources of information and novel empirical approaches. Our paper investigates the impacts of COVID-19 on consumer demand using historical and near real-time Google search data. The Google search data have global coverage and are instantaneously available, permitting timely policy analyses.

As COVID-19 continues to wreak health, economic and social havoc across the world, governments and development financing agencies have rolled out rescue packages and large-scale measures to save jobs and livelihoods. For instance, about 193 countries around the globe have already implemented various policy measures and rescue packages, in addition to aid packages from the World Bank and International Monetary Fund (IMF).<sup>1</sup> The effectiveness of these measures in weathering the impacts of the pandemic depends on the ability to assess the effects on different sectors of the
deseasonalized prices were created by regressing each of the prices on twelve monthly dummies plus a time trend. The estimated residuals from that regression are the deseasonalized prices. Third, the variables were differenced.

The adcdusted R squared can be viewed as one way of measuring the residual risk inherent in the basis. That is, the adjusted R squared indicates the percentage of the variability in cash prices that is not explained by the futures prices. The percentage of the cash price changes that is unexplained (1-R squared) is an estimate of the basis risk. The higher the unexplained variability, the lower the value of the adjusted R squared (the higher the basis risk), and the lower the usefulness of the particular wheat futures contract in hedging Polish wheat cash price risks.

The estimation of the R squared using the regression analysis described above was performed for the overall period 1990 to 1995 and for each individual year. R squares were computed for both calendar and crop years.

# **3.1.1. Correlation of Prices in** US **Dollar Terms**

Polish and LIFFE wheat prices were converted to US dollar equivalent prices. In addition to the correlations with prices in levels, the impact of seasonality on prices was assessed by using a deseasonalized data series as well as the first differences of the respective price series. Correlations See Labuszewski and Nyhoff(1988), and Smith, Smithson and Wilford (1989).

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**Table 3. Business Cycle Synchronization – orthogonal to US business cycle**

||Costa Rica|El Salvador|Guatemala|Honduras|Nicaragua|
|---|---|---|---|---|---|
|Costa Rica|1.000|||||
|El Salvador|**0.409**|1.000||||
|Guatemala|**0.488**|0.006|1.000|||
|Honduras|0.104|0.157|**0.421**|1.000||
|Nicaragua|-0.141|0.115|-0.076|-0.063|1.000|
|Panama|0.134|0.014|-0.021|0.118|0.065|

# **2.1.2 Monthly Data: 1995 - 2003**

The business cycle is usually defined in the range of 6 to 32 quarters, as such, the low frequency of annual data might be insufficient to fully assess the degree of business cycle synchronization. In this section we therefore complement our analysis in the previous section with an analysis of monthly data, where output is proxied by seasonally adjusted monthly indices of industrial production and economic activity.

We use spectral analysis to estimate the correlation at different frequencies and use the average coherence at business cycle frequency (6 to 32 quarters) of year-over-year changes in economic activity as a summary measure of business cycle synchronization (Garnier, 2003). The advantage of using cross-spectral densities over simple correlations in the analysis of business cycle synchronization is twofold. First, spectral analysis avoids possible business cycle distortions due to filtering, it is well known that the cycles change with the de-trending method (Canova, 1998). Second, contemporaneous correlation is unable to take lagged co-movement into account. As coherence measures the correlation between
as durable asset ownership, are similar across the two surveys. Thirty-two percent of households in the IHDS-3 states own motorcycles and cars and 21 percent possess air coolers and air conditioners. In the reweighted CPHS, ownership shares of these two assets are 34 and 22 percent, respectively. Growth in monetary and non-monetary indicators in the IHDS-3 therefore are consistent with the observation that poverty in 2017 is lower than in 2011.

**Another assessment of poverty since 2011 can be made by comparing rural headcounts to rural wages produced by India’s Labor Bureau.** Monthly wages for agricultural and non-agricultural occupations are available since 1998. We take a weighted average of wages across occupations to construct a composite monthly rural wages series. The series is then deflated using monthly CPI-AL series and collapsed at the yearly level by taking a simple average across months.

Figure 22 correlates the growth in average annual wages for rural workers with yearon-year changes in rural poverty headcounts from our analysis (approach 2). As real

> 28The average real consumption in NSS-2011 for the three states is 1259.01 (constant 2011 rupees). For rural and urban areas, the mean consumption in NSS-2011 is 1141.57 and 1885.60 respectively.

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where _αik,t_ , _βijk,t_ , and _γjk,t_ are parameters capturing sales/supply-potential factors of _k_ in _i_ , the trade freedom of _k_ between _i_ and _j_ , and the demand/consumption potential of _k_ in _j_ , all in logs and at time/month _t_ . When only trade but no domestic sales data are available, the unilateral frictions on _k_ enter _γjk,t_ only.

We proceed in two steps to identify the parameters _ηk_ and _ξk_ of interest: (i) estimate _γjk,t_ as importer-product-month fixed effects from monthly bilateral product-level export data _xijk,t_ on 180 sensitive products; (ii) estimate the impact of unilateral trade frictions on product _k_ from where _gjk,t_ is defined as in (2), and _γ_ ̃ _jk_ , _λ_<sup>_γ_</sup> _t_<sup>and</sup><sup>_uγ_</sup> _ik,t_<sup>areimporter-product-fixed,month-fixed,and</sup> importer-product-month residual effects, respectively.

As all ∆ _jk,t_ are binary indicators, all parameters _η_ measure (direct) semi-elasticities of imports. Note that under the assumption of a constant-elasticity-of-substitution model with (direct) trade � elasticity (1 _− σk_ ),<sup>3</sup> one can scale _γjk,t_ by (1 _− σk_ ) to obtain the iceberg-trade-cost increment associated with pandemic-related policies alone by product, importer, and month. E.g., _τjk,t_ = 0 _._ 02 then means that trade costs are (directly plus indirectly) raised by pandemic-related policies for product _k_ and country _j_ in month _t_ by about two percentage points. Apart from reporting on _τjk,t_ , we (trade-weighted) aggregate these trade costs effects for each of the 247 countries and each month _t_ to obtain _τj,t_ . Finally, we aggregate the same trade-cost effects across countries _j_ to obtain _τt_ for the average country and sensitive product.

# **4 Results*
in the share of farmers classified as employed under the revised standard, depending on spatial differences in the commercialization of agricultural production. In parts of Malawi and Nigeria 70 to 80 percent of farmers report producing only or mainly for family consumption at the beginning of the agricultural season (i.e. the growing season of the main local crop), which is not considered as employment by the 19<sup>th</sup> ICLS standards but falls under the new category of own-use production work. However, this share is much lower in parts of Ghana, where only 24 percent of farmers produce for own use. Second, we find that, in contexts where gender differences in the self-reported intended use of output are significant, women are less likely than men to report producing only or mainly for sale. Further in-depth analyses using the data collected in Malawi, which are best suited to analyze male-female differences, shows that the

> 3 This mirrors difficulties in the agricultural economics literature in defining and measuring subsistence agriculture – a term which is often used in policy circles, but without any generally accepted definition (Wharton 1969; Brüntrup and Heidhues 2002). 4 The operationalization of the 19th ICLS standards provided an opportunity for enhanced methodological testing. This has led to a redesign of labor force surveys and labor modules in multi-topic household surveys and an ambitious agenda of survey methodological research by the ILO and World Bank under the Women’s Work and Employment Partnership, of which this paper is a product. Due to the co-occurrence of these changes in survey design with the rollout of the 19<sup>th</sup> ICLS standards, some countries may see an increase in employment, especially for females, where traditional surveys often failed to capture certain types of informal employment. This paper is concerned with the rollout of the 19<sup>th</sup> ICLS standards in isolation, i.e. comparing employment ratios under the 19<sup>th</sup> ICLS standards with previous (
Policy Research Working Paper 5824

# **Abstract**

This paper investigates the short-run consumption expenditure dynamics and the interaction of public and private arrangements of ultra-poor and labor-constrained households in Malawi using an original dataset from the Mchinjii social cash transfer pilot project (one of the first experiments of social protection policies based on unconditional cash transfers in Sub-Saharan Africa). The authors exploit the unique source of exogenous variation provided by the randomized component of the program in order to isolate the effect of cash transfers on consumption expenditures as well as the net crowding out effect of cash transfers on private arrangements. They find a statistically significant reduction effect on the level of consumption expenditures for those households receiving cash transfers, thus leading to the rejection of the perfect risk sharing hypothesis. Moreover, by looking at the effects of cash transfers on private arrangements in a context characterized by imperfect enforceability of contracts and by a social fabric heavily compromised by high HIV/AIDS rates, the analysis confirms the presence of crowding out effects on private arrangements when looking at gifts and (to a lesser extent) remittances, while informal loans seem to be completely independent from the cash transfer’s reception. From a policy perspective, the paper offers a contribution to the evaluation of the very recent wave of social protection policies based on (unconditional) cash transfers in Sub-Saharan Africa, suggesting that there might be an important role for public interventions aimed at helping households to pool risk more effectively.

This paper is a product of the Finance and Private Sector Development Unit, Africa Region. 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 fstrobbe@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
first steps towards implementation took place, with no federal assistance. However, further implementation of the reform has proved challenging.

In the Alto-Tietê River Basin, the subject of this Working Paper, the advances and difficulties faced by the implementation of the São Paulo water reform are well illustrated, particularly because of the high levels of industrialization and urbanization of the basin. This case study is part of an international study of experiences in decentralizing integrated water management to the lowest appropriate level _._ The larger study, supported by the World Bank, seeks to evaluate the extent to which river basin management efforts have been successful and to identify the factors that can be associated with both positive and negative outcomes. An important consideration in this context is that “the lowest appropriate level” for integrated river basin management varies between countries, states and even river basins. For this reason, the methodology necessarily needs to take into account the hydrological, socio-economic, cultural and historical conditions in the case study areas. The present research project explores these factors both through a survey of river basin organizations throughout the world and case studies of eight river basins, namely the Murray Darling River basin in Australia, the Fraser basin in Canada, the Tárcoles basin in Costa Rica, the Brantas basin in Indonesia, the Warta basin of Poland, the Guadalquivir basin in Spain, and the Alto Tietê and Jaguaribe basins in Brazil.

In the next two sections, the analytical framework and methodology of the study are presented. The paper then briefly describes the Alto-Tietê basin, the new institutional arrangements there, and the role of stakeholders in these institutions. It then further examines these changes through the lens of the analytical framework presented by looking at how a series of factors affect the evolution of decentralization, such as initial conditions and context, the level of commitment to decentralization on the part of both central and local authorities, the capacities of central and local institutions, and the design of basin-level institutions. Finally, the paper assesses the performance of the decentralization process by examining
0.054 ****|
|**Observations**|**381**|**364**|**370**|**533**|**436**|**518**|
|**Countries**|**11**|**11**|**11**|**15**|**15**|**15**|
|**F-Test joint Commodity EPI and EMDE intera**|**ction**|||*******|*******|*******|
|Notes: Panel least squares with country fixed effects. All var<br>statistical significance at 10, 5, and 1 percent, respectively.|iables are in log-|differences. Full|refers to the samp|le with AE and|EMDE. *,**, a|nd *** indicate|
16 traditional gross or net enrollment ratios. Their most recent version of the data set also takes into account changes in school duration over time within countries.

The second global data set for average years of schooling is that of Cohen and Soto (2001), which covers 95 countries and spans the period 1960 to 2000 on a decade basis. This data set uses 3 main sources of data. They are the OECD database on education, national censuses or surveys published by UNESCO’s Statistical Yearbook and censuses obtained directly from national statistical agencies’ web pages. Based on reports from its members and other non-member countries, the OECD has published detailed information on educational attainment, beginning at the end of the 1980s. This information refers to the population aged 15 to 64 broken up in different age groups and this is the cornerstone of the Cohen-Soto data set for high-income countries. The main advantage of the OECD data set is that the information is presented in a standardized form across countries. Cohen and Soto extend the study performed by the OECD to missing periods and countries.

One key difference between the Cohen-Soto dataset is in the methodology for extrapolating the missing data. Barro and Lee extrapolate missing data for the whole population either backwards or forwards to obtain educational attainment for missing years. As opposed to using the whole population, Cohen and Soto utilize estimates for age-specific groups, which they argue tend to result in more reliable estimates. Cohen and Soto also claim that for some countries, they had more recent census information than that used by Barro and Lee.

In order to obtain the broadest coverage of countries for data on educational attainment, we combined the information from the Barro-Lee and Cohen-Soto data sets to obtain the resultant data set that covers 144 countries for the period 1960 to 2000. If educational attainment data on a specific country is contained in both data sets, then we follow Bosworth and Collins (2003) and take the simple average of two data values to obtain our final measure of the average years of schooling.

Even though average
slightly across data sets: the IHDS data do not include information on homeownership, and only the IHDS data include the occupation of the household head and the distance to the closest fuel source.

> 9“Urban” is defined in the 2011 India Census, which is based on total population, population density, and non-agricultural labor activity.

> 10In equilibrium, urban areas and rural areas differ on several dimensions. For example, urban areas have higher wages and higher returns to education than rural areas (Adukia, Asher and Novosad, 2020). Other notable differences between the two group means include the following: family size is slightly smaller among Hindus, and there are fewer Hindus in urban areas that have less than or equal to 5 years of education. The main regression analysis will control for family size and education, along with other characteristics.

> 11Note that the overall difference between Hindus and Muslims is not a weighted average of the difference

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_D_ ∑ _jit_ δ _itx_ = _<u>j</u> K_ ∑ _jit j_ (23) where _j_ indexes firms; _i_ the industrial sector, _t_ the year. _K_ is the capital stock; _I_ is amount invested; δ is the depreciation rate; and _D_ is depreciation in pesos.

For years before 1988, the equation is reversed and each year’s capital is obtained by subtracting each year’s investment and applying a depreciation factor. The depreciation rate before 1988 was not available and was estimated using 1988 data. We ran a simple OLS model for the log of total depreciation conditional on the log of gross output, capital stock, total hours and electricity usage. Using this model we predicted the before 1988 depreciation levels.

# **7.2. Tariffs**

We use data on import tariffs for the period 1985-1995 from Vaillant (2000) and Casacuberta, Fachola and Gandelman (2004).

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. (2013) all find that official recommendations are too generic (typically at a state or country level) to account for local soil heterogeneity. Smallholder farmers in the developing world typically fertilize using average recommendations, with no information on which elements are most needed, and primarily use urea and DAP. Ours appears to be the first paper to compare plot to cluster level recommendations using field experimental evidence.

Finally, we contribute to the literature on autonomy, which has emphasized both its intrinsic as well as its instrumental value on participatory decision-making (broadly construed), compliance (Malesky and Taussig, 2019; Dal Bó et al., 2010), effort (Sjöström et al., 2018) and productivity (e.g. Black and Lynch, 2001; Bonin et al., 1993; Spector, 1986). Using observational data from an agricultural context, Bardhan (2000) finds that Indian farmers are less likely to violate irrigation rules when they themselves have crafted those rules. The reactance literature in psychology (Wicklund, 1974; Fitzsimons and Lehmann, 2004) argues that recommendations by experts may backfire as agents purposefully try to contradict them. We show that providing autonomy in the decision to follow expert advice may in fact lead to higher adherence in the

> 14They study Tanzania, where fertilizer usage is low (less than 1 percent of study farmers had used fertilizer at baseline) and thus the recommendations suggest substantial increases in input use.

5
on financial development in Nepal. But, unlike their study, ours does not investigate short run and long run interactions between financial deepening and economic growth: we only assume the existence of the interactions and, on that basis, proceed to include financial deepening and economic growth variables in the economic growth and financial deepening equations, respectively.

The specification and estimation of the econometric model is based on time series data for Thailand for the period 1960-1996. We do not include 1997 and 1998 in our analysis partly because the required data is not available; in addition, the Asia financial crisis has hit Thailand and created so much economic and social disruption to the extent that it would only be prudent to analyze the 1997-98 period separately as a special case.

# **(a) Specification of the Relationship Between Land Registration and Financial Development**

It has already been argued that land registration is primarily linked to financial development through two channels: the role played by land registration in enabling land to become a collateral asset which enhances financial development by expanding the market base (the number of credit worthy borrowers) for loans and by reducing financial 4
estimated time-series models and examined partial and total factor productivity for both state and rural enterprises. While they found that the TFP growth rate in the state industrial sector was positive, averaging about 2.5 percent, it lagged behind that in the non-state sectors, particularly township and village enterprises which averaged about 13 percent. The main explanation for the increase in the state sector's labor productivity in recent years is capital deepening. However, disagreements still exist regarding the rate of TFP growth in SOEs.

# **Efficiency: Empirical Analysis**

We now explore what our data set has to say about these issues and estimate aggregate production functions in the state and non-state industrial sectors using provincial data. The provincial data is known to suffer from various aggregation problems, and findings using aggregate data analysis are likely to be biased (e.g. Jefferson, 1990 for a discussion). Partly to overcome this problem, most of existing studies turns to enterprise-level data for firm-level efficiency analysis. However, their data sample so far has only covered a limited number of industries and provinces. None of the studies look at the whole industrial level, at the sarne time covers the entire non-state sector. The industrial sector is one of the largest components of the economy, employing 30 percent of the (formal) labor force. In the Yearbook definition, the industrial sector is comprised of mining, quarrying, and manufacturing as well as electricity, gas and water.

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