rafmacalaba/datause-encoder-data huggingface dataset:
# _<u>In-depth Interviews</u>_

In contrast with surveys, in-depth interviews are conversations based on questions, many of which may be open-ended; the goal is strike the right balance between structure and flexibility. In-depth interviews are useful to investigate individual points of view, beliefs and experiences, — — yielding detailed insights into and understandings of personal context. They are especially appropriate for exploring sensitive topics and issues that are not easy (and/or ethically undesirable) to discuss in a group.

There are three types of interviews: structured, semi-structured and unstructured. Structured interviews follow a questionnaire with predetermined questions with little variation. Hence, they limit the extent to which the participant can go into detail on specific topics or issues that are not specifically addressed by the questions. However, they can be administered relatively easily and quickly (e.g., customer satisfaction surveys conducted over the phone). On the other hand, semistructured interviews use key questions as a guide to explore topics for discussion. These questions provide guidance during the conversation, allowing certain flexibility to the respondent to go into greater detail on certain topics. Finally, unstructured interviews start with an open question and continue without any predetermined list of questions. They last relatively longer and need certain expertise from the interviewer. This type of interview is recommended when indepth information is required or the researcher has little information about a specific topic (Legard et al, 2003).

Semi and unstructured interviews are the most useful for obtaining in-depth information, especially if the interaction between the researcher and the respondent is respectful and open. In these circumstances, in-depth interviews allow “the researcher to explore fully all the factors that underpin participants’ answers: reasons, feelings, opinions and beliefs. This furnishes the explanatory evidence that is an important element of qualitative research.”<sup>18</sup> After identifying and selecting the respondents, it is recommended that an interview protocol be developed in case different persons are implementing the interviews. The protocol includes guidelines to conduct the interview, how to act during the interview and what should be done after the interview to ensure that responses can be adequately compared and contrasted (Boyce

> 18 Legard et al. (2003).

27
# The Political Economy of Fiscal Policy and Inflation in Developing Countries: An Empirical Analysis*

# by Sebastian Edwards and Guido Tabellini

# Table of Contents

|I.|Introd|uction|1|
|---|---|---|---|
|II.|Inflat|ion|3|
||11.1<br>11.2<br>11.3|The Inflation Tax<br>Credibility and Inflation<br>Long Run Seigniorage, Tax Reforms, and Political Instability|4<br>6<br>10|
|III.|Fiscal|Deficit|16|
||111.1|Government "Borrowing" from the Monetary System and<br>Fiscal Deficits: The Evidence|16|
||111.2|<br>Political Instability and Budget Deficits|18|
|IV.|Stabil|ization Attempts, Devaluations, and Politics|22|
||IV. 1|The Politics of Successful Devaluation Programs: Some<br>Preliminary Empirical Evidence|27|
|||IV.1.1<br>The Data Set<br> <br>|27<br>|
|||IV.1.2<br>The Timing of Devaluations<br>IV. 1.3<br>Political Determinants of Successful Devaluations|30<br>31|
|V.|Concl|uding Remarks|35|
|Appen|dix I: T|he Theory of Optimal Taxation: Theory and Tests|39|
|Appen|dix II:|The Measurement of Political Instability|45|
|Refere|nces||49|
|Tables|||52|

> * This paper is part of a World Bank research project on "The Political Economy of Fiscal Policies in Developing Countries: A Pilot Study" (RPO 675-59). We have benefitted from extensive comments by Chad Leechor. We wish t-i thank Javad Shirazi and Ajay Chhibber for their support and the participants of two World bank seminars for helpful comments. Bela Balassa provided thorough comments to a previous version of this paper. M. Savastano, J. Santaella, and T. Harris provided excellent research assistance. A previous version entitled "Explaining Fiscal Policies and Inflation in Developing Countries" was presented at the Arizona State University Conference on Political Economy,
11 Market prices are also to some extent indicative of the quality of housing in Moscow, e.g., the Stalin-era and the elite brick buildings are the most expensive, whereas the small-sized apartments of Khrushchev period and the 1970s are the cheapest. The former are high-quality, while the latter are low-quality housing. Average value and, consequently, average quality in Moscow corresponds to standard apartments with an improved layout in the new panel multistory buildings located mainly on the outskirts of the city.

**Table 1.6.** Average price of housing unit by quality and location, Moscow, April, 1995 (thousand dollars).

|Type of Apartment||Locati|on fro|m Cen|ter*|Average|
|---|---|---|---|---|---|---|
||1|2|3|4|_5_||
|Communal apartments|65|39|33|32|30|41|
|Separate apartments, average<br>of which:|116|83|59|58|52|64|
|Khrushchev-era buildings|60|49|42|43|27|42|
|Panel buildings (1970s design)|71|49|48|52|33|51|
|Brick buildings (1970s design)|80|62|51|46|33|56|
|Modem panel buildings|81|83|71|68|56|67|
|Old low-rise stock|130|69|72|72|35|85|
|Modemrbrickbuildings|112|118|89|72|-|103|
|Stalin-era buildings|147|114|95|-|-|114|
|Average|105|<br>77|57|57|51|62|

Sources: The Moscow Longitudinal Household Survey, December 1994; author's estimates. *Location: 1- city center, 2 - near the center, 3 **-** far from the center but not outskirts, 4 - outskirts, **_5_** - outside the Moscow Ring Road. Note: The data presented in Table 1.6 and the other value estimates of Moscow apartments used in this work were obtained from the MLHS, 1994 database complemented by the market estimates made by the methods described in footnote 7.

How do the category and location of a dwelling affect privatization? Table 1.7 presents the percentage of the households who have privatized their apartments by type of apartment and its location from the center of Moscow.<sup>8</sup>

**Table 1.7.** Correlation between the category and location of housing and its privatization rate in Moscow (% of households that privatized before 1995)

|Type of Apartment|L|ocati|on fro|m Cen|ter*|Average|
|---|---|---|---|---|---|---|
||1|2|3|4|5||
|Communal apartments|13|10|-|-|-|5|
|Separate apartments, average|65|56|40|33|34|40|
|of which:|||||||
|Khrushchev-era buildings|-|37|32|27|-|29|
|Panel buildings (1970s design)|-|49|39|32|30|36|
|Brick buildings (1970s design)|50|53|40|46|-|45|
|Modern panel buildings|-|51|44|36|34|38|
|Old low-rise stock|47|25|36|29|-|36|
|Modern brick buildings|73|74|50|43|-|63|
|<br>Stalin-era buildings|77|70|50|-|-|65|
|Average|49|49|36|32|36|37|

Source: The Moscow Longitudinal Household Survey, December 1994.

* Location: 1- city center, 2 - near the center, 3 **-** far from **the** center but not outskirts, 4 **-** outskirts, **5** - outside the Moscow Ring Road.

a Here and below, **the** categories of households who privatized housing in Moscow also include the households who have recently purchased apartments.
**Table 2. School Attendance**

||(1)|(2)<br>In-person Edu|(3)<br>cation(All)|(4)|
|---|---|---|---|---|
|Female|-0.001|-0.001|0.004|0.019|
||(0.013)|(0.017)|(0.006)|(0.013)|
|Poor|-0.014|-0.014|0.018***|0.031***|
||(0.027)|(0.029)|(0.007)|(0.011)|
|Female * Poor||0.001<br>(0.022)||-0.026*<br>(0.015)|
|Child Hunger|-0.004|-0.003|-0.062**|-0.063**|
||(0.043)|(0.043)|(0.028)|(0.027)|
|Urban|0.029**|0.029**|0.011|0.012|
||(0.014)|(0.014)|(0.008)|(0.008)|
|Head Tertiary Education|0.033*|0.033*|-0.027*|-0.029**|
||(0.018)|(0.018)|(0.014)|(0.014)|
|Female Head|-0.034*|-0.034*|0.000|0.002|
||(0.018)|(0.019)|(0.006)|(0.006)|
|Power Grid|-0.031|-0.031|-0.007|-0.007|
||(0.023)|(0.022)|(0.007)|(0.007)|
|Internet|-0.070***|-0.070***|0.003|0.005|
||(0.022)|(0.022)|(0.009)|(0.009)|
|Public School|-0.015|-0.015|-0.009|-0.011|
||(0.025)|(0.025)|(0.014)|(0.014)|
|County FE|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|
|Age|3-6|3-6|6+|6+|
|R2|0.62|0.62|0.09|0.09|
|N|767|767|3210|3210|

_Significance levels:_ * p<0.1, ** p<0.05, *** p<0.01. Standard errors (svy) in parentheses. County fixed effects. Data from wave 4. Control variables: age, age squared, no. children (log), anxious due to outbreak, week count.

# _5.2.2. Well-being_

To the extent that the data allow, we now turn our analysis to the impact of the crisis on the emotional well-being of Kenyan children. We devote special attention to those below six years of age, as disruptions to child development at this stage can be particularly detrimental (Shonkoff, Boyce and McEwan 2009; Shonkoff and Garner 2012). A first sign that educations matters for well-being is that, after schools reopened, changes in both internalizing and externalizing behavior were significantly more prevalent in those not attending school: while fewer than 4 percent of children that were back in the classroom experienced changes in behavior, almost one-fifth of those not in school reported some alteration (p=0.01) – and the gap is even larger for children at the pre-primary level across all categories (Figure 3). In the first half of 2021, the cases of behavioral change in children more than tripled, jumping from around 7 percent between January and March to more than 21 percent of children between March and June (p=0.00).<sup>39</sup>

> 39 For more details, see Figure A2 in the appendix.

14
## **2.5 Welfare and other performance measures**

Important outputs of the model are the per-capita expected utility measure for each income group. As is well known from the properties of the logit model, the expected utility of income group _f_ is:

# **3. Calibration**

The model was calibrated by first expanding the distribution of choices shown in Table 2 to the population of 2005 workers in Beijing. The travel distances and times from Table 3 were replicated by calibrating the road capacities in the congestion function (9) so that congested travel times and speeds for cars given by (9) and (10), agreed with those given or implied in the data. Then, the parameters  , and<sup>_b_</sup> _f_<sup>in the utility function were set in such a way that, given</sup> the observed hourly wage rates, and the observed choice frequencies from the sample, the elasticity of the choice probabilities with respect to monetary travel cost, travel time and rent, given by (5a)-(5c), after being weighted by the choice probabilities, gave the numbers shown in Table 4. Then, the constant effects  were calculated so as to replicate the observed choice _ijm_ | _f_

frequencies for each mode-residence location-job location combination.

Table 4 shows that the own elasticity of the choice probability with respect to monetary cost, and with respect to rent decrease with income, as should be the case, using the principle that the lower is the income share of an expenditure the lower is the price elasticity with respect to the corresponding good. The own elasticity of the choice probability with respect to travel time is increasing with income, reflecting the fact that the value of time is increasing with income or with wage. Given a reasonable guess for the rent of floor space in the periphery as 425 RMB per sq. meter of floor space, the rent in the core was calculated in such a way that, given all the previous calibration results, the calibrated model for the 2005 base situation replicated the observed floor space stocks in the core and in the periphery, while generating reasonable consumption of floor space per worker in both the core and the periphery. The core rent that achieved this result was 705 RMB/sq. meter of floor space.

15
<!-- Start of picture text --> East Asia & Pacific World South Asia Sub-Saharan Africa<br>1<br>Latin America & Caribbean Europe & Central Asia Middle East & North Africa North America<br>3<br>; SS<br><!-- End of picture text --> 1985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 2015 Risk levels None Low —— Moderate —- High — Very high <!-- Start of picture text --> Upper-middle-income countries Lower-middle-income countries Low-income countries High-income countries<br>150<br>100<br>;0 aa —<br>1985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> Risk levels None Low —— Moderate — High — Very high
# **5. Conclusion**

In this study, we show that wide mangrove belts in the Caribbean have the potential to mitigate the disruption of economic activity caused by hurricanes. We use remote sensing nightlight data to measure local economic activity, a damage index derived from a wind field model calibrated for the Caribbean to measure potential hurricane destruction, and the calculated cumulated width of mangrove along the closest path to the coast to measure mangrove protection. Using these data, we estimate the impact of hurricanes on economic activity under the assumption that hurricane strikes are exogenous conditional on cell and year fixed effects. We then use a binning estimator to explore whether there is a negative and plausibly nonlinear relationship between mangroves and hurricane damages.

Hurricanes have negative short-term effects on economic activity, with losses likely to be concentrated in coastal lowlands at risk of both wind and storm surge. We further show that within the coastal lowlands, the impact of hurricanes declines with mangrove width and specifically, that the effect of hurricanes in our sample is mitigated by 0.25 kilometer or more of mangrove width. We also conduct various robustness checks and rule out that these findings are driven by the physical characteristics of the location of the mangrove habitat or by the distance to the coast.

Our results contribute to highlight the importance of mangrove conservation and restoration in the Caribbean region, especially in the coastal lowlands, for protecting local economic activity against tropical cyclones and hurricanes. The evidence suggests it is the width of the mangrove belts that give the protection, reducing the flooding impacts of storm surges during major storms, which implies that largescale efforts will be required to achieve the benefits of the mangrove protection.

Finally, there are two important points to comment. First, we are likely to have underestimated the protective value of mangroves in the long run because mangrove protection may include additional benefits—such as lives saved, health outcomes, and human capital accumulation—that are not well captured by nightlight data. Second, while climate change and the resulting intensification of storms may increase the value of conservation for protection purposes, it is vital that that decisions around areas designated for conservation or restoration consider the threat of sea level rise. Consequently, identifying areas that should be prioritized for conservation and restoration and that have the potential to be mangrove habitats is an important avenue for future research.

20
# **Table 5f—Trade Flows by Trading Partner of SADC (in percentage)**

|||||I|mports|||||||||Exports|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Kenya|Tanzania|Uganda|Rwanda|COMESA|USA|EUR|China|ROW|Kenya|Tanzania|Uganda|Rwanda|COMESA|USA|EUR|China|ROW|
|**Business Services**|||||||||||||||||||
|Air Transport|0.3|0.0|0.0|0.0|2.3|11.7|48.4|2.3|36.3|0.0|0.0|0.0|0.0|0.4|18.3|40.9|2.7|37.6|
|Communication|0.0|0.2|0.2|0.0|7.5|17.5|0.0|15.1|59.4|0.0|0.0|0.0|0.0|0.0|12.4|52.9|2.6|32.0|
|Insurance|0.0|0.0|0.0|0.0|0.0|13.4|50.9|2.0|33.6|0.0|0.1|0.0|0.0|0.0|21.6|36.5|5.0|36.8|
|Business Services nec|0.0|0.0|0.0|0.0|0.2|12.5|56.6|2.6|28.1|0.0|0.0|0.0|0.0|0.4|13.0|45.6|3.2|37.8|
|Financial Services nec|0.0|0.0|0.0|0.0|0.3|17.1|44.9|1.9|35.8|0.0|0.0|0.0|0.0|0.2|16.6|51.3|1.7|30.3|
|Transport nec|0.0|0.1|0.1|0.0|2.0|7.7|47.1|5.3|37.7|0.0|0.1|0.0|0.0|0.5|12.7|45.1|3.7|37.9|
|Water Transport|0.1|0.0|0.0|0.0|0.5|0.0|53.9|2.7|42.8|0.0|0.0|0.0|0.0|0.3|4.8|46.5|1.4|47.0|
|**Dixit-Stigliz Goods**|||||||||||||||||||
|Chemicals Mineral and Metal Products|0.3|0.9|0.0|0.0|0.2|6.3|40.3|10.3|41.7|1.3|0.5|0.2|0.0|0.7|13.9|28.3|7.0|48.1|
|Energy and Minerals|0.0|0.2|0.1|0.0|0.0|0.5|8.6|0.2|90.4|0.0|0.0|0.0|0.0|0.0|31.1|23.2|24.4|21.3|
|Food Products|1.4|0.2|0.4|0.0|0.8|4.7|32.8|2.7|57.1|1.6|0.8|0.8|0.2|1.2|5.4|54.4|1.1|34.6|
|Petroleum and Coal Products|0.1|0.0|0.0|0.0|0.4|11.9|13.1|3.8|70.7|0.8|1.3|0.1|0.0|0.7|20.7|27.4|3.5|45.5|
|Other Manufacturing|0.0|0.0|0.0|0.0|0.0|10.4|49.4|11.1|29.0|0.7|1.1|0.5|0.1|1.5|15.5|40.9|3.3|36.5|
|Textile and Apparel|0.2|0.4|0.0|0.0|0.2|1.3|15.0|50.9|32.0|0.5|0.2|0.1|0.0|0.3|31.5|51.8|1.5|14.2|
|Wood and Paper Products|0.2|0.1|0.0|0.0|0.2|5.5|50.8|12.8|30.4|1.7|1.2|0.4|0.1|1.1|4.0|40.6|2.9|48.1|
|**CRTS**|||||||||||||||||||
|Agriculture and Forestry|1.0|0.8|0.6|0.4|0.7|18.2|14.7|7.0|56.6|0.3|0.2|0.0|0.0|1.2|4.3|51.3|10.1|32.5|
|Other Services|0.1|0.0|0.0|0.0|0.9|20.0|40.5|4.6|33.8|0.0|0.1|0.0|0.0|0.6|16.3|41.3|3.4|38.3|
|Trade|0.0|0.0|0.0|0.0|0.2|4.0|47.0|10.6|38.2|0.0|0.0|0.0|0.0|0.2|4.2|49.3|11.9|34.2|
|Utilities|0.0|0.0|0.0|0.0|0.2|3.5|54.9|3.4|37.9|0.0|0.0|0.1|0.0|0.2|7.2|48.1|2.5|41.9|

# **Source:** GTAP 8.1 data set.

64
tively similar prices to that of higher-income countries. If tariffs are summed to these qualityadjusted import prices, along with other relevant factors needed to make these assets ready for use in production, we conclude that several African nations tend to face relatively high prices on digital goods, especially in low-income countries where the tariff burden tends to be higher.<sup>8</sup> These results are aligned with evidence from item-level price data from the World Bank’s International Comparison Program on digital machinery, equipment, and software, suggesting that prices in Africa are higher than other parts of the world, when accounting for these other factors.

We then examined the likely effects of AfCFTA on digital goods imports. AfCFTA is a groundbreaking trade agreement encompassing 54 African Union member states. It seeks to foster economic integration by progressively eliminating tariffs on 90 percent of traded goods, promoting trade in services, and implementing rules of origin to ensure preferential treatment of products. By 2030, the AfCFTA has the potential to create a single market of over 1.3 billion people with a combined GDP exceeding $3 trillion.

To estimate the likely effects of AfCFTA on digital goods imports, we used the detailed tariff schedule of the agreement, and applied the model proposed by Hoekman, Ng, and Olarreaga (2002) for the total demand for imported goods broken down to the 6-digit level of the Harmonized System. The model simulates a “counterfactual” trade flow resulting from tariff liberalization and compares it with the base year. Despite the comprehensive liberalization expected from the AfCFTA, our analysis indicates that the agreement is unlikely to have a significant impact on reducing tariff protection on digital goods in Africa. The average importweighted tariff on these products is expected to decrease by only 0.13 percentage points due to the agreement, from 3.89 percent to 3.76 percent. This result is partly due to the absence of tariff reductions on extra-zone imports of digital goods, which constitute the majority of digital goods imports, and the limited intra-regional trade in these products. As a result, imports of digital goods are expected to increase only by 0.3% for the whole region. These findings suggest that the AfCFTA negotiations should consider including tariff concessions on digital goods imports from non-member countries, potentially lowering costs significantly, especially in the poorest countries with the highest tariffs on digital goods. Indeed, we find that such a scenario would lead to an 8% increase in the imports of digital goods.

By way of conclusion, we would like to emphasize that the partial equilibrium approach we adopted in this paper is likely to provide a lower bound on the effects of tariff reductions on digital goods imports in Africa. Since we estimate the partial effects of a policy change,

> 8Other relevant factors that may contribute to higher prices of digital manufactured goods in these countries may include local taxes, transport cost, and installation costs.

15
several years are constrained by transportation facilities and long-term contracts. This limitation, of course, can be overcome and new entrants are likely to emerge. However, the longer-term constraint is the demand of export markets. Russia’s proven reserves are sufficient to support a doubling, or even tripling, of its production capacity. In order to absorb this volume of gas, markets in Europe would have to increase dramatically.

The key point here is that Gazprom cannot sell significantly more natural gas in Europe without impacting the price of gas there. Russian domestic consumption in 2008 of 420 BCM, was 2.7 times Gazprom sales in Europe. To sell significantly more gas, Gazprom would have to accept a lower price, i.e., it faces a downward sloping demand curve. **This means that there is no “world price” of gas that Russia faces** . Rather, Gazprom must calculate an optimal price for its gas sales in Europe that reflects the tradeoff it faces between the additional revenue from additional sales of gas and the lost revenue from the reduction of price to sell additional gas. Gazprom’s optimal price of gas in Europe will have to change over time as the demand for gas in Europe changes, but it is in Gazprom’s interest to maximize its profits on exports.

In figure 1, I present the Tarr and Thomson model. Although the data have changed since we did this analysis, the principles still apply and I use the data of our original article to illustrate the argument. I indicate below some key changes and how this affects the results of the analysis.

We assume that Gazprom is optimizing the price and quantity that it sells in Europe— this was between $79 and $99 per thousand cubic meters (TCM) plus $27 transport costs in 2000 and 2001. (Prices were about $380 per TCM in 2008.)<sup>8</sup> . The analysis reveals that than demand from Europe, the markup by Gazprom would fall. Moreover, elasticities of demand are greater in the long run than in the short run, since, for example, inter-fuel substitution is possible in the long run. Greater elasticities imply less market power and lower the optimal markup over marginal costs. We presume, however, that Gazprom has optimized its markup based on long run calculations.

> 8 Gazprom president Alexei Miller reported on March 14, 2008 that "the price [of Russian gas] in Europe now exceeds $370. We believe the average price in 2008 could be $378 and could even reach $400 per 1,000 cubic meters."  Regarding demand in Russia, he noted that the rise of national industries, such as producers of cement, building materials, and fertilizers and gas refineries, is also pushing up Russian gas demands.  Miller said. Gazprom plans to introduce market gas prices for Russian industrial consumers in 2011. See Johnson’s Russia List, http://www.cdi.org/russia/johnson/2008-56-39.cfm.

In 2009, however, the price collapsed to an estimated $280 for 2009. Moreover, Gazprom in its zeal to control natural gas sales to Europe, entered into long term contracts with central Asian suppliers 6