rafmacalaba/datause-encoder-data huggingface dataset:
These conditions produced a setting that was favorable for rapid reductions in the share of shipments that were subject to physical inspection. The fourth ingredient was the ready availability of well-developed electronic systems and risk management strategies that had been developed and improved in other countries. Ready availability meant that Albania’s implementation could be relatively rapid, and translate quickly into decreased inspection rates. While our empirical exercises are only able to identify the impact of reduced inspections, it is nonetheless helpful to review the specific policy changes that were undertaken to support the reduced level of inspections.<sup>15</sup> In April 1999, the Albanian parliament passed a new customs law designed to reconcile Albanian customs law with that of the European Union. Among the activities authorized in this law was the automation of customs procedures, a precondition for effective risk management. Beginning in 2001, the Albanian customs authority began automatic data processing using the Automated System of Customs Data system (ASYCUDA). An updated IT package, ASYCUDA++, was implemented in 2006.<sup>16</sup> ASYCUDA++ includes a risk module that can be used to separate shipments based on levels of risk. The system also facilitates feedback from physical inspections so that risk profiles can be updated. An even more advanced IT system, ASYCUDAWorld, was introduced in 2008-2009, further improving risk management capabilities. In addition to the IT improvements, Albanian customs also undertook related organizational changes, establishing a “ _Directorate for the Risk Analysis and Monitoring_ ” in April 2009. The Directorate holds a monthly committee meeting to approve and update the profiles that underpin risk-based selection.<sup>17</sup> The adoption of new inspection protocols and IT systems also required substantial efforts to train the relevant staff. Clearly it is difficult to evaluate the entire package of interlocking customs reforms that were implemented over multiple years, but we can exploit a manifestation of these reforms in our data, which is the dramatic reduction in probability that goods are subject physical inspection. Our estimates relate

> 15 The information that follows is based on interviews with customs authorities by the authors in the port of Durres, Albania in March-April 2013.  The biennial IMF Section IV reviews of Albania indicate that the customs authority reported quarterly to the IMF on the progress of the introduction of risk management in customs.

> 16 IMF (2006) offers some further detail on this implementation.

> 17 These profiles determine the weights that the IT system applies to information from the import declaration in order to determine the initial allocation to an inspection channel.  Local agents have the ability to upgrade the initial allocation (from documentary to physical inspection), but the automated decision governed by the risk profile determines the final allocation in approximately 99% of the cases, as will be documented in Section 3.

8
<!-- Start of picture text --> COSTA RICA 84 °<br>TÁRCOLES RIVER BASIN San Rafael<br>ATLANTIC<br>SELECTED CITIES<br>NATIONAL CAPITAL OCEAN<br>C O S T A R I C A<br>MAJOR ROADS<br>Puerto<br>Viejo<br>85 °<br>Cañas Lago de<br>Arenal<br>Quesada<br>Guácimo<br>San Ramón<br>Siquirres<br>10 ° Carmona Alajuela Heredia 10 °<br>Puntarenas<br>Playa Orotina SAN JOSÉ<br>Naranjo Cartago Turrialba<br>Santiago<br>San Ignacio<br>Tárcoles<br>Golfo de<br>Jacó San<br>Nicoya Marcos<br>Parrita<br>Puerto<br>Quepos<br>San Isidro<br>COSTA RICA 0 5 10 15 20 Kilometers<br>Tárcoles<br>River Basin 0 5 10 15 20 Miles<br>PACIFIC Dominical<br>This map was produced by the Map Design Unit of The World Bank.<br>shown on this map do not imply, on the part of The World BankThe boundaries, colors, denominations and any other information OCEAN<br>Group, any judgment on the legal status of any territory, or any<br>endorsement or acceptance of such boundaries.<br>85 ° 84 °<br>á a rcoles<br>General<br>T<br>n<br>s<br>ó<br>o<br>z<br>S<br>a<br>rl<br>a<br>nt<br>n<br>Ca<br>ántico<br>ve<br> dle Alt<br>ó<br>hirirp<br>C<br>Re<br>ó<br>hiiprr<br>C<br><!-- End of picture text -->
|||schemes.<br>Information and education<br>campaigns on risk maps|
|---|---|---|
|The<br>heterogeneity<br>in<br>direct<br>losses<br>across<br>households (|This parameter depends on the hazards (e.g., high winds<br>have often impacts that are less concentrated than<br>floods), but also on the diversification of the economic<br>system (e.g., if households have multiple sources of<br>income, it is more likely than losses will be shared<br>amongmore households).|Diversification<br>of<br>income<br>within<br>households<br>Diversification of activities at the<br>local and regional level.|
|The<br>ability<br>of<br>the<br>households to smooth<br>income shocks over time<br>( );|This parameter can be estimated using the fraction of<br>the population with access to financial services,<br>insurance systems, or support for access to credit in<br>post-disaster contexts.|Financial inclusion and stability<br>Household insurance programs,<br>especially when they target the poor<br>Facilitated access to credit in post-<br>disaster situation|
|The maximum loss of<br>welfare<br>that<br>an<br>household<br>can<br>suffer<br>from ( );|This parameter is largely a political choice that describes<br>the value attributed to avoiding leaving households with<br>nothing (for instance based on an acceptable estimate of<br>the “value of a statistical life”).<br>It can also account for the ability of the government to<br>provide emergency services to the affected population<br>(that depends on government resources and access to<br>foreign aid), and the basic safety net that the poorest<br>can access.|Emergency actions (in kind provision<br>of foods, shelter, health care etc.)<br>(the ability to do so will depend on<br>fiscal space and sovereign risk<br>financing and insurance)<br>Basic safety nets, including<br>conditional and unconditional cash<br>transfers|
|The<br>amount<br>of<br>risk-<br>sharing in the economy,<br>represented by|This parameter can be estimated based on the fraction<br>of the population with access to social protection,<br>subsidized insurance products, and on the ability of the<br>government to provide emergency services to the<br>affected population (that depends on government<br>resources and access to foreign aid)|Emergency actions<br>Subsidized insurance for the poor<br>Safety nets, including conditional and<br>unconditional cash transfers|

_Table 2: List of concepts, parameters, measurement tools, and policies to reduce the welfare disaster risk._ 36
debt financing in counties and county-level cities comes from SOBs. This assumption is not without foundation. The fact that the establishment of SOBs is one of the major benefits associated with city upgrading indicates that commercial banks or other credit channels are very limited at the county level in China.

In Table 10, the first two columns show that compared with non-SOEs, SOEs located in cities saw a big increase in both current debt and total debt. This suggests that part of the SOE performance differential obtained in Table 9 above could be explained by easier access to credit by SOEs (compared to non-SOEs) post-upgrading.

As a robustness check of this possible channel, we try to compare the debt profile of negative profit SOEs in cities versus counties. Our hypothesis is that if underperforming SOEs are able to access credit more easily than underperforming non-SOEs, then the debt is probably being financed by SOBs that have branches in cities (since commercial banks or other lending agents would not lend to these underperforming firms). The last two columns of Table 10 report this result. Although the coefficient is positive for cities, it is not significant (even though its p-value is not far from 0.1).

**Table 10: Debt financing of incumbent SOEs**

||**Full Sa**<br>|**mple**<br>|**Negative prof**<br>|**it SOEs Only**<br>|
|---|---|---|---|---|
|**_Incumbent Firms_**|Logcurrent debt|Logtotal debt|Logcurrent debt|Logtotal debt|
|**City**|-0.273*|-0.207|0.442|0.421|
||(0.147)|(0.142)|(0.276)|(0.280)|
|**SOE**|0.261***|0.248***|||
||(0.0819)|(0.0801)|||
|**City X SOE**|**0.356****<br>(0.172)|**0.341***<br>(0.174)|||
|**Constant**|10.49***|10.77***|9.706***|10.10***|
||(0.529)|(0.513)|(0.255)|(0.277)|
|Observations|32,240|32,732|1,494|1,515|
|R-squared|0.201|0.188|0.388|0.350|
|Block FE|Yes|Yes|Yes|Yes|
|Industry **×**Year FE|Yes|Yes|Yes|Yes|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Standard errors are in parentheses and clustered at the county-level.

3. Analysis is restricted to firms opened before 1997.

There is a vast literature showing how firms in developing countries are more likely to report access to finance as a major obstacle to their growth, especially for small firms (Bloom, Mahajan, & Roberts, 2010). Results from randomized control trials giving credit to small and medium-sized enterprises in Sri Lanka (De Mel, McKenzie, &Woodruff, 2008) and India (Banerjee and Duflo, 2008) illustrate that access to credit was indeed a big problem for disadvantaged firms in developing countries.

There is also cross-country evidence showing if banks are concentrated, with a large share being government-owned banks, then financing obstacles will increase and the chance of smaller firms 14
<!-- Start of picture text --> 35 5<br>30<br>4<br>25 \| o£<br>\ E<br>> i‘} ,TM<br>=~ 20 1 73 ¢<br>as f oOo”<br>=n!a<br>' 5 I\! o<br>2 Il ! 2 ]<br>aad ry i \ i =<br>10 -J~ : /<br>K fe Vr / \ yt ‘ 7 -<br>5 wi \! \ // \.\ i]F \_/<br>a<br>0 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009<br>—— loan growth —-—-— monthly new lending (right axis)<br><!-- End of picture text -->
# 3. Coverage of the Balance Sheet

Countries that prepare a balance sheet must determine its scope and identify the entities that will be included. At one extreme, a limited balance sheet would simply detail the assets and liabilities of the central government. A broader public‐sector balance sheet could include assets and liabilities of subnational governments, state‐owned enterprises, and even sovereign wealth funds, which may be substantial and have a significant impact on public sector assets particularly in resource‐rich commodity exporting countries. A sovereign balance sheet may jointly report the assets and liabilities of the government with the central bank.

All respondents indicated that the sovereign balance sheet includes central government. In more than half the cases (56 percent of respondents), the balance sheet also covers state‐owned enterprises (SOEs). One‐third of the responding countries included subnationals, and 24 percent included the central bank. Sovereign wealth funds (SWF) are included in sovereign balance sheets by 24 percent of the respondents. In some cases, sovereign balance sheets also include insurance companies, social security funds and pension plans (see graph 3).

<!-- Start of picture text --> Graph 3 ‐ Coverage of Balance Sheet<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>25<br>25<br>20<br>14<br>15<br>10 8<br>6 6 6<br>5<br>0<br>Central Central bank SOEs Subnationals SWFs Other<br>government<br><!-- End of picture text --> Source: Responses to SALM survey The inclusion of central banks in the sovereign balance is a complex issue. On one hand, the inclusion could be perceived as an ambiguous signal with regards to the independence of the central bank vs. the central government. On the other hand, the foreign reserves managed by the central banks are often a key part of any comprehensive balance sheet approach focusing on capturing financial risks and vulnerabilities. This topic will be discussed further in the paper (see section B.1.).

11
||Log [Self-re<br>|ported Produc<br>|tivity (XOF/ha)]<br>|<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
|Household head has Secondary education|0.018|0.017|0.033|0.007|
||(0.011)|(0.011)|(0.030)|(0.012)|
|Household head has Tertiary education|0.000|-0.000|0.000|0.002|
||(0.001)|(0.001)|(0.000)|(0.003)|
|Household head disability|0.008|0.015|0.025|0.059**|
||(0.026)|(0.025)|(0.050)|(0.030)|
|Dependency ratio|-0.059|-0.068|-0.114*|-0.031|
||(0.064)|(0.059)|(0.062)|(0.071)|
|Household Male Labor Use (Number)|0.041|0.045|-0.024|0.051|
||(0.048)|(0.048)|(0.039)|(0.046)|
|Household Female Labor Use (Number)|0.160**|0.068|-0.027|0.057|
||(0.078)|(0.078)|(0.096)|(0.113)|
|Household Child Labor Use (Number)|0.012|0.003|-0.038**|0.034|
||(0.019)|(0.017)|(0.019)|(0.026)|
|Hired Labor Use (Number)|-0.010|-0.017|0.011|-0.029|
||(0.032)|(0.029)|(0.054)|(0.032)|
|Number of Fields Managed by the household|-0.125|-0.052|-0.175|0.096|
||(0.105)|(0.104)|(0.143)|(0.153)|
|Total number of crops produced|-0.192|-0.197|0.313*|-0.588***|
||(0.122)|(0.121)|(0.183)|(0.166)|
|Household used any purchased seed|0.019|0.022|-0.026|0.012|
||(0.037)|(0.036)|(0.044)|(0.053)|
|Plots that Use Chemical Fertilizer (% of total)|-0.014|-0.006|0.045*|0.023|
||(0.014)|(0.015)|(0.024)|(0.025)|
|Plots that Use Pesticide (% of total)|-0.180***|-0.150***|-0.250***|-0.201***|
||(0.054)|(0.050)|(0.068)|(0.072)|
|Export crops dummy (1 if Household produced any||-0.106|||
|<br>export crop)||(0.093)|||
|Constant|1.410**|<br>1.017*|-0.628|1.165*|
||(0.603)|(0.591)|(0.644)|(0.670)|
|Observations|3936|3936|2823|2749|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ In Table 10, we present results from the decomposition of each decile of the productivity distribution, using the RIF method. We see that the simple overall average productivity is lower for female-headed households in the bottom six deciles (10<sup>th</sup> to 60<sup>th</sup> ) but is not significantly lower than male-headed households in the top three productivity deciles. In our 2016 data, there is a significant correlation (at the 5 percent level) between the number of assets that households own and their agricultural productivity, suggesting a close relationship between poverty and low productivity as in 2008. This implies that the productivity gender gap is present among the poorest households in 2016.

Similar to the finding from the mean decomposition, the smaller amount of land cultivated by femaleheaded household reduces the gender gap in all productivity deciles. A notable finding is that female-headed households’ lower use of household male labor leads to a widening of the gender gap in the lower half of 22
- 13 -

as arc elasticitles which Indlcate the percentage response to a given percentage change In the level of a U.S. pollcy instrument. In order to compute the elasticities for a common time period, the quarterly results of the USAGMKTS model are converted to annual averages prlor to computation of the elasticitles.

Please note that the elasticities computed from these experiments are general equilibrium elasticitles rather than partial equilibrium elasticities since they summarize responses that reflect all adjustments In related markets. This statement has one qualification, however; any adjustments In Mexico due to policy changes In the U.S. that are transmitted by means of effects on macrovarlables of the Mexican economy are neglected In this analysis due to the lack of an adequate macroeconomic model of the Mexican economy similar to the FAIRMODEL for the U.S. economy. Thus, for all simulation experlments reported here, values of linkage variables from the Mexican macroeconomy are set at historically observed levels. In addition, the U.S. agricultural pollcy adjustment experiments were simulated holding the values of the macroeconomic variables transferred fron the FAIRMODEL to USAGMKTS model at the average of their values In the macroeconomic scenarlos reported In Just ( 1989b) In Qrder to avoid effects due to macropolicy variation and yet not condition the results on any given macropolicy specification.

The elasticity estimates of the responses of prices, productlon and traded quantities In Mexican feed grain, soybeans and livestock markets to U.S. agricultural policy change under Mexican trade liberalization are reported in tables 1 through 4, with tables 1 and 2 reporting results from 1981-82 and tables 3 and 4 reporting results from 1984-
- Blonigen, Bruce A., and Jeremy Piger. _Determinants of Foreign Direct Investment._ Working Papers 16704, National Bureau of Economic Research (NBER), 2011.

- Blonigen, Bruce. "Firm-Specific Assets and the Link between Exchange Rates and Foreign Direct Investment." _American Economic Review_ 87 (1997): 447-465.

Bloom, Nicholas. "The Impact of Uncertainty Shocks." _Econometrica_ 77, no. 3 (2009): 623-685.

- Bloom, Nicholas, Stephen Bond, and John Van Reenen. "Uncertainty and Investment Dynamics." _Review of Economic Studies_ 74, no. 2 (2007): 391-415.

- Borensztein, E J, J W De Gregorio, and J W Lee. "How does FDI affect economic growth?" _Journal of International Economics_ 45, no. 1 (1998): 115-135.

- Broner, Fernado, Tatiana Didier, Aitor Erce, and Sergio L Schmukler. _Gross Capital Flows: Dynamics and Crises._ Policy Research Working Paper 5768, World Bank, August 2011.

- Calderón, César, Norman Loayza, and Luis Servén. _Greenfield Foreign Direct Investment and Mergrs and Acquisitions: Feedback and Macroeconomic Effects._ Policy Research Working Paper 3192, World Bank, 2004.

- Campos, Nauro F., and Yuko Kinoshita. "Foreign Direct Investment and Structural Reforms: Evidence from Eastern Europe and Latin America." _IMF Research Conference on the Causes and Consequences of Structural Reforms._ 2003.

- Christiansen, Hans, and Ayse Bertrand. "Trends and Recent Developments in Foreign Direct Investment." Chap. 1 in _International Investment Perspectives_ , by OECD, 9-49. Paris: OECD, 2002.

- Demirgüç-Kunt, Aslı, and Thorsten Beck. _Financial Institutions and Markets Across Countries and over Time: Data and Analysis._ Policy Research Working Paper 4943, World Bank, 2009.

- Dutta, Nabamita, and Sanjukta Roy. "Foreign Direct Investment, Financial Development and Political Risks." _Journal of Developing Areas_ 44, no. 2 (2008): 303-327.

- Eichengreen, B., and H. Tong. "Is China's FDI coming at the expense of other countries?" _Journal of the Japanese and International Economies_ 21, no. 2 (2007): 153–172.

- Gastanaga, Victor M., Jeffrey B. Nugent, and Bistra Pashamova. "Host Country Reforms and FDI Inflows: How Much Difference Do They Make?" _World Development_ 26, no. 7 (1998): 1299-1314.

- Goberman, Steven, and Daniel Shapiro. "Governance infrastructure and US foreign direct investment." _Journal of International Business Studies_ 34, no. 1 (2003): 19-40.

- Harms, Philipp, and Pierre-Guillaume Méon. "An FDI is an FDI is an FDI? The growth effects of greenfield investment and mergers and acquisitions in developing countries." _Proceedings of the German Development Economics Conference, No. 38._ Berlin, 2011.

- Hellman, J S, G Jones, and D Kaufmann. _Far from home: Do foreign invstors import higher standards of governance in transition economies._ Washington D.C.: World Bank, 2002.

- Javorcik, Beata S, and Yue Li. "Do the Biggest Aisles Serve a Brighter Future? Global Retail Chains and Their Implications for Romania." _Journal of International Economics_ 90, no. 2 (2013): 348–363.

38
# **Table 1.a Tax Compliance by Region and Income**

|**Region**|**Mean**|**Share of**<br>**Firms**|**N. Countries**|
|---|---|---|---|
|Sub-Saharan Africa|0.685|18%|29|
|Latin America & Caribbean|0.769|36%|18|
|Europe & Central Asia|0.795|20%|27|
|East Asia & Pacific|0.814|11%|6|
|Middle East & North Africa|0.833|9%|6|
|South Asia|0.921|1%|1|
|OECD|0.948|4%|7|
|Low income|0.739|21%|31|
|Upper middle income|0.770|28%|35|
|Lower middle income|0.789|46%|21|
|High income: non OECD|0.942|0%|1|
|High income: OECD|0.948|4%|6|
|Total|0.780||94|

# **Table 1.b Tax Compliance by Industry**

|**Industry**|**Mean**|**N**|**Share of**<br>**Firms**|
|---|---|---|---|
|Wood and furniture|0.713|2443|10%|
|Leather|0.722|594|3%|
|Garments|0.742|3911|17%|
|Food|0.768|3779|16%|
|Non-metallic and plastic|0.781|1770|8%|
|Textiles|0.797|1898|8%|
|Paper|0.854|358|2%|
|Beverages|0.877|1193|5%|
|**Low-Tech**|0.770|17033|73%|
|Other transport|0.735|48|0%|
|Auto and auto components|0.737|322|1%|
|Metals and machine|0.803|3696|16%|
|Chemicals and<br>pharmaceutics|0.822|1830|8%|
|Other manufacturing|0.828|1087|5%|
|Electronics|0.840|356|2%|
|**High-Tech**|0.807|6252|27%|
|**Total**|0.780|23285||

23
regions, equaled only by Sub-Saharan Africa.<sup>26</sup> Figure 5 shows that tax collection has a strong negative association with informality. The figure depicts the partial scatter plot of tax collection as percent of GDP against the estimated size of the shadow economy, after removing the effect of per capita income. Across Latin America, higher informality comes along with lower tax revenue, holding constant the level of development.

**Figure 5. Tax revenue and the shadow economy in Latin America**

<!-- Start of picture text --> 6<br>HND<br>5<br>4 ARG<br>CHL URY<br>3<br>2 NIC BRA BOL<br>1 VEN<br>0 PER<br>-1 CRI<br>-2 ECU COL GTM<br>PAN<br>-3<br>-4<br>-20 -10 0 10 20 30<br>Shadow Economy (% GDP)<br>Tax Revenue (% GDP)<br><!-- End of picture text --> Informality, of course, is not exogenous, but the combined result of poor public services (that reduce the benefits of formality), weak tax administration, and the tax structure itself. Payroll taxes in particular are widely viewed as providing a major incentive to informality, and still account for a considerable fraction of total direct tax collection in Latin America – as high as 50 percent in Brazil (Martner and Aldunate 2006). More broadly, the overall tax burden facing formal firms – combining corporate taxes and VAT along with payroll contributions **--** is higher in Latin America than in other world regions, with the exception of Sub-Saharan Africa,<sup>27</sup> and this provides small firms a strong incentive to remain informal.

Aside from evasion, tax concessions – exemptions, deductions, and other loopholes – play a major role in narrowing income tax bases across Latin America. In many countries, households with above-average income levels are exempt from the personal income tax because of very high minimum personal exemption levels. The average level of the minimum taxable income is twice the region’s per capita income, and in some countries it is much higher (Figure 6).<sup>28</sup> High personal exemptions combined with a plethora of deductions severely reduce effective income tax rates, especially for the rich. As a result, tax rates highly progressive on paper collect little revenue in practice because the higher rates only kick in at extraordinarily high levels of income, so they are rarely, if ever, effective (Tanzi 2000).

> 26 Eichhorn (2006).

> 27 See World Bank (2006).

> 28 Shome (1995) reports that these minimum exemptions are higher in Latin America than in other developing regions.

13