rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Foreign Exchange Rates: End of Period

|(US$)|||||
|---|---|---|---|---|
|Country|June 1997|September 1997|December 1997|January 1998|
|Thailand'|25.79|36.52|47.25|54.92|
|Indonesia<sup>2</sup>|2,450|3,275|4,650|10,375|
|Korea<sup>2</sup>|888|915|1,396|1,573|
|_1/_Official rate.<br>2/ Market rate.<br>_Source._IMF,_IFS,_M|ay 1998.||||

Second, capital outflows typically cause domestic interest rates to rise. One reason is that investors fleeing the local currency sell domestic debt instruments, thereby forcing up domestic interest rates. A second reason is that the central bank, in an effort to stem the deterioration of the domestic currency, may purposely drive up interest rates in order to make the holding of domestic debt instruments more attractive. In the Mexican currency crisis, money market rates, which had averaged 17 percent a year in November 1994, soared to 83 percent in March 1995, before retreating to a still extremely high rate of 47 percent in June 1995 (IMF 1998). In the Asian currency crisis, money market rates in Indonesia rose from an average of 12 percent a year in the first quarter of 1997 (before the crisis began) to 41 percent in the fourth quarter, and then to 57 percent during January 1998. Money market rates in Korea rose less drastically, but still significantly for local standards-from an average of 12 percent in the first quarter of 1997 to 16 percent in the fourth quarter, and then to 26 percent in January 1998 (IMF 1998).

Third, domestic equity prices typically decline during a currency crisis, often dramatically. The decline occurs partly because of a general loss of investor confidence and partly because interest rates on debt instruments-an alternative asset for investors to hold-tend to rise. During the Mexican crisis, the stock market index (the _Bolsa Mexicana de Valores)_ fell from 2,591 in November 1994 to a low of 1,550 in February 1995, a decline of about 40 percent (World Bank 1995, 1996). Likewise, between July 1997 and May 1998, stock market indices fell over 40 percent in both Thailand and Indonesia, and over 50 percent in Korea (Bloomberg Financial Markets, 1998).

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# Annex A.1 Determinants of disruptions: Country-level

|Dependent:|Blackouts|Water<br>disruption|LPI:<br>Timeliness|LPI:<br>Timeliness|LPI:<br>Timeliness|LPI:<br>Timeliness|
|---|---|---|---|---|---|---|
|Model<br>variables|Baseline|Baseline|No<br>governance,<br>no spending|No governance|Baseline|With country<br>FE|
|Output Gap<br>_HP residual as %_<br>_of HP trend_|-21.4333<br>_(21.6779)_|-21.1625<br>_(19.5278)_|0.0138***<br>_(0.0053)_|0.0075<br>_(0.0082)_|-0.0006<br>_(0.0066)_|-0.0093*<br>_(0.0055)_|
|GDP Growth|7.4593|8.5404|-<br>0.0128***|-0.0160***|-0.0065*|0.0014|
|_annual, in %_|_(7.2893)_|_(6.0843)_|_(0.0033)_|_(0.0041)_|_(0.0035)_|_(0.0021)_|
|Electrification|-2.8383|-2.6508|0.0003|-0.0054*|-0.0020|0.0012|
|_% of population_|_(3.6982)_|_(2.1407)_|_(0.0017)_|_(0.0030)_|_(0.0025)_|_(0.0045)_|
|Water access|-8.4012|1.3151|0.0070**|0.0077|0.0016|-0.0286*|
|_% of population_|_(7.0578)_|_(1.8621)_|_(0.0034)_|_(0.0054)_|_(0.0049)_|_(0.0169)_|
|Population|0.8897**|-0.0201|0.0001***|0.0002|0.0001|-0.0038|
|_in mil_|_(0.3648)_|_(0.0729)_|_(0.0000)_|_(0.0002)_|_(0.0002)_|_(0.0033)_|
|Population|||||||
|density|-0.1565|-0.1126|0.0006***|0.0005***|0.0004***|0.0015|
|_in 1/km^2_|_(0.1601)_|_(0.0873)_|_(0.0001)_|_(0.0001)_|_(0.0001)_|_(0.0014)_|
|Urbanisation|4.8294|2.7502|0.0088***|0.0104***|0.0045*|0.0369|
|_% of population_<br>WGI:|_(4.9504)_|_(3.5266)_|_(0.0019)_|_(0.0025)_|_(0.0027)_|_(0.0354)_|
|Government|||||||
|Effectiveness|-184.0429**|-27.6857|||0.3525***|0.2625*|
|_Index, standard_<br>_normal_|_(79.3588)_|_(42.7362)_|||_(0.0435)_|_(0.1342)_|
|Public road|||||||
|spending||||0.1574***|0.0384|0.0315|
|_per capita, in_<br>_constant 2009_<br>_USD, log_||||_(0.0291)_|_(0.0262)_|_(0.0339)_|
|CountryFE||||||YES|
|N (country-<br>years)|207|207|1233|556|556|556|
|R<sup>2</sup>|0.18|0.05|0.45|0.60|0.71|0.91|

Estimated with OLS. Standard errors in parenthesis, clustered on countries. Public road spending includes both investment and maintenance expenditures. Significance levels: *** 0.01, ** 0.05, * 0.10.

12
A further cost often associated with targeted programs is their perceived failure to achieve political support because they help but a fraction of the population, and one typically lacking political clout. Indeed, an incidence of public spending biased against the poor is professed to reflect the reality that governments "misbehave" precisely to please powerful elites and hold on to power (Birdsall and James 1993). It is also often argued that universally available public spending schemes are successful and sustainable due to the fact that a wide spectrum of the community maintain a stake in them-and perceive them as a nonstigmatizing right (Sen in PSP, Stocpol 1991). Some programs may require a socially and economically mixed group of participants for their success. For example, in the United States this argument has been invoked as grounds for not limiting public housing project units to the very neediest (New York Times 1993).

There are country examples for which these explanations are persuasive. Yet, some targeted schemes-usually ones with important indirect benefits-are known to have achieved quite widespread support, well beyond direct participants. The popularity and sustainability of Maharashtra's EGS is often explained by the scheme's indirect benefits: well-off urban dwellers, whose taxes finance the EGS, support it because it helps stem rural migration to Bombay; the rural elite derive benefits from the assets built and the fact that the scheme keeps their labor force in the area through the lean seasons (Echeverri-Gent 1988); finally, in an environment prone to drought and other sources of severe vulnerability, the scheme's promise of a job in times of need provides a form of insurance or safety net, and hence achieves support from many rural inhabitants who do not participate in normal times (Ravallion 1991). Others argue, as Jarvis and Micklewright do in PSP that the historical evolution of policies is an important factor influencing attitudes both of the public and reformers and the political economy of targeted policies.

Clearly, the fact that a program is well targeted does not ensure that it is a cost-effective way to reduce poverty since the extra costs incurred by targeting and the political-economy responses may 35
―Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between: ... Cheating on tax if you have the chance‖. The question leads to a ten scale index of tax morale with the two extreme points ―never justified‖ and ―always justified‖.

A second important data set that has been used is the Latinobarómetro (Torgler, 2005a). It is an annual public opinion survey carried out in 17 Latin American countries (since 1996). It reports the opinions, attitudes, and behaviors of the around 400 million inhabitants of the region. The survey started with 8 countries in 1995 and was extended to 17 countries in 1996. It covers most of Latin America with the exception of Cuba, the Dominican Republic, and Puerto Rico. This data set is not as well known as the WVS though economists have referred to it recently (see, e.g., Graham and Pettinato, 2002, who contributed to the happiness research). This data set has integrated a similar question which allows to measure tax morale: ―On a scale of 1 to 10, where 1 means not at all justifiable and 10 means totally justifiable, how justifiable do you believe it is to: Manage to avoid paying all his tax‖.

In addition some studies have worked with the International Social Survey Programme (ISSP) (see, e.g., Torgler, 2005b). Similar to the previous data sets, it is a continuing annual program of cross-national collaboration. It started in 1983 and has grown to include more than 30 nations (mostly European countries). The RELIGION II (ISSP 1998) module covers, e.g., the following question: ―Do you feel it is wrong or not wrong if a taxpayer does not report all of his or her income in order to pay less income taxes? (1= not wrong, 2= a bit wrong, 3= wrong, 4=seriously wrong)‖.

In general, these three data sets have the advantage that they are designed as a wideranging survey, which reduces the probability of being suspicious and of creating framing effects by other tax context questions.

8
**Figure 3: Growth accounting (ctd.)**

<!-- Start of picture text --> B. Capital/output ratio approach<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>1970‐2016 1970‐1980 1980‐1990 1991‐1998 1998‐2005 2005‐2012 2012‐2016<br>‐6<br>TFP K/Y Labor Schooling Overall<br><!-- End of picture text --> Source: own calculation based on PWT 9.0 and WDI. As the standard approach in the top panel may somewhat overestimate the contribution of capital accumulation that is really due to productivity-induced capital deepening, we alternatively follow an approach by Klenow and Rodriguez-Clare (1997) in the lower panel that focuses on the capital/output ratio. As one can see, there is little qualitative difference between both approaches, only that the contribution of capital accumulation is qualitatively less in the latter case, as should be. See appendix B1 for details on the methodology and the data.

A sectoral perspective highlights the role of services and structural change: Figure 2.B depicts a typical pattern of structural change where the share of value added declines over time. Somewhat atypically, this was not accompanied by an increasing role for manufacturing, which remained remarkably stable. Labor instead moved straight into the services sector.<sup>5</sup> This is mirrored in Figure 2.C that shows a strong sectoral contribution of services to growth, while the contribution of manufacturing is barely visible for most years. It is important to understand, however, that the relevant growth contribution of services does not stem from productivity increases within the aggregate service sector. If anything, productivity in services was declining after 2005 (see Figure 2.D; the more detailed analysis in section 3 finds no productivity change). Services’ relevant growth contribution rather stems from the fact that the overall size of the sector was large (a level effect) and growing (an effect of structural change).

By and large, one could expect this to be a favorable pattern of structural change, as labor productivity in services is about double as high as in agriculture (Figure 2.D). This aggregate picture, however, masks the productivity differences that exist within the services (and also industry) sector and the detailed changes in employment taking place. This motivates our more detailed analysis of structural change in section 3.

# **3. A detailed analysis of structural change**

In this section, we have a more detailed look at patterns of structural change and recent literature of decomposing aggregate productivity changes into productivity changes that take place within

> 5 Note that a revision of the national account statistic was responsible for the considerable increase of the service sector in 2005/2006 as several communication and personal services, recreation services, and professional services were not included in GDP before the revision. Figure 2.B may thus quantitatively overstate the described trends which are nevertheless qualitatively present.

6
15 loss froa rent-seeking is given by ABO. At the new equilibriua. FG and BF are additional coats due to rationing and rent-seeking. respectively.

The welfare benefits from removing QRs are reported in table 2. Measured in terms of GDP. welfare costs of QRs in Turkey in 1978 were estimated at over 5% of GOP. Most of the costs come from quotas on intermediates since consumer imports had a small share in overall imports. Of course. the magnitude of the results is driven by the assumed rent-seeking behavior and by the estimated value of the premium rates incorporated in the calibrated solution for the base simulation. The results are thus only 1Dd1cat1ve. but can serve to focus on the costs in terms of foregone conauaption and. more importantly. foregone growth due to reduced real investment caused by QRs and rent-seeking behavior.

Table 2

> Welfare Benefits from Removi otas in Turke Ratio to Base Simulation Value)

Quotas on Intermediate Quotas on Intermediate Imports Removed and Consumer Imports Removed leal GDP 1.052 1.054 leal Household Consumption 1.038 1.042 Real Investment 1.112 1.110 Source: Grata. de Malo and Urata (1986. table 7) • ... , ' '
survey were used to classify each household in the panel data set as having no migrants, having one or more members who migrated overseas, or having one or more members who migrated to another Indian state between 1998 and 2003.<sup>18</sup> As noted earlier, only a small fraction of sending households (less than 3 percent) saw members migrating both domestically and internationally for work between survey rounds. I dropped 12 such observations to reach a working sample of 4,783 households.<sup>19</sup> Table 1 displays summary statistics of selected variables. I determined migration status (shown in columns 2 and 3) from the 2003 KMS reports of household members who migrated for work between the 1998 and 2003 survey rounds. In addition, questions in the 2003 KMS enable one to determine the length of time a given household member spent abroad or in another Indian state between survey rounds. I used this variable to measure the duration of a household’s exposure to either form of migration.

Within the three migration categories, I used the 2003 KMS data set to obtain individual-level characteristics for household members between the ages of 19 and 60 who were in the labor force as of 2003 (shown in Table 1, rows 1 to 4). The majority of migrants departing for jobs outside Kerala after 1998 were between 19 and 40 years old. Individuals who migrated for work after 1998 tended to be 5 to 9 years younger than non-migrants. Of these, international migrants were on average 4 years older than domestic migrants. About three-quarters of the adult labor force in Kerala consisted of males; the gender breakout of domestic migrants shows a similar male-female ratio. International migrants were almost all male—close to 90 percent.

In terms of educational level, domestic migrants tended to be more educated than international migrants, who in turn had more schooling than non-migrants. The share of domestic migrants, international migrants, and non-migrants who had completed at least secondary schooling was 68

> 18 Neither the 1998 nor 2003 KMS queried if household members had ever moved within Kerala for work. In my empirical analysis, I treat households whose members moved within Kerala and those without domestic or international migrants collectively as non-migrant households.

> 19 The overall results do not change if the 12 household observations are retained.

10
0 i (2) ii ii ii ss RR tt ii where represents our measures of firm performance, YY = αα + ββ∙s ssPPs ss_i PPiissi ii + γγZZ + δδ∙DDs + ρρ∙DDi + ττ + εε is a dummy variable that is equal to one for firms that were not registered when they began operating. is a vector of firm-level YY ssPPs ss_i PPiissi ii control variables. We exploit the richness of the enterprise survey data by including a large set of control ZZ variables that the literature has shown to be correlated to firm performance.<sup>13</sup> We control for firm size and age, which numerous authors have shown to be correlated with firm productivity (Biesebroeck 2005 and Haltiwanger, Jarmin, and Miranda 2013). We also proxy for human capital within the firm by controlling for the manager’s years of experience in the sector and whether the firm has formal training programs in place for its employees, which should be associated with higher human capital (Kinda, Plane, and Véganzonès-Varoudakis 2015). Physical capital is measured by whether or not the firm purchased fixed assets in the past year. A firm’s outward orientation is measured by whether it has foreign participation in ownership and whether it is engaged in exporting activities—research has shown that such factors can expose firms to a more competitive environment and to improved technologies, with associated impacts on productivity (Dimelis and Louri 2002; López 2005; Bernard et al. 2007; Guadalupe, Kuzmina, and Thomas 2012). We control for whether or not a firm has a website—a proxy for internet and telecommunication services that may be associated with increased productivity (Clarke, Qiang, and Xu 2015). Access to finance, which has shown to be associated with firm performance (Rajan and Zingales 1998; Gatti and Love 2008), is proxied by whether a firm has a checking or savings account. Finally, we control for two factors of the business environment that may negatively affect firm performance: experience of power outages (Cole et al. 2018) and exposure to crime (Islam 2013). Finally, because research by Islam et al. 2020 has shown a significant labor productivity gap between female and male managed firms, we control for whether the firm’s top manager is female.

managed firms, we control for whether the firm’s top manager is female. Our model, summarized by equation (2), also includes controls for the firm’s sector of operation ( ), the within-country region in which the firm is located ( ) as well as year-of-survey fixed effects ( ). Again, ss DD the model is estimated separately for firms with and without female participation in ownership to see if RR tt DD ττ the relationship between informality and performance differs between these types of firms. Given that the start date of firms precedes the time at which performance is evaluated, the concerns of reverse causality are attenuated. While we cannot completely rule out omitted variable bias, we alleviate this concern as much as possible by the wide range of control variables we employ.

> 13 These correlations are discussed in greater detail in Islam, Gaddis and Amin (2020).

12
Figure 11: Utilization rate losses from infrastructure disruptions, by type of infrastructure at country level <!-- Start of picture text --> a. Electricity infrastructure b. Water infrastructure c. Transport infrastructure<br>2 2 2<br>1.8 1.8 1.8<br>1.6 1.6 1.6<br>1.4 1.4 1.4<br>1.2 1.2 1.2<br>1 1 1<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>0 20000 40000 0 10000 20000 30000 40000 0 10000 20000 30000 40000<br>GDP per capita GDP per capita GDP per capita<br>Electricity infrastructure Water infrastructure Transport infrastructure<br>infrastructure disruptions<br>Percentage of utilization rate loss due to<br><!-- End of picture text --> _Note:_ Data points represent 118 countries.

Figure 12 and Figure 13 show the most affected countries overall and by infrastructure type, respectively. In some countries, unreliable electricity infrastructure causes large fractions of industry capacity to remain unused, often significantly more so than transportation problems. At the same time, firms in many countries are barely affected by power outages as 46% of countries experience utilization losses of less than 0.05% due to electricity. Other than in the case of transportation disruptions, many countries appear to be successful in eliminating their firms’ losses due to unreliable electricity.

Unreliable water infrastructure has a small affect in most countries. Whereas some countries’ firms suffer significant losses from disrupted water infrastructure, most countries appear to be affected only moderately. This might indicate countries’ success in eliminating outages as well as firms’ lower reliance on water infrastructure.

24
13

**Figure 8. Observed Patterns of Structural Change across Country Income Groups**

<!-- Start of picture text --> Rural Share of Population, % Agriculture Share of GDP, %<br>70<br>100 60<br>90<br>80 50<br>70 40<br>60 30<br>50<br>40 20<br>30 10<br>20<br>10 0<br>0 Low income Lower Upper High income<br>Low income Lower middle Upper middle High income middle middle<br>income income income income<br>Agriculture Share of Employment, % The Case of the Republic of Korea<br>80 80<br>Agriculture Share of GDP<br>70 70<br>60 60 Rural Population Share<br>5040 50 Agricultural Share of Employment<br>40<br>30<br>20 30<br>10 20<br>0 10<br>incomeLow middle Lower middle Upper incomeHigh 0<br>income income 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br><!-- End of picture text --> Source: World Bank, World Development Indicators database. Note: Data for rural population and GDP shares are for 2008. Data for employment shares are for 2000 for low-income countries (because of limited data availability) and for 2006 for other country groups. Some small island states with very high rural population shares were removed as outliers from the high-income and upper-middle-income groups. Lines represent the range of values within each country group, and triangles the average.

Yet development strategies have often failed to deliver sustained growth and structural transformation in many developing countries, especially in Latin America and Africa. A recent assessment by McMillan and Rodrik (2011), based on a decomposition of productivity growth into two components (sectoral productivity and structural change), is illustrative.<sup>13</sup> It shows that most of the difference between the recent growth in Asia and that in Latin America and SubSaharan Africa can be explained by the variation in the contribution of structural change to overall labor productivity (figure 9).

> 13 McMillan and Rodrik (2011) construct a simple index based on the idea that productivity differentials exist both between broad sectors of the economy and within modern manufacturing activities. These gaps are indicative of the allocative inefficiencies that reduce overall labor productivity. But they can potentially be an important engine of growth. When labor and other resources move from less productive to more productive activities, the economy grows even if there is no productivity growth _within_ sectors. This kind of structural change can be an important contributor to overall economic growth.