rafmacalaba/data-use-mentions-extended huggingface dataset:
all countries, the data contain over 90 percent of employment of the old firm manufacturing sector in 1992.

For a more detailed description of the job flows and job flow productivity data, see Brown and Earle (2004). Biletsky et al. (2002) contains more information on the data for worker and job flows. Brown, Earle, and Lup (2004) gives details about the Romanian new small firm survey. See Brown, Earle, and Telegdy (2004a, 2004b) for further information about the privatization data.

# **II.3.1 Job and Worker Flows and Firm Turnover**

The first results are the basic job flow rates for the Russian and Ukrainian manufacturing sectors in Figure 1. The job creation rate is negligible in Russian manufacturing during the Soviet period, but by the late 1990s both the Russian and Ukrainian rates are well within the range of creation rates of the full manufacturing sectors in the U.S. and other market economies.

The calculated job destruction is also very low in the Soviet period, but the rate rises rapidly in the early reform period, quickly reaching the upper end of the typical range of rates found in the U.S. The Russian and Ukrainian creation and destruction rates show very similar patterns, the main difference being that Ukrainian destruction did not rise as quickly as Russian destruction at the beginning of the reform period. These results suggest that labor market flexibility increased substantially in response to market reforms.

Brown and Earle (2002) measure the persistence of these employment changes among continuing firms, i.e., the extent to which jobs added or subtracted from the firm remain gained or lost in future years. Results are displayed in Table 1. Persistence rates are calculated for one- and two-year periods, and both have fallen slightly during the reform period by comparison with Soviet socialism. Apparently, the planners had little tendency to reverse their decisions on changing employment levels! As in other countries, the persistence rate is lower for job creation than
in Ethiopia (Mascagni and Molla 2018). It is unclear if a similar pattern would be observed around the presumptive tax exemption threshold.

The design of a tax on small and microenterprises needs to consider the tradeoffs between bringing informal businesses into the tax net and the administrative costs (Keen and Mintz 2004). These considerations are particularly important for microenterprises because of their limited revenue generation potential. While medium-sized firms may benefit from tax registration by increasing their customer base and sales, micro-level businesses may not reap the benefits of formalization (see Benhassine et al. 2018 and McKenzie and Sakho 2010 for examples from Benin and Bolivia, respectively). Tax administrations’ capacity in terms of resources and skills may also be limited to monitor and collect taxes from microenterprises.

# **7. Conclusion**

This paper examines the equity and gender implications of the presumptive tax on small and microenterprises. Using nationally representative household data and the tax code, the findings show that the tax burden is borne most heavily by the smallest enterprises, thereby making it regressive. The smallest male-owned businesses face a higher effective tax rate compared to female-owned businesses because a larger share of male-owned businesses operate in sectors without an exemption threshold. The results also show that businesses in the food and beverage service subsector, which are predominantly female-owned, and the transit service subsector, which are predominantly male-owned, face a high tax burden because of the lack of a tax-free threshold for these subsectors. Such high tax rates for small enterprises could discourage entrepreneurs from registering their business for taxation and threaten the business sustainability.

The presumptive taxation on small and microenterprises in Ethiopia consists of different tax rates for 99 business sectors with 19 turnover bands (with a total of 1,881 activity-bands). The complexity of the tax schedule and the existing process of estimating tax liabilities using daily revenue to extrapolate annual revenues have led to discontent among taxpayers, which could undermine trust towards tax authorities and the perception of tax fairness. An alternative presumptive tax system that is assessed on a fixed percentage
potentially differs.

As discussed in the background section, given the overall greater sensitivity around data collection in DFA-controlled regions, it is possible that many of the sensitive questions might be more sensitive in DFA-controlled regions as well. If that were the case, we might expect there to be significantly larger deviations in sensitive questions between the internet and mobile phone surveys in DFA-controlled regions.

However, we illustrate that this is surprisingly not the case. Table 6 re-estimates specification (1) for all sensitive questions, but further allows the difference between the modalities to vary based on whether the respondent lives in regions under DFA control. The estimates are low in magnitude, vary in sign, and are not precisely estimated.

Although these results are difficult to precisely interpret, the results are consistent with questions being equally sensitive across the entire country. It is possible that individuals in regions controlled by the IRG might worry about the DFA eventually taking over more territory or the entire country, and do not want there to be a record of them being critical of them; or it is possible that the primary driver of the sensitivity could be that individuals are afraid of naming groups responsible for violence they witnessed because they do not want to be seen as being implicitly supportive of another group. More investigation can help to address some of these uncertainties and better understand the sources of sensitivity in the Republic of Yemen’s conflict, potentially using internet surveys along with the other techniques described in Section 2.

# **Section 7. Conclusion**

We investigate the ability of novel and anonymous internet-based surveys to potentially elicit more sensitive information from respondents by comparing the results of an internet survey to a concurrent mobile phone survey. Importantly, we illustrate how collecting sensitive information becomes significantly more important in conflict settingsboth because a wide range of typical sensitive behaviors become more important, and because a wider range of information that is critical to well-being and the conflict itself becomes difficult to collect. The results demonstrate how there were stronger differences 20
These findings are also consistent with analysis of other definitions of vulnerability. Using a six-year panel on households in rural China, Jalan and Ravallion (1998) estimate the inter-temporal "coefficient of variability" for consumption expenditures of 53.2 percent. They also found that the inter-temporal "coefficient of variability" for consumption tends to remain roughly constant as mean consumption expenditures increase.

These results are also consistent with calculations and analysis of other definitions of vulnerability. Jalan and Ravallion (1999), using a six year panel on households in rural China, examine "chronic" and "transient" poverty by examining which households were either persistently poor (expenditures in each period below the poverty line), chronically poor _(mean_ expenditures over all periods less than the poverty line but not poor in each period), transiently poor _(mean_ expenditures over all periods above the poverty line but experiencing at least one episode of poverty), and never poor. The results, reproduced here in table 3, are consistent with a large level of "vulnerability," as even though only 6.2 percent of households were "always poor" and a cross section in any given year would find less than 20 percent poverty, 54 percent of the sample experienced at least one episode of poverty.

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However, since public supply pushes the price of private child care downwards, sticking to a high-price strategy is optimal only if the relative size of the high-valuation segment is sufficiently large. If Segment _H_ is relatively small ( _n <_ 1 _− c_ ), the private provider will optimally respond to public preschool supply by switching from a high-price strategy to a low-price strategy, leading to the somewhat counterintuitive outcome where public supply _crowds in_ private enrollment. In this case, the presence of a public provider forces the private provider to compete for consumers (in Segment _L_ ) who would otherwise not be profitable for the private provider to target. As a result, the demand for private child care increases. Thus, using our interpretation of the two demand segments as differing in average income, public supply crowding in private enrollment might occur if demand for child care consists of a segment of consumers who are much richer than the rest but relatively few in numbers.

In light of this theoretical framework, a plausible explanation for the lack of any evidence that public supply crowds out private enrollment in Brazilian child care markets, is a demand side that is characterized by relatively large inter-household income inequalities, which implies that private providers might find it profitable to exclusively (or to a large extent) target high-income households by offering high-quality services at prices that are not affordable to low-income segments. An increase in the public supply of preschool will then fully (or mainly) benefit those households that previously could not afford private preschool, rather than crowding out private enrollment.

# **6 Concluding remarks**

We have examined if and how an expansion in the supply of public preschool affects private provision. Using rich data for municipalities in Brazil from 2000-2006, we have used an RD design to exploit the fact that federal transfers received by local governments exhibit a non-linear and non-monotonic relationship with given population estimates. The results reveal that larger federal transfers lead to a significant expansion of municipal preschool, as measured by the number of centers and enrollment, but show
# **5.3 Results Based on ACLED Data**

We triangulate our results based on interaction between post-war-onset dummies and the waraffected region dummy by running the same set of outcome variables on exposure to battles that took place within 20 km and 30 km of the household’s’ residence. More specifically, we compute the cumulative number of battles that have taken place within 20 km and 30 km radius of the residence of households prior to each survey round.<sup>31</sup> We chose a buffer of 20 km and 30 km distance to ensure that battles are close enough to have impacts on our outcome variables. The coefficients on these variables measure the effect of exposure to an additional battle event on household food security and labor market outcomes.

Table 9 presents the impact of battles that occurred within 20 and 30 km distance from households’ residence on food insecurity. For all three measures, exposure to battles leads to an increase in food insecurity. Exposure to an additional battle within 20 km of residence increases moderate or severe food insecurity and probability of food insecurity by 1 percentage point each. Likewise, battles within 30 km of residence have similar negative impact on food insecurity, though the impacts are slightly smaller. We experimented with varying battle distances from residence, including 10, 20, 30, 40 and 50 km radius. We find that the impact of exposure to battles increase the closer the battles get to the household residence.<sup>32</sup>

**<u>Table 9: Number of battles and food security</u>**

||(1)<br>Moderate or<br>severe food<br>insecurity|(2)<br>Moderate or<br>severe food<br>insecurity|(3)<br>Food<br>insecurity|(4)<br>Food<br>insecurity|(5)<br>Raw score|(6)<br>Raw
change over the electoral cycle. These patterns suggest that in countries with high political competition, the spending on social assistance and income protection appears to crowd out the spending on job protection programs over the electoral cycle. As elections approach, governments appear to reduce spending on job protection programs and increase it on social assistance and income protection policies. Once elections are behind, the spending on job protection increases, and spending on social assistance declines.

# **6. Conclusions**

In this paper, we use newly assembled data on various types of social protection spending across 154 countries during the COVID-19 pandemic in 2020 and 2021 to analyze the effect of the electoral cycle on the size and composition of the social protection pandemic stimulus budget.

Electoral cycles appear to affect the size and composition of social programs in politically competitive environments, even during systemic shocks, such as the COVID-19 pandemic. Our results demonstrate that the preferences and motivations of politicians seeking re-election should be taken into account when designing social policies. To stay in power, incumbent politicians may feel the need to show immediate results by directing resources to highly visible policies, such as social assistance, and underinvesting in other policies, such as job protection schemes, which may be particularly important in severe economic downturns. A similar argument could apply to underinvestment in policies that bring significant longer-term benefits, such as health and education. Expanding social programs at certain periods of the electoral cycle to achieve political gains may undermine the policies’ effectiveness in protecting the most vulnerable population groups <u>(Bueno 2021). Increasing the provision of public goods to gain votes could increase budget</u> deficits and repayment costs, increasing inflation and the future tax burden. Reducing taxation to secure voter support before elections may decrease the quality of social protection programs and endanger their long-term sustainability (Prichard 2018). Better governance and promotion of the rule of law could mitigate these effects and result in more effective public policies and higher welfare.

Our estimations show that the degree of political competition affects
collapsed into five-year groupings beginning with 1980-1984 and extending to 1995-1999. The dependent variable in Column 1 is the log mean nominal value (US$) of bilateral exports for the five years; the dependent variable in Column 2 is the change in log exports from the prior period. The intensive margin sample is restricted to exporter-importer-industry groupings with exports exceeding $100k in every year. The $100k threshold is chosen due to WTF data collection procedures discussed in the text. Labor productivity from the UNIDO database measures comparative advantages. Column 1 estimates Ricardian elasticities using both within-panel variation and variation between industries of a country. Column 2 estimates Ricardian elasticities using only variation within panels. Estimations in Panel A weight bilateral routes by the interaction of total exporter and importer trade in industry; estimations in Panel B are unweighted. Estimations cluster standard errors by exporter-industry. Importer-Industry-Yr FE are defined at the two-digit level of the ISIC system. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Monetary Policy Review (twice a year beginning in 2001), which will represent the Reserve Bank's inflation report. At this stage, inflation forecasts and the underlying forecasting models are not released to the public.

Following the collapse of the Thai baht in July 1997 (after almost five decades of pegged exchange rate regimes), Thailand adopted a floating rage regime under which monetary targeting was followed at first. Quarterly monetary base targets (agreed with the IMF) were implemented and achieved through daily liquidity management. However, growing evidence of instability in the relationship between money, output and inflation led Thailand to announce the adoption of inflation targeting as a monetary policy framework in May 2000. Under this regime, monetary policy is decided by the Monetary Policy Board (MPB), which was appointed in April 2000 and comprises both high-level central bank officials and external experts. An amendment to the Bank of Thailand has recently been passed to give the MPB an official status and operational independence in conducting monetary policy. At the same time, price stability was reaffirmed as the overriding objective of monetary policy. The price index targeted by the central bank is an index of core inflation, which excludes raw food and energy prices (both components deemed to be highly volatile) from headline inflation. The target core inflation rate is set in the form of a band of between 0 and 3.5 percent for 2000-01 and is explicitly aimed at allowing the Bank of Thailand to cushion temporary shocks and reduce interest rate volatility. At the time of the adoption of inflation targeting, headline and core inflation were running at, respectively, 2 percent and 1.2 percent on a yearly basis. Monitoring of deviations from the target band is done on the basis of quarterly averages of core inflation, to avoid the high degree of volatility that may characterize monthly observations. If core inflation breaches the target band, the MPB must explain why the breach occurred, identify the measures that it intends to take in response, and the amount of time required to
001)|-0.004**<br>(0.002)|-0.005***<br>(0.002)|
|Region FE|YES|YES|YES|YES|YES|YES|
|Income Level FE|YES|NO|NO|NO|NO|NO|
|Exits|61|57|42|61|52|54|
|Spells|74|70|48|61|61|75|

_Notes_ : All models include the US real interest rate, the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for recurrent spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

Until now, we have treated spells recurring in the same country as interchangeable.

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From Table 2, we see that malaria and loss of off‐farm income were the most prevalent shocks. While households faced a relatively similar probability for experiencing the malaria shock across regions, they experienced different probabilities for experiencing the off‐farm income shock, as well as rainfall and price shocks. Northern and Central faced 8‐10% deviations from long‐term mean rainfall, whereas Southern faced 15% deviations. Price deviations were smallest in Northern at around 7%, and higher in Southern and Central at 10% and 11% respectively. The probability of an off‐farm employment income loss shock was similar in Central and Southern at 13% and 15% respectively, and highest in Northern at 19%.

Table 2 also includes long‐term measures of riskiness that are hypothesized to shape peoples’ expectations ex ante, which may in turn affect their choices of risk management and risk coping strategies. The coefficient of variation for malaria and employment shocks are spatial variations, whereas those for maize prices and rainfall are generated from the time series data. Consistent with information on actual rainfall shocks realized, households in Southern face higher rainfall variability than households in Central and Northern. On the other hand, maize price variability is highest in Northern, followed by Southern, and lowest in Central. The differences in rainfall and price variability between regions are all statistically significant. Households in Northern face greater variability of malaria incidence, followed by Southern and then Central. Households in Central face the greatest risks in terms of off‐farm employment income loss, followed by Northern and Southern with relatively similar and lower levels of risk.

**Table 2. Descriptive Statistics**

||**Mal**|**awi**|**Nort**|**hern**|**Cen**|**tral**|**Sou**|**thern**|
|---|---|---|---
**Table 4. Long-Term Income Growth Rate, Russia 1994-2015 (percentage)**

||||**2015**<br>|||
|---|---|---|---|---|---|
|||Poorest tercile|Middle tercile|Richest tercile|Overall|
||Poorest tercile|317.2|543.8|965.8|503.1|
|**1994**|Middle tercile|78.6|180.9|353.4|194.0|
||Richest tercile|-13.1|34.0|125.4|44.9|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 2478 panel individuals from the 5th and 24th round of the RLMS.

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# 5 Conclusion

Due to the poor quality of international data, assessing the economic impact of the brain drain is a challenging issue. Building on the seminal work of Carrington and Detragiache (1998), this paper provides a new dataset on skilled emigration rates describing the loss of skilled workers in both developing and developed countries. By increasing the number of observations and improving their degree of reliability, our method provides useful material for econometric analysis of the causes and consequences of the brain drain.

In absolute terms, we show that the largest stocks of educated emigrants are from Europe, Southern and Eastern Asia and, to a lesser extent, Central America. Nevertheless, as a proportion of the local educated labor force, the highest rates are observed in Central America and Africa (on the Western and Eastern coasts). Between 1990 and 2000, these regions also experienced the strongest increase in brain drain.

Our dataset is obviously evolutionary. It can be improved in several ways:

- 2 This release uses survey data when census data are not available yet. By incorporating new OECD censuses, we will improve the quality of our estimates;

- 2 Adding immigration data from non-OECD countries (Gulf states, South Africa, Eastern Asia) would also strongly improve the quality of our estimates for the major countries involved (Egypt, Jordan, Indonesia, India, Pakistan, Zimbabwe, Namibia.);

- 2 Repeating the exercise for 1980 would also ease the econometric exploitation of the database.

Despite these possible developments, we believe that the current version of our database delivers information that is rich enough to assess the changes in the international distribution of migration rates, to test for the (push and pull) determinants per skill group or to evaluate the macroeconomic consequences of migration on source and destination countries.

# References

- [1] Adams, R. (2003): International migration, remittances and the brain drain: a study of 24 labor-exporting countries, World Bank Policy Research Working Paper, n. 2972.

- [2]