rafmacalaba/data-use-mentions-extended huggingface dataset:
product innovations and 53% of firms for process innovations (a description of how the cleaning exercise was implemented is provided in the Appendix). The remaining firms reported no innovations in their products and processes. The implication of this, as we will explore below, is that the correctness of the reply for non‐innovators cannot be verified. This is a problem in the analysis of the determinants of discrepancies in innovation rates since some of the elements that may bias the responses

> 12 The following analysis is based on the sample of 11,150 firms for which this information is available. Follow‐up questions to be used for the verification and cleaning process were not asked for marketing innovation or R&D. As such the innovation rates based on these two measures cannot be verified. An additional verification was conducted by using the year in which the innovation was introduced to make sure that all reported innovations were introduced in the correct reference period. The question asked whether new or significantly improved process or product were introduced during the past three years but in a few cases respondents reported innovation introduced before that period.

13
statistical capacity and have higher values for the SPI and its components. The economic complexity index ( _ECIi_ ) measures the productive capabilities of an economic system (its integration into global value chains). More complex economies require more data to manage and operate successfully, which should increase both the demand for and supply of statistical services. Economies of scale in the production of statistical information are captured by the country’s population ( _Popi_ ). Economies of scale in large countries arise from the fixed costs of setting up statistical operations, which may vary little with the size of the country (e.g., De Vries 1999).

The demand for statistical information is linked to the level of education in the country ( _EDUi_ ). The ability to read and interpret the data published by national statistical offices depends on the levels of statistical literacy and numeracy in a county (Schield 2011). We, therefore, expect countries with larger shares of educated people to have higher SPI scores. The share of the urban population ( _SURi_ ) captures the structure of the economy, which could also affect demand for data. We use the voice and accountability indicator ( _VAi_ ) to control for the role of civil society as consumers of official statistics (Gray, Lämmerhirt, and Bounegru 2016). The fragility index ( _FRGi_ ,) accounts for the state of fragility and conflict in the country. The model also includes seven regional dummies to control for regional cooperation and knowledge transfer across NSOs.

# **_Data_**

We use several sources of data. The first is the SPI and its component. In our sample, it ranges from 0.21 (Papua New Guinea) to 096 for (Australia).

An indicator of voice and accountability comes from the World Governance Indicators (WGI) database, produced by the World Bank annually since 1996 for over 200 countries (Kaufmann et al. 2010). This indicator ranges from –2.5 (lowest) to 2.5 (highest).

We control
,859|2,859|2,859|2,859|2,859|2,859|2,859|
|R-2|0.291|0.184|0.179|0.188|0.206|0.228|0.255|0.283|0.313|0.344|0.450|
|Municipalities|985|985|985|985|985|985|985|985|985|985|985|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Gini refers to the Gini coefficient. Fixed effects model by year and municipality for the years 2005, 2010, and 2014. Include 985 municipalities with at least 15,000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 34
A. Sanghi and D. Johnson data from 130 countries and explicitly assesses the long-term behavior of income distributions, tracking demographic and educational changes over time.

**Wealthier households in Sub-Saharan Africa benefit from demographic changes and wage changes across sectors** The wealthiest 40 percent of households will have higher per capita income growth from demographic changes, but the poorest 40 percent of households will see no benefit. The poorest 20 percent of households will experience slower per capita income growth when demographic changes cause changes in relative wages across sectors. Upper middle income households —those between the 60th and 80th percentile of the income distribution —will gain the most from the wage changes, earning the fastest per capita income growth<sup>8</sup>

**Poorer households are hurt by changes in food to non-food relative prices, but gain overall from Chinese slowdown and rebalancing** Changes in food to non-food prices leave the poorest 40 percent of households worse off: per capita income growth is 2.9 percent compared to the 3.07 percent if China had continued to grow at seven percent. But thanks to greater Chinse consumption, the bottom 40 percent will increase their incomes; the number of people living in extreme poverty will fall by an additional 4.04 million people. The Chinese slowdown scenario increases poverty initially, but the rebalancing reduces poverty enough for an overall drop. More important, the bottom 40 percent in Kenya will see a per capita income increase of 2.7 percent, the highest in Sub-Saharan Africa (Lakatos _et al_ 2015).

# **3 Foreign Direct Investment in Kenya**

Kenya performs poorly in attracting foreign direct investment (FDI) given the size of its economy. Despite a larger economy, Kenya attracts even less FDI than Uganda and Tanzania. Figure 14 shows Kenya’s net inflows of foreign direct investment (FDI) from 1980 to 2014. Kenya’s investment levels dropped to less than 10 percent of GDP near 2000, but has since returned to levels experienced in
TABLE 9

# DISTRIBUTION OF OUTSTANDING BANK CREDIT BY SOURCE (in percent, end of year data)

|YEAR<br>_________|free<br>credit<br>________________|government<br>directed<br>credit<br>____-<br><br>TOT|short-term<br>directed<br>credit<br>------------<br>__-________--_----_<br>AL CREDIT|investment<br>loans<br>----------|earmarked<br>loans|
|---|---|---|---|---|---|
|1971|38|62|18|14|30|
|1974|38|62|16|13|33|
|1979|29|71|22|7|42|
|1984|28|72|14|10|49|
|1986|35|65|10|9|47|
|||AGRICUL|TURAL CREDIT|||
|1971|17|83|36|33|14|
|1974|16|84|33|37|1S|
|1979|23|77'|28|13||
|1984|12|88|13|16|59|
|1986|23|77|9|12|56|

Source: Annual Bank Statistics, various issues.

answer this puzzle below when considering cost of debt.

Credit did not grow only in absolute terms, it also grew relative to product and capital (Table 7). In the economy at large, debt to product ratio increased from 0.30 in 1969 to 0.67 in 1986. Reported product in agriculture is not exactly comparable to product as calculated in GNP, but rates of growth are: debt to product ratio increased in agriculture between
Salehyan, 2021; Hirvonen, 2016). It is precisely for this reason that the capacity for migration in response to climatic shocks is much more limited than commonly assumed: poor people, who are disproportionately affected by climate change, have more incentives to migrate but often cannot leave because they lack the necessary resources (Cattaneo et al., 2019). In this perspective, migration is a costly investment in risk diversification that only richer households can undertake, while poorer households are “forced to stay” rather than “forced to move” (Kaczan and Orgill-Meyer, 2020). Depending on the interaction between the severity of the climatic event and household-specific characteristics such as wealth, number of income sources, assets and resources, we should expect a wide heterogeneity of outcomes depending on whether liquidity constraints or migratory responses ultimately prevail. Indeed, the empirical evidence is mixed, with some studies showing that the poorest households are more prone to migrate in response to weather shocks (Gray and Mueller, 2012b; Mueller, Gray, and Kosec, 2014; Mastrorillo et al., 2016), while others find that liquidity constraints trap poor people in immobility (Cattaneo and Peri, 2016; Hirvonen, 2016; Bazzi, 2017). Cattaneo et al. (2019) argue that this contradictory evidence can be explained by the different types of migration involved: poor families respond to negative shocks through low-return or even “survival” migration, taking the form of temporary movements across short distances, whereas wealthier families engage in risk management migration, which is typically costlier, semi-permanent and longer-distance migration. Koubi et al. (2022) suggest that (im)mobility depends on both the type of the climate shock that individuals experience and their adaptive capacity in terms of endowments and resources. The simulations by Choquette-Levy et al. (2021), who parameterize an agent-based model on household survey data from Nepal, support this perspective, but also highlight that cash transfers and risk transfer mechanisms may prevent climateinduced immobility of farmers.

In short,
the World Bank data that feature strong enforceability.<sup>2</sup> The red lines indicate the number of agreements, and the blue lines show the number of countries in one or more of them.<sup>3</sup> As depicted in panel (a), the number of IPAs rose from fewer than 20 in 2000 to 100 in 2015, while the number of countries involved now exceeds 100. Thus, currently around two-thirds of WTO

> 2 Appendix Table A1 gives the full list of IP-related PTAs and the year in which each agreement entered into force. 3 An important related source of information is the DESTA database described in Dür et al. (2014). It defines IPAs as agreements with an IPRs chapter, regardless of how comprehensive or rigorous.

4
global and domestic shocks in EMDEs than in advanced economies, implying weaker anchoring of expectations in the former group. The robustness of the results is tested by replacing the global shock, as described above, with an oil price shock, food price shock, global liquidity shock, and global output gap shock. These exercises lead to broadly consistent findings with the headline results.

# 5. Determinants of anchoring expectations

The improved anchoring of five-year-ahead inflation expectations over time in advanced economies and EMDEs, as suggested by the time-varying model in the previous section, may be associated with policy reforms aimed at increasing central bank credibility since the early 1990s (Mishkin 2007). Using the estimated sensitivity of inflation expectations as a dependent variable, panel regression models are used to assess which factors determine the degree of anchoring of expectations (Appendix 3). The explanatory variables in the models include the presence of an inflation targeting regime, central bank transparency, the presence of a fixed exchange rate regime, financial openness, trade openness, and fiscal sustainability.

# Inflation targeting and central bank transparency. If central banks in advanced economies

> 22 De Pooter et al. (2014) examine how foreign and domestic news surprises affect (market-based) inflation expectations in Brazil, Chile, and Mexico, using daily data. In their framework, foreign news surprises stem from macroeconomic developments in the United States and China and fluctuations in oil and food prices. They report that U.S. nonfarm payroll data releases have a significant impact on long-term inflation expectations in Chile and Mexico, while there is no corresponding impact in Brazil. The impact of news related to oil and food prices is not statistically significant.

12
|**Models/Approaches**|**Macroeconomic**<br>**Model**|**Linkage Variables**|**Specification of**<br>**Distribution**<br>|
|---|---|---|---|
||||**(Micro-Model)**|
|_1. PAMS I_|• Revised<br>minimum standard<br>model extended<br> (RMSM-X)|<br>• Growth rate of output<br>• Changes in sectoral wages<br>• Changes in disposable income<br>by group|• Unit record data|
|_2. PAMS II_|• Static CGE<br>model|• Changes in factor and<br>commodity prices<br>• Changes in employment<br>• Household-level real<br>consumption|• Unit record data<br>• Parameterized<br>Lorenz function|
|_3. 123PRSP_|• Financial<br>programming<br>model<br>• Growth models<br>• 123CGE model|• Changes in commodity prices<br>• Changes in incomes|• Unit record data<br>• Envelope model|
|_4. CGE-Microsimulation-1_|• Static CGE<br>model|• Changes in factor and<br>commodity prices<br>• Changes in incomes|• Unit record data<br>• Envelope model|
|_5. CGE-Microsimulation-2_|• Static CGE<br>model|• Changes in commodity prices<br>• Changes in incomes (wages and<br>self-employment income)<br>• Changes in employment by type<br>of occupation|• Unit record data<br>• Household<br>income and<br>occupational<br>choice models|
|_6. IS-LM Microsimulation-2_|• IS-LM model|• Changes in commodity prices<br>• Changes in incomes (
improved, leading to better subsequent returns from investments in transport vehicles or businesses relating to transportation. The service and the trade sectors fare the worst in terms of profit margin, which is also revealed in the rate of return earned on assets in all three survey years, indicating that there is room for performance improvement. The return on assets in the service sector is 61.3 percent in 2000, 62.5 percent in 2005, and 50 percent in 2010. The corresponding figures for the trade sector are 59.9 percent, 54.2 percent, and 52.6 percent respectively. The manufacturing sector seems to be exposed to high risk, as reflected in its relatively low profit margin, and the rate of return in this sector also tends to be around the average for all enterprises in all activities.

Our estimates of average rates of return to assets are consistent with findings from other countries. For example, Kremer, Lee, and Robinson (2008) take advantage of the characteristics of the retail industry in rural Kenya to create estimates and bounds on the rate of return to inventory capital in a set of retail firms. Using administrative data on whether firms purchased enough to take advantage of quantity discounts from wholesalers, they estimate a lower bound on rates of return for the median shop of greater than 100 percent per year. McKenzie and Woodruff (2006) similarly find large returns to small entrepreneurs. Exploiting county-level variation in credit supply due to the Community Reinvestment Act, Zinman (2002) estimates gross rates of return to capital in the US on the order of 20-58 percent per year. In a recent study, Anagol and Udry (2006) take the elegant approach of using data on prices of used car parts of varying expected lifetimes to estimate a lower bound to the opportunity cost of capital of 60 percent for taxi drivers in Ghana. Banerjee and Duflo (2005) compute the rate of return to capital in the economy to be about 22 percent in India, and Caselli and Freyer (2007)
Our elasticity is close to Boehm et al. (2019), who estimate an elasticity across HS-10 products that lies between 0 _._ 42 − 0 _._ 62 for non-Japanese affiliates and 0 _._ 20 for Japanese affiliates. Atalay (2017) finds an estimate of around 0 _._ 10 for 30 aggregated industries using US data.

27This is consistent with the macroeconomics literature (Houthakker, 1955; Bachmann et al., 2022; Lagos, 2006).

29
Key metrics

## _4.3.1) Hirschman-Herfindahl Index_

To study market concentration in products associated with decarbonization technologies, we compute the Hirschman-Herfindahl Index (HHI) for each HS 6-digit product p.<sup>6</sup> It is defined as follows: c p where c is the market share of country c’s export value in total global exports of product p. A HHI of 1 indicates XX that the market is a perfect monopoly (one country exports 100% of the product), while HHI scores approaching 0 XXcc that the market is a perfect monopoly (one country exports 100% of the product), while HHI scores approaching 0 indicate a much more competitive market.

## _4.3.2) Decarbonization Technology Strength (DTS) index_

To define countries’ Decarbonization Technology Strength (DTS) index, we first quantify their strengths in products, using depth and breadth dimensions. To measure breadth, we count the number of HS 6-digit products in each value chain for which a country’s export share is greater than the global average (RCA > 1), as defined in equation (1). To measure depth in decarbonization technologies, we compute a country c’s average market share in export values of HS 6-digit products across value chains (vc):

> 6 For ease of exposition, we leave out the time subscript in all equations.

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export measures at the task level,<sup>3</sup> we link sectoral measures of GVC participation to RTI at the worker level in a given sector, drawing on the methodology of Borin & Mancini (2019) based on EORA data. We also control for technology use with a country-sector share of workers who use computers at work. The country-sector level globalization and technology measures are plausibly exogenous to the decisions of individual firms and workers. The closest study to ours is Lewandowski et al. (2022), but we use much more disaggregated measures of GVC participation (especially in manufacturing) and assess the relative role of forward and backward linkages. Reijnders and de Vries (2018) also provided evidence on the role of offshoring and technological change in GVCs in explaining the increase in non-routine occupational labor demand in a sample of 37 advanced and emerging countries.<sup>4</sup> However, they assumed that occupations are identical globally and measured occupational task contents with American data (the Occupation Information Network – O*NET). We use worker-level RTI to account for cross-country task differences in comparable occupations. This is vital as theory suggests that offshoring leads to a global polarization of tasks within occupations (Grossman & RossiHansberg, 2008) and occupational task demands indeed differ between countries (Caunedo et al., 2021; de la Rica et al., 2020; Lewandowski et al., 2022; Lo Bello et al., 2019).<sup>5</sup> We find significant linkages between GVC participation and RTI. Importantly, these associations differ between backward and forward integration. Overall, backward GVC participation is not correlated with RTI, while higher forward GVC participation is associated with more routine-intensive work. Moreover, the strength of this relationship is negatively related to countries’ development – it is stronger in countries with relatively low GDP per capita and weaker in countries with high GDP per capita.

Second, this study assesses how the nature of occupations and sectors mediates the relationship between
## 5.1.3. VAT exemptions

A closer look at the foregone VAT due to exempt and zero-rated items shows that it is more concentrated amongst the poor than the standard-rated VAT is (Graph a.). Removing exemptions would make the VAT regressive as the incidence of VAT payments, as a proportion of pre-fiscal income, is decreasing with income (Graph b.). Removing VAT exemptions will therefore increase levels of inequality and poverty.

_Figure 10: Concentration and Incidence of VAT exemptions_ <!-- Start of picture text --> a. Concentration shares for foregone VAT due to b. Incidence of foregone VAT due to exemptions (as a<br>exemptions share of market income plus pensions)<br>0.40 0.06<br>0.35<br>0.05<br>0.30<br>0.04<br>0.25<br>0.20 0.03<br>0.15<br>0.02<br>0.10<br>0.01<br>0.05<br>0.00 0.00<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>VAT Foregone VAT Simulated VAT (no ex) VAT Foregone VAT<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ It is clear that future marginal increases in internal revenues from higher VAT rates or more excises will negatively impact the vulnerable. Direct taxes like PAYE could provide more revenues without threatening to impoverish already-vulnerable households. For example, the threshold (for reporting) could be lowered while the marginal rates at each bracket could be raised.

# 5.2. Expenditures

## 5.2.1. Direct Transfers

Except for the SCG program, direct transfers (NUSAF, UWEP and YLP) are _simulated_
We go on to analyze the dynamics of subjective financial well‐being and coping strategies as reported by the households. People mostly cite inflation, decrease in salaries and loss of employment as major reasons for the well‐being deterioration during the crisis. Cutting expenditure is the most popular reported coping strategy, and only few are relying on government or attempting to find new sources of income. We explore the expenditure data to verify that cutting expenditure was an almost universal response to crisis. The bottom income deciles mostly cut expenditure on food (possibly replacing it in part with in‐kind income); while the top deciles were able to cut down expenses on purchase of durable goods. Other households relied to a large extent on cutting their consumption of utility services. Finally, we show that many households not only reduced their expenditure on savings during the crisis but also consumed their previously accumulated savings. However, savings as a coping mechanism are available for the middle‐class, not for the mostly affected bottom income deciles.

The rest of the paper is organized as follows: section 2 presents a brief overview of the recent macroeconomic environment, while section 3 looks at how the macroeconomic developments translate into household income dynamics and composition during the recession. Section 4 reviews the poverty dynamics in Belarus and develops a concept of poverty vulnerability, based on the probability of falling into poverty. Section 5 analyzes household self‐reported well‐being changes and coping strategies, as well as presenting an analysis of expenditure and saving changes during the crisis. Some concluding remarks are outlined in section 6.

# 2. Macro‐Economic Environment

Belarus suffered a series of economic crises during the last decade. Triggered by external shocks, they were rooted in overheated domestic demand, while the absence of adjustment of economic policy led to the recurrence of crises. However, the crisis of 2015‐2016 differed in this regard from the previous ones. This time economic authorities pursued a policy of macroeconomic stabilization, which allowed them to balance external demand and curb inflation