rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Dynamic Panel Regressions

||FS Index|Ethi<br>Mild|opia<br>Moderate|Severe|FS Index|Ma<br>Mild|lawi<br>Moderate|Severe|FS Index|Ni<br>Mild|geria<br>Moderate|Mild|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Lagged Fractional Index (FI)|-0.161|0.009|0.027|-0.105|0.139|-0.052|0.044|-0.031|0.309|0.028|0.020|0.071|
||(0.131)|(0.126)|(0.080)|(0.054)|(0.090)|(0.087)|(0.071)|(0.043)|(0.192)|(0.156)|(0.117)|(0.075)|
|Lagged FS _×_ Lagged FI|-0.180|-0.086|-0.339*|0.525|-0.003|0.129|0.032|0.060|0.165|-0.005|0.215|0.087|
||(0.138)|(0.145)|(0.146)|(0.410)|(0.072)|(0.089)|(0.082)|(0.153)|(0.136)|(0.168)|(0.141)|(0.241)|
|Lagged Food Security (FS)|0.110|0.023|0.218|-0.556|0.079|-0.047|-0.003|-0.101|-0.111|0.093|-0.133|-0.213|
||(0.116)|(0.117)|(0.124)|(0.356)|(0.050)|(0.055)|(0.056)|(0.107)|(0.093)|(0.115)|(0.094)|(0.155)|
|Observations|12,031|12,031|12,031|12,031|9,303|9,303|9,303|9,303|3,277|3,277|3,277|3,277|

_Note_ : The table displays regression results from our dynamic panel specification with household fixed effects, round dummies, and region-time trends (see Equation 3.1). FI stands for Fractional Index while FS stands for our standardized index of food insecurity. Standard errors, clustered at the household, are reported in parentheses (*** p _<_ 0.001, ** p _<_ 0.01, * p _<_ 0.05).
- D. _Sensitivity test: Contribution of structural transformation to the change in regional intersectoral productivity gaps when the pace of structural transformation differs across regions_

In deriving our results, we have assumed a constant pace of structural transformation across regions. If structural transformation reduces the disparity in regional intersectoral productivity gaps, it weakens our argument that the disparity in these gaps undermines national productivity. It becomes important then to examine how the disparity in regional intersectoral productivity gaps relates to the differing pace of structural transformation across regions.

We cannot directly observe the long-term pattern or pace of structural transformation at the region level using the IBES since it is a single cross-section. However, the IBES collected retrospective employment data going back a year. Specifically, the IBES gathered information on total persons engaged in an enterprise for four different dates: November 30, 2013; February 28, 2014; May 31, 2014; and August 31, 2014. We use these data to estimate short-term net nonfarm employment creation between November 2013 and August 2014, adopting the calculation method used by GSS (2016b).<sup>19</sup> Across regions in general, net employment creation has been dominated by services, and within services, by the informal sector (Figure 11a). At the national level, 70 percent of net nonfarm employment creation between 2013 and 2014 was in informal services. We view these patterns as being part of the long-term process of structural transformation occurring in Ghana, although we recognize that these patterns may be due to unrelated short-term labor market adjustments. The patterns of nonfarm employment creation, combined with the patterns in sectoral employment trends presented in Section II, indicate that the largest flow of workers has been from agriculture to informal services.

At the national level, net employment creation (as a percentage of the employment level in 2013) in formal and informal enterprises in industry were 3.3 and 7.1 percent, respectively (Figure 11b). Net employment creation in formal and informal enterprises in services was slightly higher, at 3.8 and 10.3 percent, respectively.<sup>20</sup> Assuming that net employment creation in these sectors continued at the same rate in the following year (from 2014 to 2015), we can

> 19 Net employment creation between November 30, 2013 and August 31, 2014 is estimated as a sum of (1) change in total persons engaged between November 30, 2013 and February 28, 2014; (2) change in total persons engaged between February 28, 2014 and May 31, 2014; and (3) change in total persons engaged between May 31, 2014 and August 31, 2014.

> 20 In Section II, we show that the informal employment share fell in Ghana. Here, we show that net employment creation was strongest in informal enterprises. The informal employment share includes informal self-employment while net employment creation in informal enterprises only comprises wage-employment. Over time, there has been shift from self-employment to wage-employment in Ghana.

21
other South American countries grew more slowly than world exports to the United States; only Central America, along with Mexico, performed better than the world as whole. And even Mexico, despite being bound to the United States by geography and by the North American Free Trade Agreement, has not been able to keep up with China's export dynamism. By 2003 the latter had surpassed Mexico as the United States' second most important import supplier, only behind Canada.

Aggregate trade figures also hide differences in the sectoral composition of Chinese and Latin American exports to the United States (Table 2). The latter is an important supplier of agricultural and mining products (including oil) to the United States, with respective shares of around 50 and 30 percent of US important demand. Close to a quarter of all Latin American exports consists of non-manufacturing goods, or around three quarters in the case of the Andean countries. At the opposite extreme, Mexico has the highest share of manufacturing exports to the United States (86 percent), followed by Central America and South America (84 percent in both cases). Central American countries in particular saw a significant change in the composition of its exports, with a 20 percentage-point drop in the share of agricultural exports shifting to manufacturing. In contrast to Latin America, China is a relatively insignificant supplier of agricultural and mining exports, while manufactures represent over 99 percent of exports to the United States.

There are important differences within the manufacturing sector as well (Table 3). In 2003, approximately a fifth of all Chinese exports to the US market was comprised by leather (including footwear), textile and apparel products, compared with around 8 to 9 percent in the case of Mexico or South America, or with 75 percent for Central America. Moreover, machinery and equipment exports amounted to almost a half of all Chinese sales to the United States, compared to 5 percent or 10 percent for the Andean and Central American countries, respectively, or 76 percent in the case of Mexico.

China's strong export performance ---and Latin America's relative weakness--has been made patently manifest since 2000. During the 2000-2003 period, as the US demand for world goods declined at a rate of 3.2 percent per year, or 2.7 percent in the case of Latin American goods, Chinese exports to the United States expanded at a rate of 11.9 percent per annum. Figures for the manufacturing sector are more dismal, showing a yearly decline of 3.9 percent in Latin American exports, or as high as 12 and 17 percent, 3
the capital stock drives down the marginal product of capital, i.e., it drives down the rental rate on capital, until the rate of return on capital falls back to the initial level.<sup>12</sup> To analyze trade policy, this comparative steady state approach has been employed by many authors, including Harrison, Rutherford and Tarr (1996, 1997) and Baldwin et al. (1999) and Francois et al. (1996). The approach, however, dates back to the 1970s, when both Hansen and Koopmans (1972) and Dantzig and Manne (1974) used it. The approach ignores the foregone consumption necessary to achieve the higher level of investment and thus, is an upper bound estimate on the long run gains within the framework of the model assumptions.

# **IV.  Data of the Model and Evidence for Key Elasticities**

# **Input-output matrix**

The core of the model data consists of an input-output table. No official recent inputoutput table for Armenia exists, so we produced the table based on data provided by the National Statistical Office of the Republic of Armenia. Our data sources include an unbalanced supply-use table with 16 sectors for the year 2006 and detailed data on GDP for 2007 by types of income, expenditure, and production. The supply-use table contains all the elements we need for the input-output table, but supply deviates significantly from use in most of the sectors. We therefore develop a balancing procedure to arrive at a balanced input-output table. The procedure involves an optimization problem in which the elements of the table are adjusted such that the sum of the squared deviations from the initial values are minimized and subject to a number of side constraints, including supply-use balance. As part of the procedure, we also use detailed GDP data to update the dataset to the year 2007. Finally, we disaggregate two services sectors to get more details on transport, communication and financials sectors. The final table contains 21 sectors. Details of the construction are explained in Appendix G.

> 12 The rate of return on investment in our model is the rental rate on capital divided by the cost of a unit of the capital good.

16
37 payments in the past 12 months, % of adults who used a formal account to receive or send money to family members living elsewhere in the past 12 months, % of adults who used a mobile phone to pay bills or send or receive money in the past 12 months), _Insurance_ (% of adults who personally purchased private health insurance, % of adults who work in farming, forestry or fishing and personally paid for crop, rainfall or livestock insurance)

**<u>Financial Soundness Indicators</u>** database, hosted by the IMF, disseminates data and metadata on selected financial soundness indicators (FSIs) provided by participating countries.

**<u>World Development Indicators</u>** is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

**<u>International Financial Statistics</u>** (IMF) provides is a standard source of international statistics on all aspects of international and domestic finance. It reports, for most countries of the world, basic financial and economic data on international banking, money and banking, interest rates, prices, production, international transactions, international liquidity, government accounts, exchange rates, and national accounts.

**<u>Bank for International Settlements</u>** (BIS) statistics were used for the aggregate data on bond statistics, including domestic debt securities by residence and type of instrument (bonds and notes vs. money market instruments, issued by financial and non-financial corporates; based on publicly available or country reported data). Domestic debt securities (Quarterly Review Table 16) for a given country comprise issues by residents in domestic currency targeted at resident investors, whereas international debt securities (i.e. Quarterly Review Table 11) are the ones targeted at non-residents (a) in domestic currency on the domestic market, (b) in domestic and foreign currency on the international market, plus the issues (c) in foreign currency in the domestic market (further information can be found in the Guide to the international financial statistics, http://www.bis.org/publ/bppdf/bispap14.htm).

As two different collection systems are used (s-b-s for international debt securities and aggregated data for domestic debt securities), some overlap (between domestic debt securities and international debt securities) and inconsistencies (classification of issuers) might remain which differs from country to country.

Country authorities’ websites were used to re-confirm and fill in some of the gaps in the data.
Figure 4: The Number of Brazilians Staying in Japan <!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>year<br>(thousand)<br><!-- End of picture text --> Source: Japanese Ministry of Justice.

Figure 5: Nikkeijin's Length of Stay in Japan <!-- Start of picture text --> no answer less than 1 year<br>1% 5%<br>1-3 years<br>more than 10 years 16%<br>28%<br>3-5 years<br>13%<br>5-10 years<br>37%<br><!-- End of picture text --> Note: The data for 2002. Source: Sangyo Koyo Antei Sentah (Industry Employment Security Center).

46
expenditure of time by 3.1 minutes ( _|t|_ = 5 _._ 91), an unit increase in landholding by 2.8 minutes ( _|t|_ = 3 _._ 16); the former is harder to interpret than the latter. Where total monetary outlays on transportation are concerned, 256 cases involved positive amounts. The same specification yields the finding that the presence of an all-weather road increases the outlay by Rs. 54.7, but this estimate is statistically borderline ( _t_ = 1 _._ 82). An increase in distance of 1 km. increases the outlay by an estimated Rs. 7.1 ( _t_ = 2 _._ 97). Of the remaining regressors that are statistically significant at conventional levels, a larger landholding increases such expenditures, but the purse-strings are tightened, by Rs. 82.6 ( _|t|_ = 2 _._ 57), for a child of school age.

As already noted, the distance to the health facility poses problems; for whenever there is a choice of facility, distance is likewise chosen, and the use of the modal distance may not be fully satisfactory. Since an all-weather road should offer greater average speed over the whole trip, an alternative way of skirting this difficulty is to – – use speed or rather its inverse, minutes per km. as the regressand. OLS with all the other regressors and village dummies yields an estimated saving of 7.0 minutes per km. with such a road, and this is statistically highly significant ( _|t|_ = 5 _._ 12). Speed is also greater over longer stretches: 0.2 minutes per km. less with each additional km. ( _|t|_ = 4 _._ 55); but the household and demographic variables are not statistically significant at conventional levels, nor are over one-half of the village dummies.

# **5.2 The household survey: Mortality**

Over the period from the beginning of 2006 to the end of 2009, there were 53 deaths among members of the households comprising the sample, which corresponds to an crude annual mortality rate of about 10 per thousand. The distribution of deaths by age-group and year is set out in Table 11; the causes of death by age-group are reported in Table 12. The spike in deaths in 2009 is probably just sampling fluctuation. The age-specific mortality rates follow the expected pattern, being lowest among adults in prime age. The causes of death in these poor communities are overwhelmingly the usual suspects, namely, malaria, communicable diseases and complications in childbirth, whereby the latter often claim both mother and child.

The first step is to try to establish whether the disease environment is independent of the presence of an all-weather road. An important indicator of this environment is the relative incidence of the causes of death. Table 13 sets out the corresponding distributions of cases, where it should be noted that 8 of the 10 villages that had all29
Of course, the evidence presented in this section is suggestive, not conclusive. As discussed above, the distinct roles of correlated perceptions errors versus more precise estimates of governance in driving higher pairwise correlations between data sources cannot be definitively identified from the observed correlation between them. However, the fact that expert assessments from commercial business information providers are no more correlated among themselves than with firm surveys, and the fact that these correlations are not increasing over time, suggest that the phenomenon of correlated perceptions errors may not be all that important for the aggregation methodology of the WGI.

# 5.2 Alternative Weights

In this subsection, we explore the robustness of the WGI to alternative weighting schemes. In our baseline estimates, each data source is weighted according to its precision, i.e., the weight assigned to each data source is inversely proportional to the variance of measurement error in the source. This approach makes sense given the logic of the UCM – data sources that are more precise should be assigned more weight to reduce the variance of the estimate of governance. However, our estimates of the precision of each data source depend crucially on the assumption that each data source provides independent information on governance. As discussed in the previous subsection, this assumption may be questioned: if some data sources make correlated perceptions errors, the WGI methodology will assign too much weight to these data sources.

As discussed above, the extent of correlated perceptions errors is difficult to conclusively assess empirically. In this subsection, we instead investigate how our baseline estimates of governance in the WGI change if we instead use the natural alternative of assigning equal weights to each data source. This corresponds to a “neutral” assumption that all data sources are equally precise in their estimates of governance. This alternative weighting scheme also has the virtue of simplicity relative to the somewhat more complex UCM approach.

Specifically, we consider a simple alternative non-parametric aggregation methodology. Like the UCM used to generate our baseline estimates, this alternative approach converts data from different not correlated across sources. However, this would no longer be plausible when constructing an overall aggregate governance indicator out of the six WGI measures. This is because many of our data sources are used across more than one of the six aggregate indicators, raising the possibility that measurement errors in the six aggregate indicators are correlated with each other. This would, in particular, complicate construction of standard errors for the overall aggregate indicator of governance. Because we believe that these margins of error are essential to interpretation of aggregate governance indicators, we refrain from computing a single overall summary governance indicator based on the six WGI measures.

23
-3 -

indicated. Section IV applies the methodology and provides estimates of health effects of air pollution for Jakarta. Section V discusses the results and indicates the research and data on developing countries needed to enhance the accuracy of these estimates.

# II. METHODOLOGY AND BASEUNE ASSUMPTIONS FOR ESlTMATING HEALTH EFFECTS

# A. **Meth¶ dolo:y**

The estimation of the health and economic effects of air pollution involves the use of methodology similar to that used by the U.S. Enviromnental Protection Agency (EPA) in their Regulatory Impact Analysis for a new national air quality standard for particulate matter.' Estimation techniques are also derived from the analyses of economic benefits of air quality control programs in selected U.S. locations.<sup>4</sup> To estimate the economic value associated with changes in air pollution, four factors must be determined: the dose-response relationships, the susceptible populations, the relevant change in air pollution, and an economic valuation of the health endpoints. In this paper. health effects for a range of health outcomes are provided, while valuation of these is not performed.

The first step is to develop estimates of the effects of air pollution on various health outcomes. Dose-response functions chat relate health impacts to ambient levels of air pollution are taken from the published epidemiologic literature. This step involves calculating the partial derivative (or slope, b) of the dose-response fimction, to provide an estimate of the change in the prevalence of a given health effect associated with a change in outdoor air quality (A). Sufficient infonration is provided in this report to understand the sources of the selected dose-response functions, but a more complete review of the literature can be obtained in the EPA scientific review of the health effects of criteria pollutants?
20 indicates that the relationship between the M variable and growth is not highly sensitive to the inclusion of other variables, providing strong evidence as to the robustness of the results.

At this point, it should be noted that all that is really needed to show a relationship between the variable of interest and growth is the sign (and significance) of the variable of interests' coefficient in the "true" growth regression.<sup>2</sup> ' A variable which is related to growth can become insignificant, or switch signs, due to the inclusion of irrelevant Z variables, or the exclusion of relevant Z variables. On the other hand, in an area such as cross country growth regressions, where there is little agreement on the precise form of the growth regressions and where theory indicates a large number of possibly relevant variables, such a procedure may increase the readers' confidence in the results presented.

Another concern is the division of plausible variables into the I and Z variables. The I variables used in the first section are the Barro regressors from the previous sectioil. This base regression is preferred to the base regression used in Levine and Renelt (1992), due to doubts about the exogeniety of investment (one of the I variables in Levine and Renelt (1992)), and because many regressors in the Barro regression are significant at the five and ten percent levels. Results are also presented using the Levine and Renelt (1992) base regression. In a later section the Barro regressors that are insignificant in the base regression, when inequality variables are included, are dropped one at a time untll all regressors are significant at the ten percent level in at least one of the regressions. This further excludes CSEC60, secondary enrollment rates, and REVC7085, the average number of revolutions and coups.

The seven Z variables are money and quasimoney as percent of GDP averaged between 1970 and 1988 (M27088), war casualties per capita averaged between 1970 and 1988 (CS7088), ratio of claims on the private sector by the central bank to GDP averaged between 19708 and 1988 (DCPT7088), the average inflation rate between 1970 and 1988 (PI7088), Standard Deviation of the Inflation between 1970 and 1988 (SDPI7088), trade share of GDP averaged between 1970 and 1988 (STRD7088), and the ratio 2 For a discussion of **_those_** and other problems in sensitivity **analysis see** McAleet, **Pagan** and Volcker (1985).
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