rafmacalaba/data-use-mentions-extended huggingface dataset:
33 THE INTENSIVE MARGIN IN TRADE

# **5.2. Counterfactual Analysis in the Estimated Full-Lognormal Melitz Model**

For our counterfactual analysis we need a set of countries for which we have _λij_ , _hij_ , _Xj_ and _Yi_ , as well as _gij_ for all _i_ and _j_ in that set. Since we assume (see Section 4) that the variances of _φi_ and _fij_ differ by origin but not by destination, then _gij_ = _gi_ for all _i_ and _j_ . We have estimated _gi_ for a set of 37 EDD countries, and we can infer the implied _Ni_ for all those countries, so we can include any subset of those countries in our analysis.<sup>35</sup> We then construct _hij_ using data for _Nij_ and _hij ≡Hij_ ( _Nij/Ni_ ). Finally, we also need _Xij_ and _Nij_ for _i_ = _j_ . Following the approach proposed by Ossa (2012), we construct _Xii_ as manufacturing value-added in country _i_ from the World Development Indicators divided by 0.25, which is close to the average share of manufacturing value-added in gross production from the World Input-Output Database (WIOD) for the set of covered EDD countries in 2007. We set _Nii_ = _Ni_ , which would be true if there are no fixed costs for domestic sales.

We conduct our counterfactual analysis for a world composed of the 12 Latin American countries and China, for which we have estimated the full lognormal model.<sup>36</sup> We do not consider the whole EDD dataset for computational reasons. This is because some of the country pairs in the whole EDD dataset trade very little and this makes our welfare calculations imprecise (since we need to use numerical approximation to compute some of the integrals).

We are interested in comparing the counterfactual implications of the full Melitz-lognormal model with those of the Melitz-Pareto model. We proceed in three steps. First, we allow for some change in trade costs and compute the counterfactual implications using the estimated full Melitz-lognormal model. We
tax payment through withholding by employers observed in the second half of the 20<sup>th</sup> century among high-income nations (Besley and Persson 2014) likely freed up resources dedicated to marshaling tax collections through visits, in-person collections, and so on. This shift could account to some extent for the greater shares of staff in audit and investigation roles in higher-income countries relative to lower-income countries we note above.

On the other hand, technology can clearly offer productivity gains for staff by expediting formerly cumbersome processes – like managing tax records or checking returns for errors – and facilitating reallocation of effort towards higher-return activities. The introduction of a digital tax collection technology within a Ghanaian municipality sheds light on this dynamic. In 2021, the municipal tax authority provided a tablet-based technology containing a geospatial database of properties and revenue management software that helped property tax collectors more easily locate properties in the field. A randomly selected subset of local property tax collectors – whose task is to deliver tax bills and collect payments manually – were provided with this new technology. These collectors delivered almost one-third more bills and collected double the amount of revenues compared to collectors persisting in the old, entirely manual system (Dzansi et al. 2022). The timeline of the evolution of visits and payments suggests that the technology assisted collectors in learning more quickly which households were willing and able to make payments and to target their in-person collection efforts accordingly. Technology can thus have high returns in even low resource, low compliance settings.

However, tax authorities face two major challenges. The first is how best to strike a balance between using technology to improve on potential error, bias, or manipulation in the work of human agents –– while retaining the value of rich contextual knowledge of an environment that tax officials can acquire from repeated interactions with taxpayers. The tradeoffs involved likely vary with the nature of the task, even within the same setting. As an example, in Senegal, property assessment officials using a “discretionary” method produced real estate valuations that
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Net foreign reserves have grown rapidly in recent years in Turkey, and Table 23 below shows that the ratio of broad money to foreign reserves have declined. Though the ratio does not look particularly high as compared to a number of other large capital importer countries, whether the reserves are all freely usable is an issue of concern in the case of Turkey. While the central bank has fairly conservative foreign exchange management policies, and it does not engage in foreign exchange guarantees, forward contract or swap positions, a large part of the net international foreign reserves represent foreign currency deposits with the central bank by foreign residents of Turkish origin through the Dresdner Scheme'<sup>9</sup> or similar arrangements. Although the balance of such deposits does not exhibit high volatility, its stability cannot be taken for granted.

**Table 23**

|||Turkey: System|Uquidity||
|---|---|---|---|---|
|Ratio<br>to|of Short-term Debt<br>Foreign Reserves|<br>Short-term Debt<br>as Flrcentage of Total Debt|Ratio of Broad Money<br>to Foreign Reserves|Ratio of Broad Money+ Repos<br>to Foreign Reserves|
|1993|2.4|28%|4.3|**4.3**|
|1994|1.3|17%|4.0|4.1|
|1995|1.1|21%|3.4|3.4|
|1996|1.2|26%|3.0|3.1|
|1997|1.2|27%|3.0|3.2|

Source: Own calculation based on Central Bank data.

**Table 24**

||**System Uquidity in**<br>|**Seleted Countries as of June 1**<br
(under the Ministry of Home Affairs - 52,000), Indo-Tibetan Border Police (35,000), Special Frontier Force (10,000), National - Rifles (under Ministry of Defense - 30,000), Central Industrial Security Force (under Ministry of Home Affairs 90,000), Defense Security Corps (provides security at Ministry of Defense sites - 31,000), Railway Protection Force (70,000) and Home Guards (472,000).

GDP estimate is from World Table 1995 and refers to 1992. Consolidated Central Govemment wages and salaries are for 1994 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) are taken from the United Nations' Statistical Yearbook for Asia and the Pacific 1995 and refer to 1989.

Indonesia Unemployment is taken from CIA Factbook and refers to 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992.

Data on Central and local Government are taken from Indonesia - Civil Service Issues (Confidential draft of October 21, 1993), received from David Steedman (ASTTP) and relate to 1992: Whether the military is included in the number of CS or not is not clear. It should be noted however that a substantial number of CS slots are filled by the military and it is seriously hampering satisfactory career development for civilians. For our purposes, we will deduct armed forces, but risk undercounting Civilian CS." Of the 3.4 million central govemment employees, all but about 0.5 millions were seconded to the regions." Education and Health employment are from the same report and relate to 1992. Military employment do not include the paramilitary forces, i.e. the Police (174,000 people), the Kamra (people's security - part-time police auxiliary).

Wages bill of Consolidated Central Govemment and GDP estimates are from IMF Government Finance Statistics Yearbook, 1995 and are for 1993
λλ . ii ii This method estimates the portion of log income that is assumed to be time-invariant, at least when �ββ ββ using cross-sectional surveys close in time. Although this assumption does not always hold, there are several reasons why it may be reasonable to believe that the portion of income explained by timeinvariant covariates remains constant in close periods. First, personal characteristics such as education level, work experience, and skill level tend to be relatively stable over time and may have a persistent effect on income. Second, if individuals have limited mobility and are unable to easily change their geographic location or industry of employment (which is expected to be the case in close periods), this could limit their ability to access higher-paying jobs and result in a relatively constant portion of income being explained by time-invariant factors. Third, if economic conditions (such as the overall level of demand for goods and services) remain relatively constant over time, this could also lead to the portion of income explained by time-invariant factors remaining constant. If a purely parametric approach were used, the full welfare measure in round 1 would be completed by adding some residuals to i . That is not the case in the method presented in this paper. The main assumption behind this semi-parametric approach is that the portion of log income explained by timeyy� invariant covariates remains constant in both periods. As a result, and can be used to find the most similar household heads in both surveys (within the set of observations that share common ii1 ii2 yy� yy� characteristics) and thus build the synthetic panel. For every observation in the second round of data, a set of neighbors (in terms of and closeness) is found in the first-round data by looking for observations with the smallest absolute difference between the two linear predictions. Among these ii1 ii2 yy� yy� neighbors, one of them is randomly selected based on the sampling weights from the first round. In addition, the matching method requires that the closest neighbors are searched only within a
usage, property right protection, access to finance, and government expropriation. Thus, NSPF allows us to study how China’s private firms and their BEs vary over the two decades and across all the provinces. Table A1 in the appendix displays the distribution of observations across years and provinces, and Table A2, the sectoral distribution. As Table A1 shows, most private firms in this data set are from prosperous coastal provinces such as Beijing, Shanghai, Zhejiang, Jiangsu, Guangdong, and Shandong.

The second firm-level data set is an unbalanced panel of the Annual Survey of Industrial Firms (ASIF) from 1998 to 2008, compiled by China’s National Bureau of Statistics (NBS). ASIF contains an annual survey of two types of manufacturing firms: all state-owned enterprises (SOEs) and non-SOEs with annual sales over a certain threshold level (RMB 5 million before 2002 and RMB 2,000 million afterwards). On average, the sample accounts for over 95% of China’s total annual output in industrial sectors covering mining, manufacturing, and public utilities. The data set has 100+ variables from the firms' main financial statements, including balance sheets, income statements, and cash flow statements. ASIF will thus help us study the disparities in firm characteristics and their external environments, among firms with different ownership types. Table A3 presents the distribution of observations from ASIF.

The third firm-level data set consists of listed firms in China: the China Stock Market and Accounting Research (CSMAR) database constructed by GTA Information Technology. It covers all companies listed on the two major stock exchanges (Shanghai and Shenzhen Stock Exchanges) since 1990, covering both financial statements and corporate governance information. We rely on these three large-scale firm level data sets (i.e., NSPF, ASIF, and CSMAR) to make comprehensive comparisons among firms across sectors, sizes, and ownership types.

The fourth data set is the firm-level survey conducted by the World Bank in 2005, which includes 12,400 firms
<!-- Start of picture text --> o FDI stock in Ukraine in 2001 - 2007<br>oa uw<br>® as<br>rs ao o<br>me ve of<br>ez ae ra<br>2a ere a ML<br>=s CUurrrn ners sss tesa “ in<br>ER eT TS +5<br>ae eer me 2<br>_ _- D<br>S . LOS<br>we me eas<br>tates) me .<br>o m ain<br>2000 2002 2004 2006 2008<br>Year<br>———_ Total FD! stock —---—- Share of services sector<br>cooossoss> Share of manufacturing sector<br>Source: The Vienna Institute for International Economic Studies (witw) data on FDI in Central and Eastern Europe<br>Note: Services sector includes NACE 1-digit sectors J, H, 1, J, K according ta NACE REY.1 classification.<br><!-- End of picture text -->
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data study of the size of the hidden economy of a cross-section of 17 OECD countries for the period 1960-1978 and has been used in several studies thereafter (Loayza 1996, Chaudhuri, Schneider and Chattopadhyay 2006, Dell'Anno, Gomez-Antonio and Pardo 2007). The approach is based on structural equation modeling with latent variable(s), for which multiple causes and multiple indicators exist (MIMIC model). The empirical MIMIC literature is primarily macroeconomic in nature and typically the specification of the applied MIMIC model is not derived from formal economic theory. One notable exception is a paper by Siegel (1997), which uses a MIMIC model derived from formal economic theory to estimate the contribution of computer usage in productivity growth at the industry level.

This paper makes a number of contributions. First, because we have unique firm-level survey data matched with official tax data, we can apply and compare the above three approaches simultaneously for the same sample of firms. Second, unlike most of the MIMIC literature, we use formal economic theory to derive a MIMIC model to estimate the size of hidden outputs using microeconomic data. In particular, we model true sales by taking into account firm-level incentives to comply with taxes and a production function technology linking true sales with observable firm-level production characteristics. Third, we not only allow for underreporting in the official tax office data but also for underreporting in the survey data. Fourth, we estimate the extent of underreporting for a sample of formal sector firms in the transition economy of Mongolia where the extent of underreporting is expected to be prevalent but largely unknown.<sup>7</sup> The finding for Mongolia should also be 7 While not studying underreporting by formal sector firms specifically, a number of studies have looked at the broader issue of shadow economy in Mongolia before (Anderson 1998, Bikales, Khurelbaatar and Schelzig 2000).

5
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

# **References**

Arocena, P. and Waddams-Price, C. (2002). “Generating Efficiency: Economic and Environmental Regulation of Public and Private Electricity Generators in Spain”, International Journal of Industrial Organization, Vol. 20, No. 1, pp. 41-69.

- Atkinson, T., Cantillon, B., Marlier, E., and Nolan, B. (2002). “Social Indicators: The EU and Social Inclusion”, Oxford University Press: New York.

Bacon, R. (1999). “A Scorecard for Energy Reform in Developing Countries”, Public Policy for the Private Sector, Note No. 175, April, The World Bank, Washington, DC.

- Beato, P. and Laffont, J. J. (2002). “Competition in Public Utilities in Developing Countries”, Sustainable Development Department, Technical Papers Series, 2/02, IFM-127, Inter-American Development Bank, Washington DC.

Bergara, M., Henisz, W. J. and Spiller, P. T. (1997). “Political Institutional and Electric Utility Investment: A Cross-Nation Analysis”, Program on Workable Energy Regulation (POWER), Working Paper No. PWP-052, University of California Energy Institute, University of California at Berkeley.

- Bossel, H. (1999). “Indicators for Sustainable Development: Theory, Method, Applications”, International Institute for Sustainable Development (IISD), Winnipeg, Manitoba, Canada.

CAEM (2003). “Electricity Retail Energy Deregulation Index 2003”, Fourth Edition of the Electricity RED Index, A Scorecard for Measuring Progress on Energy Restructuring, Center for the Advancement of Electricity Markets (CAEM), April.

30
to be plagued by inefficiency and corruption, imposing a high cost to traded goods."<sup>9</sup> Surveys in a number of developing countries indicates that, the proportion of time managers spend in managing process regulation, ranges between 10-30 percent of their time and imputed costs on produced goods or services due to process regulation, are in the 5-15 percent range (World Bank, 1997).

The available evidence underscores the significant gains that developing countries can secure by further deregulating their economies and reducing the costs of process regulation. Estimates of those gains vary form country to country, but are a least a few percentage points of GDP (Chisari, Estache and Romero, 1996; Guasch and Spiller 1997).

,x Mercosur is a free trade area for Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay.

**19** According to the Nigeria Manufacturers' Association, pernission to clear goods in that country has to go through 27 stages and the process takes 5-8 weeks (Nigeria Manufacturers Association, 1996). These numbers are not uncommon in others developing countries.
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**Figure 5. Number of security-related incidents by district**

<!-- Start of picture text --> Security incidence per month<br>(2006-2010)<br>0.05 - 0.28<br>0.29 - 0.65<br>0.66 - 1.03<br>1.04 - 1.58<br>1.59 - 2.53<br>2.54 - 3.90<br>3.91 - 5.68<br>5.69 - 13.35<br>.<br>Source: UN Nepal Security Database.<br>Figure 6. Number of security-related incidents in Nepal<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Source: UN Nepal Security Database.<br>2006-01 2006-04 2006-07 2006-10 2007-01 2007-04 2007-07 2007-10 2008-01 2008-04 2008-07 2008-10 2009-01 2009-04 2009-07 2009-10 2010-01 2010-04 2010-07 2010-10<br><!-- End of picture text -->

**Figure 6. Number of security-related incidents in Nepal**

In general, governance matters to public procurement. In Nigeria, the majority of private firms surveyed on perceptions of public sector procurement reforms answered that they decided not to submit their expressions of interest in recent public tenders, because they did not trust the selection process. There is a general perception that contractors are predetermined or must pay a bribe. Particularly, integrity tends to be relatively weaker at the subnational levels (World Bank, 2008c).

In Nepal, ethnicity may be another issue that might affect the bidders’ entry strategy in the market. In Nepal there are 103 castes and ethnic groups.<sup>3</sup> In some areas, a dominant ethnic group accounts for nearly half the population in a district. There is a general perception that

> 3 In the 1991 census,
|—|
|Somalia_2011|16|—|—|—|—|
|South Africa_2014|47,027|44,895|95%|36,170.0|19%|
|South Sudan_2013|22|—|—|22.0|—|
|Sudan_2014|3,686|—|—|—|—|
|Swaziland_2014|95|—|—|226.3|—|
|Tanzania_2015|1,626|—|—|934.6|—|
|Togo_2013|264|—|—|162.6|—|
|Uganda_2014|892|—|—|580.0|—|
|Zambia_2014|2,366|2,211|93%|—|—|
|Zimbabwe_2012|1,970|1,257|64%|1,546.0|-23%|
|**Total for countries with available capacity data (20 countries)**|77,501|67,110|87%|||
|**Total for countries with available capacity data**<br>**(excluding South Africa)**|30,474|22,215|73%|||
|**Total for countries with available capacity data and peak demand**<br>**data(14 countries)**||56,357||45,940|18%|
|**Total for countries with available capacity data and peak demand**<br>**data(excluding South Africa)**||11,462||9,770|15%|

Source: utility annual reports and other documents _Note_ : The year following each country name is the reference year for countries included in the QFD analysis, or the latest year with available data for capacity, peak demand, or both for countries not included in the QFD analysis. For the purposes of the availability
Households in the treatment group also experienced a rainfall shock in the agricultural year previous to the baseline period. This shock was less pronounced in the control group. Finally, Table 1 reports the participation status with the NREGS at the time of the baseline data collection. As can be seen, 66% of the households in the treatment districts report having registered with the NREGS in 2007.

For the calculation of the dependent variable - a risk index of each households’ crop portfolio - survey data on input allocation to each crop is combined with District-level crop production statistics. The time series of crop production statistics are used to calculate the coefficient of variation of each crop’s yield. With this information, a risk index _Ri_ of each household’s crop portfolio is constructed given the reported allocation of inputs to each of the crops.<sup>21</sup> The risk index for household _i_ given input allocation _k_ to crop _m_ is defined as _Ri_ =<sup>�</sup> _rmkm/_<sup>�</sup> _kn_ , where _rm_ is the coefficient of variation of the yield of crop _m_ .<sup>22</sup> Note here, that _rm_ is only available for a subset of all crops _n_ (26 out of 42), such that _m ⊆ n_ . Still,<sup>�</sup> _km_ represents roughly 90% of the total allocation of inputs in the sample. To reduce potential bias, I drop all observations from the sample which have no crop in their portfolio for which risk information is available, e.g.<sup>�</sup> _km_ = 0 or _Ri_ = 0, in one or both of the survey rounds.<sup>23</sup> As can be seen in Table 1, the risk index at baseline is higher in the treatment group (0.36) than in the control group (0.26). The difference is statistically significant at
# **III. The Rapid Growth of a Few Large Economies and the Rise of the New Growth Poles**

While there was progress in development during the twentieth century, the G7 countries still dominated the global economy in terms of size, contribution to economic growth and living standards. This dominance began to decline dramatically during the first decade of the twentyfirst century—in particular, due to the fast growth of large dynamic emerging economies –the socalled ―BRICs‖ (Brazil, Russia, India and China). As a result, the G7 share of global GDP declined rapidly from 45 to 37 percent in just 8 years (Figure 4). This is an unprecedented and historic shift. After a remarkable degree of G7 dominance for an entire century, one decade of extraordinary growth has caused a dramatic shift in the balance of economic power.

It should be noted that this data uses Maddison‘s PPP adjusted figures, which increase the developing country‘s share of GDP, as compared to GDP measured at market exchange rates. On the other hand, even using market exchange rates, there was a dramatic shift during the 2000s, as the changing shares were driven by real growth. Data on the BRICs are illustrative, as we see that, even using market exchange rates, the BRICs‘ share nearly doubled in a decade (Figure 9). These recent developments are unlike the pre-industrial period of Asian dominance of the world economy (in particular China and India) when economic importance was driven by sheer population size in a context of stagnant global growth. In the recent decade, the rapid growth of the BRICs was driven by technological adaptation and integration into the modern global economy and in the context of an expanding global economy where countries need to ―run‖ just to keep their existing relative ranking in income per capita.

**Figure 8:  G7 and BRIC Shares of World GNI, 2000-2009**

<!-- Start of picture text --> 70%<br>60%