rafmacalaba/data-use-mentions-extended huggingface dataset:
>|ecializat<br>|ion Inde<br>|x<br>||||
|Low-technology products|2.24|2.36|2.39|2.41|2.55|2.50|2.28|2.28|102|90|
|Medium to high tech products|0.36|0.30|0.31|0.37|0.33|0.35|0.44|0.46|125|140|
|TOTAL MANUFACTURES|1.05|1.06|1.02|1.00|1.00|1.10|1.04|1.06|95|106|
|**_Memorandum_**:share of manu|factures|in Bulg|aria'sEU|-15 exp|orts||||||
||70.5|71.6|73.0|71.9|68.7|74.5|71.0|72.4|100|105|

Source: Own calculations based on EU-15 trade data reported to the UN COMTRADE database.

Although Bulgaria’s continues specializing in low-technology products in EU-15 markets, medium- to high technology intensive products have outperformed since 2000 lowtechnology products across all dimensions. They had the largest increase in competitiveness in EU-15 markets as measured by the share in EU external imports, which—after having been stagnant at around 0.10 percent in 1996-1999—increased 76 percent in 2000-03. Net exports of medium to high tech product, albeit still well below their imports, have considerably increased since 2000 in contrast to net exports of manufactures to EU-15 markets. This has taken place against the background of rapidly expanding imports, which stood in 2003—in terms of value—at 185 percent of their level in 2000. The value of Export Specialization index (ESI) in EU markets fell for low-technology products from its record level of
South Africa, Tanzania, Togo, and Uganda. For Nigeria and Tanzania we use panel data from the Living Standards Measurement Study–Integrated Survey on Agriculture (LSMS-ISA), partly to address quality concerns with other consumption surveys (World Bank 2014, 2015).<sup>11</sup> Only household survey series considered as comparable over time (in terms of the broad data collection method for consumption data and

> 10 We use consumption rather than income because it is generally thought to be a better measure of permanent income and is also easier to measure in economies with large agricultural or other informal sectors (see Deaton 1997).

> 11 See LSMS-ISA (database), World Bank, Washington, DC, <mark>http://go.worldbank.org/BCLXW38HY0.</mark>

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applied propensity score weighting and matching techniques to achieve a higher degree of baseline comparability across groups.<sup>40</sup>

# **b. Survey Response Rates and Attrition**

One potential problem with any study such as ours is sample attrition. Approximately one year after each baseline survey, we conducted a follow-up survey.<sup>41,42</sup> The response rates were quite high for both followup surveys (see Table 5). Overall, the survey firm was able to track and successfully interview 88 percent of the baseline survey respondents, yielding a final sample for analysis of 4,101 individuals.<sup>43</sup> High response rates such as these limit the degree to which any differences between respondents and non-respondents can affect our estimates.

Particularly worrisome is the possibility of so-called “ _differential attrition_ ”, a situation in which we reinterview trainees and non-trainees at statistically different rates. Even with low attrition rates for a panel survey, differential attrition may influence the scientific validity of results. Suppose, hypothetically, that very motivated individuals in the control group ( _i.e.,_ non-trainees) are more likely than trainees to migrate to a district where they find employment. As a result of this migration, these individuals will not show up in our analyses and this scenario could compromise the scientific validity of this study’s estimates.

In Table 19, we explore this possibility and show no evidence to support it. Table 19 shows a series of regressions on the correlates of survey attrition. We regress the panel status of respondents on their treatment status, depending on how the treatment indicator is defined. This specification provides information on whether treatment or control individuals are more likely to be lost to follow-up. Next, we add a series of covariates, such as gender, age, marital status, parental status, ethnicity, and employment, a set of district and T&E provider-specific control dummy variables. The results indicate that attrition is not correlated with
information about buyers (over traditional financial intermediaries), those with sufficient resources use that advantage to offer more trade credit in line with the redistribution view. For example, Nilsen (1994) found that during monetary contractions (when uncertainty is greater), small, likely financially constrained firms receive more trade credit from suppliers. In contrast, where relationships between banks and firms are strong such as in Germany, suppliers’ information advantages (over banks) are not as great, and trade credit is used less (Breig 1994).

But, as noted, suppliers also must have resources to extend. They can extend their own resources to buyers, and there is evidence that profitable suppliers provide more trade credit than others in multiple contexts. For example, the influential study by Petersen and Rajan (1997) was among the first to test theories of trade credit on a large set of firms, using a dataset of small firms in the U.S. compiled by the U.S. National Survey of Small Business Finance (NSSBF) in 1988-89. They found evidence that firms with more assets and greater longevity, both indications of past profitability, and higher gross profit margins, an indication of greater cashflows, extended significantly more trade credit than others (as reflected in a higher ratio of accounts receivable to sales). In a very different context, Cull, Xu and Zhu (2009) used data from over 100,000 industrial firms (including all SOEs) in China from 1998 to 2003 to show that profitable private domestic firms were significantly more likely to extend trade credit than unprofitable ones, credit which provided a substitute for loans for these firms’ customers that were shut out of formal credit markets.

Suppliers need not fund trade credit only from their internal resources. Under the redistribution view of trade credit, firms with better access to external finance redistribute the credit they receive to less advantaged firms (Meltzer 1960; Nilsen 2002). For example, in the U.S. context Petersen and Rajan (
expenditure in 2014 U.S. dollars<sup>8</sup> was $73 in urban areas and $37 in rural areas, giving a national median of $49 per capita per month.

# Measuring Access in Household Surveys

What information about access do household surveys provide? How many surveys provide enough information about kWh of electricity consumed by each household—essential information for designing targeted subsidies? How do access rates differ by income, between urban and rural, between the poor and

> 8 Expenditures were adjusted for inflation to 2014 and converted to U.S. dollars using the exchange rate in 2014.
Survey (PIHS) of 2001-02 shows that enrollment decisions for girls in rural areas are more responsive than those of boys to proximity to school (see World Bank, 2005), but this analysis does not account for settlement boundary-crossing. More suggestive evidence comes from the 2001-02 PRHS-I, in which parents of children never enrolled were asked the main reason for the child’s non-enrollment. For boys, far and away the most important reason is economic ("school too expensive" cited by 43% of parents), but the picture is quite different for girls. While economic motives still dominate (32% reported "school too expensive"), social constraints become an important consideration. Respondents were much more likely to report that they did not "approve" of

> 4Veiling or purdah in this context is the use of a large shawl (chador ) to cover the head and the body, and sometimes also part of the face.

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Policy Research Working Paper 8020

# **Abstract**

Private savings play a pivotal role in financing development and sustaining growth. Recently, there have been many theoretical developments that underpin key determinants of savings behavior, many of which merit empirical investigation. Understanding the dynamics of the determinants of savings is crucial to inform economic policy and devise reform programs. This paper builds on earlier work examining the stability of the long-run relationship between the real interest rate, financial saving, and total saving during 1960 to 1990. The paper extends the scope of the empirical investigation of the determinants of private savings behavior in the Arab Republic of Egypt, and considers the effect of financial development. The analysis uses quarterly data covering 1991–2010, adopting a vector error correction model. The key findings attest that private savings in Egypt follow the Life Cycle Model in the long term. Controlling for population growth, the analysis finds that the real interest rate and financial development are key determinates for real private savings in the long run. The negative long-run relation between the real interest rate and private savings holds under the proposed model structure as well as for that in the earlier work. However, in the short run, inflation and exchange rate movements are key determinants for private savings decisions. Robust economic policies, inclusive of macroeconomic and monetary measures, are prerequisites for maximizing private savings and financing growth in Egypt.

This paper is a product of the Office of the Senior Vice President for 2030 Development Agenda, UN Relations and Partnerships. 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 authors may be contacted at arostom@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,
>4 .45<br>.4<br>3.9<br>.35<br>3.8 Job Satisfaction<br>Hours Worked (log) .3<br>3.7 .25<br>3.6 .2<br>.64 .5<br>.62<br>.45<br>.6<br>.58 .4<br>.56 .35<br>Self-Employed<br>.54<br>Reservation Wage (log) .3<br>.52<br>.5 .25<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

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, or product restrictions 16% 28%<br>Foreign investment limits 16% 22%<br>Expat restrictions 13% 24%<br>JV requirements 15% 20%<br>Local sourcing requirements 12% 21%<br>R&D investment requirements 10% 21%<br>Minimum investment requirements 8% 19%<br>0% 20% 40% 60% 80% 100%<br>Major obstacle Moderate obstacle<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

_Note:_ JV = joint venture; R&D = research and development; tech = technology.

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leveraging panel surveys that are multi-topic and interoperable in nature. Unfortunately, such surveys remain uncommon in many developing contexts, which hinders the possibility of studying climate migration dynamics in those regions. Relatedly, even in countries where panel data are available, these are often of limited length, preventing the analysis of longer-term dynamics and impacts. This is also the case of this paper, where we can only provide short-run evidence in support of our conceptual framework. Having longer panels would allow going a step further and establishing whether the short-run relationships detected ultimately lead to permanent immobility 29
empirical insight in the extent to which food price seasonality transmits into seasonality in food consumption. With the notable exception of Dercon and Krishnan (2000) for Ethiopia, there remains little systematic information on the magnitude of these links.

The empirical application is to Tanzania, which provides a good backdrop to study seasonality in prices and consumption.<sup>4</sup> It covers a wide array of agro‐ecological settings and cropping systems and staples (maize and rice) still make up a sizeable share of food consumption (33 percent in rural and 28.5 percent in urban areas). Seasonality in maize and rice prices is estimated from 19 years of monthly prices collected during 1995 and 2013 from 20 regional markets across Tanzania. To estimate seasonality in consumption, three waves of a nationally representative household consumption panel survey are used. The surveys were conducted throughout the year on each occasion starting in 2008‐09 and subsequently in 2010‐11 and 2012‐13. The data enabled identification of the staple price effects on food consumption and simulation of the extent of price induced seasonality in calorie consumption, alongside a household fixed effect estimation strategy and controlling for other consumption seasonal factors.

The results show that the seasonal gap in staple prices is substantial in Tanzania, though variable across markets. It is almost twice as high for maize (27 percent, averaging across districts), as for rice (15 percent). It is also 2.5 to three times more than the gap observed in reference markets (10.7 percent in the South African Futures Exchange market for white maize; and 5 percent broken rice in Bangkok), suggesting substantial excess seasonality. The contribution of seasonality to overall price volatility is limited (13.1 percent for maize and 7.1 percent for rice). Price induced seasonality in caloric intake is sizeable among poor urban households and rural net food sellers (around 10 percent). No effects were discerned among net food buyers.

In what follows, section two shows how
to 30% of their production in the domestic market, and the related profits are taxed at the general rate.

Figure 1: Timeline of reform.

<!-- Start of picture text --> 0% CIT 10% CIT<br><!-- End of picture text --> <!-- Start of picture text --> Announcement<br>10% CIT if incorporated after 2007<br>or incorporated before 2008<br>and older than 10 years<br><!-- End of picture text --> <!-- Start of picture text --> Enforcement<br><!-- End of picture text --> Note: This timeline depicts the subsequent budget law postponements of the enforcement of the Corporate Income Tax (CIT) reform for Offshore firms, thus leaving the full exemption in place until 2013, when the law 2006-80 was finally applied.

# **3 Data and Descriptive Statistics**

The main source of data we use are administrative records on Tunisian registered firms, the Repertoire National des Entreprises (RNE). This data spans the universe of all private and public sector firms and includes information such as firms’ 4-digit economic activity sector, location, age and whether owners are foreign.<sup>9</sup> In joint collaboration with the Tunisian Statistics Institute (INS), the RNE microdata has been merged with tax returns to include yearly measures of firms’ declared turnover, profits, and revenue from exports; social security data on total employment and wage bill; and customs data on total value of exports and imports.<sup>10</sup> We clean the data in a number of ways. In Figure A1 in the Appendix, we provide the key steps we take to arrive at our analysis sample. We start with a full panel of almost 16 million observations and almost 1.5 million firms between 1995 and 2021. First, we focus

> from the
retest structure and differs from previous studies in that a randomly selected portion of the sample, the RCT treatment group, received an actual improved Mirt stove after completing the survey in 2013. Therefore, when the same sample was given an identical survey in 2016, a portion of them had already experienced cooking with the new, efficient stove technology. Results from this study not only contribute to the growing body of research about temporal stability of preferences, but also contribute to the existing literature about global sustainable development through the adoption of clean technologies.

# **3. Empirical Approach**

Given the improved cookstove RCT and the repeated choice experiments that were conducted in 2013 and in 2016, our data allow us to study temporal stability and the evolution of preferences elicited using a choice experiment survey within a difference-in-difference framework. The choice experiment in 2013 was conducted immediately prior to the improved stove RCT.

## _Choice Experiment Design_

Choice experiments are a stated preference valuation method used to determine individuals’ willingness to pay (WTP) for goods that do not have attainable market values. A choice experiment survey was chosen for this study over other stated preference valuation methods because they provide information about individual attributes of the good being valued.

We follow standard practice in the choice modeling literature for this research (Adamowicz et al. 1997, Adamowicz et al. 1998, Carlsson and Martinsson 2003, Louviere et al. 2000). We initially conducted informal focus groups, engaged in discussions with researchers and then finally conducted 15 formal focus groups and a field trial of the survey. Through this process we identified five attributes that we use for the final choice experiment; durability of the stove, the reduction of fuel use, the reduction of smoke, decrease in cooking time, and the cost of the stove. The attribute levels are presented in Table 1.

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# **REFERENCE**

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- Briceno-Garmendia, Cecilia, Karlis Smits, Vivien Foster. 2008. Financing public infrastructure in Sub-Saharan Africa: Patterns and emerging issuers. Africa Infrastructure Country Diagnostic, Background Paper No. 15. The World Bank.

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- Deller, Steven, David Chicoine, Ganapathi Ramamurthy. 1986. Instrumental variable approach to rural water service demand. _Southern Economic Journal_ , Vol. 53, pp. 333-346.

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- ESMAP. 2009. _Climate Vulnerability Assessments: An Assessment of Climate Change Vulnerability, Risk, and Adaptation in Albania’s Energy Sector_ . Energy Sector Management Assistance Program.

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is associated with a 2% drop in income. Converting all variables into standard-deviation units, each one standard deviation increase in the correlation between morality and ability is associated with an increase in the log of per capita income of one-fourth of a standard deviation, approximately equal to $3600 for the median-income country in our sample.<sup>15</sup> This standardized coefficient of .25 is comparable to that for schooling (-.23 standard deviations), and larger than for geography or morality (both at .16), and openness (.08). Only rule of law (.38) has a larger standardized coefficient).

The ability and morality variables are computed from survey data, with different sample sizes in each country’s surveys. Values estimated from larger samples will tend to be more precise, so the observations for those countries contain more information. In equation 2-5, we report estimates using weighted least squares, with observations weighted by the number of WVS respondents. Results from WLS estimation differ only trivially from the OLS results in equation 2-4.

Equation 2-6 shows how results change when the most influential observation in favor of our key hypothesis is dropped. Figure 1 depicts the partial correlation between the dependent variable and the morality-ability correlation from equation 2-4. Thus, the slope of the least-squares line imposed on the figure equals the regression coefficient 3.616. Tanzania is clearly the most influential case in our favor. When this observation is dropped in equation 2-6, the coefficient of the morality-ability correlation declines from 3.62 to 2.85, but remains significant at the .01 level. Two other relatively influential cases are Uganda and Kyrgyzstan.<sup>16</sup> When they and Tanzania

> 15 The standard deviation of (log of) per capita income in the sample is 1.13, so a standard deviation increase in the morality-ability correlation will be associated with an increase of about .28 (=1.13 x .25) in the log of
2015). It provides aggregate results that are similar but makes it possible to consider explicitly differential vulnerabilities for people with different socioeconomic status.

We start from household surveys using the latest vintage of the World Bank Global Monitoring Database, an aggregation and harmonization of country-level household surveys. The household surveys used here were made at different dates for different countries, but all are before the COVID-19 pandemic (cf. Appendix 1). The analysis covers 86 countries representing 64 percent of the total poor population. To provide a global estimate, the simulated poverty headcount is scaled up in accordance with the fraction of missing population in each region.

In each country, the population is described by a set of representative households, with their demographic information, education level, sectoral employment, and consumption or income level (here consumption and income are considered interchangeable). Each household has a _weight_ that measures its importance and representativeness of the entire population.

First, we create scenarios for the future income distribution of households in each country in 2030, without accounting for climate change impacts. We factor in possible changes to demography, education, labor force participation, economic structure (share of population employed in various sectors), productivity (for skilled and unskilled labor), and redistribution (through pensions and social protection). We explore a range of possible futures for these factors and combine them to generate hundreds of scenarios representing a wide range of plausible socioeconomic development pathways for each country.

In practice, we first manipulate the weight of each household to match demographic and macroeconomic projections detailed in one of the five Shared Socioeconomic Pathways (SSPs). The SSPs describe plausible future changes in demographics, human development, economies, institutions, technologies, and the environment (O’Neill et al., 2014, 2017; Samir & Lutz, 2017). Within each country, representative weights of the households are adjusted so that the population matches SSP projections in terms of population size and composition (including for instance the number of children and education level of