rafmacalaba/datause-encoder-data huggingface dataset:
# D. Additional regression results

**Table D.1: Firms whose employees walk to work are more likely to come to work and be on time compared to employees that use the bus. Informal businesses struggle more with latecomers**

||(1)<br>unable to come|(2)<br>Share of worke<br>unable to come|(3)<br>rs that are ...|(4)|
|---|---|---|---|---|
||to work (rainy|to work|late|late|
|VARIABLES|season)|(dry season)|(rainy season)|(dry season)|
|**Primary mode of transport**(bus)|||||
|Train|-0.195***|-0.0185|0.237|-0.0981***|
||(0.0372)|(0.0135)|(0.253)|(0.0182)|
|Walk|-0.0957***|-0.0144***|-0.126***|-0.0561***|
||(0.0210)|(0.00527)|(0.0321)|(0.0124)|
|Bike|-0.0620|0.0109|-0.176**|-0.0210|
||(0.0675)|(0.00986)|(0.0870)|(0.0234)|
|Motorbike|0.0527|0.00263|0.0196|0.00158|
||(0.0605)|(0.0142)|(0.0658)|(0.0273)|
|Car|-0.0310|-0.0154|0.00293|-0.0654***|
||(0.0569)|(0.0115)|(0.0972)|(0.0180)|
|Other|-0.129***|0.0374|-0.322***|-0.0593***|
||(0.0485)|(0.0327)|(0.0818)|(0.0200)|
|**Share of employee types**|||||
|Seasonal|-0.0451|-0.0114|-0.00167|-0.0532***|
||(0.0370)|(0.00973)|(0.0476)|(0.0195)|
|Daily|0.0246|-0.0290***|-0.0533|-0.0317|
||(0.0535)|(0.0101)|(0.0483)|(0.0335)|
|Family|0.134**|0.0101|0.262***|0.0246|
||(0.0619)|(0.0212)|(0.0794)|(0.0288)|
|**Total number of employees**|-2.45e-05***|-6.99e-06**|-5.46e-05***|-2.26e-05**|
||(9.07e-06)|(3.13e-06)|(2.01e-05)|(8.76e-06)|
|Fixed effects|YES|YES|YES|YES|
|Observations|752|755|752|756|
|R-squared|0.085|0.038|0.189|0.071|

_Note:_ Fixed effects include region and sector-specific fixed effects. For categorical variables, base categories are mentioned in parentheses. The low R-squared indicates that results should be interpreted cautiously. Standard errors are clustered on the ward level:<sup>*</sup> p<0.1<sup>**</sup> p<0.05<sup>***</sup> p<0.01

**Table D.2: Regression results for input inventory sizes, using region interactions**

||(1)<br>Input inventories|(2)<br>Input inventories|
|---|---|---|
|VARIABLES|||
|**Risk perception # region:**|**Direct risk**|**Indirect risk**|
|**Dar es Salam**(no risk)|||
|Low risk|-1.268 (9.034)|-0.543 (5.317)|
|Moderate risk|-8.514 (6.518)|-3.392 (4.730)|
|High risk|-11.58 (7.169)|-5.049 (4.866)|
|Very high risk|-16.21** (7.1)|5.992 (13.82)|

40
||**ExchangeRate Arrangements and**<br>**(AsofDecember**|**AnchorsofMonetaryPoilcy**<br>**31,2000)**|
|---|---|---|
|**Country**|**ExchangeRateRegime**|**MonetaryPolicyFramework**|
|Myanmar|Otherconventionalfixedpeg<br>arrangement(againsta<br>composite)|Exchangerateanchor|
|New Zealand|Independently floating|Inflationtargetingframework|
|Philippines|Independently floating|Two nominalanchors:<br>(1) Monetaryaggregate target<br>(2)Fund-supported orothermonetary<br>program|
|Singapore|Managedfloating withno<br>preannouncedpathfor<br>exchangerate<br>(Singapore describesits<br>regimeas abasket,band,and<br>crawl(BBC)withundisclosed<br>parameters.)<sup>28 8</sup>|Other(not specified)|
|South Korea|Independentlyfloating|Other (notspecified)|
|Taiwan|Managedfloating"g|N.A.|
|Thailand|Independentlyfloating|Inflation targeting framework|
|Vietnam<br>NOTE:Taiwanisnoti|Peggedexchangeratewithin<br>horizontal band(0.1%daily<br>movement, one-sided)<br>(Defactoarrangement undera<br>formallyannounced policyof<br>managedorindependent<br>floating.)<br>ncluded intheIMFlisting.|Exchangerate anchor|

Source: [MF, Intemational Financial Statistics, July 2001 There is, however, some controversy about the true nature of these countries' announced regimes, which for most are indicated as independently floating. Several recent studies 90 are reviewed in Branson (2001). These studies examine variability in exchange rates, intemational reserves and interest rates of certain "floater" countries relative to the variability in these indicators for the U.S., Japan and Germany. They find that, for the most part, countries are neither adopting a hard fix nor are they actually freely floating but rather are managing their exchange rates against the dollar, yen or 102
# **I. Introduction**

1. Although the financial crisis of 2008 officially came to an end in the United States in June 2009<sup>2</sup> , its repercussions continue to be felt across the globe. In many advanced economies, industrial production lingers below pre-crisis levels. Unemployment remains stubbornly high and balance sheets of governments, European financial institutions and U.S. households continue to be weak. In continental Europe’s highly-indebted economies, a crisis of confidence has led to plummeting stock markets and widening spreads. Signs of vulnerability are also surging in emerging market economies. The anxiety over a weak global growth outlook is rising. World growth is expected to slow down from 4 percent in 2010 to around 2.5 percent through 2012, as growth in advanced economies is projected to contract (World Bank 2012). The combination of excess capacity, low returns on investment, high risks and lower growth in advanced economies has been referred to as the ―new normal.‖<sup>3</sup> If this ―new normal‖ becomes entrenched, several advanced countries may face a lost decade<sup>4</sup> —with negative consequences for the entire world.

2. What the world needs now is a growth lifting strategy. With a looming public debt crisis in Europe and high public debt levels in the U.S., the private sector should ideally become the driver of growth; however, as long as excess capacity persists and investment risks remain high, private sector investment is likely to remain subdued. In order to reduce debt levels, many governments have turned their attention to implementing austerity measures and structural reforms, but austerity measures bear the danger of further weakening growth and worsening unemployment. Structural reforms, while key to boosting growth in the medium term, will only gain traction once demand increases. This raises the question of how governments can support demand and employment without adding further to debt levels in the medium run. Investments in green technology, education, and infrastructure come to mind. Under current economic circumstances, however, investing in bottleneck-releasing infrastructure projects that are selffinancing may be the best option. Infrastructure investments create jobs in sectors such as construction and manufacturing, which have been hit hard by the crisis, while also enhancing countries’ future competitiveness and growth. In addition, countries could explore innovative

> **2** According to the Business Cycle Dating Committee of the National Bureau of Economic Research at http://www.nber.org/cycles/cyclesmain.html

> 3 PIMCO (2009).

> 4 For a recent reference to the lost decade, see Lagarde 2011.

2
- **, Yunhua Wu, and Scott Rozelle** , “Moving off the farm and intensifying agricultural production in Shandong: A case study of rural labor market linkages in China,” _Agricultural Economics_ , 2009, _40_ , 203–18.

- **Huang, Yipin** , _Agricultural reform in China: Getting institutions right_ , Cambridge University Press, 1998.

- **IIASA and FAO** , 2012. Global Agro-ecological Zones (GAEZ v3.0).

- **Janzen, Sarah A. and Michael R. Carter** , “After the drought: The impact of microinsurance on consumption smoothing and asset protection,” 2013. NBER Working Paper No. 19702.

- **Jedwab, Remi** , “Cash crop windfalls and development: On cocoa and cities in Ivory Coast and Ghana,” 2011.

- **Jensen, Robert and Nolan Miller** , “Giffen behavior and subsistence consumption,” _American Economic Review_ , Sep. 2008, _98_ (1), 1553–1577.

- **Kaboski, Joseph and Robert Townsend** , “A structural evaluation of a large-scale quasi-experimental microfinance initiative,” _Econometrica_ , 2011, _79_ (5), 1357–1406.

- **Kraay, Aart and David McKenzie** , “Do poverty traps exist? Assessing the evidence,” _The Journal of Economic Perspectives_ , 2014, _28_ (3), 127–148.

- **Kwak, Sungil and Stephen C. Smith** , “Regional agricultural endowments and shifts of poverty trap equilibria: Evidence from Ethiopian panel data,” _The Journal of Development Studies_ , 2013, _49_ (7), 955–975.

- **Lagakos, David and Michael Waugh** , “Selection, agriculture, and cross-country productivity differences,” _American Economic Review_ , 2013, _103_ (2), 948–80.

- **Lin, Justin Yifu** , “The household responsibility system reform and the adoption of hybrid rice in China,” _Journal of Development Economics_ , 1991, _36_ , 353–372.

31
they operate. As discussed in the methodological section, we work with a specification that assumes two different channels: (i) a productivity-driven effect and (ii) a scale effect. Since our TFPR measure confounds both prices and efficiency, our productivity-driven effect is not fully comparable to the displacement effect cited in the literature (Acemoglu and Restrepo 2018, 2019b). This is because the price-related component of this effect could be laboraugmenting if the efficiency gains are passed-through onto prices, and the price reduction can increase product demand. The scale effect is labor-augmenting. It is associated with an expansion in firms’ profits due to a reduction in marginal costs or the scale-up of demand as digitization allows firms to reach a larger potential customer base.

# **7.1 on Jobs**

Table 4 presents the results from estimating equation 4.8, the effects of digital-technology adoption on jobs, for each of the endogenous TFPR specifications we estimated in the previous section (Table 3 columns 1-4). Unfortunately, due to WBES limitations in questionnaire design, we cannot measure the impact of digital-technology adoption on the skill composition of labor in a straightforward manner, especially across regions. This is because there are discrepancies in the questionnaires across countries and time. For example, while some surveys collect information on high-school education, others do it for college graduates. Further, the type of information collected is not independent of the level of development of the country. The WBES questionnaires for upper middle-income countries focus on college graduates, whereas those for low-income countries collect information on the share of high-school graduates. Column (1) displays estimated labor-demand effects, when assuming an endogenous TFPR process that is a function of learning by exporting. Column (2) excludes exporting effects and assumes an endogenous TFPR process that is a function of digitization. Column (3) assumes an endogenous TFPR process that is a function of managerial experience. The most complete specification is the one displayed in Column (4), which shows the effects of changes in digitization, learning by exporting, and accumulation of managerial experience 26
discusses the data and the estimation strategy. Section 5 presents the empirical results.

The final section concludes.

# **2. Background on HIV/AIDS in Kenya**

## **a. HIV Prevalence**

Until recently AIDS prevalence in Kenya was estimated to be around 15%, based on data from antenatal clinics. The 2003 Kenya Demographic and Health Survey (KDHS), which included HIV testing of survey respondents, estimated that 7% of Kenyan adults are infected with HIV. Measured prevalence levels among young women rise quickly with age (3% are infected in the 15-19 age group and 9% are in the 20-24 age group). Among men, prevalence reaches its peak (8.8%) in the 40-44 age group (Central Bureau of Statistics, Kenya 2004).

## **b. HIV/AIDS Awareness**

A number of institutions are currently implementing HIV/AIDS prevention programs in Kenya. These programs involve schools, health clinics, and the media. The Government of Kenya created the National AIDS Control Council (NACC) in 2000 to coordinate all HIV/AIDS activities in the country. Voluntary Counseling and Testing (VCT) services are available in each district. Radio programs on HIV/AIDS can be heard daily. Population Services International conducts social marketing of “Trust” condoms at the very low cost of 3 condoms for 12 US cents. Large wall-painted advertisements for Trust Condoms are present everywhere from large towns to rural villages. A number of 5
WPS4123

# **Differentiated Products and Evasion of Import Tariffs**

# Beata S. Javorcik<sup>*</sup>

# and

# Gaia Narciso<sup>**</sup>

**Abstract:** An emerging literature has demonstrated some unique characteristics of trade in differentiated products. This paper contributes to the literature by postulating that differentiated products may be subject to greater tariff evasion due to the difficulties associated with assessing their quality and price. Using product-level data on trade between Germany and 10 Eastern European countries during 1992-2003, we find empirical support for this hypothesis. We show that the trade gap, defined as the discrepancy between the value of exports reported by Germany and the value of imports from Germany reported by the importing country, is positively related to the level of tariff in 8 out of 10 countries. Further, we show that the responsiveness of the trade gap to the tariff level is greater for differentiated products than for homogeneous goods. A one-percentage-point increase in the tariff rate is associated with a 0.6% increase in the trade gap in the case of homogeneous products and a 2.1% increase in the case of differentiated products. Finally, the data indicate that greater tariff evasion observed for differentiated products tends to take place through misrepresentation of the import prices.

Keywords: differentiated products, tariff evasion, transition countries World Bank Policy Research Working Paper 4123, February 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * World Bank and CEPR, MSN MC3-303, 1818 H St, NW, Washington DC, 20433. Email: bjavorcik@worldbank.org.

> ** Bocconi University, IEP, via Gobbi 5, 20136, Milano, Italy. Email: gaia.narciso@unibocconi.it. The authors wish to thank Torfinn Harding, Leonardo Iacovone, Bernard Hoekman, Molly Lipscomb, Maurice Schiff, Antonio Spilimbergo and David Tarr for their helpful comments and suggestions and Geoff Revell for research assistance.

1
below the extreme poverty line, followed by South Asia (18.8 percent) and East Asia (7.2 percent). Table 8 summarizes the global and regional poverty measures (percent poor, depth of poverty and number of poor) for a selection of reference years between 1990 and 2012.<sup>59</sup> In contrast to some of the large country-level revisions, the regional poverty profile based on the $1.90 poverty line and 2011 PPPs largely maintains the picture of global poverty based on the $1.25 line and 2005 PPPs.

The global count of poor with the new data is very similar to the global count based on the $1.25 line and the 2005 PPPs. For 2011, the most recent year for which we have estimates for both the $1.90 and $1.25 lines, the global poverty headcount rate is 14.1 percent using the 2011 PPPs, compared to 14.5 percent with the 2005 PPPs. The difference in the number of poor is 28 million people. It is important to note that the aggregate global distribution is extremely dense around the current and past poverty lines. Edward and Sumner (2015) show that moving the poverty line by as little as 10 cents would move the count of global poor by more than 100 million. The high density of the distribution around the poverty line has also been an important aspect contributing to the rapid decline in poverty in recent years, such as the estimated reduction of number of poor by more than 80 million people between 2012 and 2011.

Although the global number of poor remains similar in the revised numbers, some of the regional changes are more significant. In the East Asia and the Pacific region, poverty in 2011 increases from 7.9 percent under the previous estimates based on the $1.25 line to 8.5 percent in the new estimates using the $1.90 line. This increase is largely driven by an upward adjustment of the poverty headcount rate in China, as a result of the new PPPs and new rural-urban adjustments. The new estimates lower the aggregate poverty rate in Sub-Saharan Africa and South Asia by around 2 percentage points in the most recent update, to 44.4 and 22.2 percent, respectively.<sup>60</sup> In the Latin America and Caribbean region, poverty estimates are revised up with the 2011 PPPs, corresponding to the lower upward adjustment in the PPP-adjusted welfare measures in these regions compared to the rise in the global poverty line. While the absolute

> 59 A more complete set of estimates will be available in PovcalNet in due course.

> 60 For India, new PPPs and the revised spatial adjustments work in opposite directions.  Revised PPPs suggest that as a whole, India is richer than we used to think.  Adjusting only for the new PPP, lowers the poverty rate nationally. Revised spatial adjustments (that are based on India’s official poverty lines) suggest that the gap between urban and rural areas in the cost of living is half of what was implied in the earlier official poverty lines. Effectively, rural areas are not so much cheaper to live in than urban areas, as we used to think.

36
**<u>Table 6: Test for labor market efficiency for preharvest and total labor</u>**

||**1982**|**-99**|**1999**|**-08**|
|---|---|---|---|---|
||**Pre-harvest**<br>**(1)**|**Total**<br>**(2)**|**Pre-harvest**<br>**(3)**|**Total**<br>**(4)**|
|Log of land|0.377***|0.381***|0.365***|0.344***|
||(17.062)|(17.182)|(16.823)|(17.335)|
|Agricultural wage|-0.588***|-0.138***|-0.173***|-0.200***|
||(-14.100)|(-3.285)|(-2.980)|(-3.766)|
|Non-agricultural wage|<br>-0.062|<br>-0.199***|<br>-0.091*|<br>-0.195***|
||(-1.566)|(-5.010)|(-1.815)|(-4.261)|
|Price of fertilizer|-0.012|-0.009|0.151***|0.150***|
||(-0.574)|(-0.422)|(7.316)|(7.951)|
|Price of bullocks|<br>0.177***|<br>0.119***|<br>0.008|<br>-0.031|
||(8.916)|(5.985)|(0.192)|(-0.861)|
|Log of household size (α1)|0.108***|0.098***|0.029|0.010|
||(2.893)|(2.611)|(0.693)|(0.260)|
|Share males 14-64 years old (α2)|-0.091|-0.161*|-0.094|-0.113|
||(-0.962)|(-1.696)|(-0.903)|(-1.196)|
|Share females 14-64 years old (α3)|<br>0.047|<br>-0.007|<br>0.035|<br>0.036|
||(0.396)|(-0.061)|(0.280)|(0.317)|
|Share males >64 years old (α4)|0.021|0.019|0.019|0.006|
||(0.099)|(0.089)|(0.091)|(0.030)|
|Share females >64 years old (α5)|0.084|-0.086|-0.198|-0.223|
||(0.413)|(-0.425)|(-1.071)|(-1.321)|
|Head’s age|0.886|1.205|-2.079|-2.364**|
||(0.943)|(1.278)|(-1.624)|(-2.021)|
|Head’s age squared|<br>-0.107|<br>-0.148|<br>0.273|<br>0.310**|
||(-0.854)|(-1.182)|(1.620)|(2.012)|
|Head’s education|-0.043*|-0.022|0.002|-0.010|
||(-1.898)|(-0.967)|(0.081)|(-0.521)|
|Head is female|<br>0.196**|<br>0.176**|<br>0.093|<br>0.080|
||(2.363)|(2.114)|(1.241)|(1.166)|
|Constant|3.940**|2.662|8.089***|9.928***|
||(2.224)|(1.497)|(3.316)|(4.455)|
|No. of observations|<br>8,314|<br>8,314|<br>7,400|<br>7,400|
|R-squared|<br>0.291|<br>0.375|<br>0.236|<br>0.257|
|Number of farmers|4,925|4,925|4,439|4,439|
|Joint F-test (α1+ α2+ α3+ α4+ α5=0)|2.899|3.437|0.819|0.942|
|P-value|0.0129|0.00423|0.536|0.452|

Source: Authors’ computation from 1982, 1999, and 2008 ARIS-REDS surveys.

Note: Dependent variable is the log of preharvest and total labor days used for agricultural production. Absolute values of asymptotic t-ratios are in parentheses. All specifications include farmer fixed effect and year fixed effect. Standard errors adjusted for clustering effect at the village level. All prices are in logarithms.

21
information for each social protection program listed in the GDSPRC that was announced before June 1, 2021 from official documents (including IMF Article IV revisions and related documents from other international organizations), government websites, and news sources. We quantified the expenditures of social protection programs in 154 countries. This set of countries forms the core sample for the empirical analysis in this paper (see <u>Appendix Table A.1</u> for the list of countries).

We also used several auxiliary data sources. Information on countries’ pre-pandemic social protection landscape comes from the ASPIRE database (World Bank 2021a) and OECD’s Social Spending database (OECD 2021b). Data on employment, inactivity, and growth outcomes comes from <u>ILO (2021), the IMF (2021), and the World Bank (World Bank 2021b, 2022b). Poverty</u> projections are from Lakner et al. (2020). Information on the size of the informal sector is from the Informal Economy Database produced by the World Bank (Elgin et al. 2021).

An essential feature of our analysis is the distinction between social programs focused on income protection and programs focused on job protection. <u>Table 1</u> shows a correspondence between social protection areas, categories of social protection, as defined in GDSPRC, and the policy focus (on either income protection or job protection). All categories included in the social assistance policy areas focus on income protection; the categories of social insurance and labor market policy areas are split between those focusing on income protection and those focusing on job protection.

# **4. The nature of the social protection response and the impact of the pandemic**

Countries across the world implemented substantial economic measures in response to the economic shock of the pandemic. The size of the economic mitigation measures (also called the “stimulus budget”) averaged about 5.6 percent of GDP (Table 2). In high-income countries, the average stimulus package reached 10 percent of GDP; in lower-middle and low-income countries, the average size of the stimulus package was about 3 percent of GDP.

Pandemic stimulus packages also included infrastructure spending and general business support measures. The share of these non-social protection policies in the total stimulus budget was highest at the extremes of the world income distribution (Figure 1). Low-income countries allocated about 35 percent of their total mitigation budgets to health spending. Health spending represented only 13 percent of the total size of the mitigation budget in high-income countries.

8
Figure 1: Scatter plots of the shares of services value added in GDP against income level, for financial and business services, year 2005 <!-- Start of picture text --> LUX<br>GBR<br>FRA<br>LUX<br>BELUSA<br>DEU<br>NLD<br>IRL<br>SWE<br>LUX<br>AUS<br>GBR HUNSVNITADNKAUT<br>USA<br>IRL ESTCZE<br>MLT JPNFIN<br>NLD ESP<br>FRABEL PRT<br>DEUAUS BRA MEXPOLSVK CAN<br>TWN KOR<br>SWE IRL RUSLVA<br>CYP<br>BRA LVAESTHUNCYPPRTMLTKORSVNTWNESPITAJPNDNKAUTCAN GBRNLDAUSUSA LTU<br>IND MEXRUSPOLSVKCZE FIN BRA LVA CYPPRTKORJPNBELCAN IND CHN ROMTUR GRC<br>IDN CHN ROMBGRTURLTU GRC INDIDN CHN BGRTURMEXRUSPOLSVKESTHUNCZEMLTSVNGRCESPITAFRADEUDNKSWEAUT BGR TWN<br>ROM LTU FIN IDN<br>8 9 10 11 8 9 10 11 8 9 10 11<br>log(GDP/capita) log(GDP/capita) log(GDP/capita)<br>.4 .15<br>.25<br>.2<br>.3<br>.1<br>.15<br>.2<br>.1<br>Financial Serives Value Added / GDP Business Services Value Added / GDP .05<br>Financial & Business Serives Value Added / GDP<br>.1<br>.05<br>0 0 0<br><!-- End of picture text --> Notes: The vertical axis represents the share of services value added in GDP for financial & business services combined, financial services only, and business services only, respectively. The horizontal axis represents log(GDP/capita). Only the data for year 2005 are used. Data sources: WIOD and PWT.

29