rafmacalaba/datause-encoder-data huggingface dataset:
# **4 MACROECONOMIC VOLATILITY: DETERMINANTS**

Most of the decline in output growth volatility can be explained by improved domestic structural features, more stable macroeconomic policies and, until the current financial crisis hit, a lower frequency of external and exogenous shocks. In particular, the literature studying output growth volatility has identified three main sources of volatility: (i) domestic structural features that affect the ability of the economy to absorb shocks; (ii) domestic shocks generated by self-inflicted policy mistakes and weak institutions; and (ii) external shocks such as “sudden stops” of capital inflows, large changes in the international terms of trade, and volatility of main trading partners. Next, we review these factors in detail and their trends in East Asia.

# **Improved Domestic Structural Features**

Domestic structural features, such as institutions, the sophistication and depth of financial markets, technology and business practices, not only affect countries’ ability to absorb shocks, but can also induce or magnify them. Deaton and Miller (1996), Hoffmaister, Roldós, and Wickham (1998), and Raddatz (2005, 2007), among others, all find that internal conditions and domestically induced shocks explain a large share of output fluctuations in poor countries. Improved institutions, deeper financial markets, and more stable macroeconomic policies have all played an important role in reducing volatility in East Asia and all over the world.

## **Improved Institutions**

Greater institutional quality tends to be associated with increased capacity of adjusting to major economic shocks, and lower output growth volatility. Acemoglu _et al._ (2003), for instance, document a strong relationship between the quality of institutions and volatility, with countries that inherited worse institutions having a higher probability of suffering high volatility and economic crises. They motivate the findings by suggesting that societies with better institutional quality are expected to experience less internal conflicts, hence governments are able to pursue more enduring and less volatile policies.

Since the 1960s East Asian countries have significantly improved overall institutional quality, which remains nonetheless well below the OECD average (Figure 7). There exist various datasets providing quantitative measures of institutional quality. The one used in this study is the Polity IV (2004), which looks at the quality of checks and balances in place on the executive branch of the government. It shows that, on average, non-OECD East Asia improved its institutional quality score from 2.2 in the 1960s, to 3.8 nowadays. Although this represents a significant improvement, overall institutional quality remains fairly low (the best institutional quality in the Polity IV dataset is 7), and even nowadays remains lower than institutional quality of OECD countries in the sixties. Moreover, most of the progress 11
**<mark>Table 15 - Tunisia: Composition of Foreign Currency Debt and Reserves</mark>**

|as of end Dece|mber 2016,millions EU|R<br>|||||||
|---|---|---|---|---|---|---|---|---|
|||USD|EUR|JPY|GBP|SDR|Other|Total|
||EUR mil. eq.|2,06<br>4|2,55<br>0|117|617|-|41|5,389|
|Reserves|Percent of total|38.3|47.3|2.2|11.<br>4|-|0.8|100.0|
||Percent of GDP|5.2|6.4|0.3|1.5|-|0.1|13.5|
||EUR mil. eq.|4,76<br>8|5,99<br>7|2,06<br>9|-|1,51<br>4|645|14,99<br>3|
|External Debt|Percent of total|31.8|40.0|13.8|0.0|10.1|4.3|100.0|
||Percent of GDP|11.9|15.0|5.2|-|3.8|1.6|37.5|
|Concentratio<br>n|Ratio FX debt/<br>reserves|2.31|2.35|17.6<br>8|n/a|n/a|15.73|2.78|

Source: Ministry of Finance and Banque Centrale de Tunisie _Applied FX Rate: 1.05USD/EUR; 1.17 EUR/GBP; 123JPY/EUR; 2.4 TND/EUR as of Dec. 31, 2016_ The currency composition of this external debt reflects large shares of EUR and USD debt followed by obligations in JPY and SDR (40 percent, 32 percent, 14 percent and 10 percent respectively). The largest shares of Tunisia’s foreign exchange reserves are also held in USD and EUR (38 percent and 47 percent respectively) and the composition of shares for reserves and debt match relatively closely. When the SDR holdings are broken into their components, reserves and debt composition match even more closely (47 percent and 43 percent for EUR and 38 percent and 36 percent for USD, see Figure 10).

**Figure 10 – Tunisia: FX Currency Composition of:**

Reserves <!-- Start of picture text --> Other<br>JPY 1%<br>2% GBP<br>12%<br>USD<br>38%<br>EUR<br>47%<br><!-- End of picture text --> <!-- Start of picture text --> Debt (SDR in components)<br><!-- End of picture text --> <!-- Start of picture text --> CNY Other<br>1% 4%<br>GBP<br>1%<br>JPY<br>USD<br>15%<br>36%<br>EUR<br>43%<br><!-- End of picture text --> Despite its high FX debt-to-reserves ratio, Tunisia has a history of intervening in the foreign exchange market, selling reserves to smooth excessive fluctuations in the dinar’s exchange rate. Since May 2016, the dinar has traded more flexibly, abandoning its history of crawling within a 2 23
To exploit this variation in both decisions and timing, Clarke and Cull (2000a) estimate a discrete-time hazard model using data from between 1992 and 1996. The probability of privatization was allowed to vary across periods, making it possible to disentangle factors that affected all provinces (e.g., the Convertibility Plan and the Tequila Crisis) from provincespecific effects (bank quality, fiscal pressures, and internal political pressures). They find strong empirical support for the hypothesis that political economic incentives affected the likelihood of privatization. In particular, poor performance (which might reflect failures in corporate governance) encouraged privatization, while overstaffing (a reflection of the power of workers) and size (relative to the local banking sector) decreased the likelihood of privatization. Other factors, including political affiliation of policymakers and fiscal performance of the province, did not appear to have a consistent effect on the likelihood of privatization.

# **B. Determinants of Contract Features**

In this sub-section, we study characteristics of the banks and provinces that affected the outcomes of the negotiations between the private buyers and the provinces. Ideally, we would be interested in factors that might affect either the preferences of policymakers or the attractiveness of the bank to potential buyers. Unfortunately, the small number of observations severely limits our ability to specify a complete model, test plausible variables and control for demographic factors that might affect contract provisions.

The contract provisions studied are: restrictions on layoffs; the percent of privatized assets guaranteed by the province; the percent of public bank assets taken by the privatized bank rather than put into the residual entity; the price paid relative to size of the bank; and the length of the service contract. We do not look at branching restrictions because there was not enough variation to estimate a model with this as the dependent variable.<sup>12</sup> In general, we would expect policymakers to prefer fewer layoffs, smaller residual entities, smaller guarantees and a higher price. Although it is not immediately clear that politicians would prefer shorter to longer service contracts, as noted above, the new owners tended to prefer longer contracts. Since we do not

> 12 That is, if we estimate a simple probit model with a dummy variable indicating branching restriction, there are only two cases with no restrictions (see Table 1). A multivariate model would be go far beyond the data constraints.

23
2 Table 1: Cotton's Share in Total Agricultural Export Revenues in Selected Countries

||1971<br>_1981_|1991|
|---|---|---|
||---------<br>.-<br>(U---||
|Francophone Africa|7.3<br>9.7|23.5|
|<br>rso|24.2<br>49.7|46.4|
|Pakistan|34.2<br>41.7|40.7|
|Turkey|181<br>26.9|11.5|
|<br>-China|2.0<br>1.1|0.7|
|World|5.1<br>_<br>3.1|2.5|

Source: IECIT estimates using data obtained from FAO International Trade Tapes.

The recent marketing liberalization effortL in many cotton producing developing countries are likely to expose participants to market forces and make hedging instruments such as futures and options attractive in reducing intertemporal price volatility. However, even without liberalization, there is scope for using hedging instruments. In most cases where government intervention is prevalent, the government effectively internalizes (assumes) the cotton price risk. For example, governments in China and Uzbekistan and cotton parastatals in Turkey and the FPA countries could make good use of hedging instruments in reducing cotton price volatility. FPA countries use forward sales for this purpose, but this instrument only provides limited coverage (see Satyanaryan, et. aL9., 193).

The usual arguments for hedging are that by doing so market participants can increase the predictability of future cash flows, lock-in profit margins, and reduce the price uncertainty of
## **3.2 Plant Size**

The WBES only interviews enterprises with 5 or more employees and Turkish enterprises have 3.6 employees on average. Consequently, the WBES dataset cannot be used to analyze the plant size distribution of Türkiye. Instead, I use the Annual Industry and Services Statistics provided by the TurkStat.<sup>6</sup> This data is confidential and contains detailed information for all the enterprises in Türkiye. TurkStat annually reports summary statistics, instead of providing micro data. Table 3 reports the plant size distribution in Türkiye and the US.<sup>7</sup> Note that these numbers are the average over the period 2009-2014 for Türkiye and 2009-2015 for the US.

The mean plant size in Türkiye is almost one-fourth of that in the US. Given that the unit of observation in Türkiye is the enterprise whereas in the US it is the establishment, this difference would be bigger if establishments were measured in Türkiye. This is because enterprises may contain more than one establishment. There are only 3.6 employees in an enterprise, on average, in Türkiye. As many as 97.3% of enterprises have 19 employees and 99.6% of enterprises employ fewer than 100 workers. However, enterprises with more than 100 employees employ 43.6% of all workers.

Compared to the US, the plant-size distribution in Türkiye is skewed to the left. However, Türkiye’s total employment size distribution for big plants is similar to that of the US. Türkiye’s frequency of plants with size between 20 and 99 workers is only 2.2% but it is 13.2% in the US. Türkiye’s plant size distribution is an example of the “missing middle" literature proposed by Tybout (2000) and Krueger (2007), there are very few middle-sized firms, and employment is concentrated in a few big firms, and there is a large number of small firms.

# **4 Model**

My model’s environment is similar to that in Guner _et al._ (2008) which puts forward a Lucas (1978) span-of-control framework in a growth model. The innovation in this paper is the introduction of exogenous corrupt officials who create distortions and that it is possible to decrease some of the distortions by paying (endogenously chosen) bribes. There is a single infinitely-lived representative household which consists of a continuum of members. The household has a preference over the stream of consumption, _Ct_ , discounts the future with

> 6http://www.turkstat.gov.tr/UstMenu.do?metod=kategorist

> 7https://www.census.gov/programs-surveys/cbp.html

8
<mark>The CEQ methodology can capture both scenarios. If deferred income, pension income is included in market income, and the income concept is thus labeled market income plus pensions. If transfers, pension income is added to net market income to get to disposable income, alongside other components; and pension</mark> contributions are considered a tax.

<mark>Our benchmark analysis focuses on the scenario of pensions as deferred income. Nevertheless, as robustness check, we also compare the difference in results between the different scenarios. Figure B1 and B2 show that excluding pensions from market income in the pure transfer scenario leads to a higher prefiscal inequality and poverty, highlighting the important role of pensions for households and their large size compared to other sources of market income. Not surprisingly, inequality and poverty drop sharply from net market income to disposable income, as pensions benefits (as direct transfers) are added. Interestingly, the poverty-increasing effect of the full fiscal system is not present in the scenario of pensions as transfers. In this case, poverty declines from 23 to 8 percent ($2.5/day, 2005PPP), compared to 7.5 to 8.1 percent in the case of pensions as pure deferred income.</mark> <!-- Start of picture text --> Figure B1. Gini coefficient by income concept, and Figure B2. Poverty headcount ($2.5/day) by income<br>by pension scenario concept, and by pension scenario<br>Pensions (Pure Deferred Income) Pensions (Pure deferred Income)<br>Pensions (Pure Transfer)<br>Pensions (Pure Transfer)<br>0.50 0.25<br>0.48<br>0.46 0.20<br>0.44<br>0.42 0.15<br>0.40<br>0.38 0.10<br>0.36<br>0.05<br>0.34<br>0.32<br>0.00<br>0.30<br>Market Net Market Disposable Consumable Final Income Market Net Market Disposable Consumable<br>Income Income Income Income Income Income Income Income<br>Source: Own estimates using the Albania HBS 2015. Source: Own estimates using the Albania HBS 2015.<br><!-- End of picture text -->

# **VI. Incidence, Progressivity and Marginal Contributions of Taxes and Social Spending**

**How each of the fiscal interventions contribute to the observed changes in poverty and inequality?** One way to answer this question is by examining the extent to which taxes and transfers affect household income across the welfare distribution. Another way is by looking at the marginal effect<sup>36</sup> of each fiscal component on the Gini coefficient and the poverty headcount. Intuitively, a tax or transfer has a sizable poverty and distributional impact if it is strongly targeted to those at the bottom of the distribution (as

> 36 The marginal effect is the change in market income plus pensions due to adding or subtracting only the given benefit or tax from market income plus pensions.

21
_55_

# IV, Conclusions

Since Goldsmith (1969) documented the relationship between financial and economic development thirty years ago, the profession has made important progress. Rigorous theoretical work carefully illuminates many of the channels through which the emergence of financial markets and institutions affect -- and are affected by -- economic development. A growing body of empirical analyses, including firm-level studies, industry-level studies, individual countrystudies, and broad cross-country comparisons, demonstrate a strong positive link between the functioning of the financial system and long-run economic growth. Theory and evidence make it difficult to conclude that the financial system merely -- and automatically -- responds to industrialization and economic activity, or that financial development is an inconsequential addendum to the process of economic growth. I believe that we will not have a sufficient understanding of long-run economic growth until we understand the evolution and functioning of financial systems. This conclusion about financial development and long-run growth has an important corollary: although financial panics and recessions are critically important issues, the finance-growth link goes beyond the relationship between finance and shorter-term fluctuations. Undoubtedly, the financial system is shaped by nonfinancial developments. Changes in telecommunications, computers, nonfinancial sector policies, institutions, and economic growth itself influence the quality of financial services and the structure of the financial system. Technological improvements lower transaction costs and affect financial arrangements (Merton 1992). Monetary and fiscal policies affect the taxation of financial intermediaries and the provision of financial services (Bencivenga and Smith 1992; Roubini and Sala-i-Martin 1995). Legal systems affect financial systems (LaPorta, et. al. 1996), and political changes and national
**Appendix:** Entrepreneurial Psychology Survey Questions following McKenzie & Woodruff (2010)

Responses to all questions are on a scale of one to five, with five indicating “agree strongly” and one indicating “disagree strongly.”

# **1. Impulsiveness:**

I plan tasks carefully (scale reversed). I make up my mind quickly. I save regularly (scale reversed).

# **2. Tenacity:**

I can think of many times when I persisted with work when others quit. I continue to work on hard projects even when others oppose me.

# **Polychronicity:**

I like to juggle several activities at the same time.

I would rather complete an entire project every day than complete parts of several projects (scale reversed). I believe it is best to complete one task before beginning another (scale reversed).

# **3. Locus of control:**

It is difficult to know who my real friends are (scale reversed) I never try anything that I am not sure of (scale reversed) A person can get rich by taking risks

# **4. Achievement:**

It is important for me to do whatever I’m doing as well as I can even if it isn’t popular with people around me. Part of my enjoyment in doing things is improving my past performance.

When a group I belong to plans an activity, I would rather direct it myself than just help out and have someone else organize it.

I try harder when I’m in competition with other people. It is important to me to perform better than others on a task.

I try harder when I’m in competition with other people.

It is important to me to perform better than others on a task.

# **5. Power motivation:**

I enjoy planning things and deciding what other people should do. I find satisfaction in having influence over others.

I like to have a lot of control over the events around me.

# **6. Passion for work/work centrality:**

I look forward to returning to my work when I am away from work. The most important thing that happens in life involves work.

It is important for me to do whatever I’m doing as well as I can even if it isn’t popular with people around me. Part of my enjoyment in doing things is improving my past performance.

# **7. Organized person:**

My family and friends would say I am a very organized person.

# **8. Optimism:**

In uncertain times I usually expect the best. If something can go wrong for me, it will (scale reversed). I’m always optimistic about my future.

I hardly ever expect things to go my way (scale reversed).

I rarely count on good things happening to me (scale reversed).

* The attitude variables are constructed by rescaling each question from -2 to 2. The questions within each attitude category are then summed and divided by the number of questions to produce an indicator of range [-2,2].

26
reexamining the impact of immigration on the labor market’, _Quarterly Journal of Economics_ , **118** (4), 1335–74.

Borjas, G.J. (2014), _Immigration Economics_ , Cambridge, MA: Harvard University Press. Borjas, G.J. and B. Bratsberg (1996), ‘Who leaves? The outmigration of the foreignborn’, _The Review of Economics and Statistics_ , **78** (1), 165–76.

Calero, C., A.S. Bedi and R. Sparrow (2008), ‘Remittances, liquidity constraints and human capital investments in Ecuador’, _World Development_ , **37** (6), 1143–54.

Card, D. (2001), ‘Immigrant inflows, native outflows, and the local market impacts of higher immigration’, _Journal of Labor Economics_ , **19** (1), 22–64. Card, D. (2009), ‘Immigration and inequality’, _American Economic Review_ , **99** (2 **)** , 1–21.

Carletto, C. and C. Azzarri (2007), ‘Migration in Albania: trends, determinants and impact on poverty’, Background Paper to the 2007 Albania Poverty Assessment, mimeo, Washington, DC: The World Bank.

Carletto, C., K. Covarrubias and J.A. Maluccio (2011), ‘Migration and child growth in Guatemala’, _Food Policy_ , **36** (February), 16–27.

Carletto, C. and A. de Brauw (2008), ‘Measuring migration using household surveys’, Migration Operational Vehicle, Operational Note No. 2, Washington, DC: The World Bank.

Carletto, C. and T. Kilic (2011), ‘Moving up the ladder? The impact of migration experience on occupational mobility in Albania’, _Journal of Development Studies_ , **47** (6), 846–69.

Center for Global Development (2009), ‘Migrants count: five steps toward better 48
|Ranking of the Top Business Environment Obstac<br>% firms choosing the informal sector as the biggest obstacle|le for Finms<br>se.7|
|---|---|
|% firms choosing tax rates as the biggest obstacle|14.35|
|% firms choosing political instability as the biggest obstacle|12.2|
|> firms choosing business licensing as the biggest obstacle|68|
|SS firms choosing access to finance as the biggest obstacle|63|
|% firms choosing trade regulations as the biggest obstacle|6.2|
|% firms choosing crime and disorder as the biggest obstacle|4s|
|So firms choosing transportation as the biggest obstacle<br>& firms choosing corruption as the biggest obstacle|2.9<br>25|
|® firms choosing poorly educated workers as the biggest obstacle|VF|
||o<br>S<br>10<br>15<br>20<br>25:<br>30<br>35<br>40<br>PercentofFirms|
- 9 -

Table 2. IPCC Climate Projections around Project Sites

||Vish<br>Temperat|inugad Pi<br>ure (°C)|Precipi<br>(mm/m<br>palkoti (Ind|tation<br>onth)<br>ia)|Upp<br>Temperat|er Kotmal<br>ure (°C)|e (Sri Lan<br>Precipi<br>(mm/m|ka)<br>tation<br>onth)|<br>Temperat|Thac Mo<br>ure (°C)|(Vietnam)<br>Precipi<br>(mm/m|tation<br>onth)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Avg.|Proj.|Avg.|Proj.|Avg.|Proj.|Avg.|Proj.|Avg.|Proj.|Avg.|Proj.|
||1961-90|2020s|1961-90|2020s|1961-90|2020s|1961-90|2020s|1961-90|2020s|1961-90|2020s|
|Jan|6.3|7.3|47|34|26.6|27.3|63|97|21.5|21.9|44|51|
|Feb|7.2|8.0|55|47|26.9|27.6|71|68|22.5|22.7|26|38|
|Mar|11.0|12.2|58|39|27.7|28.3|129|137|20.0|20.8|34|73|
|Apr|15.5|17.2|35|6|28.2|28.8|255|252|25.5|26.1|48|61|
|May|17.8|19.7|66|52|28.3|28.9|401|335|26.2|26.3|122|135|
|Jun|18.6|20.0|138|135|27.9|28.7|179|187|26.5|26.4|154|167|
|Jul|17.4|18.2|327|340|27.6|28.4|130|138|26.6|27.0|170|135|
|Aug|17.0|17.4|293|332|27.5|28.3|92|111|26.8|27.2|160|168|
|Sep|16.2|16.3|201|198|27.5|28.3|241|289|25.7|26.3|311|313|
|Oct|14.3|15.1|43|56|27.0|27.6|382|379|25.1|25.7|363|397|
|Nov|11.1|11.5|7|0|26.7|27.4|308|285|23.7|24.3|312|309|
|Dec|8.4|9.6|24|27|26.6|27.2|170|156|22.2|23.1|110|113|

Sources: IPCC DDC database and one of the SRES scenarios, CSIRO/A1a; and NOAA GHCN Monthly database version 2. Note that the estimated incremental changes are given by the IPCC model. The historical series are based on NOAA database for VPHEP and UKHPP. The Thac Mo case relies on IPCC DDC database for historical data as well.

# **III. MODEL AND DATA**

Following the existing literature (e.g., Salas, 1993; Mohammadi _et al_ ., 2006), a simple multivariate stochastic model, VAR, is considered. Stationarity is a key requirement for performing the VAR model. Hydrologic data defined on an annual time scale are generally characterized stationary unless there are large-scale climate variability, natural disruptions and human-induced changes such as reservoir construction (Salas, 1993). A typical hydrologic process is composed of three parts: (i) a deterministic part resulting from natural physical periodicities, (ii) an aperiodic deterministic part which is often referred to as trends, and (iii) a stationary random component. Once detrending and adjusting seasonarity, the remaining time series tends to be covariance stationary (Koutsoyiannis, 2006).

Our hydrological data seem to have strong seasonal regularities on an annual basis (Figure 2). Outstanding hikes in water flow have been observed twice a year in the Sri Lanka’s case and once a year in the rest of the cases. Another intuitive finding from the figure is that while a water flow in the Kotmale River appears to have declined over the past five decades, the Thac Mo discharge may have had an increasing steady tendency in recent