rafmacalaba/datause-encoder-data huggingface dataset:
# <u><mark>ie</mark></u> ~~<u><mark>.</mark></u>~~

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Table 5: Impact of the ITN campaign on politician approval by district level malaria prevalence

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||village chair|village exec|councillor|ward exec|mp|
|**Panel A: effect in hi**|**gher malaria**|**prevalence di**|**stricts**|||
|post x high malaria|0.149***|0.235***|0.177*|0.0817|0.256***|
||(0.0553)|(0.0603)|(0.0890)|(0.0958)|(0.0698)|
|high malaria|-0.134**|-0.181***|-0.181**|-0.160*|-0.236***|
||(0.0505)|(0.0454)|(0.0833)|(0.0888)|(0.0585)|
|post|0.0399|-0.0536|0.0612|0.0691|-0.00841|
||(0.0448)|(0.0472)|(0.0538)|(0.0582)|(0.0610)|
|constant|0.823***|0.875***|0.820***|0.822***|0.797***|
||(0.0371)|(0.0261)|(0.0395)|(0.0473)|(0.0278)|
|_N_|741|560|540|412|536|
|**Panel B: effect in hi**|**gher malaria**|**prevalence dis**|**tricts, zonal**|**fxed effect**|**s**|
|post x high malaria|0.107|0.132|0.0404|0.0965|0.240***|
||(0.0692)|(0.0964)|(0.0887)|(0.0943)|(0.0889)|
|high malaria|-0.0691|-0.0401|-0.0399|-0.201**|-0.238***|
||(0.0655)|(0.0800)|(0.0543)|(0.0882)|(0.0874)|
|post|0.0378|-0.0676|0.101*|-0.0290|-0.130**|
||(0.0699)|(0.0858)|(0.0582)|(0.0712)|(0.0603)|
|constant|0.782***|0.862***|0.808***|0.967***|0.940***|
||(0.0774)|(0.0696)|(0.0335)|(0.0893)|(0.0622)|
|_N_|741|560|540|412|536|

Robust standard errors clustered at district level in parentheses * _p <_ .1, ** p _<_ .05, *** p _<_ .01 29
our regressions. Nonetheless, we do not see evidence of such a shock when looking at VAT collection (refer to Appendix Figure A.3) – no large changes in tax collected are observed around the same period we see the significant increase in TOT taxes, suggesting that the response we document is much more likely to be an evasion response than a real economic shock. Over time, it is possible that the higher TOT rate may have real economic implications as businesses have fewer resources to reinvest in their operations.

The responsiveness of taxpayers to a change in tax obligations is consistent with findings from the surveys showing that respondents were relatively better informed about the TOT rate they needed to pay compared to other aspects of the TOT regime. Specifically, 44.6 percent of registered small businesses in the in-person survey and 31.8 percent of TOT payers in the SMS survey were aware of the existing TOT rate. The lower level of knowledge in the SMS survey may be a result of the tax rate changes in the preceding 12-month period.

# **4.1.3 Requiring a percentage to be paid**

Thirdly, requiring taxpayers to calculate a percentage of their monthly turnover to be paid in tax appears to be challenging for some businesses, especially those that do not have extensive transaction records. Instead, it appears many small businesses rely on heuristics to determine their tax liability, as a non-trivial share consistently pays the same amount in TOT each month despite numerous policy changes altering their tax liability up and down (see Table 2).

Both surveys reveal that registered small businesses struggle to calculate percentages of turnover. The in-person survey shows that just over half (53.5 percent) of registered small businesses could calculate 1 percent of 5 million Kenyan shillings. The SMS survey illustrated that just over two-thirds (68.9 percent) of TOT payers who responded to the survey could calculate the tax obligation required from businesses earning 5 million Kenyan shillings.<sup>16</sup>

> 16In the case of the SMS survey respondents were not informed of the actual rate of TOT while in the in-person survey they were informed prior to this question. As such, in the case of the SMS survey, the

23
# **Table 9. Controlling for recapitalizations**

The dependent variable is the quarterly stock return. See Table 4 for a detailed description of the estimated model. In this table estimate the model introducing a dummy variable that takes value one if the bank has received a capital injection from the government in that quarter. Most recapitalizations have occurred in 2008. We have identified 103 recapitalizations in the full sample and 44 in the large bank sample. Sources include reports from central banks, finance ministries, and press articles. The sample period is Q1-2006 to Q1-2009. All control variables are lagged one year. Crisis is a dummy that takes value one from Q3-2007 to Q1-2009. Tier1 (Tier2) is Tier1 (Tier2) capital scaled either by risk-weighted assets (RWR) or total un-weighted assets (LR). Tier1 capital includes shareholder funds plus perpetual, non-cumulative preference shares, plus retained earnings. Tier2 capital includes subordinated debt, hybrid capital, loan loss reserves, and valuation reserves. We report estimates for the whole sample and the sample of large banks. Large banks are defined as banks with total assets above 50 billion US $. Standard errors clustered by bank are reported in brackets, and the p-value for the test of significant difference between the pre-crisis and crisis coefficients in parentheses.

||(1)<br><br>Full sa|(2)<br><br>mple|(3)<br><br>Larg|(4)<br><br>e banks|
|---|---|---|---|---|
||**RWR**|**LR**|**RWR**|**LR**|
|Tier1*PreCrisis|0.040|0.121**|-0.105|0.042|
||[0.047]|[0.061]|[0.130]|[0.148]|
|Tier1*Crisis|0.114|0.158|0.236|0.552***|
||[0.081]|[0.110]|[0.183]|[0.199]|
||(0.219)|(0.677)|(0.048)|(0.005)|
|Tier2*PreCrisis|0.088|-0.001|-0.125|-0.261|
||[0.055]|[0.070]|[0.144]|[0.176]|
||0.079|0.093|0.066|0.321|
|Tier2*Crisis|[0.099]|[0.179]|[0.251]|[0.347]|
||(0.924)|(0.644)|(0.456)|(0.170)|
|Recapitalized|-5.374***|-5.118***|-8.577***|-8.202***|
||[1.142]|[1.201]|[1.956]|[2.290]|
|Constant|1.547|-1.413|13.933|13.998|
||[2.765]|[2.498]|[17.081]|[14.104]|
|Controls*Crisis|x|x|x|x|
|Controls*PreCrisis|x|x|x|x|
|Country*Year FE|x|x|x|x|
|Nber. of Obser.|4049|3726|827|736|
|R squared|0.24|0.24|0.36|0.37|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. In parentheses we report the pvalue for the test of equality of effects during crisis and pre crisis.

26
needs. Indeed, there is evidence that such inertia in the labor market reduces the responsiveness of employment to growth. Plotting the estimated OLC against hiring and firing scores from the Doing Business Report reveals an interesting relationship: more rigid labor market regulations result in a lower OLC. This holds for the short-run, yielding a considerable R-squared of 0.6 (Figure 3). Considering the Philippines an outlier (since its OLC is statistically insignificant), the effect is similarly pronounced in the medium run (Figure 4).

East Asia generally has comparatively flexible labor market regulations. Singapore and Hong Kong SAR, China have the most flexible hiring and firing rules in the world. In the medium run, employment has responded most sensitively to economic growth in these countries, while the effect was weaker in more strongly regulated countries like China and Taiwan, China.

# **Results 2: Okun’s Law and economic crises**

Having established that the strength of Okun’s Law varies across countries, it is interesting to explore if it varies across time. This is important especially to establish of the effect only works in one direction; in other words, does an economic expansion create jobs in the same way that an economic contraction destroys jobs? Just looking at the association captured by the OLC does not answer this question. Thus, the sample was split to isolate times of crisis – the Great Recession of 2008-2011 – from a period of calm between 2001 and 2007. Table 4 presents the short-run results.<sup>5</sup>

**<mark>Table 4:</mark>** <mark>Total employment, 2001-2007, 2008-2011, Short Run</mark>

||**2001-200**|**7**|||**2008-2011**||||
|---|---|---|---|---|---|---|---|---|
||**OLC**|**S.E.**|**R2**|**Obs.**|**OLC**|**S.E.**|**R2**|**Obs.**|
|China|1.62<sup>***</sup>|0.56|0.96|28|7.70<sup>***</sup>|1.47|0.95|14|
|Hong Kong SAR,<br>China|15.93<sup>***</sup>|2.30|0.96|28|12.18<sup>***</sup>|1.72|0.99|14|
|Korea,Rep.|1.66|2.64|0.86|28|4.33<sup>*</sup>|2.43|0.69|14|
|Malaysia|6.36|6.73|0.34|28|12.94<sup>***</sup>|1.00|0.94|14|
|Philippines|3.29|25.76|0.51|28|-2.16|5.87|0.52|14|
|Singapore|-4.53|3.24|0.85|28|7.63<sup>*</sup>|4.24|0.78|14|
|Thailand|2.33<sup>*</sup>|1.21|0.97|28|9.01<sup>***</sup>|0.98|0.99|14|
|Taiwan,China|2.68|3.00|0.73|28|1.33|1.34|0.71|14|

<mark>*** p<0.01; ** p<0.05; * p<0.1; ChiSq significant at 5% level or higher for all countries.</mark> Table 4 does not convey a clear message. On average, there appears to be a somewhat stronger relationship between growth and employment creation in the crisis period, as more coefficients reach statistical significance and tend to be larger in magnitude. Especially in China, the coefficient increased from 1.62 between 2001 and 2007 to 7.7 between 2008 and 2011. Yet in Hong Kong SAR, China, the OLC

> 5 Estimating the medium-run OLC in times of turbulence with considerable variation in the variables makes the coefficients more difficult to interpret.

8
**Table 22: Piecemeal Sensitivity Analysis of Tanzania-EU FTA**

||**Par**|**ameter val**|**ue**|**% Welf**|**are Chang**|**e (EV)**|
|---|---|---|---|---|---|---|
|Parameter|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj)– services sectors|1.5|3|4.5|2.11|0.35|0.27|
|σ(qi, qj)–goods sectors||See below||0.45|0.35|0.32|
|σ(va, bs)|0.625|1.25|1.875|0.27|0.35|0.45|
|σ(D,M)|2|4|6|0.31|0.35|0.40|
|σ(L,K)|0.5|1|1.5|0.34|0.35|0.36|
|σ(A1,...An)|NA|0|0.25|NA|0.35|0.36|
|σ(D,E)|2|4|6|0.33|0.35|0.37|
|εTZA|Central va|lues of all 4 s|ets of ε|0.34|0.35|0.36|
|εEU|parameters|are listed in|table 6B.|0.19|0.35|0.45|
|εAFR|Lower and|upper value|s are 0.5|0.35|0.35|0.35|
|εROW|and 1.5 t|imes central|values.|0.36|0.35|0.35|
|θr|NA|0|1|NA|0.35|0.27|
|θr - CRTS model|NA|0|1|NA|0.04|-0.03|
|θm|0.025|0.05|0.075|0.35|0.35|0.35|
|σ(qi, qj)–goods sectors|||||||
|Processedfood|4.8|9.6|14.4||||
|Beverages & tobacco products|1.5|2.3|3.1||||
|Textile &leatherproducts|2.8|3.6|4.4||||
|Manufacture ofbasic &industrialchemicals|1.7|2.5|3.3||||
|Manufacture of fertilizers & pesticides|1.6|2.4|3.2||||
|Petroleum refineries|2.8|3.6|4.4||||
|Glass & cement|2.3|4.6|6.9||||
|Ironsteel&metalproducts|3.7|7.3|11||||
|Mining & quarrying|2.8|3.6|4.4||||
|Grain milling<br>**Key:**|2.4|3.2|4||||

σ(qi, <u>qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic and imported varieties σ(L, K): Elasticity of substitution between primary factors of production in value added</u> σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods <u>σ(D, E): Elasticity of transformation (domestic output versus exports)</u> εTZA: Elasticity of national service firm supply with respect to price of output εEU: Elasticity of EU service firm supply with respect to price of output εAFR: Elasticity of AFR service firm supply with respect to price of output εROW: Elasticity of Rest of World service firm supply with respect to price of output θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports <u>Source: Authors’ estimates.</u> 90
changes using log first differences. The 5-year breakeven inflation rate is used from 2003 onwards.<sup>6</sup>

# **3.2 Impact on EMDEs**

The data included in the panel local projection models are provided in table A2 and include financial, real and fiscal variables (for details, see appendix figures A1-A3). Seasonal adjusted data are used when available or adjusted using X13-ARIMA-SEATS (U.S. Census Bureau 2017). The data are mainly sourced from Haver Analytics, with all available data collected for as long a time period as possible at a quarterly frequency. Quarterly data is used since it is the highest available frequency for most fiscal-related variables—a key focus of this paper. The datasets used to measure the impacts of different U.S. interest rate shocks differ based on the dependent variable. The most restricted country sample size, in the short-term yields, includes 19 economies from 1997Q4 to 2019Q4—an unbalanced panel of 768 total observations (table A3).<sup>7</sup> The largest country sample used, for example with capital flows, includes 36 EMDEs over 1997Q2 to 2019Q4—an unbalanced panel of 1547 observations. The EMDEs included across all regressions are provided in table A4.

To transform the monthly monetary policy shocks identified earlier into a quarterly frequency and to reflect a one-percentage-point change in the 2-year U.S. yield, the shocks are adjusted in two ways. First, given that monthly shocks are in first differences, shifting to a quarterly change is done by adding monthly changes within each quarter. Second, to make interpretation across shocks comparable, the contribution of all shocks from the historical decomposition of the 2-year yield is used.<sup>8</sup>

# **3.3 Correlates of crises**

The probability of crises is estimated using logit and probit models based on a panel dataset of 139 EMDEs on annual data from 1985-2018. The variables selected are based on empirical findings in the early warning indicators literature on crises (see Kaminsky, Lizondo, and Reinhart 1998; Frankel and Saravelos 2012; and Chamon and Crowe 2012 for an extensive review) and Kose et al. (2021). The panel includes data on debt (both public and private), the balance of payments, and the real, banking, and financial sectors (table A5).

# **<u>4. Results</u>**

# **4.1 Decomposition of movements in U.S. interest rates**

The sign-restricted Bayesian VAR model described above is used to decompose movements in U.S. bond yields into the contributions of their respective shocks. Evolving expectations about

> 6 The 5-year maturity is used based on data availability and as a compromise between capturing information in both the 2-year and 10-year yields. Prior to 2003, the inflation expectations series is based on model estimates by Haubrich, Pennacchi, and Ritchken (2012).

> 7 The models exclude observations between 2008Q4-2009Q4 to account for the period of the global financial crisis.

> 8 The historical decomposition divides the 2-year yield into the contribution of each of the shocks to its evolution over time.

8
<!-- Start of picture text --> $ billions 700 $ billions<br>400<br>China China<br>600<br>350<br>UAE<br>500 300 Singapore<br>400 Singapore 250 Malaysia<br>Brazil India<br>200<br>300 India Russia<br>UAE<br>Russia 150<br>Mexico<br>200 Mexico<br>Other MNA<br>100<br>Other MNA<br>Other LAC<br>100<br>50<br>Other EAP Other EAP<br>0 AFR 0 Other<br><!-- End of picture text --> Source: Authors' calculations, based on Thomson-Reuters SDC Platinum Figure 2: Top source countries of emerging-market cross-border M&A in emerging economies (left) and advanced economies (right), by value, 1997–2010 total. AFR, EAP, LAC, and MNA correspond to the World Bank’s regional classifications for Africa, East Asia and the Pacific, Latin America and the Caribbean, and Middle East and North Africa.

As may be expected, large and fast-growing emerging economies are responsible for the bulk of cross-border M&A activity. China is the single largest emerging market source country for M&A deals, and accounts for $80 and $132 billion of the total $426 and $698 billion invested in emerging and advanced economies, respectively, over the 1997–2010 time period. Other emerging economies with significant presence among source countries include Singapore, the United Arab Emirates,<sup>4</sup> India, and Russia (see Figure 2). Much of these flows are destined for developed markets—primarily the United States, United Kingdom, Canada, and Australia—but China, Singapore, and Brazil are major destination markets as well.

In terms of sectoral composition, the major emerging market M&A transactions appear in high-value, nontradable service sectors: financial services (the top sector for cross-border M&A activity among emerging-market firms, amounting to $227 million of the total of $1.12 billion for 1997–2010), telecommunications (a distant second, amounting to $103 million), resource extraction, and utilities. There is little difference in the sectoral composition of M&A deals in emerging versus advanced economies, which suggests that, to the extent that there are distinct patterns between Southern and Northern investments, they lie more in the nature of the respective economies, rather than in the type of businesses involved.<sup>5</sup>

> 4The relatively high standing of Singapore and the UAE, in spite of their relatively small size, is attributable to the large number of sovereign wealth fund acquisitions in these economies. Singapore’s Temasek Holdings and GIC Real Estate, for instance, accounted for 32 and 31 deals, respectively, in the sample period.

> 5The sectoral distribution differs somewhat when considering the number, rather than value, of deals, with

5
<!-- Start of picture text --> Total losses as % of GDP, average per IG<br>25.0%<br>20.0%<br>HIC<br>15.0%<br>UMIC<br>10.0% LMIC<br>LIC<br>5.0%<br>0.0%<br>HIC UMIC LMIC LIC<br><!-- End of picture text -->

**Figure 2.** Economic losses as percentage of GDP, 1980-2011; _Source: MunichRE, NatCatSERVICE, Wirtz_ (2013)

In addition to these indicators, there exists evidence that in low-income countries various other macroindicators are particularly negatively affected by disasters. Cochrane (1994), for instance, provides evidence that, especially in countries where recovery and reconstruction costs have to be financed through external borrowing (i.e. typically low-income countries), disasters lead to increased stocks of external debt, and reduce longer-term growth prospects.

# **2.2. Evidence at the subnational level**

Evidently, the country-level macro data presented in the previous section fails to reflect conditions at the subnational level: Certain cities or regions within a country may be particularly exposed or vulnerable to disasters. When tracking the recovery of a disaster-affected economy, the slow recovery of an affected region may be disguised behind strong development in non-affected regions.

**_Regions._** In fact, only on rare occasions is an entire country affected by a single adverse natural event. Far more frequent are regional disasters, which leave the rest of the country unaffected or at least less affected. There is evidence that even in highly industrialized economies with large reconstruction and recovery budgets, the occurrence of a disaster can lead to significantly reduced per capita income in the long term. For instance, duPont and Noy (2012) find that the 1995 earthquake in the Hanshin area (Kansai region, Japan) caused a persistent, long-term decline in the regional GDP per capita. They estimate that without the earthquake, income per capita in the region would have been 13% higher 15 years later.

**_Cities._** A study by Hallegatte et al. (2013) provides comprehensive evidence for the exposure and vulnerability of the world’s 136 largest coastal cities in low-income countries. The authors develop different scenarii, based on varying assumptions about future adaptation measures and sea level rise, and assess the economic costs of future urban flood losses. In line with the evidence at the country level, the authors find that the relative economic losses (i.e. expected average annual flood losses, relative to the city’s GDP) are particularly large in coastal cities of low income and lower-middle-income countries. A ranking of the 20 cities with the largest _relative_ annual losses includes only four cities from high-income countries. In this context, the authors highlight the importance of adaptation: without significant investments in flood defenses, cities will incur enormous costs from flooding – with cities in low-income countries incurring the largest (relative) losses.

6