rafmacalaba/datause-encoder-data huggingface dataset:
2004): _yi_ = _f_ ( _d i_ , _xi_ ) . In the context of observational studies, the key assumptions underlying matching methods seek to mimic conditions similar to an experiment so that the assessment of the impact of the program can be based on a comparison of outcomes for a group of participants (i.e. those with **di=1** ) with those drawn from a comparison group of non-participants ( **di=0** ).

To yield consistent estimates of program impact, matching methods rely on a fundamental assumption known as “ _conditional independence_ ” or “ _selection on_ ”<sup>5</sup> _observables_ . This assumption can be formally stated as<sup>6</sup> : The above expression states that potential outcomes are orthogonal to treatment status, given the observable covariates. In other terms, conditional on observable characteristics, participation is independent of potential outcomes. Assuming that there are no unobservable differences between the two groups after conditioning on **xi** , any systematic differences in outcomes between participants and nonparticipants are due to participation If one is only interested in the mean impact for the treated, then the assumption of unconfoundedness can be weakened by focusing on potential outcomes in the nonparticipation state (Imbens 2004). This weaker version can be stated as follows.

In other terms, the outcome in the counterfactual state is independent of participation, given the observable characteristics. Thus, conditional on the observables, outcomes for the non-treated (the comparison group) represent what the participants would have experienced had they not participated in the program. Obviously, this makes sense in the particular situation where selection into the program is based entirely on

> 5 This assumption is also known as the _exogeneity_ or _unconfoundedness_ assumption or _ignorable treatment assignment_ (Imbens 2004)

> 6 The symbol ⊥ represents orthogonality between two variables.  Thus conditional independence may also be referred to as conditional orthogonality.

6
_Increases in unemployment, after factoring in the size of the shock, were larger outside of Europe and in countries with higher redundancy costs._ After controlling for GDP growth, unemployment increases were particularly low in Eastern Europe and Central Asia (Figure 10). This suggests that concerns that European institutions exacerbated unemployment increases during the crisis were exaggerated, at least for the middle income European countries in our sample. Activity rates and unemployment rates increased in higher-income countries, as laid-off workers in these countries were more able to afford to search for work, in part perhaps because spouses or younger members of the household rejoined the labor force. Finally, countries with higher redundancy costs experienced significantly larger increases in unemployment. During the crisis, unemployment rates increased most for youth, who may have faced particular difficulty obtaining employment in countries with higher redundancy costs.

<!-- Start of picture text --> Unemployment rose less in European and<br>lower redundancy cost countries<br>East Asia (omitted)<br>Eastern Europe and Central Asia **<br>Latin America<br>Middle East<br>Low middle income (omitted)<br>Middle middle income **<br>Upper middle income ***<br>Small manufacturing sector ...<br>Large manufacturing sector<br>Less dependent (omitted)<br>More dependent<br>Fixed or Peg (omitted)<br>Managed float<br>Full float<br>Low difficulty of redundancy<br>High difficulty of redundancy<br>Low redundancy cost<br>High redundancy cost ***<br>Change in activity rate trend<br>Change in unemployment rate trend<br>Region<br>Income<br>Structure<br>Exports<br>Exchange rate<br>Regulation<br>-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0<br><!-- End of picture text --> Note: See notes from figure 10. All data are from household surveys. Unemployment regressions contain 36 or 37 observations, while activity **Figure 10** regressions contain 29 observations.

**Figure 10**

11
We obtain the indicators of expropriation vulnerability as follows. First, we construct a dummy variable indicating whether a treatment firm’s previous land tenure had expired at the time of the reform.<sup>23</sup> We obtain the maturity of the tenures of the lands from the special report or annual report of the listed companies. Second, we construct a dummy variable indicating whether the usage rights of the land at the time of the reform were contested (i.e., whether the firm illegally sold or rented their Allocated land to other firms).<sup>24</sup> Those illegitimate transactions could result in multiple stakeholders with claim over the land and large compensations for current users of the land to vacate. We obtained relevant information from the annual reports and the self-disclosure reports of the titling firms.

In columns (1) and (2) of Table 7, we include the dummy variable indicating tenure expiration interacting with _Titling Firm_ .<sup>25</sup> The results render support to the property right hypothesis: according to the preferred column (2), titling firms whose land tenure was not expired do not have significant titling effects, but titling firms with expired land tenure had a titling effect on the investment rate of 42 percentage points. In columns (3) and (4) of Table 7, we include the interaction term of _Titling Firm_ and the dummy of contested land rights. Relatively to other program participants, the titling effect on investment rates for titling firms facing contested land rights around the time of the reform almost sextuples (i.e., from 0.08 to 0.47), again supporting the property rights hypothesis.

# **5. Political Connections and the Differential Impact of the Reform**

We now allow distinct effects of titling for firms with various degree of political connections.

_5A. Titling Effects by Ownership_

> 23 In 1981, the Shenzhen government allowed tenures of 20 or 30 years to Allocated land. Many of those pieces of land expired or approached expiration in 2012.

> 24 The illegal way to sell allocated land was to sign a rent contract that had a length equal to the tenure length of the land.

> 25 We do not include the dummy indicating tenure expiration directly because this variable is available only for titled firms.

28
proportion of Tier 1 capital to have a greater impact in reducing risk in countries that impose greater stringency. The results suggest that a higher proportion of Tier 1 is less effective in reducing risk in the ECA region in both high-income and developing countries (Table 3, columns 2 and 4).

These results highlight the importance of how capital is defined. In particular, we find that simple leverage ratios are more informative than regulatory capital when it comes to explaining bank risk. Lower quality capital that is not common equity has lower loss absorptive capacity. Properly measuring risk exposure for computing risk-weights is also very difficult especially for large and complex financial organizations. There is also much discretion in the calculation of risk-weights, which creates the possibility of manipulation. As a growing number of countries in ECA have adopted or implemented components of Basel II and III, more banks (especially large banks) are relying on internal models for the calculation of risk-weights. Figure 14 below shows Basel implementation in each ECA group. While smaller, low-income countries have been shifting out of Basel I, most have adopted Basel II/III compared to other developing countries.

# **Figure 14: Basel Implementation as of 2016**

<!-- Start of picture text --> EU CE & BC WB SC CA Russia Turkey EE RoW<br>Basel I Basel II Basel III<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Basel III addresses some of the weaknesses related to quantity and quality of capital that we have highlighted. In particular, under Basel III the common equity ratio is 4.5% of risk-weighted assets, up from 2% under Basel II. Basel III also introduces an additional capital conservation buffer of 2.5% of common equity. Tier III capital is eliminated, and a supplemental minimum 3% leverage ratio is imposed as a backstop to the risk-based capital requirement. This leverage ratio is calculated as Tier 1 capital over total assets and is thus not based on risk-weighted assets. Although the use of a simple 18
of growth while Rivera-Batiz (1989) discusses a case in which they slow down economic growth. This variety of theoretical possibilities is not surprising given the presence of increasing returns and monopolistic competition. It is well known that second best results are more complex and less Intuitive than their first best counterparts.

# II.4 POVERTY TRAPS

A striking feature of the growth phenonmeon in this century is that a large number of countries have failed to Improve "tir standard of living. Easterly (1991) shows that the ra '- f growth of per capita GDP Is not significantly different from zero in 46 out of 87 developing countries included in the Summers and Heston (1988) data set. This evidence suggests that some countries mav have been caught in a "poverty trap": a stable steady state which involves stagnation at low levels of Income. Models which display poverty traps have been proposed In the endogenous growth literature by Azariadis and Drazen (1990), Tamura (1989), and Becker, Murphy and Tamura (1990). 12 The mechanism at work in these models can be explained within a stylized example inspired by Azariadis and Drazen (1990). Consider a Lucas-Uzawa type model In which the equations that pertain to output production and to the accumulation of the capital stock are: The production function for human capital takes the following special form: 12The first poverty trap model was proposed by Nelson (1956). See Neves (1990) **for** a through discussion and critical evaluation of poverty trap models.

18
Buchinsky (1998) examines various estimators for the asymptotic covariance matrix and concludes that the design matrix bootstrap performs the best. In this paper, the standard errors are obtained by bootstrapping using 200 repetitions. This is in line with the literature.

# **4. Sources of Income**

Except in the metropolitan area, most Haitian households have low annual per capita incomes. In 2001, the median income per capita of extremely poor households (1,080 gourdes) was around one-tenth of the median income of the nonpoor (10,304 gourdes). Median income varies greatly across regions and locations. In 2001, median household income per capita in the metropolitan area (7,293 gourdes) was far higher than elsewhere in Haiti. The households with the lowest median incomes per capita are located in the Northeast region, where they stand at 617 and 804 gourdes in urban and rural areas, respectively (Table 4.1). In the West region, by contrast, the median income per capita of households is 5-6 times higher in rural and urban areas (excluding the metropolitan area) than in the Northeast. These figures reveal how access to imported goods and to a large market like Port-au-Prince can make a difference in people’s well-being.

Table 4.1: Median Annual Income per capita, 2001 (gourdes)

|Region|Metropolitan|Urban|Rural|Total Haiti|
|---|---|---|---|---|
|Artibonite|NA|1,723.0|2,134.8|2,000.0|
|Center|NA|2,316.7|2,430.0|2,389.7|
|Grand-Anse|NA|1,654.2|1,900.0|1,829.2|
|North|NA|3,304.2|1,585.0|1,900.0|
|Northeast|NA|804.2|616.7|671.7|
|Northwest|NA|2,470.7|1,500.0|1,734.2|
|West|7,292.5|4,014.6|3,100.0|4,366.7|
|South|NA|2,761.0|1,696.2|1,921.6|
|Southeast|NA|3,240.0|2,371.7|2,507.5|
|Haiti|7,292.5|2,264.7|2,035.0|2,403.1|

_Source:_ Own calculations based on HLCS 2001.

There are significant differences in the distribution of per capita household income (PCHI) by geographic locality. The PCHI of a household in the first decile of the income distribution is 199, 166, and 910 gourdes in rural, urban, and metropolitan areas, respectively (Table 4.2). Hence households on the low end of the income distribution in metropolitan Haiti have much better incomes than those in other urban or rural areas. Rural households are better off than urban households on the low end of the income distribution. The per capita income difference between households in rural and metropolitan areas is fairly constant across the income distribution. At a given location in the distribution of income, metropolitan households earn roughly four times more than households in rural areas. One important explanatory factor may be the greater number of 17
|**Average per capita DAU allocation**|544,808|710,779|128,791|851,087|
|---|---|---|---|---|
|**Maximum per capita DAU**<br>**allocation (IDR)**|810,767|887,157|322,956|1,053,690|
|**Minimum per capita DAU**<br>**allocation (IDR)**|147,101|330,507|49,547|642,642|
|**Max to min ratio of per capita**<br>**DAU allocation **|5.51|2.68|6.52|1.64|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|2,932,250|3,442,057|2,811,546|3,452,077|
|**Minimum per capita revenue after**|854,038|910,819|324,900|967,542|
|**transfer (IDR)**|||||
|**Max to min ratio of per capita**<br>**revenue after transfer **|3.43|3.78|8.65|3.57|
|**Coef. of variation**|0.395|0.423|0.730|0.400|
|**Weighted coef. of variation**|0.371|0.397|0.693|0.375|
|**Relative mean deviation**|0.135|0.148|0.260|0.147|
|**Gini Index**|0.189|0.199|0.346|0.195|
|**Weighted Gini Index**|0.181|0.193|0.339|0.190|
|**No. of Observed C3 Group Region**|58|58|58|58|
|**No. of Receiver**|56|57|39|58|
|**Average DAU allocation (million)**|272,053|271,306|50,566|303,549|
|**Average per capita DAU allocation**|1,387,206|1,285,945|269,172|1,455,062|
|**Maximum per capita DAU**<br>**allocation (IDR)**|2,308,378|1,526,098|519,377|1,973,908|
|**Minimum per capita DAU**<br>**allocation (IDR)**|361,909|232,243|58,569|914,632|
|**Max to min ratio of per capita**<br>**DAU allocation **|6.38|6.57|8.87|2.16|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|5,769,565|5,115,631|5,769,565|7,029,313|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|927,534|924,709|283,775|1,644,595|
|**Max to min ratio of per capita**<br>**revenue after transfer **|6.22|5.53|20.33|4.27|
|**Coef. of variation**|0.333|0.275|0.757|0.367|
|**Weighted coef. of variation**|0.345|0.282|0.750|0.350|
|**Relative mean deviation**|0.118|0.083|0.205|0.107|
|**Gini Index**|0.168|0.124|0.299|0.150|
|**Weighted Gini Index**|0.178|0.127|0.302|0.143|
|**No. of Observed C4 Group Region**|11|11|11|11|
|**No. of Receiver**|11|11|6|11|
|**Average DAU allocation (million)**|214,035|149,815|15,376|153,297|
|**Average per capita DAU allocation**|3,557,909|2,304,629|271,529|2,385,820|
|**Maximum per capita DAU**<br>**allocation (IDR)**|7,301,673|2,594,625|530,996|3,242,229|
|**Minimum per capita DAU**<br>**allocation (IDR)**|2,291,657|1,991,547|18,214|1,743,818|
|<br>**Max to min ratio of per capita**<br>**DAU allocation **|3.19|1.30|29.15|1.86|

17
12 substantial (Keefer and Khemani 2005). Societies may be socially polarized or political competition rooted in clientelist promises of privately targeted and immediate benefits even in settings where citizens are well-informed (Khemani 2007; Keefer and Vlaicu 2008). Ferejohn (1986), for example, demonstrated that when politicians cannot make credible pre-electoral commitments to voters, voters cannot act collectively to expel incumbents and political shirking increases. Persson and Tabellini (2000) specifically predict that lack of political credibility suppresses public good provision. Keefer (2007) argues that the relative inability of political competitors in younger democracies to make credible pre-electoral commitments to voters explains why, compared to older democracies, public goods are more scarce in younger democracies and targeted policies and corruption more prevalent. If even informed citizens confront insuperable obstacles to collective action aimed at holding governments accountable, media access would have a smaller effect on public good provision than otherwise.

The political setting in Benin is precisely one in which the capacity of citizens to act collectively to sanction non-performing incumbents is weak. Credible political commitments are, in developed democracies, mediated by political parties. In Benin, these parties are absent. Since competitive elections were first held in 1991, the number of parties has fluctuated between 129 and 27, rising to over 100 prior to the 2007 elections (Economist Intelligence Unit). None of the four parties emerging from the 2007 elections had a programmatic identity – a basis for making credible commitments to groups of citizens. Party fractionalization is high: in 2006 the probability that two randomly selected legislators do not belong to the same party was 80 percent in Benin, compared to 52 percent in Ghana (Beck, et al. 2001). The political setting in Benin may therefore reduce the effects of media access on citizen ability to hold government accountable for service delivery.

However, even if media access does not have a large effect on government accountability, it may still influence the private decisions of citizens if it informs citizens of the value and availability
household-level data spanning 2006-2010 (that also forms a community-level panel) to examine how well households have been able to smooth consumption amid income fluctuations in the crisis, and also respond to a specific set of fiscal and credit initiatives supported by the government. Data for the Thai Socio-Economic Surveys (SES) are collected on an on-going basis; every month a sample is collected that is large enough to allow one to track the evolution of measures such as income or consumption spending with a considerable degree of precision at the national level.

To address simultaneity of changes in government spending and per capita expenditure, we estimate a dynamic panel regression with the community-level data, instrumenting the stimulus effect with second-order lagged outcome variables, and estimating the model via Generalized Method of Moments (GMM) (Arellano and Bond, 1991, Jalan and Ravallion 2002, 1998).

# **2. Thai Macroeconomic Experience in Recent Years**

In what follows we briefly summarize the recent macroeconomic experience of Thailand, in order to demonstrate that the country faced a significant economic shock in 2008/2009. As mentioned earlier, between the third and fourth quarters of 2008, Thai GDP fell by 4.2%, and it declined by a further 0.4% in the first quarter of 2009, as Figure 1 shows. The drop can be attributed to both external and local factors. Domestically, political instability, including an opposition blockade of the two main international airports and a change in government in late 2008, deterred tourists and investors. Globally, the world financial crisis contributed too, as Thai exports of high-tech manufactures fell sharply in the latter half of 2008 (see Figure 2), and consumers (including potential tourists) restrained their spending. Thailand was also buffeted by a rapid rise in the price of energy imports in 2008-09, but was a net beneficiary from the increase in the price of rice experienced in 2008 (and which was partly, but not completely, reversed in 2009), as Figure 2 also shows.

Some of the effects were dramatic. The number of tourist arrivals fell from 1.5 million in February 2008 to 1.1 million in February 2009. The unemployment rate in the export sector rose from 2% in the last quarter of 2008 to 9% in the first quarter of 2009. And the total number of 5
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71
# I. INTRODUCTION

While the literature that attempts to explain growth performance in Korea and other Asian NIC's has grown<sup>in recentyears,,</sup> little of it has explicitly examined the role of taxes<sup>in the growth process. Moreover,</sup> literature on Korean tax policy has merely<sup>documented changes in tax</sup> structure as growth has occurred2 and made illustrative calculations on such issues as the impact of taxes on the cost<sup>of capital, and has failed</sup> to evaluate the role of tax policies in the growth<sup>process. Capturing all</sup> the elements underlying Korean growth performance<sup>(high savings rates,</sup> human capital accumulation, intersectoral resource<sup>shifts) inasingle</sup> model is difficult and is well beyond current<sup>capabilities. Nonetheless,</sup> given the current state of the literature, we believe that some modelling evaluation of the contribution of tax policy to growth in Korea can be done and is useful.

In this paper, we use an applied general equilibrium<sup>model that we</sup> have used earlier (Trela and Whalley (1989))<sup>to investigate the</sup> contribution of outward oriented policies<sup>to the earlier years of Korean</sup> growth, through induced intersectoral resource<sup>transfers and impacts on</sup> effort and labor supply in agriculture and<sup>manufacturing sectors. While we</sup> have only focused on one aspect of the Korean<sup>growth experience,</sup> what seems to emerge from the model calculations is that<sup>one should look beyond tax</sup> policy for the main factors underlying strong<sup>Korean growth. Model</sup> calculations portray the tax component of<sup>outward orientedpolicies</sup> **as** /1 See Chonery et al. (1986).

I/ See World Bank (1987a), Choi (1988) and Kim (1988).