rafmacalaba/datause-encoder-data huggingface dataset:
# **APPENDIX A: WHO THINKS HIGHER SALARIES WILL IMPROVE PERFORMANCE OF INCUMBENT TEACHERS?**

As Appendix B notes, the standard economic model does not predict that unconditionally higher salaries will improve the performance of incumbent teachers; that is, it does not predict effects on the intensive margin. But there is considerable evidence that stakeholders in education often believe that such intensive-margin effects matter. This appendix gives examples of that view, taken from both developing-country and developed-country contexts. Because of the focus of this study, the majority of the quotations are taken from the education sector, but we also include other quotes to show that the argument is applied more broadly to the civil service.

Appendix B formalizes the intuition implicit in these quotes and derives comparative statics.

# _A. Teachers in Indonesia_

Before the Teacher Law and in the early years of its implementation, it was commonly argued in Indonesia that low pay corrodes the motivation and performance of teachers, even when they have some intrinsic motivation. Teachers’ union officials pressed this argument through the media in the year before the Teacher Law was passed<sup>1</sup> : _The high absence rate of elementary school teachers is understandable, as they are paid far below their monthly cost of living, said the head of an educators union._ Indonesian Teachers Union (PGRI) chairman Mohammad Surya said on Thursday the government lacked appreciation for teachers, who, like other professionals, needed good salaries and a clear status.

. . . A recent study by the SMERU Research Institute for the World Development Report 2004 showed that Indonesia ranked third in the average absence rate of elementary school teachers at 19 percent, following Uganda at 39 percent and India at 25 percent. . . .

_. . . Surya said the government's failure to improve teachers' quality of life would keep the absence rate high, . . ._ (Santoso 2004)<sup>2</sup> The _Jakarta Post_ echoed this argument in another article in the same year entitled “Low Salaries Force Teachers to Moonlight”, saying that: Subur and Wawan are two of millions of teachers in the country, who have to take side jobs to make ends meet. Some say it is noble. However, _others blame their side jobs for the increasing absenteeism_ among teachers in the country. (Suwarni 2004)

> 1 Throughout this appendix, we have italicized text for emphasis.

> 2 The minister of education is quoted in the same article as dismissing this argument, but the second sentence of his rebuttal implicitly accepts that teachers’ pay influences their performance:  "Teachers should realize they need to discipline themselves, as they are carrying out a duty to improve the standard of national education, regardless of their salary. Besides, they also receive allowances.” (For clarification, the various financial allowances received by teachers were not generally performance-based, but were top-ups to salaries that were either unconditional or conditional on being in certain locations.)

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There is also a growing literature using customs data on export promotion and their impact on exporting firms’ intensive and extensive margins of exports, where the extensive margin is defined either as the introduction of new export products or entry into new export-market destinations. For example, using this type of data Volpe and Carballo (2008) found in a sample of Peruvian firms that export promotion affects exports mainly along the extensive margin, both in terms of markets and products, but has little impact on the intensive margins of exports. However, because of the use of customs data, they only have exporters in their data set and they cannot examine the impact of export promotion activities on firms entering or exiting the export market. This is where the contribution of our paper lies.

Thus, none of the aforementioned papers distinguish the impact of export promotion on entry into export markets and survival in exporting activities. Our empirical strategy allows us to look at the impact of export promotion on both entry and survival.

To identify the impact of export promotion activities on firm entry, exit, survival, and export intensity, we used firm surveys from seven Latin American countries from 2006 and 2010. We estimated a multinomial logit model to explain the probability of observing four potential paths of the status of a firm: from non-exporting to exporting (entry); continuity in exporting (survival); from exporting to non-exporting (exit); and continuity of non-export status.<sup>3</sup> For ease of exposition, the model’s coefficients are estimated with respect to the probability of exiting exporting activities. Our variable of interest is whether the firm used the services of an EPA between 2006 and 2010. We also explored the treatment effect of EPA services on the change in the share of exports over total sales (export intensity) within firms, which provide estimates of the effect of EPAs on the intensive margin of exports.

The results suggest that having used export services significantly increases the probability of entry and survival (with respect to the probability of exiting export markets). It also decreases the probability of remaining a non-exporter. In contrast, firms that use export services do not seem to increase their export intensity. These results appear in the (unconditional) descriptive data, in the estimate of conditional EPA treatment effects, and are robust to the use of three different

> 3 Because a previously exporting firm cannot really enter the export market or continue not to export, we also model the decision to survive in a sample of previously exporting firms, as well as the decision to enter the export market in the sample of firms which were initially non-exporters.

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When we break the importance of country and income class in explaining one’s position in the global income distribution, we find that 63 percent is due to the country of residence, and 31 percent to income class.<sup>22</sup> However, income class can be fully treated as “effort” only if we are willing to argue that (1) income class a person is assigned at birth and income class he is in now are totally orthogonal, and that (2) the latter is dependent on his effort (and luck) alone. More formally, we can express that situation as the one where the correlation between one’s current income and his parents’ income is zero. At the other extreme, with no social mobility at all, one’s income class at birth determines his current income class ( _i.e_ ., the one observed in the surveys). In that case, the entire income class variable has to be “ascribed” to circumstance.

The situation in the real world will, of course, differ between the countries and will lie somewhere between the two extremes. Ideally, if we had the data for the correlation of children’s and parents’ income by country, we could use these countryspecific coefficients to estimate the actual role of one’s inherited position. Unfortunately, we have such data for only a dozen, mostly rich, countries. They show that intergenerational mobility is relatively high in the Nordic European countries and Canada, that it is less in the United States and the UK, and (arguably) even less in the continental Europe (see Solon, 1999, pp. 1784-89; Checci et al. 1999; Bjorklund and Jantti 1997), Blenden, Gregg and Macmillan, 2007, p.C49). The coefficients of intergenerational earnings’ elasticity, ρ, in rich countries range between 0.2 in Nordic countries (and in some studies only), and 0.6.<sup>23</sup> For a few Third World countries in Asia and Latin America where estimates are available, the coefficients tend to be high and range from just under 0.5 to 0.7 (Lam and Schoeni, 1993; Grawe 2001; Ferreira and Veloso 2006). Based on a survey of the literature, we have incorporated these results into

> 22 This is obtained by the analysis of  the variance and is independent of the order with which the regressors are introduced.

> 23 The coefficients are calculated from the regressions of children’s on parental earnings with both earnings expressed in  logarithms.  We can treat the elasticities as the correlation coefficients if we assume that the standard deviations of parental and children’s earnings are approximately the same. Note finally that the estimates of intergenerational elasticity used here apply to earnings and not to income as we would ideally like.

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based controls were effective in reducing the total volume of capital inflows. Rincon (1999) used monthly data from September 1993 to June 1998 on net flows of short-term capital to the private sector from the foreign exchange balance and from the “ _cuentas de compensacion_ ” in order to assess the effects of the capital controls and concluded that the controls were effective in reducing short-term capital inflows.

Rincon & Villar (2000) performed an indirect test for the effect of the URR on the real exchange rate, using a single equation co-integration procedure (Engle-Granger) to determine the behavior of the latter variable. They concluded that, since capital flows affected the behavior of the real exchange rate, the URR had a significant indirect impact on that variable.

To sum up, the econometric literature on the capital controls in Chile seems to present rather positive results on the effectiveness of the deposit requirements in terms of reducing short-term inflows and increasing monetary policy autonomy. Nonetheless, the results concerning the effects of capital controls on the real exchange rate and on total capital flows are inconclusive so far. In the Colombian case, there is evidence of the effectiveness of the controls in reducing the total volume of flows. The econometric analysis of the impact of the URR on the maturity structure of flows is hampered by the lack of high frequency data distinguishing short-term from medium and long-term flows.

# **4. Data, Econometric Specifications and Results**

The sources and definitions for the variables used in the empirical part are described in Appendix C. The data on capital flows at a monthly frequency employed in this study refers to bilateral capital flows between the U.S. and the two countries (including bond, equity, bank loans and other securities, as detailed below) compiled by the U.S. Treasury Department in its international capital reports (TIC) system<sup>ii</sup> . Information is collected from commercial banks and other depository institutions, bank holding companies, securities brokers and dealers, and non-banking enterprises in the United States, including the U.S. branches, agencies and subsidiaries of foreign-based banks and business enterprises.

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This article presents evidence that the introduction of the National Rural Employment Guarantee Scheme (NREGS) reduces households’ uncertainty about future income streams and enables them to produce a higher share of high-risk, high-profit crops. The National Rural Employment Guarantee Act (NREGA) was passed in India in September 2005; the implementation thereof began in 2006. The NREGA entitles every rural household to up to a 100 days of work per year at the state minimum wage. In the financial year 2010/11 the NREGS provided work to close to 55 million rural households, generating a total of 2.5 billion person-days of employment (Ministry of Rural Development, Government of India, 2012).

The empirical analysis builds on the Young Lives data; a household panel that is representative of the state of Andhra Pradesh in southern India. The quality of implementation of the NREGS has been shown to vary immensely across India (Dutta et al., 2012). In most states the provision of work under NREGS is far too unpredictable to completely offset the effects of a shock. Under such circumstances, the NREGS would not affect households’ risk expectations. Andhra Pradesh, however, is one of the states with the highest number of days of employment generated per rural household. I find that the provision of work in Andhra Pradesh does effectively respond to weather shocks and thus supports households in managing agricultural production risks.

The estimation strategy builds on the sequenced introduction of the NREGS at the district level, and explores the fact that the scheme was introduced in four out of the six survey districts in 2006 and in the remaining two districts in 2008 and 2009. Because this approach relies heavily on the parallel trends assumption, a number of robustness checks are warranted. The use of alternative treatment variables (e.g. block-level spending and employment days generated under the NREGS, as well as households’ registration with NREGS) does not change the results. Several additional robustness checks rule out the possibility that the observed effect is due to alternative mechanisms.

The results of this article suggest that employment guarantees can trigger important gains in agricultural productivity in the medium term. These gains go far beyond the direct income effect that the provision of employment in agricultural lean seasons has on 2
outline the 95% confidence interval on the relationship as estimated by Stata’s lincom command.

Panel (c) and (d) suggest that people in Peru do better with either very little rain or with a lot of rain. For intermediate amounts of rain, incomes and life expectancies are considerably lower. This is somewhat counter-intuitive, but the results are very robust.

# **4. Recent climate change in Peru**

In this section we will analyze climate data from Peru from May 1948 to May 2008 to test whether there are any significant trends, and whether these trends differ between regions.

Most of the data come from the Monthly Climatic Data for the world database collected by the National Climatic Data Center (NCDC) in the US. This project started in May 1948 with 100 selected stations spread across the world. Peru started contributing with data from four stations (Piura, Chiclayo, Lima, and Cuzco) in August 1948. Since then, many more stations have been included in the data base, and 20 Peruvian stations have contributed more or less regularly. The original data were organized in 61 printed volumes with 12 issues in each (one for each month of the year), totaling 723 months. All data were qualitychecked and published by the NCDC about 3 months after the raw data had been collected. Data for 5 stations (Arequipa, Chachapoyas, Iquitos, San Juan and Tarapoto) were very incomplete in the original publications, but NCDC had the data in their database, and made it available to us.<sup>4</sup> Monthly data from 4 other stations (Augusto Weberbauer, Campo de Marte, Granja Kcayra, and La Pampilla) were obtained directly from the National Meteorological and Hydrological Service of Peru (SENAMHI).

In total we have obtained temperature and precipitation series for 24 stations in Peru. These are listed in Table 4.

**Table 4: Meteorological stations in Peru with reasonably** **<u>complete monthly data from 1948 to 2008</u>**

|**Station**|**Latitude**<br>**(oS)**|**Longitude**<br>**(oW)**|**Elevation**<br>**(m)**|**Complete**<br>**-ness**|
|---|---|---|---|---|
|AREQUIPA|-16.33|-71.57|2539|85%|
|AUGUSTO WEBERBAUER|-7.15|-78.48|2660|71%|
|CAJAMARCA|-7.13|-78.47|2622|55%|
|CAMPO DE MARTE|-12.07|-77.03|159|53%|
|CHACHAPOYAS|-6.20|-77.85|2540|49%|
|CHICLAYO|-6.78|-79.82|30|87%|
|CUZCO|-13.53|-71.93|3249|85%|

> 4 The data request was made in the website of the NCDC, through the Global Climate Observing System’s portal (GSNMON).

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# 2. Some stylized facts about sovereign credit ratings

Sovereign credit ratings in some form have been in existence for nearly a century. The major rating agencies—Standard and Poor’s and Moody’s—started rating sovereign Yankee bonds in the early 20th century. By 1929, 21 countries were rated by Poor’s Publishing, the predecessor to S&P, and even included several of today’s emerging markets, e.g. Argentina, Colombia, Uruguay (Bhatia 2002). Moody’s started rating debt instruments in 1919, and within the next decade had rated bonds issued by about 50 governments (Cantor and Packer 1995). However, demand for ratings declined during the Great Depression and most ratings were suspended in the period following World War II. Rating activity revived briefly in the post-war period, but declined again in the late 1960s with the introduction of the Interest Equalization Tax (IET), a 15 percent levy on the interest from cross-border lending in the United States (Bhatia 2002). Rating activity for sovereigns resumed from the mid-1970s onwards with the withdrawal of the IET in 1974. In 1980, there were eight high-income countries that were rated by one or the other of the three leading rating agencies viz. Moody’s, Standard and Poor’s (S&P) and Fitch. By the late 1980s, almost all the high-income OECD countries had been rated.

Sovereign credit ratings for the developing countries (as currently defined by the World Bank) began in the late 1980s, after the debt crisis (figure 2). The number of rated countries increased significantly during the 1990s emerging market phenomena. By December 2006, 131 countries – 45 high-income and 86 developing countries – were rated by one or more of the three premier agencies.

**Figure 2: Evolution of sovereign credit ratings in 1980-2006**

<!-- Start of picture text --> Developing countries<br>High-income (others)<br>High-income (OECD)<br><!-- End of picture text --> _Sources:_ Standard and Poor’s, Moody’s, Fitch Ratings, and authors’ calculations.

Sovereign ratings issued by different agencies tend to be highly correlated. The bivariate correlation coefficient between the ratings of the three agencies as of December 2006 ranged from 0.97 to 0.99 (figure 3). The ratings are exactly the same across the three agencies for most AAA-rated countries. For most developing countries, the ratings are similar across the three 5
Table 9. Industry Interactive Effect

||S|ervice Interacti|on<br>LP||Service|LP||Manufacturi|ng<br>LP|
|---|---|---|---|---|---|---|---|---|---|
|Dep. Variables|TFP|log LP|<br>Growth|TFP|Log LP|<br>Growth|TFP|log LP|<br>Growth|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|FemHead|-0.091**|-0.245***|-0.030***|-0.039|-0.107**|-0.008|-0.093***|-0.218***|-0.024**|
||(0.035)|(0.042)|(0.011)|(0.148)|(0.045)|(0.007)|(0.035)|(0.040)|(0.010)|
|FemHead*Service|0.122|0.183**|0.030**|||||||
||(0.175)|(0.071)|(0.013)|||||||
|Foreign|0.387***|0.493***|0.051***|0.298|0.555***|0.063***|0.390***|0.464***|0.045***|
||(0.049)|(0.049)|(0.012)|(0.189)|(0.073)|(0.014)|(0.049)|(0.052)|(0.013)|
|OwnLargest|-0.112|-0.352***|-0.036***|-0.408**|-<br>0.320***|-0.038***|-0.092|-0.339***|-0.030***|
||(0.070)|(0.088)|(0.010)|(0.161)|(0.073)|(0.010)|(0.066)|(0.088)|(0.010)|
|Middle|-0.049*|0.177***|0.032***|-0.080|0.160***|0.030***|-0.044|0.202***|0.034***|
||(0.027)|(0.028)|(0.005)|(0.100)|(0.028)|(0.005)|(0.028)|(0.040)|(0.006)|
|Large|-0.158***|0.283***|0.045***|-0.142|0.145***|0.027***|-0.153***|0.362***|0.056***|
||(0.050)|(0.051)|(0.007)|(0.117)|(0.045)|(0.009)|(0.053)|(0.062)|(0.008)|
|Age6_10|0.053|0.090***|-0.009|0.197|0.069**|-0.010|0.042|0.107***|-0.009|
||(0.047)|(0.022)|(0.007)|(0.176)|(0.030)|(0.009)|(0.048)|(0.030)|(0.007)|
|Age10plus|0.014|0.089***|-0.015**|0.199|0.104***|-0.013|0.002|0.076*|-0.017**|
||(0.041)|(0.028)|(0.008)|(0.197)|(0.036)|(0.009)|(0.039)|(0.041)|(0.008)|
|Exporter|0.198***|0.227***|0.029***|0.376***|0.175**|0.041***|0.186***|0.259***|0.024***|
||(0.038)|(0.045)|(0.006)|(0.130)|(0.075)|(0.007)|(0.034)|(0.041)|(0.006)|
|Initial LP|||-0.104***|||-0.112***|||-0.099***|
||||(0.017)|||(0.017)|||(0.017)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,157|51,079|45,711|1,365|21,128|18,688|18,792|29,951|27,023|
|Adjusted R<sup>2</sup>|0.283|0.389|0.372|0.278|0.396|0.398|0.287|0.398|0.357|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

The share of female-headed firm in service sectors is 12.6 percent and in manufacturing sectors is 9.8 percent.

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absorb FDI spillovers. Overall, studies confirm that FDI spillovers are larger in countries that are more open towards trade (Meyer and Sinani 2009; Du et al. 2011, Havranek and Irsova 2011).

Weak _institutions_ – including corruption, red tape, or intellectual property rights – are linked to protectionism with regard to local firms, network-driven business practices, and inefficient markets. Foreign investors may, in such cases, be constrained from exploiting fully their competitive advantages. This may influence the type of FDI attracted in the first place, as well as the domestic firms’ absorptive capacities. Empirical evidence is mixed. Using firm-level data for 17 emerging countries over the period 2002-2005, Gorodnichenko, Svejnar, and Terrell (2007) find no evidence that the extent of FDI spillovers is affected by the degree of corruption (measured as bribes) or red tape (measured as manager’s time spent with officials). Measuring transparency with a corruption perception index, Meyer and Sinani (2009) find evidence that a country’s level of transparency has a U-shaped effect on FDI spillovers, i.e. countries with a medium level of transparency benefit the least from FDI, while countries with a low and high level of transparency benefit show stronger FDI spillovers.

Finally, competitive pressures from multinational firms might be lower if the local firm already faces a high level of _competition_ at the sectoral level. As in the case of exports, local firms in competitive sectors might have a lower incentive to improve, resulting in lower benefits from FDI spillovers. Sinani and Meyer (2004) confirm that more sectoral competition both of foreign firms and of domestic firms have a positive impact on the growth of sales of local firms in Estonia, but do not control for the interaction of competition with FDI. On the other hand, local firms could be better equipped to benefit from positive demonstration effects. Barrios and Strobl (2002) find that less sectoral competition, captured by a higher Hirschman-Herfindahl index (HHI), increases the productivity gains from FDI for Spain, pointing towards a lower incentive to improve.

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