rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10978

# **Abstract**

This paper proposes a new method for estimating a full-coverage spatial price index using data typically available in household budget surveys. The food component of the index is estimated at the household level using reported expenditures and quantities, while the nonfood component is derived indirectly as a ratio among subnational poverty lines. The paper extends the analytical framework described in Deaton and Zaidi (2002) and discusses the advantages of this new methodology.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at mranzani@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 in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
between January and April 2019. This differs too much from the data collection schedule in Burkina Faso, Mali, Niger, and Senegal for the data from all five countries to be combined when comparing the waves. However, Chad is retained for Section 3 to provide additional descriptive evidence on potential strategies for managing and coping with seasonality.

While useful, further temporal disaggregation to isolate how welfare differs for particular months of the lean season should be approached carefully. The timing of the second wave in the EHCVM data collection approximately coincides with the 2019 agricultural lean season for Burkina Faso, Mali, Niger, and Senegal, but the correspondence is not perfect. Indeed, the second wave starts and ends slightly too early in these four countries to capture the full extent of the 2019 agricultural lean season, and is better aligned with the pastoral lean season. Focusing on month-specific results may help to ascertain how welfare changes as the agricultural lean season evolves. However, the EHVCM is not designed to be representative at the month level, so these additional month-specific results should be interpreted with some caution.

Ramadan overlapped with the lean season in 2019, which could lead the analysis to underestimate the effects of seasonality. In 2019, Ramadan took place from 5<sup>th</sup> May to 3<sup>rd</sup> June, falling directly in the middle of the lean season wave of the EHCVM data collection. All of the countries in the sample are majority Muslim – with Mali, Niger, and Senegal being almost exclusively Muslim – so this may impact consumption patterns. All other things equal, consumption typically rises for Muslim households in Ramadan and reported food insecurity declines (see, for example, MVAM (2021)). Thus, it may be that the overall lean season losses estimated in this analysis represent an _underestimate_ of what might happen when Ramadan does not correspond with the lean season. Notwithstanding the caveats outlined above, looking at month-

> 9 The rains in 2019 arrived late in some western
households surveyed in the second round.<sup>2</sup> Lower and upper bound estimates of mobility are derived from two different sets of assumptions about the correlation between the error term in the first round and in the second round.

Lanjouw et al. (2011) argue that the correlation between both error terms is likely to be non-negative.<sup>3</sup> If we are willing to assume zero correlation between the first round and the second round error terms, Lanjouw et al. (2011) propose to predict consumption in the first round by randomly drawing with replacement for each household _i_ in the second round from the empirical distribution of first round estimated residuals (denoted by ) as follows: (2) Equation 2 allows us to compute estimates of movements in and out of poverty. For example, the fraction of poor households in the first round who escaped poverty in the second time is given by: (3) Since we are randomly drawing from the empirical distribution of estimated errors, we need to repeat the procedure _R_ times and take average of equation 3 in order to estimate movements in and out of poverty.<sup>4</sup> In all likelihood, however, the correlation between error terms will be positive. By assuming no correlation, equation 3 will provide an _upper bound_ estimate of the mobility in and out of poverty. Lanjouw et al. (2011) propose estimating a _lower bound_ on mobility by now assuming a perfect positive correlation between error terms. In this case estimates of residuals

> 2 Section (5) discusses the robustness of changing the forecasting direction.

> 3 Correlation between error terms will be non-zero in two cases: (i) the error term includes an individual fixed effect and (ii) shocks to consumption persist over time. Lanjouw et al. (2011) argue that correlation between error terms will almost certainly be positive if the condition (ii) holds.  In their study using Vietnamese and Indonesian data they present empirical
To assess the robustness of our results, we conducted additional sensitivity analyses not reported here.<sup>26</sup> First, we controlled for a potential non-linear relationship between outreach indicators and firms’ financing obstacles and patterns by including a squared term. This term did not enter significantly.

Second, as the WBES provides survey responses to more detailed questions on financing obstacles, we also estimated the regressions using survey responses on: (i) the extent to which firms report needing special connections to access finance; and (ii) the degree to which access to long-term loans are obstacles to firms’ operation and growth. Our main finding that higher penetration of physical bank outlets and more extensive use of loans are associated with lower financing obstacles is confirmed in those estimations.

# **7.  Conclusions**

This paper introduces a new set of financial sector outreach indicators – indicators of the access to and use of deposit and lending services. While admittedly crude, they are the first such indicators for a broad cross-section of developed and developing countries. They are an important complement to indicators of the depth and efficiency of financial systems commonly used in the finance literature.

We also show the predictive power of our aggregate measures by relating them to userbased household and firm surveys. In particular, we show that our indicators of deposit and loan use predict the share of households with bank accounts and the share of small firms with bank loans. While preliminary results are based on a limited number of observations, they underline the usefulness of aggregate indicators, especially in the absence of consistent household and firm surveys for a large cross-section of countries.

> 26 These results are available upon request.

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resulting figure is the net unitary benefit per individual or household after receiving a public service. .

As for data requirements, the first three steps rely on information about public spending in the relevant sectors and the beneficiaries or users of those services, which is typically available from the implementing agencies of the government. The last four steps require information on household consumption and expenditure patterns by income category, which can be obtained from national survey data, in this case the Zambian LCMS.

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Pyramids (CP) to assess whether the historical predictors of rural non-farm employment retain their relevance with more recent data.

The Consumer Pyramids is a large household level survey conducted by the Centre for Monitoring the Indian Economy (CMIE). The surveys were initiated in 2014 and covers 170,000 households. Unlike the NSS, the surveys are of the form of a panel with each household being visited once every four months, aggregating to three surveys per year. The data includes individual characteristics such as age, gender, years of education, and nature of employment. Unlike the NSS which provides detailed employment sectors, the CP data only provides broad occupational categories among non-farm workers. We consider the year 2015 for our analysis since a number of significant macro-economic policy interventions were initiated since 2016 (starting with the “demonetization” episode in November 2016), which can affect non-farm employment.

# **2.4 Descriptive Trends**

Basic summary statistics corresponding to the three surveys are shown in Tables 1A, 1B and 1C. We discuss some key descriptive trends using the NSS data in this section prior to a more rigorous empirical analysis.

## **2.4.1 Rural Employment**

Figure 1 shows the broad trends in labor force participation and employment in the farm sector for rural individuals over this period, disaggregated by gender. Although rural males have significantly higher levels of LFP than rural females, there is a marginal decline in male LFP over this period. This decline is significantly accentuated for rural females, whose LFP decline from 47 percent in 1999-00, to 37 percent in 2011-12. Conditional on participation in the labor force, there has also been a steady decline in employment in farm activities. Thus, while 70 (85) of rural male (female) workers were engaged in farm work in 1999-00, this reduced to 58 (74) percent in 2011-12.

Figure 2 shows the fraction of rural workers engaged in non-farm activities by occupation type (left panel) and sector (right panel)
|Zambia|2010|LCMS-VI|2011|2010|2015|LCMS-VII|2021|2015|
|---|---|---|---|---|---|---|---|---|
|Zimbabwe|2017|PICES|2011|2011|2019|PICES|2021|2019|

Note: CONS: the welfare type is consumption/expenditure, and INC indicates the welfare type is income. Joint distribution indicates there are joint distribution of household survey data with social protection (ASPIRE) and financial inclusion (Findex).

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, and information provision on emigration rates, migration duration, repatriated savings and the extent of business creation in Bangladesh.

Existing microdata typically capture temporary migrants at one point in time in one location (abroad or at home after return), while a dynamic analysis of temporary migration phenomena requires information on the entire migration and employment history of the migrants and household members. Ideally, surveys collecting this data would also include questions on expectations of the workers on their wages, employment, savings and return decisions. While comprehensive panel data information remains very sparse and takes time to collect, dedicated migrant surveys capturing retrospective information on workers’ full employment and migration history and that of their household members is a promising alternative. Policy-relevant research on temporary migration would also greatly benefit from the inclusion of more comprehensive modules on past migration episodes overseas in standard national household surveys in origin countries, in the spirit of those included in the Egyptian 34
area. They have 6.75 years of education, which is slightly above primary school completion. On average, respondents worked 32.5 hours in the week prior to the interview.

The GHS-Panel also collects information on household-level adverse events, which we describe as shocks, deaths, and conflicts. We disaggregate shocks into two categories: idiosyncratic and covariate shocks. Idiosyncratic shocks include a variety of scenarios, including death or disability of an adult working member of the household, death of someone who sends remittances to the household, or illness of an income earning member of the household.<sup>8</sup> Covariate shocks include poor rains and floods that caused harvest failure, pest invasion that caused harvest failure or storage loss, loss of property due to fire or flood, and more.<sup>9</sup> Deaths include a death of anyone in the household (from illness, accident, injury, murder, suicide, or other reasons).<sup>10</sup> Finally, conflicts are defined if any family member of or if an individual was killed (in a non-natural death), suffered physical aggression, was injured or disabled, suffered sexual violence, or more.<sup>11</sup> Although the GHS-Panel has more detailed information on the adverse events, our analysis will primarily focus on binary indicators of whether a household has suffered from a shock (idiosyncratic or covariate), conflict event, or death. Table 1 summarizes experience of adverse events between 2014 and 2016 among sampled households. Seventeen percent of households in our sample experienced one or more covariate shocks, 19 percent experienced one or more idiosyncratic shocks, 11 percent experienced one or more deaths of household members, and 4 percent experienced one or more conflict event.

In addition to the data collected on standard welfare measures and information about adverse events, the survey included a module collecting certain symptoms of depression of the primary respondent.<sup>12</sup> The questions asked
# _6.3 Effects on profits and turnover_

For both, beneficiaries of cash grants and beneficiaries of matching grants, we find negative treatment effects on turnover (total revenues on products and services over the last six months) and profits (total profits on products and services over the last six months) (Table 4 and Figure A.2 and Figure A.3). The unconditional posttreatment CDFs show that the distributions are clearly different between the 70th and 90th percentile and between the 80th and 95th percentile, respectively. Yet, for recipients of matching grants, the negative treatment effects turn insignificant if we use the ANCOVA specification. For treated firms that already existed at baseline, the reduction of profits is in the order of XOF 60,000–70,000 (or US$108–US$126). For turnover, the estimates are less precise and for the subgroups of already existing firms in case of the ANCOVA specification, they are insignificant. In the simple OLS specification, the effect has roughly a size of −7 percent (or XOF 97,000, that is, US$175). The reduction in revenues and profits may surprise, especially because of the positive effect on investment, but beneficiaries may need time to transform their business, for example, to reach out to new customers, develop new products, and learn to deal with new technologies and forms of organization. It might also be due to increased competition, which we investigate further when we explore spillover effects. In a comparable experiment with cash grants in Nigeria, McKenzie (2017) showed that the impacts on profits and sales were insignificant in the first year for all retained indicators except one but reached significant and economically sizeable effects in the second and third years after the rollout. Of course, it could also be that the COVID-19 pandemic and the associated decline in demand made it difficult for firms to market a higher output. The possible consequences of COVID-19 will be analyzed in more detail later.

If we analyze heterogeneous
of the global antidumping database, and the limited data that are available for them from the WTO. Section 5 describes the detailed data on countervailing duty use for three countries that we were able to collect. Section 6 discusses some additional caveats to using the antidumping data and concludes.

# **2 Countries and Data Included in the Global Antidumping Database**

The basic contribution of the data set is to provide a standardized set of information for as many users of the antidumping policy instrument as possible. To that end, we have collected a reasonably consistent set of detailed data for 15 different user countries, listed in table 2.1.

**Table 2.1 : Antidumping User Countries with Detailed Data in the Database**

|**Country Code**<br>**(3-letter CTY)**|**Country**<br>**Name**|**Excel Workbook**<br>**File Name**|**Spreadsheet Names within**<br>**Country Workbook**|**Available**<br>**Years of**<br>**Data**|**Current**<br>**Version**<br>**(last updated)**|
|---|---|---|---|---|---|
||||AD-ARG-Master ,|||
|ARG|Argentina|AD-ARG-v1.0.xls|AD-ARG-Products ,<br>AD-ARG-Domestic-Firms ,<br>AD-ARG-Foreign-Firms|1996-2004|v1.0<br>(8/2005)|
||||AD-AUS-Master ,|||
|AUS|Australia|AD-AUS-v1.0.xls|AD-AUS-Products ,<br>AD-AUS-Domestic-Firms ,<br>AD-AUS-Foreign-Firms|1989-2004|v1.0<br>(8/2005)|
|BRA|Brazil|AD-BRA-v1.0.xls|AD-BRA-Master ,
_Pre-crisis, mid-crisis, and post-crisis estimations._ In order to examine possible shifts in the "observed" role of the tri-polar currencies before and after the currency crisis, we have run exchange-rate regressions (2) for twelve emerging East Asian economies, including the Asian NIEs (Hong Kong SAR, Korea, Singapore, and Taiwan POC), the ASEAN-4 (Indonesia, Malaysia, the Philippines, and Thailand), the smaller ASEAN-3 (Cambodia, Laos, and Vietnam), and China. In particular, to examine shifts in exchange rate arrangements, we consider the period before, during, and after the Thai baht devaluation. Using daily exchange rate data up to December 1999, we have chosen to divide the sample into the pre-crisis period of January 1996 - June 1997, the mid-crisis period of July 1997 - December 1998. and the post-crisis period of January 1999 - December 1999.

Regression results are summarized in Table 4. Results for earlier 18-month periods are also reported, to compare with later periods. The table confirms that in the pre-crisis period (January 1990 - June 1997), the estimated coefficients of the US dollar were statistically significant and close to unity for all the economies studied (except for the smaller Indochina countries). This again supports the proposition that many East Asian economies were on _de facto_ US dollar-pegged systems until the time of the crisis. Nonetheless, the estimated coefficients of the Japanese yen were significant, for some sub-sample periods, in Thailand (a maximum value of 0.18 during January 1996-June 1997), Singapore (a maximum value of 0.10 during July 1994December 1995), Korea (0.08 during July 1994-December 1995), Taiwan POC (0.06 during July 1994-December 1995), Malaysia (0.06 during July 1994-December 1995), Hong Kong SAR (0.01 during January 1996-June 1997), but were much smaller than the coefficients for the US authorities, may have chosen the estimated coefficients.

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may simply be the timing of the launch of EPZ programs in the context of global trade and investment trends. With the exception of Senegal and Lesotho, the other African programs in the study were only operationalized well into the 1990s – probably too late to take advantage of the massive globalization of manufacturing that accelerated during the 1980s and 1990s. One hypothesis is that the structural adjustment seen in Honduras, Dominican Republic, Vietnam, and Bangladesh is not a story of economic zones but rather of broader macro trends, which African countries were unable to tap into for any number of reasons (weak micro competitiveness, lack of fiscal and political stability, etc.). An alternative hypothesis is that African countries missed the boat on these opportunities _precisely because_ most African countries lacked an effective instrument like EPZs through which to channel trade and investment.

Analyzing structural changes _within_ the EPZs may provide a measure of the degree to which the zone programs facilitate sectoral adjustment, or instead hinder it. To this end, it is instructive to look at how EPZ programs have responded to changing competitiveness in the garment sector, particularly in the post‐MFA environment. In Jayanthakumaran’s (2003, p.64) seminal study on EPZs concludes that there is “ _a strong correlation between the growth of EPZs and the Multi‐Fiber Arrangement in general_ ” and that the phasing out of MFA and guaranteed market access “ _will eventually result in lower rates of return and will be a possible threat to the existing and new EPZs”_ . Figure 8 illustrates clearly, at a broad regional level, the winners and losers in the garment sector since the expiration of the MFA at the end of 2004. In terms of exports to the US market, East and South Asia (particularly Bangladesh) have grown rapidly at the expense of Central America, the Caribbean, and of Sub‐Saharan Africa, which has experienced declines of up to 40% over just four years. More recent trade data suggest that the crisis 21
objective ‘gold standard’ measures of key variables, such as GPS land area measurement or crop cutting, if only for a subsample of farms. That choice, too, has cost implications. To evaluate the trade-off between quality and cost, survey designers require an empirically-informed understanding of the extent to which data quality varies with differences in the recall period, among other data quality considerations. Policy makers and other data users benefit from an appreciation of the limitations and biases in existing data related to recall length.

Previous work by Beegle et al. (2012a) found no consistent evidence of significant recall length effects in agricultural surveys, using data from the early 2000s. In contrast, in this paper, we find a significant effect of the recall length on key agricultural statistics. We take advantage of the more recent availability of highquality and very detailed agricultural survey data to overcome some of the limitations of those data and assess and quantify the impact of the recall length on the quality of agricultural data. Using data from nationally representative surveys conducted by the National Statistical offices of Tanzania and Malawi with support from the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) program, we find that longer recall periods are associated with overreporting of plot-level outcomes – production quantity, labor input, and fertilizer input – consistently across the three data sets in the analysis. At the same time, respondents appear to forget listing plots as the recall period increases. Our results point to the need to devise and promote the adoption of survey design choices that minimize this source of measurement error.

The remainder of the paper is structured as follows: Section 2 reviews the literature on error in the measurement of agricultural outcomes, especially that related to recall length. Section 3 describes the data used, the variables generated, and the empirical strategy this analysis employs. Section 4 presents the main results. Section 5 discusses conclusions, policy implications, and offers recommendations.

# 2. Background and related literature

This paper contributes to a large