rafmacalaba/data-use-mentions-extended huggingface dataset:
commodity prices? Could it be capturing a commodity price cycle which is not perfectly correlated with the cycle in economic activity that is already captured by GDP growth?

To analyze these issues, we follow the methodology of Céspedes and Velasco (2014) in separating GDP growth from the commodity price cycle by estimating a policy reaction function (i.e., equation (1)) that includes the price of commodities. By removing the potential comovements between fiscal policy and the price of commodities from the residual, it is possible that the volatility of commodity exporters gets closer to that of the other countries in the sample.

We run two variations of this exercise: first, we include the country-specific commodity export price index (from the IMF’s Commodity Terms of Trade database)—a weighted average of the export price of commodities, where the weight is the share of exports of a particular commodity. The second specification makes use of the price of oil.<sup>21</sup> Figure 8 shows the results for our four measures of fiscal policy volatility. As is clear from the four panels, the inclusion of the variation in commodity prices does not change the fact that commodity exporters have a much more volatile fiscal policy than other countries. In fact, there is almost no difference between the three specifications regardless of the fiscal policy variable being used. One potential explanation of our result is that the direct effect of commodity prices cannot be separated from the indirect effect that they have through economic activity. This is consistent with the evidence presented in Husain, Tazhibayeva, Ter-Martirosyan (2008). An alternative explanation is that the relationship between commodity prices and fiscal policy is much more complex than our simple fiscal policy rule.

# **5. Macroeconomic effects of fiscal policy volatility**

So far, we have documented the pattern of fiscal policy volatility with a focus on EMDEs and commodity exporters. We have shown that countries with certain institutional characteristics and policies have a more volatile fiscal policy. But what are
Studies of India similarly suggest that expectations have become better anchored in recent years. For example, Asnani et al (2019) analyze the inflation expectations of households and find that inflation expectations have become better anchored during the inflation targeting period; in particular, there is only limited spillover from food inflation to food and non-food inflation expectations in the inflation targeting period.<sup>38</sup> The RBI has been conducting its Inflation Expectations Survey of Households (IESH) since 2005, recording survey respondents’ perceptions of current inflation and expectations of inflation three months and one year ahead. The survey records both qualitative and quantitative responses. It was conducted quarterly (viz., Mar, Jun, Sep and Dec) until March 2014. At that point two additional rounds in May and November were added to align it with the bi-monthly monetary policy review cycle.

The RBI has also been conducting a survey of professional forecasters since the second quarter of 2007-08, drawing responses from forecaster with both financial and non-financial institutions. Initially, the survey was conducted at quarterly frequency, but this was changed to bi-monthly in 2014-15. The survey collects annual quantitative forecasts for two financial years (the current year and next year) and quarterly forecasts for five quarters (the current quarter and next four quarters). We analyze how the inflation expectation series for India has changed since the implementation of inflation targeting. For the analysis below, we use both the household and professional forecaster series averaged at quarterly frequencies.<sup>39</sup> We use the CPI inflation expectations of professional forecasters, and compare household and professional forecasts with CPI inflation.<sup>40</sup> Both professional forecasts and households’ expectations of inflation declined with the shift to IT. Even so, household expectations of inflation consistently exceed actual inflation, and the deviation has not declined. Figure 7 shows that the average of professional forecasts has been close to actual inflation, while household expectations have often exceeded actual inflation. In the last few years
<!-- Start of picture text --> @ Bovine Meat @ Mutton & Goat Meat @ Poultry Meat<br>40<br>36<br>as 33<br>30 29 29<br>> 26<br>SB 25<br>s<br><a20 7 17<br>15,<br>10 7<br>. 5<br>°<br>Actual Consumption 2016 FBDG-scaled consumption EAT-Lancet-scaled consumption<br><!-- End of picture text --> <!-- Start of picture text --> Bovine Meat Mutton & Goat Meat TM Poultry Meat<br>1400<br>1238<br>1200<br>@= 1000 869<br><<br>22o800<br>© 600<br>400<br>232<br>200 04 70 94 81 25 71<br>0<br>Actual Consumption 2016 FBDG-scaled consumption EAT-Lancet-scaled consumption<br><!-- End of picture text -->
Figure 10. Reasons for Not having an Account, 2011 <!-- Start of picture text --> 80 72<br>70<br>70 66<br>60 54<br>50<br>50 46<br>40 36<br>27 27 29<br>3020 14 22 21 19 21 15 14 22 23 18 13 22 15 17 17 20 17 24 16<br>10 7 4 6 9 6 5<br>0<br>Turkey BRIC Rest of Developing World Developed World Rest of ECA<br>Too Far Away Too Expensive Lack Documentation<br>Lack Trust Lack of Money Religious Reasons<br>Family Member Already Has One<br>Notes: Multiple responses allowed.<br>Sources : FINDEX 2011<br>Percent of Respondents<br><!-- End of picture text -->

# **ii. SAVINGS & SAVINGS USING A FINANCIAL INSTITUTION**

Turkey is among the countries with the lowest savings rates, both at the individual and the household level (Figure 30 and Figure 32). In the European region (which is the coverage of the LITS household level survey), Turkey has the fifth lowest savings rate among 35 countries (10 percent). In the FINDEX database, Turkey also ranks the fifth lowest in the world among 145 countries, with 9.6 percent of individuals reporting that they save.

The rate of savings at a financial institution in Turkey is low compared to the rest of the world, including only developing countries (Figure 11). In 2011, rate of savings using a bank in Turkey’s top quintile is similar to the rate of the BRICS’ lowest quintile. Turkey’s overall bank savings rate stands at 4 percent which is less than a fourth of the savings level in the BRICS countries and less than half of other ECA countries. Considering both the account usage and average income in Turkey is higher than the compared groups in 2011
available. We therefore calculate the present value of the orchard under the assumption that the orchards are halfway through their assumed lifetime.<sup>14</sup> For the expected price, we calculate a fiveyear moving average of actual domestic producer prices, converted to nominal dollars, centered on the current year (two periods forward and two periods lagged). The income per hectare is imputed forward in time (with discounting) for each crop and then aggregated. We use the U.S. interest and inflation rates to calculate a real interest rate, which serves as the discounting factor. Finally, we convert the result to constant (1990) dollars by applying the U.S. agricultural GDP deflator.

# **Data**<sup>_15_</sup>

The economies included in the agricultural investment data set are Australia, Austria, Canada, Cyprus, Denmark, Egypt, Finland, France, Greece, India, Indonesia, Italy, Kenya, Republic of Korea, Malawi, Mauritius, Morocco, the Netherlands, Norway, Pakistan, Peru, the Philippines, Sri Lanka, Sweden, Republic of Tanzania, Tunisia, Turkey, United Kingdom, United States, and Uruguay.

For most countries included in the data set on agricultural capital stocks, the series on the sectoral breakdown of fixed investment begin in the 1960s, thus sufficiently complete to estimate capital stocks beginning in 1967. All series on livestock and treestock begin in 1961. Looking forward, there is potential for the expansion of the data set on agricultural fixed capital to include more countries, but with a later starting date for the series.<sup>16</sup>

# **Evolution of capital stocks**

Having a data series on agricultural capital which is comparable across countries and over time allows us to examine the evolution of capital stocks and assists us in gaining insight on the process of agricultural growth. In Figures 2 and 3, we present the frequency distribution of growth rates of the capital components for
|—|
|Niger|Linear|Yes|0.43|1|—|13.6|13.6|—|
|Nigeria|IBT|Yes|0.15–2,38|5|20|0.9|6.5|622|
|Rwanda|Linear|No|—|1|—|14.6|14.6||
|Senegal|IBT|No|—|3|—|23.8|26.2|10|
|South Africa|IBT|No|—|2|50|0|7.2|—|
|Tanzania|Linear|Yes|1.93|1|—|10.8|10.8|—|
|TZN–low use|IBT|No|—|2|50|4.1|13|217|
|Uganda|IBT|Yes|1.1|2|15|3.4|23.3|585|
|Zambia|IBT|Yes|1.3|3|300|1.6|3.7|131|
|Zimbabwe|IBT|No|—|3|50|0.60|13.5|2,159|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 2. MWI = Malawi; MOZ = Mozambique; TZN = Tanzania. — Not available.

The structure of blocks—their number, size, and respective price levels—is what ultimately defines how tariffs reflect costs, affect demand, and address equity issues. The sample countries have adopted widely differing approaches to tariff design, with one third of countries practicing linear tariffs (as in Rwanda and Tanzania) and the remaining two thirds practicing IBTs.

Looking at the two thirds of sample countries that practice IBTs, most have adopted relatively simple structures (figure 2a) with two or three blocks (as in Benin, Burkina Faso
height and weight separately by sex and age groups (1-3, 4-9, 10-17, 1830, 31-60, 60+) for a large population of individuals drawn from around the world including tropical and developing countries.

The anthropometric micro-data used come from India’s National Family Health Survey (NFHS), a household survey that measures height and weight

> 3Basal Energy Expenditure (BEE) is closely related to REE. Measuring BEE requires a minimum period of fasting before testing and is lower than REE, which incorporates the energy cost of metabolizing food. Most laboratory measures use REE. It is possible to directly quantifying the energy cost of metabolizing different macronutrients but the differences over time and across households appear minimal.

5
cities where unemployment is expected to be continue to be high or rise further. Our view is the common trends assumption is likely to be more plausible when the units being compared are more similar to one another to begin with, which is where combining matching with DiD can be of use (e.g. Gibson and McKenzie (2014)). Newer, “doubly-robust” approaches to DiD build on this idea to yield valid estimates when either the selection on observables needed for propensity scores or the parallel trends modelling assumption used in DiD holds.

A second common form of difference-in-differences estimation in migration studies is to examine the impact of staggered policies introduced into different states or different countries at different points in time. For example, Otsu (2021) uses panel data from 1999 to 2020 to examine the impact of sanctuary cities (cities that do not share data on immigrants with the Federal government) on crime, with cities adopting this policy over time. At the country level, Kahanec et al. (2016) examine the impact of different countries joining the European Union’s free movement of workers on bilateral migration flows. In cases like these, where treatments are staggered over time, recent literature has shown that in the presence of treatment effect heterogeneity or dynamic treatment effects, the standard twoway fixed effects difference-in-differences estimator is a weighted average of many different comparisons, and it is possible that some of these weights may even be negative, making it hard to interpret the estimate (Goodman-Bacon, 2021). Different estimators have been developed to deal with this problem, which involve making sure that treated units are only ever compared to ‘clean controls’ and not to earlier treated units (see de Chaisemartin and D’Haultfoeuille (2022) and Roth et al. (2022) for a survey). A key point to note here is that with treatment heterogeneity, researchers need to clearly specify precisely which weighted average they are interested in estimating.

A third type of difference-in-differences commonly used in the migration literature involves situations in
Our main variable of interest is the natural logarithm of the market access index defined in Formula (1). It makes uses of the road panel dataset constructed by Jedwab and Storeygard (2016), which we modified to be used with our spatial unit and the dates for data availability in HYDE 3.1. Table A2 in the Appendix presents the evolution of the categories over time for the period 1970-2005. This table clearly provides evidence that the period has significant increases in paved and improved roads, but the majority of roads remain of poor quality (dirt roads / other). Map A2 represents the same changes on a map of Sub-Saharan Africa for 1970 and 2005. The construction of the market access index also makes use of a regional panel database of urban populations in Sub-Saharan Africa—that we assemble using the City Population data—for the same dates and spatial unit as in the HYDE 3.1 data (see Appendices 4 and 5). Map A3 illustrates the increase in urban population in the region between 1970 and 2005.

The control variables that we include in our regressions—average annual rainfall, population density, and distance to nearest major port—are from several sources. We processed monthly rainfall data from PREC/L (Chen, Xie, Janowiak, and Arkin, 2002), which we aggregated to the annual level, averaged over each decade, and aligned with the HYDE spatial unit. The population density was calculated dividing the number of people in a grid cell (available from HYDE) by the land area in that cell. Finally, we also use the Euclidean distance from each grid cell to the nearest major port.

Table A4 in the Appendix presents summary statistics of the variables that we use in the regressions. All values are at the cell level (roughly 10 km  10 km, or 10,000 ha). Cropland area varies significantly across cells (from uncultivated to almost fully cultivated areas) and its mean 10
Table A.8 in the Appendix presents by province, the predicted per capita expenditure, poverty rate, number of poor, and share in the total number of poor in the country. Lai Chau, Ha Giang and Dien Bien are three poorest provinces with the poverty rate of more than 70%. As expected, Hanoi and Ho Chi Minh city are the richest cities, followed by Da Nang, Hai Phong, Quang Ninh, Binh Duong, Ba Ria-Vung Tau.

Figure 1 shows the spatial distribution of poverty by provinces and districts in 2009. Poverty rates are highest in the mountainous Northern areas and lowest in the Mekong and Red River Deltas. Disaggregating down to the district level reveals a greater degree of heterogeneity in terms of both pockets of extreme poverty and pockets with particularly low levels of poverty. As we shall see below, such heterogeneity across sub-national localities translates into gains from spatial targeting of anti-poverty resources.

Figure 2 graphs the density of the poor across the country. Because of their large populations, the Mekong and Red River Delta regions still account for a significant number of the poor in the country. However, as we shall see below, the picture in 2009 is much less accentuated than at the time of the preceding census, and as such indicates a clear attenuation of the pattern described in earlier studies of poverty in Vietnam (see Minot et al, 2003) where the distribution of the number of poor people was inversely correlated with the spatial distribution communities’ poverty rates. Then, poverty rates were highest in relative sparsely populated localities and these thus accounted for only a modest fraction of the poor. Today, poverty rates remain spatially concentrated but the distribution of poor people is more evenly spread across the country. Consequently Vietnam’s poorest communities account for a larger share of the poor population.

Despite the ongoing urbanization process, poverty in Vietnam is currently still largely a rural phenomenon: 95 percent of the poor live in rural areas. The poverty rate in urban areas is generally low, and there
across countries, the determinants of inflation are not purely domestic anymore.

Following results presented in table 1, in table 2 we extend the analysis by using value-added data for trade flows instead of gross trade flows. Doing so is important because, for example, gross import flows might contain value added produce in a third country, in which case domestic inflation would be more likely to be synchronized not with the _direct_ import partner but with the country of origin of value added. Looking at column (1), we first find that the finding related to total trade is _reversed_ : while an 13
judgment to account for risk factors different than drought (Hoefsloot et al. 2013). In particular, as section III.2 showed, beneficiary numbers are subject to a detailed review process which can lead to changes in estimated beneficiary numbers. Given that historical beneficiary data are thus informed not only by current drought data but also by informed judgements, the LEAP tool is being extended to base beneficiary estimates on household livelihoods data (see section IV.2 below).

Due to the use of historic beneficiary numbers in computing needs, LEAP early warning information has so far not been used to inform insurance<sup>46</sup> . Upon the integration of household livelihood data to compute beneficiary numbers and hence elimination of subjective information in the computation of beneficiary numbers, the LEAP tool could serve as a basis for weather index insurance.

- (ii) **_Integration of market and price data:_** A further constraint of the LEAP tool in its current version is that the tool does not take into account market and price data. In particular, the ability of households to acquire food is determined not only by drought conditions (impacting on farmers’ ability to produce crops and livestock), but also by their ability to buy food in the market. Farmers’ ability to buy food is determined, for instance by the availability of food in the market, the distance of the household to the market, and food prices. To enhance the accuracy of beneficiary estimates obtained through the LEAP tool, the LEAP tool is being reviewed to include these variables.

- (iii) **_Integration of data for pastoral regions:_** Ethiopia is composed of a range of diverse livelihood zones, with livestock production representing a key source of income particularly in the regions of Afar, Borena and Somali. At present, the LEAP tool is centered mainly on developing beneficiary numbers for the crop seasons _meher_ and _belg_ . By integrating a pastoral index into the tool, LEAP could additionally generate beneficiary estimates for
) Addison-Wesley Publishing Company, Inc., U.S.A.

# DATA SOURCES

Deininger, Klaus and Lyn Squire (1996). A New Data Set Measuring Income Inequality. World Bank Economic Review. Vol. 10, pp. 565-591.

Easterly, William (1997). “Life During Growth.” Journal of Economic Growth. Vol. 4, Issue 3, September 1999.

Gastil, R.D. (1973-1989). “The Comparative Survey of Freedom.” <u>Freedom at Issue. Various</u> Issues.

Heston, Alan and Robert Summers (1991). “The Penn World Table (Mark 5) : An Expanded Set of International Comparisons, 1950-1988.” Quarterly Journal Economics. May, pp. 327-368.

International Monetary Fund. International Financial Statistics. Various Issues.

International Monetary Fund (1998). International Financial Statistics. CD-Rom International Monetary Fund (1999). International Financial Statistics. CD-Rom Taylor, Charles Lewis (1985). World Handbook of Political and Social Indicators III, 1948-1982 (ICPSR file 7761).
in dealing with government regulations (Time Tax). For power outages, we use total hours of power outages experienced by the firm in a typical month over the last year. As with corruption, we use country-industry cell average for regulation and power outage variables. Data source for these variables is ES.

Regression results for the falsification test concerning red tape or regulation are provided in table A5 in Appendix A while the same for power outages are provided in table A6 in the appendix. For brevity, results are shown with all the baseline controls included and with and without the various interaction term controls used in section 5.1. The results easily pass the falsification test. That is, we find no evidence that the impact of greater regulatory burden or more power outages on the likelihood of a firm being financially constrained is smaller (less positive or more negative) in countries with better financial institutions.

# **6. Do some firms suffer more from corruption?**

In this section, we analyze how the impact of corruption on financial constraints faced by a firm may depend on the type of firm considered. We argued above that, problems associated with corruption, such as uncertainty and asymmetric information that make it more difficult to obtain finance, are likely to affect those firms more that otherwise enjoy better access to finance. We focus on two groups of firms that are known to enjoy better access to finance: firms with all male owners vs. the rest; and better performing firms in terms of annual sales (firm-size), labor productivity, growth rate of annual sales, and exporting status.

# 6.1 _Gender_

25
the recall surveys, plot characteristics are similar, suggesting that recall farmers do not systematically forget certain types of plots.<sup>22</sup> Instead, it may be possible that the plots that are forgotten are the prime responsibility or exclusive domain of people who are themselves likely to have gone unreported. If this were the case, then the omission of a person (say, one who worked little or infrequently) from the household reporting may also result in the omission of her or his plot. To test this, we look for significant differences in the average number of plots worked per person and the average number of people working per plot. These data are presented in table 7. They suggest both that many different people work on a given plot (an average of 3.2 people in weekly visit households) and that a given person will work on many different plots (an average of 3.6 plots). Thus, it is unlikely that the omission of a single household member would necessarily result in the omission in their plots.

# _Reporting on people_

We now perform a similar analysis of which household workers may be likelier to be forgotten in recall-based reporting and why.

First, as above, we examine the entry and exit of people from the household using weekly data. In the baseline survey, households reported on household members who were working on each of the household plots. Figure 3 shows that, in this earliest survey, an average of 2.1 people per weekly visit household were reported as engaged in agricultural work. This number grew

> baseline question’s intent is unlikely to have driven the weekly recall gap in the number of plots.

> 22 The large and significant differences by ownership status are driven by technical difficulties in the collection of ownership information among some weekly surveyed households.

17