rafmacalaba/datause-encoder-data huggingface dataset:
Polac,Pnning, and Romrwh

# Trade Policy

"Measured income elasticities in developing countries are generaUy higher than I -- and relative prices, although mostly inelastic, significantly affect demand for imports. When a lack of foreign exchange or, more generally, a restrictive trade regime effectively constrains import flows, the measured impact of price and activity variables becomes less pronounced. To recover structural elasticities in such a case, one can develop a direct modeling of quantitative restrictions (which is arduous) or use the experience of a structurally similar country (which is quicker). In gencrai, a less restrictive trade regime is associated with greater responsiveness to economic incentives.

Econometric evidence that does not allow for the impact of import controls cannot be used reliably to assess the effect of a devaluation on the trade balance. Indeed, devaluation combined with trade liberalizadon (a common feature of many adjustment programs) will have a more pronounced effect on import demand than available evidence would suggest.

The authors compare three approaches to modeling and estimating import demand--which is arduous when trade controls are pervasive: _Option 1:_ Trade import equations work well when import controls are relatively stable over time, but it is difficult to determine if this is the case. Without _a priori_ information, and short of using aU misspecification tests, ont: could perhaps rely on a comparison between estimated elasticity and the "norm" computed in this study. If the difference between the two values is deemed too high, consider.

_Option 2:_ This takes into account the impact of foreign exchange availability. If it seems clear that the country has foreign exchange constraints, the traditional specifications should be bypassed, but incorporating this constraint into the import demand equation is difficult for several reasons. At a minimum, the authors recommend using the broadest possible instrument list, including indicators of world demand, competitors' prices in export markets, exogenous capital flows, and international reserves.

Option 3: The direct incorporation of quantitative restrictions is the main method of recovering structural (notdonal) demand parameters and assessing, for example, the impact of removing import restrictions. Unfortunately, a good indicator of quantitative restrictions is not usually available and, even if it exists, interpreting its behavior may be difficult.

This paper is a product of theTrade Policy Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Karla Cabana, room N8-065, extension 61539.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
**Table 2: Projected changes in household poverty from a 20% decrease in maize prices**

||**Rural**|**Urban**|**Total**|
|---|---|---|---|
|**percentage of poor out of poverty**|2.00%|1.72%|1.95%|
|**percentage non-poor into poverty**|0.17%|0.01%|0.12%|
|**Net change in poverty**|-1.77%|-1.69%|-1.75%|
|**Contribution to net change**|82.37%|17.63%||

_Source: KNBS, author calculations_

**A decrease in maize prices of 20 percent would result in a significant decline in poverty**<sup>**93**</sup>

**in both rural and urban areas.** We estimate that a maize price decrease of 20 percent (through the estimated average equivalent income gains of 1.2 percent) would result in a _net_ decline in poverty of 1.8 percent.  This result is consistent with other studies linking maize prices and poverty in Kenya<sup>94</sup> . Table 2 above shows the make-up in the net changes in poverty, including those moving from poor to non-poor and those moving from non-poor to poor across both rural and urban areas.  The net poverty gains are concentrated in rural areas, with 82 percent of the net poverty gains living in rural areas. It is unfortunate that any households would be harmed by such reform, but those who gain very substantially outnumber those who lose. The number of households that move _into_ poverty as result of the decline is roughly 10 percent of the number that move _out_ of poverty.

# **3.4 Actions by the Competition Agency in the maize sector**

**The Monopolies and Price Commission of Kenya (now the Competition Authority of Kenya) conducted an investigation into potential collusion by large-scale millers of maize over the period 2009 – 2010.** This followed spiking maize prices and recommendations of the maize sector survey report prepared by CAK in December 2008. The report highlighted anti-competitive concerns, particularly focusing on the Cereal Millers Association (CMA), which had clauses suggestive of price coordination in its constitution. The Monopolies and Price Commission did not determine whether a price fixing cartel was in fact being operated by the CMA, but the CMA was asked to amend its constitution to remove clauses that could facilitate price fixing.  This process started in early 2010 and was concluded with a final edition forwarded by the CMA in February 2011.

> 93 When we talk of poverty here, we mean those that have household per adult equivalent expenditures below the national poverty lines.

> 94 See Jayne et al (2008) for a review.  These results most look at the welfare impact of a price _increase_ .

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As time for pretesting the survey and training the interviewers was limited<sup>to a few hours,</sup> the survey should be viewed as an indication of characteristics rather than a prec,se<sup>scientific</sup> inquiry. The resu!ts show that both insured and uninsured patients are young adults,<sup>of whom</sup> about a third are emiployed. Insured patients tend to be slightly more likely to be employed ^nd have better jobs than uninsured, but differences are small.

Table C.5, based on this survey, shows actual paymeLl's reported by insured and<sup>uninsured</sup> patients for their current episode. The results show confirm that uninsured patients do pay more than insured patients, but both groups do pay something. These data from an independent source (the patients) confirm that actual charges are consistent with the official price list.

Table C.5 Kinshasa Survey: Iltness and Expenditures.

|.................................<br>............................<br>Characteristic<br>.......<br>............<br>............<br>.........................|............................<br>Insured<br>(074)<br>Mean|SO|Uninsur<br>**WMzl3**<br>Mean<br>..<br>..<br>.............|...................**..**....<br>ed<br>**l)**<br>SD|
|---|---|---|---|---|
|Duration of present tltness<br>episode in days|**53**|225|27|109|
|**First**visit for this episode.|35X||39X||
|Number of**previous**visits for<br>current episode|**2.0**|**2.6**|**1.6**|**4.2**|
|Payment**for**current<br>**visit**<br>**In**Zaires|**222**|521|511|856|
|Total payment for current visit incLudi|ng:||||
|**Lab.,**x-ray**end**consultation<br>SO.00- **t0.99**(O- 430**Zafres).**|**90°**||**76**||
|***1.00**-<br>**#2.00**(430 - 860 Zaires).|4X||7X||
|**52.01**- S5.00(861 - 2150Zaires).|4X||10X||
|<br>Morethan S S (More than 2150Zaires)|1X||7r||
|<br>MAN|0.5||1.2||
|Nedicine<br>included in total payment:|||||
|<br>**Altl IncLuded**|57X||56X||
|Some inctuded|42X||43X||
|None included|1X||1X||
|**Total payment for**sit<br>**previous**visits<br>**for**current<br>episode<br>**in**Zefresmeen<br>**SD)**|<br>115|309|<br>**308**|1,106|
|Expect**wny follow**<br>**up for**<br>**this episode**|**85**||**85X**||

Table _C.5_ also compares utilization data of insured and non-insured respondents. The results demonstrate some moral hazard or adverse selection, in that insured respondents<sup>had more</sup> previous visits. Table C.6 further describes the attitudes of the two groups. Insured persons tend to be slightly better off than uninsured; for example, the time they would require<sup>to</sup> mobilize 50,000 Z (S 125) was less than for uninsured respondents. Table C.7 presents questions asked separately of members and non-members. It is striking that 43% of non41
levels, and regional competition. Chapter 3 deals with non-energy trade and applies a gravity model to evaluate missing exports and to put into perspective current trade pattems. Chapter 4 is dedicated to trade in energy. Chapter 5 summarizes the previous results in the framework of the BOP. Chapter 6 takes up an issue of possibly adding the South-North flows to the currently dominant East-West flows. Chapter 7 provides conclusions.

# **2. Current Trade Patterns**

# **2.1. Trade Volumes and Balances**

All three Caucasus countries run high trade deficits (see Table **1** below). The deficit-to-exports ratios have been very high in Armenia and Georgia during the whole period - from 162 to 252 percent in Armenia and from 162 to 232 percent in Georgia. The Azerbaijani deficit has been lower - between 40 and 154 percent of exports. In 1999, breaking the adverse trend, the trade deficits of all three countries shrank.

Over the past four years, Armenian and Georgian exports were stagnant, while Azerbaijani exports increased by 60 percent, thanks to large foreign investments in the oil sector.

**Table 1. Trade Volumes and Balances in South Caucasus, 1995-99**

_(UJS Dollars Million)_

||_1996_|_1997_|_1998_|_1999_|
|---|---|---|---|---|
|Armenia:|||||
|Exports|290,3|232,5|220,5|231,7|
|Imports|855,8|892,3|902,4|811.3|
|Trade Balance|-565,5|-659,8|-681,9|-579.6|
|As % of exports|-162|-240|-252|-227|
|Azerbaiian:|||||
|Exports|643.7|808.3|677.8|1,025.2|
|Inports|1,337.6|1,375.2|1,723.9|1,433.4|
|Trade Balance|-693.9|-566.9|-1,046.2|-408.2|
|As % of exports|-108|-70|-154|-40|
|Georgia:|||||
|Exports|310.0|376.5|300.0|329.6|
|Imports|897.5|1,162.8|994.5|863.4|
|Trade Balance|-587.5|-786.3|-694.6|-533.9|
|As % of exports|-190|-209|-232|-162|

_Sources: National Statistical Agencies ofArmenia, Azerbaijan, and Georgia._ 6
Table 4: Panel regression, OLS

|Dependent variable|CPIA cluster D|Heritage|
|---|---|---|
|number of cumulative PSG conditions|.005|.132|
||(.002)<sup>_∗∗_</sup>|(.042)<sup>_∗∗∗_</sup>|
|number of cumulative PSG conditions, squared|-.00004|-.001|
||(0,00001)<sup>_∗∗∗_</sup>|(.0002)<sup>_∗∗∗_</sup>|
|log GDP per capita (PPP)|.498|7.392|
||(.139)<sup>_∗∗∗_</sup>|(2.539)<sup>_∗∗∗_</sup>|
|aid over GDP|1.525|-9.018|
||(.339)<sup>_∗∗∗_</sup>|(12.151)|
|Political Rights|-.005|-.302|
||(.021)|(.404)|
|_country fxed efects_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|
|Observations|1690|1607|
|Countries|139|132|
|_R_<sup>2</sup>|.144|.169|

* significance at 10%; ** significance at 5%; *** significance at 1%; constant not reported. Standard errors reported in brackets are adjusted for country clustering of observations.

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# **2.2.4 Term Annuities and Phased Withdrawals**

2.39 Term annuities consist of a series of payments made to their beneficiaries over a specified period of time. They are in fact quite similar to phased withdrawals but with a fixed term that may reach up to 25 years but normally runs for 5 or 10 years. They do not offer protection against longevity risk. Their treatment of investment and inflation risks depends on their features. They lack liquidity during their term but they allow for bequests and also permit a faster use of accumulated balances. They appeal to workers with impaired health and a short life expectancy.

2.40 Term annuities can be for fixed terms and fixed benefits calculated at the prevailing rate of interest or for fixed terms but variable benefits, with the latter consisting of "guaranteed" and "bonus" components. Term annuities are favored by pensioners who wish to have higher levels of income and spending during their early years of retirement relative to later years. The demand for term annuities is stronger when pensioners have access to universal healthcare services.<sup>19</sup> 2.41 Phased withdrawals consist of a series of fixed or variable payments whereby pensioners withdraw a fraction of their accumulated capital. They do not provide full protection against longevity risk, but by placing limits on annual withdrawals, they stretch balances over a longer period. Phased withdrawals allow for bequests but are exposed to investment and inflation risks. But unlike term and life annuities, they are portable and can be transferred to other financial institutions. Like term annuities, they appeal to workers with impaired health and a short life expectancy.

2.42 Phased withdrawals can be classified by the special rules covering the pace of withdrawals, including a fixed benefit rule and a fixed percentage benefit rule.<sup>20</sup> The most important type is the life expectancy phased withdrawal where the withdrawal fraction is each year set equal to the inverse of the remaining life expectancy of the account holder.

2.43 The account balance of phased withdrawals that follow the remaining life expectancy rule may increase initially if the rate of investment returns exceeds the withdrawal fraction. However, as pensioners grow old and their remaining life expectancy decreases, the withdrawal fraction is bound to surpass the rate of return and thus both the account balance and the annual benefit will start falling and will eventually become too small for long-lived individuals.

2.44 In Chile, prior to the 2008 changes, the monthly benefit from phased withdrawals could not fall below the MPG level. When the account balance was exhausted, the government assumed the payment responsibility. Thus, longevity risk was covered at the level of the MPG. After 2008, the MPG has been replaced by the PBS, which is now the

> 19 Term annuities, alongside life annuities from public schemes as well as from corporate plans, are popular in both Denmark and Sweden (Andersen and Skjodt 2007, Palmer 2008).

> 20 This discussion draws on the classification of phased withdrawals presented in Dus et al (2003).

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12 agricultural policy support from the 1980s, which has resulted in Oceania having the lowest levels of distortion among OECD countries (Anderson, Lattimore, Lloyd and MacLaren 2009).

The country level aggregate measures in Tables 1 and 2 hide the degree of variation in commodity NRA and CTE estimates within countries. The case studies in Anderson (2009) report standard deviations around weighted mean NRAs for covered products in each country, showing that variation to be significant and not declining. One indication of the extent of variation between groups of products is provided by a comparison of the average NRA for import-competing and exportable product groups. The extent of this variation is shown in aggregate for EU countries and the 34 focus countries in Figure 1. There is a significant gap between the average NRA for import-competing and exportable products over the period shown, which reflects the extent of antitrade bias that has also persisted through time except for the most-recent period when international agricultural prices were rising.

Notwithstanding the valuable contribution of the measures reported in the case studies in Anderson (2009), sector averages of NRAs can be misleading as indicators of the aggregate extent of price distortion within the sector as it affects trade and welfare. They can also be misleading when compared across countries which have varying degrees of dispersion in their NRAs for farm products. Hence the need for supplementary TRI and WRI series for the additional insights these measures can provide.

# **TRI and WRI Estimates**

Table 3 reports the TRI estimates for all covered farm products from 1960 to 2007 for all 34 focus countries and six regional groupings. For all of the regional aggregations except Oceania, agricultural policy overall was trade-reducing, with Northeast Asia and Western Europe experiencing the largest reductions in trade. The regional aggregations hide some of the country level variation in agricultural policy, however, and there were even some decades in which policies were trade expanding in some countries, for example Finland, Sweden and several transition economies in addition to Australia and New Zealand.

The TRI time series for the focus countries and the EU group are shown against the NRA time series in Figure 2. The most striking observation for these
The survey data are not without some limitations. For example, the survey does not contain any information on the household from which the migrant originated. This implies that we have no information on the potential recipients of migrant remittances or for what purpose the remittances were used. It is also unfortunate that non-migrants are those found in the destination areas only and this essentially prevents any analysis of the process governing the migration decision. Nevertheless, the data do contain detailed information on migrants themselves, and this allows for an investigation of the effects of various factors on migrant remittance behavior.

# **4. The Empirical Variables**

The empirical model specified in this study is eclectic in nature and guided by some of the theoretical considerations outlined in an earlier section, but also reflects strongly the Vietnamese context within which the analysis is situated. Table 1 reports the dependent variable and the explanatory variables used in our analysis, and contains selected summary statistics.

# _[TABLE 1 ABOUT HERE]_

The key dependent variable is expressed in millions of dong and is defined as the total value of money/goods a migrant sent back home to relatives in the 12 month period prior to the survey interview date.<sup>5</sup> Among those who remit, the average amount of remittances is about two million dong. However, for a large number of individuals in the sample the variable is censored at zero requiring use of a specific econometric approach for the 11
# **V Robustness checks**

Thus far, both the instrumental variable method and the structural model have shown that the demand side of corruption appears to be the driver of firms’ tax evasion, while the bribe supply strategy seems to be geared toward securing government contracts. In what follows, we perform a variety of sensitivity analyses to check the robustness of the findings, examining potential selection bias issues, conducting falsification tests, identifying potential outliers, and cluster fixed effects, and alternative methodology using a propensity score matching method.

## **V.1 Potential selection bias issues**

Assessing illegal activities such as bribery is challenging as the survey responses may suffer from desirability bias. Some firms might decide to reveal their tax evasion and bribery behaviors while others may intentionally decide not to respond to such sensitive questions. The missing bribery and tax evasion data raise questions about potential selection bias issues, particularly if the group of responders and non-responders are statistically different in their observable characteristics. Although we do not have information on why some firms did not respond to corruption and tax evasion questions, we can check whether the groups of responders and non-responders differ in terms of their observable characteristics. Table .8 in appendix reports a set of regressions using individual characteristics of firms as dependent variables. The approach follows Svensson (2003), where the regressor is a dummy variable taking the value of 1 if a firm has missing data on corruption. We extend the analysis by using a dummy variable taking the value of 1 if a firm has missing information on tax evasion and bribing for contracts. Moreover, we consider two additional dummy variables that account for the combination of missing data on (i) corruption and tax evasion, and (ii) corruption and bribing for government contracts. All regressions include country, sector, and year fixed effects given that some missing data on corruption and tax evasion seems to be specific to the country, the sector, and the year of the survey. As seen in panel A, there is no strong evidence that responders and non-responders are statistically different in their observable characteristics. A limited number of variables are barely significant at the 10% level and not robust across specifications. Panel B of Table .8 considers three additional key characteristics of firms: capital stock, investments, and profits.<sup>22</sup> The findings show that responders and non-responders do not differ significantly relative to any of these aspects. Thus, there is no observable evidence that potential selection bias drives our results.

> 22We did not employ these variables in our main analysis because of missing data.

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Figure 14 illustrates the previous econometric results. Dependency ratios of AA and BB countries were somewhat comparable until the early 1980s, but then the ratio started to fall sharply in AA countries and continued to climb in BB countries, thus enlarging the gap between the two groups. The gaps are also big for life expectancy and under-5 mortality. In the 1960s, aid per capita was comparable for various groups, but the gap started to widen toward the end of the decade. In 1990, AA countries received, on average, almost three times more aid than did BB countries ($112 and $41, respectively). The gap has since narrowed, mainly as a result of a reduction in the aid received by AA countries.

Figure 14: Selected variables by country-group <!-- Start of picture text --> Dependency ratio Life expectancy at birth<br>1 60<br>0.95<br>55<br>0.9<br>50<br>0.85<br>45<br>0.8<br>0.75 40<br>0.7 35<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002<br>AA BB BA AB AA BB BA AB<br>Mortality rate under 5 Aid per capita - current $<br>350 120<br>300 100<br>80<br>250<br>60<br>200<br>40<br>150 20<br>100 0<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002<br>AA BB BA AB AA BB BA AB<br><!-- End of picture text --> Finally, we estimated multinomial logit regressions of country clubs against each of these variables at a time: GDP per capita, growth, savings, capital, trade, agriculture value added, life expectancy, and other independent variables. The estimated model is as follows: _t_ Pr _ob_ ( _Gi_ = _j_ | _X_ ) = α + β _X i_ + ε

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