rafmacalaba/datause-encoder-data huggingface dataset:
# _From Agriculture to Industry_

Trade and economic growth typically bring about change in the sectoral composition of output. One of the stylized facts of development, postulated as far back as 1939 (Fisher 1939 and Clark 1940), is that it comes with shifts in output from the primary (agriculture) to the secondary (manufacturing, mining, and construction) and the tertiary sectors (services). Figure 6.A traces the changes in Thailand’s output composition at the threesector level since 1951 and Figure 6.B presents the average real growth rates by sector and overall, separating once again the five growth episodes.

On the basis of comparative advantage, Thailand’s leading sectors should be agriculture and related processing industries. Yet, the largest sector already in the 1950s was services, contributing around 45 percent of GDP. Since the service sector remained roughly constant as share of GDP over the last half a century, structural change came about as substitution from agriculture to manufacturing. During the 1960s and 1970s, protection of capital-intensive manufactures and export taxes on rice and other commodities suppressed the size of the agricultural sector, which declined from just under two-fifths of GDP in the early 1950s to just over one-fifth of GDP by the end of the 1970s. While these policies were abandoned during the 1980s, the shift away from agriculture continued, as industry remained the most dynamic sector. Agriculture fell below 10 percent of GDP for the first time in 1996. While agricultural growth picked up subsequently, so did the expansion of the other sectors, and agriculture contributed 9 percent of GDP in 2004.

While agriculture declined, industry increased. The expansion was initially fueled by import-substitution policies, and then from the 1980s onwards by growing industrial exports (Figure 6.B). The boom period from 1987 to 1996 saw double-digit industrial growth rates, while the contraction in 1997 and 1998 brought about a sharp fall in industry. The recovery since 1999 is also led by industry with growth rates twice as high as in the other two sectors. As a result, industry is since 2003 Thailand’s largest sector, contributing 46 percent of GDP.

The expansion in industry is foremost due to manufacturing, while services center increasingly on transportation and communication. Manufacturing accounted just over four-fifths of industry’s value-added in the early 1950s, declined to 70 percent by the mid-1980s, and increased again to four-fifths by 2003 (Figure 6.C and (Figure 6.D). Over the same period, transportation and communication as percent of the service value added increased from 9 percent to 15 percent and to 23 percent.

Focusing on the dynamics since 1996, Figure 6.E and Figure 6.F present a slightly different GDP breakdown reflecting a recent revision in Thailand’s national account classification. Among these 15 subsectors, manufacturing, electricity and gas and transportation and communications have led the recovery, while construction, financial intermediation and wholesale and retail trade are still struggling with the aftermath of the Asian crisis.

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WPS3784

**Table 11. Income Gains from Switching from Subsistence to Coffee Farming**

|**Sample: all household heads**|Matching<br>method:<br>Epanechnikov|Matching<br>method:<br>Normal kernel|
|---|---|---|
|Observed average agricultural income coffee farmer|99876.3|99876.3|
|Observed average agricultural income subsistence farmer|74072.8|74072.8|
|Subsistence farmers' agricultural income had they participated|91256.2|96267.1|
|Gain from participation in coffee farming|17183.4|22194.4|
|_Proportionate change in subsistence farmers' income_|23.2%|30.0%|
|_Impact on the headcount level_|-24.9%|-27%|
|_Impact on the poverty gap_|-12.5%|-12.9%|
|_Total sample (household heads)__~~1~~_|2753||
|_Number of people that switch and gain_|1073||
|_Percentage of people that switch and gain_|39%||

_Note:Missing values are excluded, so the sample size reported here is smaller than the actual number of head farmers_ _* = Statistically significant as established through bootstrapping._ 26
6 (resurfacing) cost only. Routine maintenance is ignored (assumption V). Note that in this formulation, road maintenance costs omit fixed maintenance custs and any maintenance costs which may depend on the volume of traffic, rather than on the passage of heavy vehicles.

16. The road system is optimally designed by first choosing values of capacity w and strength S to minimize total costs:

# Min TC = N [v*+V(N,w)+bRJ + r [K+wK(S)] + w M(D/w,S). (4)

By taking the partial derivatives of TC with respect to w and S and equating them to zero, we have the minimization conditions:J/ N V, + rK + M + wMD,,(-D/w-) = **0** _(5)_ r'wK 8 + wMs = **0.** (6) Rewriting equations (5) and (6), we have

-N-V,, = rK + M - (D/w)MD,W (7) w*Ms = r*w'Ks. (8)

17. The left-hand side of (7), representing the reduction in user time-costs due to an additional lane, is the marginal private benefit of capacity MPB,. The first two terms on the right-hand side of (7) represent the increased costs of adding capacity, since K is the road construction costs per unit of capacity and M are the variable periodic maintenance costs of that unit of capacity. The last term in (7) represents the reduction in periodic maintenance costs per unit of capacity since an additional unit of capacity reduces the number of heavy axles per unit of capacity and hence decreases the total periodic maintenance costs of the road. The three right-hand terns sum to become the marginal costs of adding an additional unit of capacity; they can also be written as r*K + M(1 - O), where t is the elasticity of M with respect to D/w. Equation (7) therefore prescibes that for an optimally designed roadway, the marginal benefit of adding capacity should equal the marginal costs of capacity. For this conclusion to be meaningfui, it must be assumed that road capacity can be smoothily aujusted to traffic (as per assumption p).

18. Likewise, the left-han( 'de of (8) represents the marginal benefits of additional strength through a reduction in periodic maintenance costs (M8 is negative), and the right-hand side of (8) represents the marginal increase in road construction costs due to additional strength. Equation (8) then prescribes that an optimally designed roadway should choose the strength of roads appropriately so as to eq marginal benefits of strength with marginal costs of strength.

U In the following, we suppru **the arguma** of **al fwcions** to **faciltte the** undenanding of vanous conditions. **Like** Mohring and **Harwitz, Newbery** implicitly auumed **the exisence** of **the** econd-order sufficient conditions.
**<u>Table 3: Village level summary statistics</u>**

||**Total**|**Reservat**<br>**Ever**|**ion Status**<br>**Never**|**T-test of**<br>**difference**|
|---|---|---|---|---|
|**Panel A: Village characteristics**|||||
|Population|2,619|2,472|2,766|-294|
|Households|483|448|519|-710|
|SCs|0.208|0.204|0.212|-0.008|
|STs|0.115|0.120|0.110|0.010|
|Share of Hindu|0.898|0.901|0.894|0.007|
|Share in agric.|0.563|0.582|0.543|0.039|
|Has prim. school|0.921|0.905|0.937|-0.032|
|Has sec. school|0.926|0.926|0.926|0.000|
|Has prim health center|0.537|0.558|0.516|0.042|
|Has pucca road|0.505|0.516|0.495|0.021|
|Dist. to district HQ (km)|51.170|50.440|51.900|-1.460|
|Dist. to town (km)|14.940|13.970|15.910|-1.940|
|Dist. to bus station (km)|4.240|4.980|3.490|1.490|
|Dist. railway station (km)|25.130|25.590|24.660|0.930|
|Dist. to post office (km)|1.940|1.990|1.880|0.110|
|**Panel B: Pradhan's Characteristics**|||||
|Earlier contested|0.158|0.137|0.179|-0.042|
|Held position before|0.474|0.442|0.505|-0.063|
|Up to high school|0.263|0.263|0.263|0.000|
|High sec. & above|0.342|0.263|0.421|-0.158|
|Higher education|0.163|0.137|0.189|-0.052|
|SC|0.537|0.579|0.495|0.084|
|ST|0.116|0.116|0.116|0.000|
|OBC|0.126|0.105|0.147|-0.042|
|OC|0.216|0.200|0.232|-0.032|
|Hindu|0.563|0.484|0.642|-0.158|
|Muslim|0.089|0.084|0.095|-0.011|
|No. of obs.|190|95|95||

_Note:_ Author’s own calculation from 2014/15 REDS follow-up survey. To test difference in means, p values from regressions with district fixed effects and standard errors clustered by village panchayat are reported in the last column.

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that are smaller than those permitted by the overall-deficit rules, and the overall-deficit rules are not the only rules that constrain fiscal policy. But macroeconomic modeling shows that Albania and Montenegro in particular will need strong commitment to compliance with the rules—probably stronger commitment than has been demonstrated in the recent past. Moreover, all the countries in the region need to keep in mind that how debt evolves will depend on unpredictable economic shocks. Taking account of possible shocks, the modeling shows that, even if government commitment is strong enough to reduce debt in a timely manner under normal economic conditions, the range of possible outcomes for future debt is wide. To be confident of reaching a debt target, governments will thus have to aim for debt that is lower than the target.

- A review of assessments of public financial management in the region shows that many of the basic elements needed to support fiscal rules are in place. But accounting and statistics are not always robust enough to reveal when success in complying with fiscal rules has occurred at the expense of, say, accumulating arrears or shifting spending responsibilities to state-owned enterprises. Moreover, fiscal forecasts have often been optimistic, making future compliance with the rules seem easier than it really is. Recent attempts to solve this problem have included the creation of independent fiscal councils and the use of forecasts produced by the IMF or European Commission.

The paper suggests five principles to guide economic policy toward fiscal rules in the region:

- First, more emphasis should be given to ensuring that fiscal rules are widely understood and enjoy the support of a broad range of stakeholders.

- Second, policy toward fiscal rules should be consistent with accession to the EU, though the rules should be simpler than the EU’s, the debt limits should be lower, and the changes that are prioritized should be those that will be beneficial even if accession to the EU is delayed (a no-regrets approach).

- Third, limits should not be mistaken for targets. For example, if the deficit limit is 3 percent of GDP, it should not be assumed that a deficit of 3 percent is usually reasonable. During economic booms, a surplus may be appropriate.

- Fourth, the aspects of public financial management that support the implementation of fiscal rules, including independent statistical agencies and monitoring institutions, should continue to be improved. This will be beneficial no matter which rules are in place.

- Fifth, and somewhat more technically, the rules and targets in place should both establish a long-term objective—normally for public debt—and specify the steps that should be taken in the short term—normally in public spending or the deficit—to ensure that progress is made in achieving the long-term objective.

4
Two important inferences emerge from this discussion. First, the amount of resources available at the provincial level is so low in absolute terms that it is unlikely to have more than a very limited impact on poverty. Second, the distribution of resources among the provinces appears to favor those who have, in the sense that the relatively better off provinces on the whole spend more in per capita terms. Although some unit costs of providing some public services may be higher than the national average in more urbanized areas, others will be lower. Cost differentials alone seem unlikely to account for these results. Indeed, in the north mountainous region, where low population density means that the unit costs of providing public services are also likely to be well above average, per capita expenditures were actually slightly below average.

# _Role of Local Governments in Poverty Alleviation_

What is the role of local govermnents in undertaking expenditure policies targeted to reduce poverty? Although the potential inter-provincial mobility of population limnits the efficacy of sub-central governments in undertaking redistributive policies.'° local authorities should have a comparative advantage in identifying the poor. They may also be more efficient providers both of social and community services such as health and water supply and economic services such as agricultural extension, irrigation and roads, particularly in rural areas. In addition, as noted earlier, in principle an essential function of local governments is to satisfy the varying preferences of people residing in different jurisdictions by providing differing mixes and levels of public services. Local governments may thus play an important role in deciding and implementing expenditure policies, including those intended to alleviate poverty.

- **10** See, Brown, C.C. and W.E. Oates, "Assistance to the Poor in a Federal System" _Jounal of Public Economics,_ Vol. 32 pp. 307-330, 1987 and Ladd, H., and F.C. Doolittle, 'Which Level of Government Should Assist the Poor", _National Tax Journal,_ Vol. _35,_ 1982.

15
decision of “how much” protection to offer by defining this as the coverage ratio of total industry imports that are made up of 6-digit Harmonized System (HS) products that face definitive antidumping measures. We discuss construction of this coverage ratio variable in substantial detail below.

# **3.1 Econometric model**

Our econometric approach uses maximum likelihood to estimate a Heckman (1979) selection model to examine determinants of a 3-digit ISIC industry’s decision to pursue an antidumping investigation and the coverage ratio of how much protection from imports under antidumping policy the domestic government provides. Our regression equation for determinants of the coverage ratio of 3-digit ISIC industry _I_ in country _C_ is given by However, we assume that our coverage ratio dependent variable is not observed for those industries that do not pursue an antidumping investigation. As discussed in section 2.2, industries likely to receive little protection under antidumping (i.e., a smaller share of their total industry imports covered by an antidumping measure) may be less likely to pursue an investigation in the first place due to the costs associated with the process. The assumption’s implication for our econometric approach is that the dependent variable of the antidumping coverage ratio in equation (1) is only observed for a particular industry-country pair if In the regression equation (1) and selection equation (2), _XI,C_ and Z _I,C_ are matrices of explanatory variables, α and β are vectors of parameters to be estimated, and we assume that _ε1I,C_ ∼ _N(0,_ σ _)_ , _ε2I,C_ ∼ _N(0,1)_ and _corr_ ( _ε1I,C, ε2I,C)=_ ρ . If ρ≠ _0_ , then ordinary least squares regression of (1) alone would yield biased estimates for β . The Heckman model treats (2) as the first-stage selection equation, and in our application this is the 3-digit ISIC industry’s binary decision of whether to pursue an antidumping investigation. The Heckman model addresses the concern of potential sample selection bias in the regression equation, and it also offers a parsimonious modeling approach to our two-pronged question of determinants of the industry decision to pursue antidumping and determinants of how much protection the domestic government provides.<sup>11</sup>

> and Niels (2004) for research on Mexico. Blonigen and Prusa (2003) and Nelson (2006) provide extensive surveys of this literature.

> 11 This approach is closest to that used by Hansen (1990) in her two-stage nested logit examination of determinants of a US industry decision to pursue antidumping protection and determinants of the US International Trade

8
# _Loss of Productivity_

Loss of productivity (also referred to as the "change in productivity" method or "effect on production") is a valuation method that computes the loss in output due to illness or some other event. The loss of productivity method is applied in two situations.

First, environment-induced health effects reduce production. Foregone income as a result of illness, which is assumed to be evenly distributed over time, is valued by using the average wages in the formal and informal sectors (see Cesar and others 2000 for a further discussion). Assuming an annual increase of 2.45 percent the formal and informal daily wage level in 2010 are US$ 24.8 and US$ 10.3, respectively (2010 values in 1999 prices).<sup>14</sup> For those air pollution-related health effects where we are not able to identify the age of the people affected we use the population-weighted wages for the whole ZMVM population. This leads to an average daily wage of US$ 6.49 (2010 values in 1999 prices). Using total population-weighted wages to estimate morbidity damage for specific age groups in the ZMVM would lead to an underestimation of the damages if only adults or children are affected and an overestimation of the damages if only the elderly are affected. 15 For effects in the elderly we assume no economic losses occur. For effects in adults and children we use an adult population-weighted wage in of US$ 9.52 (2010 values in 1999 prices). The assumed number of "days lost" due to air pollution is presented in table 6.2

> 14 In the absence of data on the expected wage growth in Mexico we have used the growth in GNP per capita as a proxy by deducting the population growth rate (1 percent) from the expected growth in GNP (3.7 percent).

> 15 Effects resulting in loss of time for children leads to productivity losses in adults resulting from care for the children.

_3]_
19

# 6.  Marginal Expenditures: Estimation of the Model

Equation (13) was estimated on all 3884 households in the Ghana survey. The equation was estimated in two ways: first, with no dummy variables for the receipt of internal or international remittances; and second, including both remittance variables. In both estimations the model was estimated for each of the six categories of expenditures described in Table 6 using both OLS and instrumental variables.

Estimating equation (13) presents several challenges. First, expenditure is endogenous or could be measured with error, which might lead to biased estimates of the Engel curve (Hausman, 2001). In the past, a standard solution to this problem has been to use income as an instrument for expenditure (Liviatan, 1961); however, recently the assumptions under which this can be done have been criticized (Hausman, 2001). Other authors have suggested the use of polynomials on age, education or consumption lagged (Hausman, Newey and Powell, 1995). In our estimation, we use two instruments for income: (1) a polynomial on age; and (2) annual per capita household expenditure in the ethno-religious group, excluding the income of the household studied.<sup>17</sup> Second, given that we found that selection matters in the receipt of international remittances (from African or other countries), it appears that estimating the effect of receiving international remittances faces the problem of selection bias. To address this issue, we use as instruments the seven variables on migration networks and remittances at the ethnoreligious level described above.<sup>18</sup> A third problem in estimating equation (13) is that when we divide different variables in the model by expenditure, we generate a number of potentially endogenous variables. To meet this problem, we use as instruments interactions of annual per capita household expenditure at the ethno-religious level with each of the characteristics that are divided by expenditure. Fourth, to insure that the variables measured in the ethno-religious