rafmacalaba/datause-encoder-data huggingface dataset:
Brazilian frost that reduces the country's export potential presumably creates only a temporary terms of trade improvement for other coffee exporters. If, however, the temporary production lull in Brazil sets in motion jockeying for market shares among exporters and a re-negotiation of export quotas under the International Coffee Agreement, it may well have permanent effects on the export prospects of Brazil's competitors. As implied above when discussing Brazil's rapid restoration of its export capacity fol jwing the 1976 frost, misjudgments about the degree of permanence or temporariness of shocks are all too common.

In reality, there are virtually no shocks that are truly permanent in the sense that they will prevail, with certainty, into the indefinite future. Rather one must view the world economic environment in general, and primary commodity prices in particular, as stochastic. In the special case where a particular commodity price happens to follow a random walk, all changes in the price should be viewed as "permanent" in the sense that the current price is the best predictor of future prices. Yet, future prices may be highly uncertain. Intelligent policy responses to booms take this uncertainty into account. If future incomes from commodity exports are highly variable (and unpredictable), a high proportion of any export windfall should be saved. ll/ This would, of course, be advisable even if the price change was deemed to be "permanent" in the stochastic sense described above.

An interesting conclusion from Cuddington-Urzaa (1987) study of primary commodity price behavior iv that the simple time series models used to distinguish permanent and temporary price shocks typically explains less than ll/ This conclusion presumes the weak conditions necessary to generate a precautionary demand for saving are satisfied.

13
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37
Table 3 takes advantage of the nested structure of our data set at three different levels (country, year and censorship events), and shows Hierarchical Linear Model results.

Table 3: Severity of censorship by event characteristics, country, and year (Hierarchical Linear Models, all censors)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|
|Foreign media|-1.09***<br>(0.18)|||||-1.21***<br>(0.23)|
|Media reach||-0.515***||||-0.44***|
|||(0.0737)||||(0.09)|
|Publication frequency|||-0.23||||
|(for print media only)|||(0.20)||||
|Media location||||-1.58***|||
|(Abroad)||||(0.28)|||
|Gender|||||0.88***<br>||
||||||(0.27)||
|Economic constraints|0.00|-0.0252|-0.04|0.07|<br>0.01|-0.00|
||(0.04)|(0.0367)|(0.06)|(0.05)|(0.05)|(0.04)|
|Legal constraints|<br>-0.12***||<br>-0.08~|<br>-0.08~|<br>-0.12**|<br>-0.10**|
||(0.03)|-0.0891**|(0.05)|(0.04)|(0.04)|(0.03)|
|GDP pc (log)|-0.13|(0.0298)|-0.25|-0.14|-0.12|-0.07|
||(0.10)||(0.16)|(0.14)|(0.12)|(0.10)|
|Population (log)|<br>0.10|-0.116|<br>0.07|<br>0.22*|<br>0.18*|<br>0.08|
||(0.07)|(0.0955)|(0.12)|(0.10)|(0.09)|(0.08)|
|Political competition|<br>-0.00||<br>-0.00|<br>-0.01~|<br>-0.01|<br>0.00|
||(0.00)|0.0819|(0.01)|(0.00)|(0.00)|(0.00)|
|Gov. fractionalization|0.85*|(0.0707)|0.16|0.55|0.53|1.05**|
||(0.37)||(0.65)|(0.52)|(0.46)|(0.40)|
|Incumbent tenure|0.01|-0.000733|0.01|-0.01|0.00|0.01|
||(0.01)|(0.00305)|(0.02)|(0.01)|(0.01)|(0.01)|
|Corruption|<br>0.70||<br>0.52|<br>-0.25|<br>0.64|<br>0.42|
||(0.56)|0.588|(0.98)|(0.89)|(0.68)|(0.61)|
|Political constraints|0.12***|0.111***|0.11*|0.06~|0.11***|0.12***|
||(0.02)|(0.0246)|(0.04)|(0.04)|(0.03)|(0.03)|
|Ethn. fractionalization|<br>0.31|<br>0.182|<br>0.96|<br>-0.13|<br>0.14|<br>0.23|
||(0.45)|(0.443)|(0.76)|(0.63)|(0.53)|(0.49)|
|Intercept|<br>4.99**|<br>6.972***|<br>7.20**|<br>4.83*|<br>4.57*|<br>5.96***|
||(1.55)|(1.552)|(2.64)|(2.25)|(1.91)|(1.69)|
|lns1_1_1|-0.34*|-0.423*|0.02|-0.21|-0.39|-0.34~|
||(0.16)|(0.185)|(0.21)|(0.22)|(0.24)|(0.18)|
|lns2_1_1|0.21**|0.189*|0.45***|0.06|0.41***|0.18~|
||(0.08)|(0.0900)|(0.13)|(0.19)|(0.09)|(0.10)|
|lnsige|1.11***|1.131***|1.06***|1.03***|1.07***|1.12***|
|_|(0.02)|(0.0160)|(0.04)|(0.03)|(0.02)|(0.02)|
|AIC|<br>15216.723|<br>16044.051|<br>4579.034|<br>4982.870|<br>8806.454|<br>11552.555|
|BIC|15306.434|16134.487|4650.510|5056.168|8887.959|11643.738|
|N (censorship events)|2924|2772|867|979|1692|2206|
|<br>Countries|133|137|116|113|130|132|
|Years|15|15|15|15|15|15|

Standard errors in parentheses (two-tailed tests) ~ p<0.10, * p<0.05, ** p<0.01, *** p<0.001 34
In the first half of the last decade, Peru grew relatively steadily and quickly, and its public budget expanded. Both the GNP and public expenditure in regions of Peru increased annually by 4% on average, whereas regional health expenditures grew by almost 7% every year. During this period, the country experienced increased awareness of the importance of early childhood, and its policy agenda featured the announcement of the National Plan of Action for Childhood and Adolescence (PNAI 2002–10). However, child nutrition worsened, and disparities between urban and rural areas widened. This is a serious concern because nutritional deprivation negatively impacts cognitive development (Glewwe et al. 2001; Walker et al. 2005) and hinders human capital formation (Glewwe and Jacoby 1995; Maluccio et al. 2006). By 2005, 30% of Peruvian children younger than five years of age endured chronic malnutrition<sup>1</sup> , with drastic urban–rural differences. In urban areas, 14% of children were malnourished, compared to 46% in rural areas. This difference of 32 percentage points represented an increase from 26% in 2000<sup>2</sup> . Van de Poel et al. (2007) found that Peru had the largest urban–rural gap in the nutritional levels among 47 developing countries.

This paper aims to determine who benefits from increasing public spending in Peru and to identify potential explanations for the increase in the urban–rural malnutrition gap. I analyze the impact of changes in total regional public expenditure on changes in child nutritional outcomes using longitudinal data from a panel of children who were studied from their first birthday until age six. This approach contrasts with most previous papers in the literature, which perform cross-country analyses (Filmer and Pritchett 1999; Baldacci et al. 2002; Gupta et al. 2003). The use of panel data allows me to control for unobserved household–child heterogeneity.

> 1 Source: 2004–6 Continual Demographic and Family Health Survey (ENDES). This is a nationally representative survey. The percentages belong to children in the same age group in two different points of time.

2
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114
**Figure 2. Wages and Openness to Trade**

<!-- Start of picture text --> 0.3-<br>0.2 -<br>0.0 -<br>-0. 1<br>-0.4<br>After 1 year After 2 years After 3 years After 4 years After 5 years<br>Effect of an extra 1% of GDP in trade...<br><!-- End of picture text --> _Source:_ Based on work in progress by Freeman, Oostendorp and Rama (2001). This figure reports the results of a regression explaining the log of wages, in PPP dollars, as a function of the trade-to-GDP ratio (based in dollar figures). There is one observation per country, occupation and year, over period 19831998. The regression includes fixed effects for all countries, occupations and years. It also controls for the ratio of foreign direct investment to GDP. Data on wages are from Freeman and Oostendorp (2000) and data on other variables from the World Bank. The solid line reflects the point estimate of the coefficient of interest. The dotted lines indicate the 95 percent confidence interval.

Based on figures 2 and 3, the short-term impact of trade liberalization on wages is negative, whereas the short-term impact of foreign direct investment is positive. If the results in these figures are to be interpreted literally, an increase in foreign trade by 10 percent of GDP leads to a 3.5 percent decline in wages. An additional one percent of GDP in foreign investment, on the other hand, would raise wages by almost 2 percent. The combination of these two figures highlights the importance of the investment climate. If the opening up of the economy fails to attract foreign capital, wage losses could be sizeable.

While the impact of foreign direct investment on wages fades over time, the impact of openness to trade becomes significantly positive. According to figure 3, foreign direct investment has no significant effect on wages after five years. If figure 2 is to be interpreted literally, the negative effect of openness to trade on wages is offset after three years. In practice, the precise timing of this reversal depends on the specification and the openness indicator used. For instance, the change in the sign of the coefficient takes place in the fifth year when using the openness indicator constructed by Sachs and Warner (1995), instead of the trade-to-GDP ratio. The indicator by Sachs and Warner combines the average tariff rate, the coverage of non-tariff barriers, the black market premium for foreign exchange, the existence of a marketing board for commodity exports, and the socialist nature of the economy.

_5_
Venezuelan migrants. We focus on crimes committed in capital cities after 2017 and before 2021.

The results in Table 6 are estimates for total crime rates as well as for crimes committed by men and women, separately. The estimates are in line with our main results suggesting actual reductions in almost all crime rates, although the estimated effects are imprecise. Moreover, we only observe increments in sex crimes committed by men, in line with the story that more reports are being made by females against potential partners of the same nationality. The estimated effects are robust to the exclusion of the control for total crime (Table B5). Moreover, the event studies across all outcomes show increases only in domestic and sexual crimes committed by Venezuelan migrants, as would be mechanically expected based on the results presented in the previous section (Figure B2).

31
US security brokers and dealers —as suggested by Bruno and Shin (2013). This leverage ratio is defined as the ratio of equity and total liabilities to equity, using the information from the U.S. flow of funds.

_Policy uncertainty_ in the advanced world will also affect the behavior of foreign investors and, hence, the likelihood of surges. Baker, Bloom and Davis (2013) build an index of U.S. economic policy uncertainty by combining three groups of measures: (i) newspaper coverage of policyrelated economic uncertainty (from the 10 major US newspapers), (ii) number and size of federal tax code provisions set to expire in subsequent years, and (iii) disagreement among economic forecasters in the Philadelphia Federal Reserve’s Survey of Professional Forecasters.<sup>15</sup> Finally, another index of global uncertainty is used in our analysis. It focuses on common movements in stock market volatility for major advanced economies. Specifically, stock market volatility is measured as the monthly standard deviation of daily stock returns in each advanced economy, and our index is the common factor in stock market volatility of the United States, Japan, Italy, France, the United Kingdom, and Germany. The data was obtained from Bloom, Kose and Terrones (2013). Finally, foreign investors can be driven not only by the fundamentals of the domestic country but also by the regional contagion —this implies favorable economic prospects of other countries in the same region. We create a regional contagion dummy that takes the value of 1 for the domestic country in period t is another country in the same region experienced a surge over the past two years.

# **4.3 Empirical Analysis**

In this section we assess the factors that drive the likelihood of surges in net and gross capital inflows. We argue that both pull and push factors can help explain the massive flow of funds from foreign investors. Therefore, we test whether some determinants help explain surges in net inflows and/or gross inflows. We conduct Probit and Complementary log-log panel regression analysis for a sample of 67 countries using quarterly data from 1975 to 2010.

# **4.3.1 Panel Probit Analysis**

Table 4 reports our first empirical estimates 9 for the incidence of surges in net and gross inflows. It shows the role of pull factors only in our regression analysis. When controlling for domestic drivers of capital inflows only (“pull” factors), our findings show that current account movements appear to matter most for surges in net rather than surges in gross inflows. Consequently, current account deficits tend to trigger surges in net inflows (and this is partly motivated by the need to finance external imbalances). The exchange rate regime does not play a significant role in inducing a surge in net flows. Equity inflows tend to mitigate the likelihood of surges in net inflows while debt inflows increase that likelihood.

> 15 A more detailed description of the construction of this index could be found in Baker, Bloom and Davis (2013). The data can be downloaded from www.policyuncertainty.com.

18
appears to have shifted toward more labor-intensive industries in the aftermath of the BTA. Alternatively, Figure 7.2 demonstrates the change in the proportion of _unskilled_ -labor-intensive industries in total industrial enterprise employment during the same period.<sup>18</sup> Figure 7.2 reveals a similar pattern, a shift of labor toward unskilled-labor-intensive industries paralleling the shift toward labor-intensive industries shown in Figure 7.1. However, this parallelism is expected since in Vietnam, labor-intensive industries are generally unskilled-labor-intensive industries.

During the same period, “formal” industrial employment as a whole expanded substantially. Thus, besides labor reallocation within the industrial sector (if it occurred), there appear to have been two additional sources of labor supply for the expansion of labor-intensive manufacturing. First, the increase in industrial employment may reflect a shift of labor from agriculture to industry. While industrial employment grew rapidly in the recent years, agricultural employment declined from 65.3 percent in 2000 to 57.9 percent in 2004 (WDI). It is likely that this move was partly induced by trade openness (Dodzin & Vamvakidis, 2004; Fu & Balasubramanyam, 2005).<sup>19</sup> Second, the expansion of formal enterprise employment is likely to reflect a move of labor from the “informal” to the “formal” sector. The implementation of the Enterprise Law (2000) was a major step toward reducing numerous barriers to private business and has greatly encouraged the establishment of new enterprises (Ramstetter & Ngoc, 2007). It is also likely that this trend was partly accelerated through trade, as the largest expansion of industrial enterprise

> 18 Unfortunately, the _Enterprise Survey_ data 2000-2007 do not break down different types of workers either in terms of education or occupation. However, the _Industrial Survey_ (GSO, 2000) breaks down employees of 17 provinces into production workers, technicians and administrative workers for the year 1998. The “unskilled-labor intensive industries” here refer to the industries whose proportion of production workers in total labor force is above median (85.7 percent) in 1998.

> 19 Applying the Smith-Myint model of “vent for surplus” to China, Fu and Balasubramanyam (2005) find that the expansion of exports from labor-intensive manufacturing has accelerated the transfer of surplus labor from the agricultural to the export sector. Using a panel of 92 developing countries in the period 1960-2000, Dodzin and Vamvakidis (2004) find that an increase in openness to trade leads to an increase in the industrial value added share of production at the expense of the agricultural share suggesting that trade leads developing countries to industrialization.

11
Internationally, UNESCO<sup>8</sup> compares the “Education for All Development Index (EDI)” for various nations, in which India figures at the 105<sup>th</sup> position among 127 nations in 2005 (UNESCO- GMR, 2004), but moved a couple of places up to be ranked 102<sup>nd</sup> among 129 nations (UNESCO- GMR, 2009). There have been various efforts to construct Education Development Indices (EDIs) in India. The Ministry of Human Resource Development (MHRD)-supported study in 1998-99 (GOI, 1999) and the Planning Commissionsponsored study conducted by the Institute of Applied Manpower Research (IAMR) (Yadav and Srivastava, 2005) are the two notable efforts in this context. Both studies endeavor to compare EDIs across states. All these estimations of EDIs, however, look at certain outcomes in education sectors, leaving out the process and inputs. These efforts were also limited at state level comparisons, which camouflaged variations within these states.

The effort towards developing district level educational development indices, and that too taking into account education development related indicators related to dimensions such as inputs and equity was carried out by Jhingran and Sankar (2006)<sup>9</sup> for the year 2003-04. For this particular analysis, separate indices were developed at district level for the status of various dimensions of education development as well as a consolidated “Education Development Index” (EDI) indicating the overall progress towards achievement of Universal Elementary Education (UEE). The EDI constructed for this analysis is a summation of the following indices—input index, equity index and outcome index.

The parameters used for this analysis and their definitions are described below.

**_(i) Input Index_** : This index sums up the different indices that describe the extent of inputs—both physical and qualitative—including those related to access, infrastructure and human resources,  i.e. teachers. These indices are:

- _Access Index_ : This summarizes the indicators related to primary school coverage (both in terms of area as well as the number of children in the age group of 6-10 years of age and the availability of upper primary schools (in comparison with primary schools). It includes coverage of the following:

- (a) Primary schools’ coverage – an index using primary schools per 1000 child population of 6-10 years and number of primary schools per square km (thus adjusted for both area density and population density).

(b) Ratio of the number of upper primary schools to primary schools.

- _Infrastructure Index_ : This is an integration of indicators relating to the availability of classrooms, toilets and drinking water facility, at both the primary and upper primary levels. It covers the following:

- (a) Percentage of classrooms available against requirement

- (b) Percentage of schools with common toilets

- (c) Percentage of schools with drinking water facility.

8
15 maternal mortality is higz and maternal health services are weak (WHO, 1985), the savings generated by family planning services could be invested in saving the lives and health of women who do want to have children. Instead of a zero-sum game, family planning services can help women to have the number of children whon they wish; safeguard their health and their lives; and generate savings to provide badly needed maternal health care. It would be difficult to find more compelling reasons to provide family planning services to the many women in developing countries who are denied them.