rafmacalaba/datause-encoder-data huggingface dataset:
22 ores; non-ferrous ores; coal; coking products; perfume oils; silk products; electro-technical equipment; radio-electronics; shipbuilding; precision instruments; and other miscellaneous production. These sectors only accounted for more than 10% of exports of seven republics: Ukraine (17%), Moldova (13.2%), Estonia (12%), Latvia (37%), Lithuania (21%), and Armenia (22%).

In order to obtain a comprehensive assessment of the sensitivity of NIS exports to EC trade barriers, we employed the following three-step procedure. First, a concordance between the SITC (Rev. 1) and the 110-sector disaggregation used in Goskomstat estimates of inter-republic and extrarepublic trade was established and then tariff rates and NTB coverage ratios were computed for each group using the World Bank--UNCTAD SMART data base. Next, we aggregated trade into two groups (vulnerable and non-vulnerable) using the criteria described earlier. The trade shares of these groups in both total inter- and extra-republic exports are given in columns "a" and "b" of Table 8. To assess the relative importance of the two types of barriers, the portion of FSU exports vulnerable to NTBs and tari%f are shown in (columns "c" and Vd") while columns ("en and "f") show the share of exports vulnerable to relatively high tariffs and columns (ng" and "h") provide similar information for nontariff barriers.

The key points evident from this table are as follows. First, many products which were both traded within the FSU and shipped to outside partners are highly vulnerable to EC trade barriers. The share of vulnerable products is high in the trade of all former republics and particularly so for most -Asian NMSs, especially from the CAR which is the least developed region of the FSU.<sup>1</sup> ' Second, any '<sup>6</sup> The vulnerability of exporters of cotton products _may_ be overstated, however, because the Soviet 110-sector breakdown lumps all cotton products together without distinguishing between their level of fabrication. In the above tabulations we treat them as final stage products (SITC. 65), although some of their exports may include prinmary stage products (e.g., raw cotton-SITC. 263) which are neither subjec **t** o non-tariff barriers nor to higher tariff rates in most OECD countries. Since the FSU exported to the OECD mainly raw cotton (its share was around 35 % of all 1990 shipments of cotton product), one may suspec **t** hat almost 100% of exports originating in the CAR was raw cotton rather than NTB-ridden cotton products. Recalculating the shares of exports from the CAR subject to either discriminatory tariffs or NTBs (see Table 10) yields the following results: for Kyrgyzstan the inter-republic and extra-republic share falls to 73% and 91%, respectively; for Tajikistan to 74% and 81%; for Turkmenistan to 79% and 33 %; and for Uzbekistan to 68% and 40%. This is clearly not the case of many European NISs-highly vulnerable to EC trade barriers--which, because of geographical proximity, could be competitive in EC markets. The coverage is very high, especially for Belarus and Moldova.
# **Figure 13: Herfindahl Index by Broad Economic Category, Ranked by 2007-2008 Growth Rate**

<!-- Start of picture text --> 11.56% 121, Food and beverages, Processed, Mainly for industry<br>9.26% 111, Food and beverages, Primary, Mainly for industry<br>8.55% 11, Food and beverages, Primary<br>8.00% 62, Consumer goods not elsewhere spec., Semi‐durable<br>6.82% 21, Industrial supplies not elsewhere spec., Primary<br>6.18% 7, Goods not elsewhere specified<br>4.82% 112, Food and beverages, Primary, Mainly for household ...<br>3.64% 6, Consumer goods not elsewhere spec.<br>1.74% 1, Food and beverages<br>1.11% 51, Transport equipment, Passenger motor cars<br>0.49% 4, Capital goods (exc. transp. equip.)<br>0.42% 41, Capital goods (exc. transp. equip.)<br>‐0.16% 22, Industrial supplies not elsewhere spec., Processed<br>‐0.29% 3, Fuels and lubricants<br>‐0.30% 31, Fuels and lubricants, Primary<br>‐0.76% 63, Consumer goods not elsewhere spec., Non‐durable<br>‐0.82% 2, Industrial supplies not elsewhere spec.<br>‐0.94% 61, Consumer goods not elsewhere spec., Durable<br>‐1.00% 42, Capital goods (exc. transp. equip.), Parts and accessories<br>‐1.01% 122, Food and beverages, Processed, Mainly for household ...<br>‐1.07% 12, Food and beverages, Processed<br>‐1.08% 521, Transport equipment, Other, Industrial<br>‐1.09% 52, Transport equipment, Other<br>‐2.66% 5, Transport equipment<br>‐3.09% 53, Transport equipment, Parts and accessories<br>‐4.19% 32, Fuels and lubricants, Processed<br>‐4.29% 322, Fuels and lubricants, Processed, Other<br>‐9.60% 522, Transport equipment, Other, Industrial, Non‐industrial<br><!-- End of picture text --> Source: Own calculations. Data: Data: UN Comtrade. Retrieved from: World Integrated Trade Solution.

**Figure 14: Export Growth and HHI Growth by BEC, 2007-2008 (%)**

<!-- Start of picture text --> 121<br>111<br>62<br>21<br>7<br>112<br>51<br>41<br>22 31<br>42 61 63 521122<br>53<br>322<br>522<br>-10 0 10 20 30 40<br>Export Growth 2007-2008 (%)<br>10<br>5<br>0<br>HHI Growth 2007-2008 (%) -5<br>-10<br><!-- End of picture text --> Source: Own illustration. Data: UN Comtrade. Retrieved from: World Integrated Trade Solution.

25
Table S.27: Estimating non-attrition <u>probability (OLS)</u>

|Dependent variable:||O|ne if the out|come data|are not miss|ing||
|---|---|---|---|---|---|---|---|
|Outcomes of|Exam|Exam|Children’s|Per-day|Log of ex|penditures|(BDT)|
|interest:|results<br>in 2013|results<br>in 2014|time use|hours<br>using<br>light<br>sources|Education|Kerosene|Total|
||(a)|(b)|(c)|(d)|(e)|(f)|(g)|
|All products|0.016|0.010|0.008|0.016|0.010|-0.696***|0.016|
||(0.011)|(0.012)|(0.034)|(0.012)|(0.013)|(0.060)|(0.012)|
|One product|0.024|0.029**|-0.000|0.020|0.029*|-0.053**|0.020|
||(0.016)|(0.014)|(0.041)|(0.016)|(0.017)|(0.025)|(0.016)|
|HH baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.094|0.084|0.075|0.088|0.088|0.499|0.088|
|No. of obs.|882|882|882|882|882|882|882|
|No. of obs. having no|n-missing <br>850|values at <br>845|follow up<br>620|847|842|631|847|

Notes: (1) A dependent variable is one if the outcome data are not missing at follow-up and zero otherwise. (2) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (3) Standard errors are robust to heteroskedasticity and clustered residuals within each village. (4) The baseline controls include household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

72
_Average monthly wage (USD, PPP)_ is the average monthly wage received by the individual during that 12-month period. If the student worked at least one month, average wage is equal to total wages divided by the number of months he worked (i.e., it is equal to her average monthly wage conditional on working). If she did not work at all, average wage is zero. Purchasing power parity (PPP) adjustment of wages was done using the 2019 (Ecuador) or 2017 (Brazil) PPP conversion factor.

# **Other variables based on individual-level administrative data**

_Student administrative variables (PCA score)_ consist of an index created for Brazil and Ecuador through a Principal Component Approach with the goal of reducing the dimensionality of the student characteristics included in estimation. The index includes the following student characteristics: age, gender, mother’s education level (less than primary, primary, high school, higher education, unknown), whether the student has children (Ecuador), and socioeconomic status index (Ecuador).

_Peer administrative variables (PCA score)_ follows the same logic, but for peers’ (average) characteristics. The variables included in the calculation of the index were average characteristics at the student’s cohort level, which is defined as all students (excluding the student herself) who entered (for Brazil) or graduate in (for Ecuador) the same program in the same year. Thus, the calculation of the index includes peers’ average age, percentage of peers who are women, percentage of peers with each level of mother’s education, percentage of peers with children (Ecuador), and peers’ average socioeconomic index (Ecuador).

# **Outcomes from survey data**

_Dropout rate_ is the percentage of students that dropped out among those who were supposed to graduate the academic year before the survey.

_Extra time to graduate (ETG)_ is the average percentage of additional time that students take to graduate relative to the theoretical duration of the program. For example, if a program is supposed to last two years and students take, on average, three years to graduate, then ETG=50% (=1 extra year *100 / 2 years). To calculate ETG, we asked directors (i) the average time it took the last cohort to graduate from the program and (ii) the theoretical duration of the program.

57
- [17] Cohen, W.M. and R.C. Levitt, ”Innovation and Market Structure,” in R. Schmalensee and R.D. Willig, eds., Handbook of industrial organisation (North-Holland, Amsterdam).

- [18] Dasgupta, P. and J. Stiglitz, 1980, ”Industrial Structure and the Nature of Innovative Activity,” Economic Journal, 90 266-293.

- [19] Dinopoulos, E. and P., Segerstrom, 2004, ”A theory of North-South Trade and Globalization,” Stockholm School of Economics, mimeo.

- [20] Dixit, A. and J. Stiglitz, 1997, Monopolistic Competition and Optimum Product Diversity, American Economic Review, LXVII 97308.

- [21] Feenstra, R., 1994, ”New Product Varieties and the Mesurement of International Prices”, American Economic Review, 84(1), 157-77.

- [22] Feenstra, R., and P., Lipsey, 1997, ”International Trade Data,” NBER.

- [23] Geroski, P., ”Market Structure, Corporate Performance, and Innovative Activity”. Oxford Press.

- [24] Ginarte, J., and W. Park, 1997, ”Determinants of Patent Rights. A Cross National Study,” Research Policy, 26 3 283-301.

- [25] Grossman, G. and E., Helpman, 1992, ”Innovation and Growth in the Global Economy”. Cambridge MA: MIT Press.

- [26] Gurley, J. G., and E., Shaw, 1955, ”Financial Aspects of Economic Development,” The American Economic Review, 45, 515-538.

- [27] Hallak, J., and P. Schott, 2005, ”Estimating Cross-Country Differences in Product Quality,” Working Paper.

- [28] Helpman, E., 1993, ”Innovation, Imitation and Intellectual Property Rights,” Econometrica 61, 1247-1280.

- [29] Howitt, P., 1999, ”Steady Endogenous Growth with Population and _R_ & _D_ Inputs Growing,” Journal of Political Economy, 107 715-730.

- [30] Hummels, D., 2001, ”Time as a Trade Barrier,” Working Paper.

27
_ing from differences between subgroups means._ ”<sup>9</sup> Initially, the Gini index was not thought to be suitable for decompositions but there are now some methodologies that can be used for an exact decomposition of the Gini such as the Shapley value method (Shorrocks, 1999). All these contributions continued to focus on the within and between groups decomposition of the Gini.

The definition of the individual contribution to inequality presented in the previous section opens the possibility for a different form of additive decomposition by population subgroups. The individual contributions to the Gini are considered as a measure of the individual degree of diversity. When we aggregate these individual degrees of diversity across groups such as males and females, we can simply add up the individual values by group and obtain the Gini. If we take the share of Gini by group, we obtain an exact decomposition by subgroup.

As an example, let _i_ = 1 _,_ 2 _, . . . , m_ be the set _M_ of male individuals in the population and _j_ = _m_ + 1 _, . . . , n_ the set _W_ of female individuals in the population, where _M ∪ W_ = _N_ . Then, the Gini index can be written as the sum of males and females individual diversities.

This decomposition is determined by both group size and within-group inequality. Groups’ size being equal, the more unequal group accounts for more inequality. Groups’ inequality being equal, the larger group accounts for more inequality. We consider population size and within-group inequality as equally legitimate contributors to total inequality.

To illustrate the decomposition proposed, we took a reduced sample from the 2000 British Household Panel Survey (BHPS) restricting the population to employees age 41-50 and using as a measure of welfare income net of taxes. Table 4 reports group means and population size as well as the decomposition by groups of total inequality. We can see that men contribute to total inequality by almost 56% of the total. This is due to both

> 9Both definitions can be found in the abstracts of the respective papers.

9
# **Characterizing Business Cycles in Small Economies**<sup>*****</sup>

Viktoria Hnatkovska<sup>†</sup> and Friederike Koehler-Geib<sup>‡</sup> Keywords: small states, volatility, business cycles, fiscal rule JEL Classification: E30, O11, O54

> * This paper benefitted from useful comments from Carlos Vegh, Edgardo Favaro, Cesar Calderon, Norbert Fiess, Klaus Schmidt-Hebbel, Raimundo Soto, Emilia Skrok, Jan Gaska, Elena Ianchovichina, and participants at the Authors’ Workshop for the World Bank’s Regional Study for Latin America and the Caribbean on Fiscal Rules. The authors acknowledge financial support from the Office of the Regional Chief Economist for Latin America and the Caribbean.

> † University of British Columbia, 253-6000 Iona Dr., Vancouver, BC V6T1L4, Canada, Viktoriya.Hnatkovska@ubc.ca.

‡ Lead Economist and Program Leader, Central America Department, The World Bank, 1818 H Street Washington, DC 20433 USA; fkoehler@worldbank.org.
on, including fixed effects by the administrative ward in Harare �J′���. This is akin to claiming that the tolerance for corruption can vary by neighborhood, ward, or precinct. As harassment by public officials and police may be a primary mechanism for the extraction of bribes, HARASS is included as a control in later specifications. However, as this certainly introduces simultaneity problems (if harassment is a means of bribery extraction, businesses may be solicited for a bribe because they are harassed or harassed to solicit a bribe), an additional measure is used. This measure averages the harassment experiences of similar businesses—those located nearby—as a proxy for harassment exposure, without including the direct experience of an individual business. Specifically, location data are used to estimate the rate of harassment in the 500-meter radius around each location point, exclusive of those points within 200 meters of each informal business. Other studies on firm-level corruption have used “leave-out” averages, where an average of an inter-sectional stratum (e.g. sizesector-location) is calculated omitting the firm-level input (see, e.g. Freund _et al_ , 2016); the measure of harass, called “harass donut”, uses GPS data to provide a location-based structure of these measures. As such, it proxies an informal business’s exposure to harassment. Lastly, as proximity to fixed premises may vary, controls for the measured location to eight municipal city offices are included.

# _<u>Descriptive Statistics</u>_

Table 2 presents the descriptive statistics of the main variables and controls; as described above, the data are representative of the Harare administrative area. Nearly one in four informal business operators reports having to pay bribes or give informal gifts to remain un-registered (BRIBE=1). Similarly, roughly one-quarter of the informal sector reports being harassed by police or government officials in the last month; the proxy, ‘donut’ measure is a lower rate of 10%. The average informal business had between one and two employees in the last month and earned just under $318 in revenue from the business activity.<sup>30</sup> Roughly 40% of informal businesses do not have fixed premises, referred to as “hawkers”, this followed by just under 30% with fixed, but temporary premises. Nineteen percent are household-based businesses, which on its face, may appear to be an under-estimate, a concern if enumeration does not adequately reach in-home, informal activity.

> 30 The exponent of the mean shown in the table: exp(0.5) ≈1.6. The same value for sales last month (in USD) is exp(5.8)≈318. Due to the loss of precision by presenting the tables at one-digit values of logs, the levels in the text are calculated from the full, precise log estimates.

(19)
# **1. Introduction**

Policy makers have plenty macroeconomic reasons to care about high inflation. But there are also important microeconomic (firm- and household-level) considerations to control the inflation rate, including the potential differential effects of price changes on purchasing power and household well-being across different populations. Inflation is typically measured and tracked using aggregate price indices such as the Producer Price Index (PPI) or Consumer Price Index (CPI), often the standard practiced employed in most price measurement programs across the world. While informative, these indices are based on aggregate bundles of goods and services sold or consumed by the “median” firm or household. In the specific case of households, in particular, the allocation of their budgets across different types of goods and services varies substantially across income and demographic groups in ways that do not necessarily match the preferences and budget possibilities of the median household. Therefore, assessing how inflation behaves at the household level requires understanding how heterogenous changes in consumer prices affect household choices and well-being differently.

An empirical regularity in Emerging Markets and Developing Economies (EMDEs) is that households from the bottom of the distribution tend to allocate a higher budget share to food and housing expenditures. If the prices of these items increase faster than core inflation (which excludes price changes in the food and energy sectors), low-income households will face higher effective inflation rates. Beyond variation in consumption bundles, poor households can also bear a higher burden if the prices that they pay for the different goods and services are above the average for reasons such as buying smaller but pricier quantities, dealing with stronger information asymmetries, inability to smooth the seasonality of agricultural production and weaker bargaining power (Kaplan and Schulhofer-Wohl 2017 and Orhun and Palazzolo 2018).<sup>1</sup> Heterogeneity in the effects of inflation can also arise due to variation in opportunities to substitute expenditures with affordable options that ensure the same level of utility,<sup>2</sup> access to financial instruments that insulate the nominal value of assets from inflation or in the indexing of pensions, wage negotiations, tax brackets or public transfers.

> 1 Analyzing over 500 million transactions between 2004 and 2013, Kaplan and Schulhofer-Wohl (2017) find that lower-income households experienced higher inflation and nearly two-thirds of the variation comes from differences in prices paid for identical goods.

> 2 For instance, it is not rare to see low-income households opting for fewer proteins in their diets to guarantee a minimum calorie intake when food is scarcer or more expensive.

2
# **Table 2: Summary Statistics of Respondents and Household (HH) Characteristics**

|**Variables**|**Variable descriptions**|**Returned**<br>**from Gulf**<br>N=|**migrants**<br>**countries**<br>332|**Returned**<br>**from Sau**<br>n=2|**migrants**<br>**di Arabia**<br>56|**N**<br>**mig**<br>N8|**on-**<br>**rants**<br>,614|
|---|---|---|---|---|---|---|---|
|||_Mean_|_sd_|_mean_|_sd_|_mean_|_sd_|
|**_Sex:_**||||||||
|Female respondent|1 if respondent was female|0.133|0.340|0.102|0.303|0.576|0.494|
|**_Age:_**||||||||
|14–29 years old|1 if respondent was between 14 and 29 years<br>old|0.030|0.171|0.012|0.108|0.086|0.280|
|30–44 years old|1 if respondent was between 30 and 44 years<br>old|0.226|0.419|0.305|0.461|0.290|0.454|
|45–69 years old|1 if respondent was between 45 and 69 years<br>old|0.440|0.497|0.453|0.499|0.355|0.479|
|70–89 years old|1 if respondent was between 70 and 89 years<br>old|0.304|0.461|0.230|0.422|0.269|0.443|
|**_Religion:_**||||||||
|Hindu|1 if respondent was Hindu|0.428|0.495|0.238|0.427|0.660|0.474|
|Christian|1 if respondent was Christian|0.184|0.388|0.125|0.331|0.207|0.405|
|Muslim|1 if respondent was Muslim|0.386|0.487|0.633|0.483|0.132|0.339|
|**_Household status:_**||||||||
|Currently married|1 if respondent was currently married|0.916|0.278|0.910|0.287|0.787|0.409|
|HH head or spouse|1 if respondent was HH head or the spouse of<br>the HH head|0.819|0.385|0.859|0.348|0.819|0.385|
|Number of years living abroad|Number of years living abroad for returned<br>international migrants|12.247|10.086|11.777|9.296|||
|**_Education level:_**||||||||
|Primary and less|1 if respondent completed primary school or<br>had less than primary school education|0.274|0.447|0.266|0.443|0.282|0.450|
|Secondary|1 if respondent completed secondary school|0.563|0.497|0.625|0.485|0.607|0.489|
|College and above|1 if respondent completed college and above<br>education level|0.163|0.370|0.109|0.313|0.111|0.314|
|**_Socioeconomic status:_**||||||||
|Currently employed|1 if respondent was currently employed|0.605|0.489|0.703|0.458|0.419|0.493|
|Received loan|1 if respondent ever received any loan|0.214|0.411|0.281|0.450|0.152|0.359|
|**_Behaviors:_**||||||||
|Any HH member smoked|1 if respondent from a HH with any HH<br>member who had a smoking habit|0.105|0.308|0.148|0.356|0.067|0.250|

17
40

# Debt-EquitX SwaDs

In recent years debt-equity swap programs have been widely used as an additional incentive in FDI policies. Because the host government buys the debt instruments from the investor at a smaller discount than the investor receives from the commercial bank, the host government is subsidizing the investor's equity investment. Host governments commonly restrict the investment opportunities to selected sectors, and they also often restrict investors' profit remittances. After a slow start in the mid-1980s, debt-equity swaps initiated by developing countries had reached $8.9 billion by 1988 (World Bank, Quarterly Review of Financial Flows to Developing Countries, March 1989). In addition, there were informal conversions directly between creditors and debtors without formal government involvement, as well as exit bonds and other types of debt reduction programs, so the total magnitude of debt conversion transactions in that year amounted to $21.1 billion.

These transactions have been highly concentrated, with more than 90 percent in four Latin American countries: Brazil, Mexico, Chile, and Argentina. Among the other eleven selected countries, however, the recently developed program of Nigeria has attracted much interest. Asian trading firms already in Nigeria have been particularly active in using debt-equity conversions as a means of diversifying their portfolios into additional sectors. At the first three auctions between November 1988 and February 1989, limits of $40 million, $30 million, and $25 million were established to constrain the domestic inflationary impact of the conversions. Over the three auctions bids totalling $334.4 million were received, at discounts ranging from 36 to 58 percent.

Although these and other debt-equity swap programs may offer individual investors highly attractive opportunities for FDI projects, their ability to stimulate additional new investment that wou'd not otherwise have taken place and their monetary and balance of payments consequxences pose a variety of issues. In any case, such programs are likely to remain important, if specialized, features of the FDI policy framework of many countries for the foreseeable future.