rafmacalaba/datause-encoder-data huggingface dataset:
afford the upfront costs of migrating, even though they would benefit from moving. We will discuss next what some of the costs of migrating are, since, all else equal, the budget constraint will be more likely to bind the higher is _C_ . But at a minimum, we may think more about just being able to pay the costs of travel. Bryan et al. (2014) found that an $8.50 incentive, enough to pay for the cost of a bus to the city, was enough to induce people near subsistence to migrate. However, they view this as not simply capturing liquidity constraints, but also insurance against the risk of migrating not working out. Cai (2020) finds that randomized access to microcredit increased internal migration in China, especially in places with low asset levels and high migration costs, which is taken as evidence of liquidity constraints being important there. The costs are higher for international travel, and several studies which look at the impact of an exogenous increase in income or wealth for poor people have found increases in migration (e.g. Gazeaud et al. (2019) in Comoros; Bazzi (2017) in Indonesia, and Angelucci (2015) in Mexico). Likewise, at the macro level, international migration rates are low for poor countries, and increase at first with the level of development, with financial constraints being one key for people in the poorest countries not migrating (Dao et al, 2018). Liquidity constraints therefore seem to be one important factor, although we have not seen studies that offer credit for international migration and measure the impacts. At one level we might think liquidity constraints bind very tightly for many people – for example, if the only opportunity to migrate legally to the United States is to pay $50,000, $100,000 or more for enrollment in a university program here, there are likely to be many people around the world for whom this could be a good investment, but who cannot pay the costs. As an even more extreme example, many countries offer special investor visas for those with enough money – which in the case of the U.S. EB-5 program, requires investing US$1.8 million (or $900,000 if investing in targeted employment areas).

_Explicit policy barriers that directly limit migration._ Policy barriers such as limits on legal migration can be viewed as making the cost term _C_ extremely large (or almost infinite) in some cases, so that no matter the gains from migrating, there is no way for some people to take advantage of them. One type of policy barrier is an explicit quota on the number of migrants who can come in through a certain category, or from a particular country. An ILO (2004) survey found that most quotas tend to be partial in nature, applying only to certain sectors, types of firms, or migration streams. Perhaps more prevalent are labor market tests that require employers to show that there is a lack of qualified applicants, or requiring that migrant workers be offered at least the prevailing 11
ASIA DEPARTMENT III

# 1. **Eutimates** and Projections

|Towr|pqutatlm|**IW.**<br>ratio|Pwad|growth<br>**rate**|**cm**|cm|TFU|**@0**<br>Nlut|GO0q<br>**Fmtm**|**In**|XlOOG|
|---|---|---|---|---|---|---|---|---|---|---|---|
|195|1044554|53.9|1985-89|1.43|21.0|6.6<br>|2.40<br>|68.4<br>|70.6<br>|32<br>|39<br>|
|**1990**|1121960|47.7|1990-94|1.37|20.4|6.7<br>|2.28<br>|69.6<br>|71.7<br>|27<br>|32<br>|
|**1995**<br>**2000**|1201556<br>1275464|48.5<br>50.1|1995-99<br>2000-04|1.19<br>1.00|18.6<br>16.6<br>|6.6<br>6.5<br>|2.16<br>2.11<br>|70.9<br>72.2<br>|73.0<br>74.4<br>|22<br>18<br>|26<br>22<br>|
|**2005**|13410i7|48.0|2005-09|0.86<br>|15.3<br>|6.7<br>|2.10<br>|73.1<br>|75.5<br>|15<br>|19<br>|
|2010|**1399MN4**|45.1|2010-14|0.79|14.8|6.9<br>|2.10<br>|74.1<br>|76.7<br>|13<br>|16<br>|
|2015|1456442|44.4|2014-19|0.75<br>|14.7<br>|7.1<br>|2.09<br>|75.1<br>|77.9<br>|10<br>|13<br>|
|**2020**|**1512424**|47.4|2020-24|0.70<br>|14.3<br>|7.3<br>|2.08<br>|76.1<br>|79.3<br>|8<br>|10<br>|
|**2025**|1566159|50.2|2025-49|0.38<br>|13.0<br>|9.2<br>|2.07<br>|78.2<br>|U.3<br>|4<br>|6<br>|
|**2zS0**|**1720696**|6A.0|2050-74|0.12<br>|12.3<br>|11.1<br>|2.06<br>|80.3<br>|85.4<br>|3<br>|4<br>|
|2075|1774?|69.3|2075-99|0.07|12.0<br>|11.3<br>|2.06<br>|81.3<br>|87.2<br>|2<br>|4<br>|
|2100|1604227|71.9|2100-24|0.04|11.8<br>|11.3<br>|2.06<br>|**82.2**<br>|**88.6**<br>|**2**<br>|3<br>|
|**2125**<br>2150|16474<br>1834915|73.4<br>74.2|2125-49|0.02|11.7|11.5|2.06|U.5|89.1|2|3|

# 2. **Age** structure (percent):<sup>1990 and 2025</sup>

<!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>60-64<br>55.59<br>50-54<br>E sS ~~~~45-49<br>4044<br>35-39<br>30-34<br>g§2ES{ FUmPUn 1 25-29 6 l<br>20-24<br>15-19<br>10-14<br>5.9<br>0-4<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 3. Distribution **of** population<sup>(percent): 1990 and 2025</sup>

<!-- Start of picture text --> A C7.M) AB s t, 3 C, c7 41, Al.. CS ,<br>£34 CN 2_,4<br>0 A4 ()' 25)<br><!-- End of picture text --> 30
As the World Bank Enterprise Surveys (WBES) reveal, bribing and corruption occur frequently (though less commonly in the Western world), they are obstacles to doing business, and they carry a large cost for businesses. In Argentina, almost 9.5 percent of establishments in the economy participated reported bribing of a government official, almost 1 percent more than the average in Latin America and the Caribbean but below the worldwide average. In addition, bribes in Argentina were sizable, averaging 6.5 percent of the establishments’ annual sales.

Motivated by this evidence, this paper aims to quantify the efficiency cost of rent-seeking activities in the form of bribes in Argentina. To this end, I introduce bribing into a general equilibrium model of firm dynamics as in Atkeson and Bursteinn (2010). In this model, firms are heterogeneous with respect to their levels of productivity and behave in an environment with imperfect competition.<sup>4</sup> Productivity in this model evolves stochastically, but the probability of increasing the level of productivity depends on research and development (R&D) expenditure. In in the spirit of Restuccia and Rogerson (2008) and Hsieh and Klenow (2009), firms also face idiosyncratic subsidies and taxes that affect their size, which generate static misallocation in the model. However, unlike the static subsidies and taxes presented in those papers, these subsidies and taxes also evolve stochastically, conditional on the rent-seeking efforts (bribes) that each firm chooses to undertake. As a result of these choices, some firms will invest in rent-seeking activities and others will invest in improving the quality of their production process (by improving physical productivity, management practices, or intangible capital).<sup>5</sup> By diverting talent and resources away from production and innovation activities, rent-seeking activities can have sizable negative effects on aggregate efficiency.

For the quantitative analysis, I calibrate an undistorted version of the model to match key features related to the size distribution of firms in the U.S. economy, as it is usual in the literature. Next, to evaluate the quantitative implications of the model, I use data from the WBES, which is one of the few surveys that provide establishment-level evidence related to bribing. I use this micro-level evidence of bribing in Argentina to calibrate the distorted version of the model. In particular, I use the frequency of bribing, the average cost of bribing in terms of sales, bribing expenditures across the size distribution of firms, and statistics from the firm-level productivity

> 4 In the model, productivity captures physical productivity as well as management practices and intangible capital. 5 In another background paper, Castro et al. (2019) find evidence supporting the fact that firms that pay bribes tend to have worse management outcomes, which would be reflected in productivity. This evidence supports the modeling strategy of this paper and the paper’s general interpretation of productivity.

4
# **Table A.3: Factors related to provincial poverty, with additional control variables**

|Explanatory variables||Poverty headc|ount||Depend<br>Poverty gap inde|ent variable is the p<br>x|overty indexes of<br>P|provinces<br>overty severity in|dex|
|---|---|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|L f er aita exenditre|-0.420***|-0.328***|-0.274***|-0.036***|-0.104***|-0.103***|-0.013***|-0.047***|-0.049***|
|og o p cp pu|(0.013)|(0.061)|(0.057)|(0.003)|(0.015)|(0.015)|(0.001)|(0.009)|(0.009)|
|Gini index|-0.507**|-0.350*|-0.090|0.180**|0.085|0.144***|0.086**|0.040|0.069*|
||(0.203)|(0.201)|(0.158)|(0.073)|(0.055)|(0.052)|(0.041)|(0.036)|(0.035)|
|Log of state spending|||0.001*|||0.000|||0.000|
||||(0.001)|||(0.000)|||(0.000)|
||||0.002|||0.003*|||0.002**|
|Log of investment spending|||(0.005)|||(0.002)|||(0.001)|
|L f lti dit|||0.290***|||-0.010|||-0.015|
|og o popuaon ensy|||(0.102)|||(0.021)|||(0.011)|
|h f b li|||0.003***|||0.001***|||0.000***|
|Sare o uran popuaton|||(0.001)|||(0.000)|||(0.000)|
|Share of population with high-school|||0.143|||-0.041|||-0.017|
|diploma|||(0.299)|||(0.047)|||(0.027)|
||||0.001***|||0.000***|||0.000***|
|Share of ethnic minority population|||(0.000)|||(0.000)|||(0.000)|
|Share of wage income|||-0.316***|||-0.032|||-0.009|
||||(0.104)|||(0.031)|||(0.017)|
|Share of non-farm income|||-0.258**|||-0.042|||-0.020|
||||(0.113)|||(0.030)|||(0.016)|
|Share of village with year-round passable<br>road|||-0.064|||-0.040***|||-0.024***|
||||(0.046)|||(0.011)|||(0.007)|
|Share of village with daily market|||0.039|||0.018|||0.010*|
||||(0.041)|||(0.011)|||(0.006)|
|Distance from village to the nearest town|||-0.007|||-0.001|||0.000|
||||(0.011)|||(0.003)|||(0.002)|
|Constant|4.078***|3.355***|3.238***|0.311***|0.898***|0.814***|0.112***|0.400***|0.348***|
||(0.100)|(0.479)|(0.440)|(0.025)|(0.119)|(0.099)|(0.013)|(0.073)|(0.056)|
|Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|

39
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 44 4. Repeat steps 2-3 for _l_ = 1 _, . . . , L_ . The MC approximation of the EB predictor of _δd_ is obtained by averaging the indicators over the _L_ simulated censuses, that is, In step 2, it is necessary to simulate _L_ vectors **y** _dr_<sup>(</sup><sup>_l_)fromamultivariate</sup> Normal distribution of size _Nd − nd_ (which can be huge in many real applications) with covariance matrix **V** _dr|s_ , making the process computationally unfeasible. Molina and Rao (2010) proposed a procedure that avoids the generation of huge multivariate normal vectors, by noting that **V** _dr|s_ given in (6.8) is the covariance matrix of a random vector **y** _dr_<sup>(</sup><sup>_l_), whose</sup> elements arise from the following nested error model,

where _v_<sup>(</sup><sup>_l_)</sup> and _ε_<sup>(</sup><sup>_l_)allindependentandsatisfy,respectively,</sup> _d di_<sup>are</sup> Using the model (6.13)–(6.14) to generate the non-sample elements _ydi_<sup>(</sup><sup>_l_),</sup> _i ∈ rd_ , instead of generating a multivariate Normal vector of size _Nd − nd_ , we only need to generate 1 + _Nd − nd_ independent Normal random _ind ind_ variables, _vd_<sup>(</sup><sup>_l_)</sup> _∼ N_ (0 _, σu_<sup>2(1</sup><sup>_−γd_))and</sup><sup>_ε_(</sup> _di_<sup>_l_)</sup> _∼ N_ (0 _, σe_<sup>2</sup><sup>_k_</sup> _di_<sup>2),for</sup><sup>_i∈rd_.By</sup> using the vector **y** _dr_<sup>(</sup><sup>_l_)withthesegeneratedout-of-sampleelements</sup><sup>_y_</sup> _di_<sup>(</sup><sup>_l_),</sup> _i ∈ rd_ , in step 3, we construct the census vector **y** _d_<sup>(</sup><sup>_l_)</sup> = ( **y** _ds_<sup>_′,_(</sup><sup>**y**</sup> _dr_<sup>(</sup><sup>_l_))</sup><sup>_′_)</sup><sup>_′_and</sup> calculate the indicator of interest _δd_<sup>(</sup><sup>_l_)</sup> = _hd_ ( **y** _d_<sup>(</sup><sup>_l_)).</sup> For an area _d_ that is not sampled (i.e., with _nd_ = 0), as there is no insample part in this case, we generate the whole census vector **y** _d_<sup>(</sup><sup>_l_)</sup> = **y** _dr_<sup>(</sup><sup>_l_)</sup> from the model (6.13) by setting _γd_ = 0 (limiting value as _nd →_ 0).

Obtaining an analytical estimator for the MSE of the EB predictor _δ_<sup>ˆ</sup> _d_<sup>_EB_</sup> of _δd_ in general is challenging. Molina and Rao (2010) proposed a parametric
_25_ will have to be adopted. It is unlikely that any measures relating to OECD tariffs could be effectively utilized since African countries now often face zero duties in these markets (see Table 3). This precludes any attempts to restore pre-Uruguay Round preferential tariff margins. OECD countries couid, however, target those nontariff measures that are restricting African trade -- like the quotas and variable import levies or sugar -- for full and early removal. OECD countries could also increase assistance to Africa where such help is required for efficiency-increasing structural adjustment reforms.

Probably one of the most promising areas for compensatory reforms centers in the African countries' own trade regimes. Detailed statistics compiled by UNCTAD (1987) on developing countries trade barriers show that African tariffs are generally far higher than average, and that their trade regimes are often NTM-ridden. Extensive studies by the World Bank (see Thomas and Nash, 1991) document the important economic gains that can result from trade policy reform and it appears that such reform could have a major positive impact in Africa. In short, the best response to an erosion of trade preferences in foreign markets would appear to be an aggressive liberalization of African countries own trade barriers.
discern the direction of causality is in this case, however in the context of studies of tax morale, it is likely that problems with tax administration are the driver behind perceiving tax rates as an obstacle, as troublesome tax administration can hurt tax morale.

**Figures 1.a. and 1.b. Tax administration as an obstacle vs. corruption as an obstacle (left) and tax rates as an obstacle (right) (means of indicator values)**

<!-- Start of picture text --> Kyrgyz Republic Romania<br>Kosovo<br>Romania Kosovo<br>Ukraine Armenia<br>Moldova TajikistanSerbia PolandCzech RepublicCroatia<br>Russian Federation<br>Bulgaria Bosnia and HerzegovinaLithuania Albania Bosnia and HerzegovinaHungarySlovenia Kyrgyz RepublicSlovak RepublicUkraineLithuania<br>Kazakhstan AS ovak Republic l bania SerbiaCroatiaTajikistanCzech RepublicPoland Bulgaria Macedonia, FYRMontenegro Russian Federation<br>BelarusTurkeyMacedonia, FYRSloveniaHungary Armenia MoldovaKazakhstanEstoniaTurkey Belarus Latvia<br>Latvia Georgia<br>Estonia Azerbaijan<br>Montenegro<br>Uzbekistan AzerbaijanGeorgia<br>Uzbekistan<br>0 .5 1 1.5 2 2.5 .5 1 1.5 2 2.5 3<br>Tax administration - obstacle to current operations (mean) Tax rates - obstacle to current operations (mean)<br>3 2.5<br>2<br>2<br>1.5<br>1<br>1<br>.5<br>Corruption - obstacle to current operations (mean) 0 0<br>Tax administration - obstacle to current operations (mean)<br><!-- End of picture text --> No significant correlations have been found between actual tax rates (PricewaterhouseCoopers &World Bank Group, 2013) in a country and the average perceptions of either tax rates or tax administration as obstacles to current operations (scatterplots not shown, see Figure 1 and Figure 2 in the Appendix). There are also no significant correlations between tax rates and level of tax scrutiny (e.g. percentage of firms reporting tax inspections); see Figure 3 in the Appendix.

These observations have important implications for further analysis. If the perception of tax rates as an obstacle is insensitive to the actual tax rates, then firms might be confounding general difficulties associated with tax administration (tax compliance cost) with actual costs of taxes (the number of taxes and tax rates). In particular, tax administration may be perceived as an obstacle when and because it involves bribes or the level of discretion of tax administration leads to ambiguity in compliance rules and practices.

Analysis of determinants of perceptions of tax rates and tax administration at the firm level supports this supposition (see Table 2 in the Appendix). Being tax-inspected predictably reduces the likelihood that firms will agree that tax rates and tax administration are not an obstacle (see Appendix Table 2, models 1 and 4). However, intensity of tax scrutiny (e.g. number of tax inspections), and also paying a bribe in the context of a tax inspection (Table 2 Models 3 and 6) have no effect on perceptions of tax rates as an obstacle, but increase the probability of companies perceiving tax administration as an obstacle. This suggests a distinction between 6 | P a g e
|Constant|0.46***|0.88***|0.06***|0.01**|
|---|---|---|---|---|
||(0.03)|(0.02)|(0.01)|(0.01)|
|**E. WEALTH INDEX**|||||
|_Reference Category: Bottom 20%_|||||
|Second Quintile|0.08***|-0.00|-0.01|0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Third Quintile|0.11***|-0.01|-0.00|0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Fourth Quintile|0.16***|-0.01|-0.02**|-0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Fifth Quintile|0.15***|-0.03**|-0.04***|-0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Constant|0.51***|0.87***|0.08***|0.01***|
||(0.01)|(0.01)|(0.01)|(0.00)|

Notes: Standard errors in parentheses. * Significant at ten per cent; ** significant at five per cent; *** significant at one per cent. _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014.

26
other loan term regressions are at the level of facility (in the case of Maturity, Spread and Collateral) or package (in the case of Covenant). Since a loan may have several lenders, we aggregate lender characteristics by taking their unweighted average, including the characteristics of the countries where lenders reside.<sup>3</sup> We matched Dealscan with monetary policy rates from the International Financial Statistics (IFS) database of the IMF. Our main dependent variable, IR, is the lender-country central bank policy rate (replaced by the discount rate at which commercial banks can borrow from the central bank against eligible securities in case of a few countries where the central bank policy rate data was missing). Similarly, IR (Borrower) is the borrower-country central bank policy rate or the discount rate. In some specifications we use deviations from an estimated Taylor rule type monetary policy rate for the lender country as an alternative measure of the stance of monetary policy in this country. To calculate this variable, called Taylor residual, we regress the monetary policy rate, IR, on real GDP growth and the inflation rate separately for each country, and then take the errors from these regressions. According to Table 1, the average monetary policy rate, IR, over the whole sample period was 2.48%, while Taylor residuals averaged -0.005. An additional lender-country monetary policy variable is QE, which indicates that a quantitative easing program was in place in a lender country in a given month. This variable reflects that the Fed, the European Central Bank, Bank of England, and the Bank of Japan implemented various quantitative easing programs at different points in time (see the appendix for the exact dates<sup>4</sup> ) in efforts to

> 3 <mark>By taking the unweighted average of lender characteristics we avoid the problem of missing loan volume contributions. For example, using non-missing loan shares as weights would reduce the number of loans from 92,191 to 18,133 in the first maturity regression in Table 4. In addition, unweighted averages are less affected by endogenous changes in loan contributions.</mark>

> 4 <mark>In the reported regressions we do not distinguish between the different rounds of QE in the United States. The results are robust, however, to specifying the QE to reflect the three periods corresponding to QE1, QE2 and</mark>

8
# **3 The Data**

The dataset consists of transaction-level records obtained from the Lao PDR Customs Department reporting all<sup>3</sup> legal export and import transactions covering the entire Lao exporter population. The following information is included: the checkpoint used; the form type (for special status, re-exports, tariff exemptions, etc.); the date (year, month, and day); HS 8-digit code of the product (a mix of revision 2002 and 2007 which has been harmonized for the analysis); the export license number and the firm name; the destination (using only a 2-letter code); the unit, amount and weight; and the value in US dollars (USD) and Lao kip (LAK). The analysis uses only the 6-digit HS code for international comparison purposes. Data coverage spans the period from October 2005 to September 2010 – the entire period of 60 months for which Customs have recorded data in this format. The 1138 firms, 668 HS-6 products and 88 destinations in the analysis sample combine to create 5053 FPDs after several manipulations for survival analysis.

Firm names have been discarded for anonymity purposes, and the firm identification is carried out based on export license numbers. This helps the analysis because firm names, recorded in Lao script, can have multiple transliterations and formatting (e.g. including or not the word “firm”, before or after the name) making individual identification by name unreliable. Very small firms or individuals are given a generic code. There are also shortcomings in using the export license number for the identification strategy. The crucial assumption is that licenses are owned by exporting producers and this can be invalidated when the license is in fact used by other entities which are irrelevant for export survival behavior (e.g. brokers, freight forwarders). Moreover, license numbers could be shared by multiple firms because of mergers, splits or for tax evasion purposes. Nevertheless, anecdotal evidence from personal interviews indicates that this is not at all prevalent (personal interview, Laos, 2012).

There are some further limitations related to this type of data. First, since the partner firm is not recorded (only the country is) the data cannot reveal if the contract changes to another firm or if there are multiple partners in the same country. Second is the heterogeneity of contractual arrangements: some firms receive punctual, one-time orders, while others have long-term contracts with a pre-arranged delivery schedule. The data cannot discriminate between these cases, which imply different survival behavior (i.e. in the former, another order is not guaranteed, whereas in the latter there is stronger hysteresis because the contract locks in the partners despite occasional dissatisfactions). Neither one is a serious concern: experience of the kind investigated here is likely to be country- or region- rather than firm-specific, while contractual

> 3 There are a few exceptions such as electric power, an optional Harmonized System (HS) heading and a significant Lao export, which is not always reported and is excluded.

8