rafmacalaba/datause-encoder-data huggingface dataset:
Bureau of Statistics; (c) Labour Force Survey 2000 by Bangladesh Bureau of Statistics; and (d) National Income Estimates by Bangladesh Bureau of Statistics.

We use an aggregate version of the SAM of Bangladesh that includes 15 sectors and four factors of production: skilled and unskilled labor, agricultural and non-agricultural capital. An important feature of the SAM is the decomposition of households into nine groups based on location **–** urban and rural – and assets – land or education. In the case of rural households, there are five groups -- Landless (No cultivable land); marginal farmers (up to 0.49 acre of land); small farmers (0.5 to 2.49 acres of land); large farmers (2.50 acres of land and above); NonAgricultural -- while for urban households there are four -- Illiterates (no education); Low Education (class I to class IX); Medium Education (class X to class XII); High Education (graduate and above).

Table 1 summarizes the basic structure of the 2000 Bangladesh SAM. Import duty rates ranges from as low as 1 percent (ready-made garments) to as high as 55.2 percent (petroleum). The sectoral import penetration ratio (ratio of imports to domestic demand) is highest for Readymade Garment (44 percent), followed by Petroleum (43 percent), while the highest shares in total imports are for Machinery (32.8 percent), and followed by Petroleum (12 percent). The sectoral export-orientation ratio (exports as a share of output) is highest for Ready-made Garment (92 percent), followed by Leather (31 percent), and Ready-made Garment exports account for 67 percent of total exports. Together, the service and construction sectors account for 60 percent of total value-added in the economy. The contributions of agriculture and manufacturing sectors in total value-added are 17 percent and 23 percent respectively. The highest shares of intermediate consumption in output are for Rice-Ata Milling (85 percent), followed by Other Food (81 percent). The share of intermediate consumption in total demand is highest for the Cereal Crop sector (113 percent).<sup>4</sup> 9
7 objectives; that is the prerogative of domestic social welfare and income tax policy measures. However, if empirical studies reveal that national trade-related policies are worsening particular countries’ poverty or inequality, they provide yet another reason – on top of the usual national gains-from-trade reason – for those countries to reform their policies unilaterally. Should the inequality and poverty alleviating effects of national trade-related policy reforms be contingent on the rest of the world also reforming, that provides a further reason for that country to participate actively in promoting multilateral trade negotiations under the World Trade Organization (WTO). And should global modeling studies reveal that multilateral trade reform would alleviate global inequality and poverty, it underlines the importance of bringing the WTO’s Doha Development Agenda (DDA) expeditiously to a successful conclusion with ambitious reform commitments. On the other hand, a negative finding (e.g., that trade liberalization would increase poverty in a particular country) need not be a reason to shun welfare-enhancing trade reform. Rather, such a result could be used to provide guidance as to where tax or social programs need to be targeted so that all groups in society can share in the economic benefits from such reform (see Ravallion 2009). Global reform results could also provide bargaining power to developing countries seeking aid-fortrade side payments to alleviate any increase in poverty projected to result from multilaterally-agreed trade reform.

The paper begins with an outline of the analytical framework and the common empirical methodology adopted by the global and national case studies being summarized. It then compares modeling results from both global and national models, before mentioning some caveats and drawing out policy implications. The findings are based on two studies that each use a global model to examine the effects of farm and non-farm price and trade policies on global poverty and its distribution within and across many identified countries, plus nine
6 Under the assumption that firms minimize costs of production subject to a given output constraint, derived demand curves for imported intermediate goods and the two capital stocks can be determined from the production function system (equations 4 to 6). We derive the cost functions corresponding to the production functions which allows us to endogenize two relevant domestic prices, namely the ones of gross output (PQ) and value added (Py) (equations 7 and 8). The effect of price changes of imported inputs on the domestic price level of value added and gross output, which reduce the real impact of nominal exchange rate devaluations, can hence be assessed. It should be pointed out that the price rise stems solely'from the supplypush effect and that it does not mirror shifts in the composition and level of aggregate demand or monetary assets.

We assume Ecuador to be small on the import side so that import prices are determined exogenously. Imports of intermediate goods are directly determined from the derived factor demand function while imports of capitml goods are deduced from the change in the stock of imported machinery, taking into account depreciation on past investment (14) 6 The residual import category ('other imports') consists mainly of consumer goods and constitutes a minor share in total imports. Demand for these goods is modelled as a function of domestic income and the relative price between this type of imports and the home-made goods (9).

Product differentiation is introduced on the export side. An export supply function is incorporated while staying witiln the framework of a one-sector economy (Melo and Robinson 1989). In contrast to the import side, the export price is not _assumied_ to be determined exogenously which might seem to be an unusual assumption for a small country. However, for coffee and bananas (which constitute Ecuador's main exports besides oil), consumers tend to differentiate products by origin. As we assume a one-good economy, export demand is not product specific. Instead it is depicted as a function of the relative price between Ecuadoran products and the OECD price deflator on the one hand, and OECD real income level on the other. The export market is depicted by equations 10 to 12.

Distribution parameter and long-run elasticity estimates from non-linear simultaneous estimations are summarized in Table 2.7 Focus rests with the estimates

- 6 Capital stocks are calculated by inventory method assuming a depreciation rate of 12.5 _%._ A relatively wide dispersion of rates fbr developing counties are put to use in the literature. E.g., while Bergsten et al. (1985) assume an average zate of 20%, Keil and Marchese (1991) employ a 1255% rae as used inthis study- Field (1987, p.1285) and Ikemoto (1986, p.387) apply a rate of 10%. while the IMF (1989, p.66) posits a rate of 5%.

- 7 Estimations are carried out in several blocks: Block A: the demand for intemediate inputs (Mh) and the output price (PQ); Block B: demand for the two capital stocks and the GDP price _deflator_ **(Py);** Block C: export supply and demand (XD and Py). The demand for other imports (Moh) is estimated with OLS.
substitute strongly for tenant effort is hired labor and, if such substitutability were high in all tasks (not just for harvesting; see subsection 2.6), there would be little demand for share-tenants. Landowners could simply self-cultivate using wage labor.

# 2.2 Data

Our empirical analysis draws upon agricultural production data collected in two separate household surveys from rural Pakistan. The fi rst is round 14 of the IFPRI panel survey, fi elded in 1993 in four districts and 52 villages. The second is the nationally representative Pakistan Rural Household Survey (PRHS) of 2001, which collected data from about 2,800 households sampled across 17 districts and 150 villages. All of the households in IFPRI-93 were purposively included in PRHS-01. Overall, about 60% of the households surveyed in 2001 were farm households and, as in 1993, a considerable fraction of these operated multiple plots. Because much of the plot-level agricultural production and tenancy data are comparable across the two surveys, part of the empirical work uses both data sets together. Our analysis of landlord supervision, however, relies solely on the 2001 survey, since the relevant questions were not asked in 1993.

# 2.3 Context

As a consequence of Pakistan’s extreme land ownership inequality, the fraction of tenanted land is high (more than a third), and about two-thirds of this land is under sharecropping. Despite numerous tenancy laws on the books (regarding eviction and the sharing of output and costs), contracts are de facto unconstrained, as enforcement is practically nonexistent.

Sharecropping is the predominant form of tenancy in Sindh province, where the land ownership distribution is particularly skewed. According to reports by surveyed tenants, the median landlord owns 28 acres, whereas nearly 80% of the share-tenants are landless farmers. Large landlords in this region often employ labor supervisors (kamdars) to manage their many tenants. In the Punjab, the second of the major provinces of Pakistan, tenancies are split more evenly between share and fi xed rent contracts. Landlords in Punjab are also typically much smaller than in Sindh, with a median holdings of only 7 acres, and are more likely to be resident in the same village as their tenants.

Although nearly three-quarters of share-tenants in our data report a 50-50 output sharing rule, this probably overstates the degree of uniformity in these contracts. For example, it is common for tenants to borrow from their landlords, often by taking an 4
**Table 14.  Impact of WTO Accession on Output by Sector and Regional Market. (Results are Percentage Change from Base Year)**

|**Good**|**Central**|**Siberia**|**South**|**North**|**Urals**|**Far East**|**Moscow**|**St. Pete.**|**Tumen**|**Northwest**|
|---|---|---|---|---|---|---|---|---|---|---|
|`FME`|26.6|13.1|37.4|24.7|13.7|23.3|38.5|293.0||12.0|
|`CHM`|8.6|20.9|11.2|3.3|12.4|5.2|6.2|8.8|47.6|25.5|
|`TMS`|13.7|8.6|13.2|20.3|0.4|21.5|-0.7|21.3|10.3|21.9|
|`TRK`|10.1|6.8|8.8|11.5|4.3|13.6|5.1|14.5|5.4|15.2|
|`COL`|7.8|6.4|8.0|8.8|6.7|8.6|||||
|TRD|6.7|4.7|4.9|5.3|4.2|6.1|5.0|7.8|3.0|9.3|
|`NFM`|5.8|3.6|20.2|8.1|14.5|4.6|22.6|-6.6||-22.3|
|PST|3.9|3.1|4.0|4.5|2.4|4.9|2.8|4.2|4.2|4.7|
|`OIL`|4.9|0.6|5.4|5.1|0.4|6.2|1.8|1.3|8.7||
|ELE|3.3|3.0|3.5|3.4|2.7|3.2|2.0|4.1|3.6|5.7|
|HOU|2.1|2.2|2.8|3.1|1.5|3.1|2.2|2.6|3.2|2.9|
|`RLW`|3.7|0.8|1.9|1.9|1.4|2.1|0.4|6.1|0.5|4.3|
|`CRU`||1.5|2.5|2.8|1.5|3.0|||2.7|1.9|
|`SCI`|1.7|2.0|0.5|1.8|0.1|4.5|1.1|7.5|-4.3|6.8|
|HEA|1.4|1.3|1.7|1.5|1.2|1.7|1.5|1.7|1.2|1.7|
|CLI|1.0|-0.4|1.1|1.2|0.3|1.2|0.0|-1.9|6.0|-2.9|
|`MWO`|-4.1|0.9|1.3|-0.3|-4.3|2.3|8.4|2.1|2.8|-5.4|
|`CON`|0.4|-0.9|0.8|-0.2|0.3|-0.4|1.8|-1.8|3.4|-3.6|
|`OTI`|-0.9|-2.6|0.1|-0.1|-1.2|-1.4|2.6|0.5|2.6|-2.0|
|`AIR`|-0.8|-3.7|-0.9|2.8|-3.1|2.7|-1.6|-0.3|2.9|-0.6|
|`TPP`|-6.2|-5.0|-9.4|0.6|-1.9|17.1|-11.3|-7.2|16.3|-9.2|
|`AGR`|-1.4|-1.1|-0.2|-1.7|0.5|-2.5|-0.8|-6.7|6.4|-8.6|
|`GAS`||-3.0|-1.3|-1.8|-2.5|-2.0|||2.9|-5.0|
|`PIP`|-1.3|-2.8|-2.1|-3.3|-1.9|-2.3|-3.7|-3.3|0.1|0.5|
|`CNM`|-2.5|-4.8|-1.2|-1.3|-2.7|-1.9|-4.5|-5.9|6.2|-7.8|
|`OTH`|-2.0|-6.5|-3.0|-4.9|-1.2|-6.5|0.1|-8.7|3.7|-13.0|
|`FIN`|-5.5|-6.1|-5.4|-5.8|-6.3|-5.3|-5.6|-4.9|-7.5|-4.1|
|`TRO`|-4.3|-9.5|-6.3|-1.3|-15.1|0.6|-18.1|1.0|-13.2|1.6|
|`FOO`|-8.4|-6.8|-6.1|-3.8|-6.4|-7.1|-11.0|-8.4|4.5|-11.6|
|`MAR`|-6.4|-11.4|-8.0|-4.9|-6.9|-4.3|-9.2|-5.8|-6.1|-7.6|
|See table 1|for sector c|odes.|||||||||
|**Source: A**|**uthor's est**|**imates.**|||||||||

52
for a fresh graduate within a week, and 94 percent within four weeks. Thus firms do not appear to be spending large amounts of time with unfilled vacancies.

In order to provide further evidence on how firms in Jordan fill positions, we conducted a 4 round panel survey based off of the United States Bureau of Labor Statistics Job Openings Labor Turnovers Survey (JOLTS) on 350 firms in Amman that employ recent graduates. During the non-holiday months, 26 percent of firms hired new employees who were under the age of 26. On average, 245 new youth employees were hired, 105 youth employees quit, and 8 youth employees were laid off. Given the high level of churn among these 350 firms; many firms are constantly looking to fill positions. On average, 51 percent of positions that firms seek to fill are perennially available. The other 49 percent of positions that firms seek to fill are unique to the firm’s circumstances and timing. Firms are able to fill 88 percent of these positions within 4 weeks, which means only 6 percent of positions overall require greater than 4 weeks to hire a new employee. Different types of jobs are filled at different rates. 57 percent of low prestige positions are perennially available in contrast to 43 percent of high prestige jobs and 37 percent of technical jobs. Figure 1 below reveals the cumulative proportion of the number of weeks open by position type; 75 percent of high prestige positions are filled within 4 weeks and low prestige jobs and technical jobs fill positions even quicker.

Therefore for many firms it appears that they are able to fill positions reasonably easily and lowering search costs may not generate a lot of new employment. This might explain the firm response to the matching intervention, but the question then remains as to why many youth turned down the opportunity to be matched, and turned down actual job offers.

# _3.4.5 Reservation wages and reservation prestige_

Recall that job candidates turned down 28 percent of the match opportunities they were given, and turned down or quickly quit 83 percent of the job offers given. The main reasons given for rejecting the opportunity for a potential interview were that the graduate was not interested in the company or type of job. When we asked those who had turned down job offers the reason for doing so, only 8 percent said that the wage was too low, whereas 41 percent said that it was not in their career path, 8 percent said they didn’t want to work outside, and 8 percent said it was not a culturally appropriate job. It therefore appears that job candidates have a “reservation prestige”, 18
# **4.2.  Crises and Foreign Participation: Cross-Country Regressions**

To further assess the role that crises play in promoting foreign bank participation we estimate equation (1), in which we regress the change in foreign bank participation between 1995 and 2002 on a dummy for whether countries had crises over this period along with a number of controls: ∆ _Foreign Participation_ is the change in the share of banking sector assets held by foreign banks in country _i_ between 1995 and 2002, based on the data from Micco _et al._ (2006). _Crisis_ is a dummy variable equal to one if a country experienced a systemic banking crisis at any point during the period of study as defined in Caprio and Klingebiel (2003). _Macro_ is a matrix of two variables, inflation and total output growth, intended to capture the attractiveness of an environment for prospective foreign entrants. Inflation is averaged over the period; output growth is the total change in real output from 1995 to 2002.<sup>9</sup> We expect foreign bank participation to increase with GDP growth because it reflects opportunities for the profitable provision of financial services (especially lending), and to decrease with the level of inflation because it reflects uncertainty and, perhaps, macroeconomic mismanagement by the government. Both of those factors make medium and long-term lending problematic.

The _Banking Index_ is taken from the Heritage Foundation which reports a yearly assessment of the level of banking restrictions in a country. As noted, the index ranges from 1 to 5 with higher scores indicating greater restrictions. Among the restrictions that the Heritage Foundation considers in constructing this index are the ease with which foreign banks can open branches and subsidiaries; government interference in the allocation of credit, including government ownership of banks; the ability of private banks to operate without government regulation such as deposit insurance; and the ability of banks to provide a wide range of financial services (including real estate and securities transactions, and insurance). We would expect that more restrictive environments would be less receptive to foreign bank participation. For their

> 9 The construction of these variables is intended to minimize the influence of yearly observations that are outliers. In the fixed effects regressions in the next section, which are of necessity based on yearly observations, we remove observations for inflation and output growth on the tails of their respective distributions (< 1<sup>st</sup> percentile, > 99<sup>th</sup> percentile).

17
# 3.1.2 Behavioral approaches

As already noted, a general problem underlying evaluations of policy impacts, including public-spending changes, is the lack of the relevant counterfactual. Ideally, we would like to compare situations with and without a spending policy change. What would have happened had the policy not been implemented or the public good not provided? But we are rarely able to observe such a baseline. Many facets of the economic behavior of beneficiaries and nonbeneficiaries alike-including labor supply, consumption, saving and investment decisions-may be affected by public policies. It is difficult to accurately trace responses and their full general-equilibrium effects. Yet these responses have potentially important implications for a policy's final impact. This issue comes up repeatedly in PSP, and a number of the included studies can be interpreted as attempts to resolve the problem by modelling behavioral responses.

Various estimation-based techniques have been developed that attempt to capture key behavioral responses. The general approach has consisted of trying to measure the program beneficiary's own valuation of the benefit received. The latter is deemed sufficient for-or at least relevant to-attributing benefits from public services. (Alternative nonwelfarist approaches [discussed earlier, and by Sen in PSP] tend to downplay the relevance of such subjectivist evaluations, though not dissimilar problems emerge in these approaches, as I discuss later.) The conventional welfarist measures allow for behavioral responses to price and income changes. They are generalizations of the old notion of consumer surplus-the monetary value of a change in welfare due to a change in prices. In the simplest version, this is measured by the area under the Marshallian demand curve. As such, it can be interpreted as the sum of incremental benefits from each additional consumption unit valued at marginal willingness-to-pay.

As is well-known, the fundamental problem with consumer surplus is that it ignores the income effects of price changes. This has led theorists and some practitioners to turn to the Hicksian 9
# **b.2) Regional growth in Mexico**<sup>1</sup>

GDP per capita growth in Mexican states has been distributed in different ways throughout the years, as shown in the histograms of Figure 3. In the period from 1970 to 1985, all states experienced a positive annual growth between 0.6 and 5.3 percent, but the distribution was biased towards the smaller values, with a mode between 1.5 and 2 percent. During the “lost decade” between 1985 and 1995, the distribution of annual GDP per capita growth had a much larger standard deviation with growth rates ranging from -4.1 percent to +4.2 percent, but with 80 percent of the states having negative growth during this period, with a mode between 0 and minus 0.5 percent. The last period from 1995 to 2006 had again a much smaller standard deviation and with exception of the tourism-oriented state of Quintana Roo, which had negative growth, the values ranged from 0.8 to 3.4 percent.

**Figure 3: Number of Mexican states per annual GDP per capita growth rate (grouped by half percentage point ranges) for three different time periods between 1970 and 2006.**

<!-- Start of picture text --> 1995 ‐ 2006<br>1970 ‐ 1985 1985 ‐ 1995<br>10 10 10<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br><!-- End of picture text --> Source: Calculations based on information from INEGI, excluding Campeche and Tabasco.

We replicate a table from Chiquiar (2004) with more recent data (Table 1). The author divided the country in five regions, the border, northern central, southern central, Mexico City<sup>2</sup> and the south. In order to visualize the patterns of convergence-divergence among Mexican regions, Figure 4 compares their per-capita GDP with the national average. First, we can observe that the regions 1 As suggested by Chiquiar (2004), we exclude in this section the states of Campeche and Tabasco from the analysis, because a very large and volatile share of these states’ GDP is generated from the exploitation of oil reserves by the government-owned oil company PEMEX. The growth rates are in consequence strongly affected and their per-capita output overestimated and not representative of their welfare, as the profits are going directly to the federal government.

2 As used in recent literature (Aguayo-Tellez, 2006; Aroca, Bosch, Maloney, 2005; Chiquiar, 2004), Mexico State was merged with the Federal District because together they form the metropolitan area of Mexico City and have long been part of a common industrial aggregate with strong labor market links between them. In addition many private enterprises report profits in the Federal District, where their headquarters are located, and not in Mexico State, where the plants are located. Finally the population in the Federal District remained stable over the last two decades, while doubling in Mexico State, which leads to overestimate the Federal District’s per capita income.

7