rafmacalaba/datause-encoder-data huggingface dataset:
respective set of underlying BEEPS variables, and three separate tests—the Guttman-Kaiser criterion, Cattell’s scree test, and Humphrey-Ilgen parallel analysis—confirm the decision to retain only the first principal component.

The estimation strategy follows a two-step approach and exploits cross-cell (defined by country, sub-national location, and firm size) variation in the changes across time of the five synthetic business environment indicators, as well as a sixth measuring the level of competition (based on the four-firm concentration ratio for each 4-digit NACE industry), to determine their effect on firm-level productivity.<sup>3</sup> First, a production function equation whose residuals measure total factor productivity (TFP) is estimated using the methodology of Levinsohn and Petrin (2003), which corrects for the crucial simultaneity bias arising from the fact that firms make input choices with knowledge of their productivity. Second, a first-differenced equation in firm characteristics, whose dependent variable is the two-year change in log TFP and whose main regressors of interest are the lagged two-year changes in six different business environment indicators, is estimated using ordinary least squares with White correction for heteroskedasticity. The availability of four years of production function data from the AMADEUS database allows the model specification to control for lagged productivity in this second step. This feature is particularly important for consistency given the assumption in Levinsohn and Petrin (2003) of a Markov process for productivity (Fernandes, 2007).

The results of the regression analysis confirm that firm-level productivity growth is directly linked to important factors in the business environment and strongly support the presence of large TFP gains from successful efforts to improve these microeconomic foundations of economic development, even after controlling for unobserved firm, industry, sub-national location, and country heterogeneity. The main findings of the paper are as follows. Over the period 2001 to 2004, (i) a one standard deviation increase in the _infrastructure_ indicator raises TFP of the average firm by 9.8 percent; (ii) a one standard deviation increase in the _financial development_ indicator raises TFP of the average firm by 7.8 percent; (iii) a one standard deviation increase in the _governance_ indicator raises TFP of the average firm by 3.2 percent; (iv) a one standard deviation increase in the _labor market flexibility_ indicator raises TFP of the

> 3 NACE Rev.1 ( _Nomenclature générale des activités économiques dans les Communautés européennes_ ), the standard industrial classification of economic activities within the European Communities, is identical to the United Nations Statistical Division’s International Standard Industrial Classification of All Economic Activities (ISIC Rev. 3) at the one- and two-digit levels.

3
_Figure 3: Initial labor productivity growth versus its growth rate in the secondary sector_ Unconditional convergence in the secondary sector is confirmed by the regressions presented in Table 3. This table follows the same specification as Table 1 but it does so for the secondary sector. Column 1 presents convergence estimations for the aggregated secondary sector. Columns 2 and 4 present the same with data disaggregated into sub‐sectors of manufacturing, water and electricity, and construction. In each case, the estimated convergence coefficient is positive and statistically significant. Also, convergence remains once controlling for initial poverty level ( **Table 4** ). However, as shown in Columns 4‐ 5 of **Table 3** and Column 3 of **Table 4** , results are weaker for the most disaggregated specifications.

**_Table 3: Labor productivity convergence regressions, Secondary sector_**

|||Second<br>|arysector<br>|||Mfi|NMf|
|---|---|---|---|---|---|---|---|
||Total<br>aggregation|Disaggreg<br>Manufact<br>NonManuf|ated by<br>uring &<br>acturing|Disaggre<br>all se|gated by<br>ctors|anuacturng<br>only|onanu.<br>only|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Dependent variable:||||||||
|Growth of VAper worker over a decade||||||||
|Log initial VA per worker|‐0.026***|‐0.025***|‐0.032|‐0.018*|‐0.035|‐0.032***|‐0.021*|
||(0.007)|(0.006)|(0.019)|(0.010)|(0.030)|(0.007)|(0.010)|
|Constant|0.272***|0.262***||0.203**||0.326***|0.233**|
||(0.068)|(0.058)||(0.097)||(0.062)|(0.100)|
|Department FE|�|�||�|<br>�|�|�|
|Industry FE|�|�||�|<br>�|�|�|
|Observations|24|48|48|72|72|24|24|
|R‐squared|0.368|0.249|0.776|0.070|0.446|0.525|0.157|
|Stardard errors in parenthesis.||||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||||

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Summarizing the above, we would argue that Southeast Asia stands to reap significant potential gains from improved trade facilitation. This can be achieved both through increased trade flows and entry into new export markets and higher productivity. The measures included in any reform program will necessarily cut across a number of policy areas that are relevant to the specific costs facing exporters. These will include infrastructure, customs services, regulatory reform, efficiency of trade-related services, and governance. Policymakers and stakeholders therefore need to prioritize reforms: they are often costly and difficult to implement, and therefore they cannot all be tackled simultaneously or to the same extent. The remainder of this paper aims to provide some first indications as to what the priorities might be in Southeast Asia.

# **3 Moving Goods across Borders in Southeast Asia**

This Section provides a “snapshot” of the costs of exporting and importing in Southeast Asia. It then examines recent progress towards trade facilitation goals, by comparing scores on key indicators over the period 2000-2005. We follow the approach of Wilson et al. (2005) and adapt it, where needed, due to non-availability of certain data.

## **_3.1 A “Snapshot” of Trade Costs in ASEAN_**

We now address in detail the state of trade costs in ASEAN as of 2006 (or most recent data). Data from the World Bank’s _Doing Business_ database show that the overall cost of importing in ASEAN is relatively low by world standards (Figure 1). For the first time in 2006, the “Trading Across Borders” component of _Doing Business_ captured the total official cost for importing or exporting a standardized cargo of goods, excluding ocean transit and trade policy measures such as tariffs. The four main components of the costs that are captured are: costs related to the preparation of documents required for trading, such as a letter of credit, bill of lading, etc.; costs

- 5 -
the participation date and the survey date. Finally, it is likely that the effect of a reduction of subjective well-being on civil conflict does not only come from the increased propensity of ‘suffering’ civilians to participate in uprisings, but also from the fact that civilians that might not ‘take to the streets’ themselves, can support and legitimize an uprising. Therefore, we use as our dependent variable the number of events of civil resistance within a country in a year using data from the CNTS (2015). We calculate the number of such civil resistance events as the sum of strikes and demonstrations and transform it using the hyperbolic inverse sine. This transformation resembles a normal logarithmic transformation and can be interpreted as such, but it can take zeros into account.

The CNTS data were collected through New York Times newspaper articles since 1815 and are used in several other studies to measure strikes and demonstrations (e.g. Braha, 2012; Collier & Rohner, 2008; Schatzman, 2005). Demonstrations are included in the CNTS data set if they are (1) peaceful public gatherings, (2) with at least 100 participants, (3) whose primary purpose is to display or voice opposition to government policies or authority, and (4) do not have a distinctly anti-foreign nature such as anti-globalization protests. Strikes are included if (1) 1,000 or more industrial or service workers participate, (2) they involve more than one employer, and (3) they are aimed at national government policies or authority. While the average total number of demonstrations and strikes over the period of analysis (2006-2014) is 16 per country, there is considerable cross-country variation, from none in for example Finland, Bahrain and the United Arab Emirates, to 121 in the Arab Republic of Egypt, 129 in China and 209 in the United States. Any differences in the number of non-conflict events reported due to biases in the U.S. press coverage are absorbed by the country-fixed effect.

# **Independent variable: Subjective well-being indices**

Several recent empirical studies show that measures of subjective well-being complement financial and social indicators as independent variables in research on human development (e.g.

10
Consequently a long standing, complicated public management issue had been resolved. Those harsh complaints with each other at the beginning of the roundtable dialogue were replaced by the happy and grateful greetings at the end of the dialogue meeting.

Since the first stakeholder roundtable dialogue held in early 2006, 220 dialogue meetings have been organized by local community workers in Donglin District and reportedly 350 public management issues were solved by the dialogue. It was unthinkable to solve them by using the traditional public management means. Mutual understandings between the different stakeholders and the stakeholders’ understandings in various public issues have been significantly improved. Now the citizens there have formed a habit to request dialogues whenever they face a public issue, rather than to file complaints or wage conflicts as they traditionally did. One example is the resettlement caused by the new urban construction projects in Donglin. At the beginning, 78 households had serious complaints against the government and were not willing to accept the resettlement packages. After a series of roundtable dialogues the total number of household complaints gradually reduced to 0 and the issue was finally successfully solved.

# **_Case 2. Industrial Pollution Control (Jiangyan City, Jiangsu Province)_**<sup>**_11_**</sup>

Jiangyan, a city in Taizhou Municipality of Jiangsu Province, is located on the northern side of Yangtze River between Shanghai and Nanjing, with a land area of about 1000 square kilometers and a population of 0.91 million. A modestly well developed area in Jiangsu, one of the richest provinces in China, Jiangyan enjoyed a fast economic growth in the past two decades. However, its people also suffered from serious industrial pollution, which caused tremendous complaints from the citizens and sometime serious social conflicts. The major pollution sources were small metal processing companies, which provided a significant portion of jobs to the local residents and tax revenues to the local government. Jiangyan city environmental authorities tried their best to enforce relevant environmental regulations and won a national award for its outstanding performance in environmental inspection in 2007. However, the environmental quality was still far from satisfactory to its citizens and the environmental authority was too weak to bring those polluters into compliance, due to various political, economic as well as technical reasons.

> 11 Source: Documents Submitted by Jiangyan Municipal Environmental Protection Bureau.

11
sults disappears when the data used by Cerutti et al. (2017c) are analyzed at the annual frequency and aggregating across flow types. The mean adjusted _R_<sup>2</sup> increases from 0.05 to 0.22 in their sample of non-large (mainly emerging) countries, and from 0.07 to 0.44 in their sample of advanced countries, with the rest of the discrepancy attributable to differences in country and time sample coverage as well as estimation methodology. If in addition the factors are augmented with global variables, the mean adjusted _R_<sup>2</sup> rise to 0.45 and 0.61, respectively. Unsurprisingly, aggregation across flows and/or over time smooths out high-frequency flow-specific idiosyncratic shocks, thus raising the relative role of common shocks. Figure 5 depicts the variance contribution of the global and group factors across individual countries, along with the bootstrap-based two-standard error bands of their combined total. There is considerable heterogeneity in the quantitative role of the common factors, even across countries within the same group. Their role is biggest in the Netherlands, where almost 90 percent of the variance of both gross inflows and outflows is driven by common shocks. The same country exhibits the largest variance contribution of global shocks – over 70 percent for both inflows and outflows. The latter figures are very similar to India’s, which is the emerging market exhibiting the biggest relative contribution of common shocks. At the other end, New Zealand shows the smallest contribution among advanced economies, while among emerging markets that role corresponds to Pakistan. On the basis of the computed standard errors, common shocks represent a statistically significant force in the vast majority of advanced countries (the only exception is New Zealand in the case of gross outflows, plus Japan and Finland in the case of inflows), but in less than half of the emerging countries shown. Still, the largest emerging economies in the sample – Brazil, India, China – do exhibit significant effects of common shocks, both statistically and quantitatively.

The preceding results refer to the fraction of the variance of capital flows attributable to the common factors. From the macroeconomic perspective, however, a more relevant measure of countries’ vulnerability to common shocks is the absolute (rather than relative) exposure of their financial flows, expressed as percent of their respective GDP. In this vein, Table 6 shows the standard deviation of gross inflows and outflows explained by the factor model.<sup>26</sup> 26This is simply computed as the square root of the product of the variance of the flow 19
Putting the results of these various exercises together, we conclude that direct reports of profits are likely to be less noisy and more reliable than asking firms for all the details of the revenues and expenses. The reports one gets seem to give reasonable rankings across firms in terms of observed transactions, but are likely to understate the true profit levels.

The remainder of the paper is structured as follows. Section 2 describes the data and Section 3 calculates the correlations between profits and revenue minus expenses. Sections 4 and 5 adjust for unreported categories and for mismatching of revenue and expenses respectively. Section 6 examines recall errors, Section 7 describes our bookkeeping experiments, and Section 8 considers deliberate misreporting. Section 9 concludes.

# **2. Data**

We use data from two panel surveys of Sri Lankan microenterprises, both designed by the authors and collected in 2005 and 2006. The first survey we use is the Sri Lanka Microenterprise Survey (SLMS), carried out in three Western and Southern districts of Sri Lanka: Kalutara, Galle and Matara.<sup>1</sup> The baseline survey was carried out in April 2005, with firms then re-interviewed at three-monthly intervals, with data from eight waves used here. A door-to-door screening survey of households was used to identify enterprises with invested capital of 100,000 Rupees (about US$1000) or less, excluding investments in land and buildings. We chose 618 enterprises in retail trade and manufacturing operated by owners aged 22 to 65, and with no paid employees. The enterprises include common self-employment activities such as running small grocery stores, selling tea, food preparation (e.g. string hoppers), sewing clothes, making lace products, and coir production. They therefore cover a range of typical small-scale activities in many developing countries.

The second survey is the Kandy Microenterprise Survey (KMS), a specialized survey taken of retail trade firms in Kandy area for the purposes of a bookkeeping and

> 1 Fieldwork was carried out by ACNielsen Lanka (Pvt) Ltd. See de Mel, McKenzie and Woodruff (2007) for further details.

4
Administrative Justice (CHRAJ) has reported severe cases of human rights abuses<sup>30</sup> associated with large-scale mining in Ghana (see Table 4 ).<sup>31</sup> 67. Stakeholders have expressed concern about small-scale mining activities on similar grounds. Artisanal miners lack knowledge about the harmful impact of their mining activities on their health and safety and on the community. Harmful impacts on the community are a result of the contamination of local water supplies by the careless use of mercury and other inefficient production methods, as well as the miners’ unwillingness to participate in land rehabilitation. To mitigate such potential problems, the government in 1992 approved the establishment of a Minerals Development Fund. The fund is to receive 20 percent of mineral royalties paid to the IRS. Half of the amount paid would be distributed among the DAs, landowner representatives, and people or communities adversely affected by mining (Tsikata 1997). Given severe ineffectiveness in the management of this fund, however, it was recreated in May 2008 and renamed the Mining Common Fund. This new fund will support the development of communities in mining areas. In addition, the government has set up a highlevel committee on mining to better secure positive impacts of mining in the communities where these mines are situated (Dalberg/World Bank Group 2007).

# **Table 4: Findings of CHRAJ on Investigation into the State of Human Rights in Mining Areas in Ghana**

Evidence of widespread violations of human rights abuses of individual members of the communities and communities’ collective rights in some mining areas; Widespread pollution of communities’ water sources, deprivation and loss of livelihood; Recklessness by the security agencies and the security contractors of the mining companies with accompanying brutalities; Inadequate compensation for destroyed properties; Denial use/misuse of communities’ benefits from royalty taxes; Ill-conceived alternative livelihood projects; Absence of effective channels of communications/consultations between companies and communities; Collusion of some traditional authorities and state institutions with companies against communities; Sustained brutalities against galamsey operators; Health problems; Reckless spillage of cyanide; Unfulfilled promises of employment; Mining companies maintain private security personnel to protect their concessions and property from encroachers/trespassers; Use of state security and guard dogs in the fight against the encroachment of galamsey operators; Child labor in the small-scale mining sector; and Incidents of police and military brutalities in mining communities through their ―Operation Flush Out.‖ _Source:_ CHRAJ 2008.

26
to the fluctuations of the supply of primary factors of production such as labor and capital. In this sense, the link between trade in inputs and GDP co-movement between two countries comes from the _complementarity_ between foreign and domestic factors of production and is not necessarily related to the share of domestic production that is re-exported. Since production for the domestic market is typically much larger that production for exports, it is only natural that the former has more impact on GDP movement than the latter. Finally, the table highlights the importance of _trade network_ with significant and positive relationship between the third index and GDP correlation.

**Table. 10.** Global Value Chain: All countries

||corr G|DP<sup>_HP_</sup>|corr G|DP<sup>_BK_</sup>|corr|∆GDP|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ln(_DVAx_f in_)|0.015||0.045<sup>_∗∗_</sup>||0.048<sup>_∗∗_</sup>||
||(0.022)||(0.021)||(0.021)||
|ln(_DVAx_int_)|**0.090**<sup>_∗∗∗_</sup>||**0.105**<sup>_∗∗∗_</sup>||**0.108**<sup>_∗∗∗_</sup>||
||(0.028)||(0.028)||(0.028)||
|ln(_DVARF_)|_−_0.109<sup>_∗∗∗_</sup>||_−_0.145<sup>_∗∗∗_</sup>||_−_0.136<sup>_∗∗∗_</sup>||
||(0.025)||(0.025)||(0.025)||
|ln(_Gexp_f in_)||0.008||0.039||0.026|
|||(0.025)||(0.024)||(0.024)|
|ln(_Gexp_int_)||**0.117**<sup>_∗∗∗_</sup>||**0.141**<sup>_∗∗∗_</sup>||**0.152**<sup>_∗∗∗_</sup>|
|||(0.036)||(0.036)||(0.036)|
|ln(_GEREF_)||_−_0.108<sup>_∗∗∗_</sup>||_−_0.153<sup>_∗∗∗_</sup>||_−_0.144<sup>_∗∗∗_</sup>|
|||(0.027)||(0.027)||(0.027)|
|_thirdex_|**0.158**<sup>_∗∗_</sup><br>|**0.156**<sup>_∗∗_</sup><br>|**0.121**<sup>_∗∗_</sup><br>|**0.118**<sup>_∗∗_</sup><br>|0.093<br>|0.093<br>|
||(0.061)|(0.061)|(0.060)|(0.060)|(0.060)|(0.060)|
|_thirdIM_|_−_0.050|_−_0.048|_−_0.072|_−_0.068|_−_0.010|0.004|
||(0.076)|(0.075)|(0.075)|(0.074)|(0.075)|(0.074)|
|Country-Pair FE|Yes|Yes|No|Yes|No|Yes|
|Time Window FE|Yes|Yes|No|Yes|No|Yes|
|_N_<br>|17,120|17,224|17,120|17,224|17,120|17,224|
|R<sup>2</sup>|0.179|0.178|0.203|0.202|0.179|0.178|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. In parenthesis: std. deviation. Data are from 1990 to 2009, with two time-windows: 1990-1999 and 2000-2009.

# **3.7 Trade in Sticky products**

As argued in Antras (2016), the nature of buyer-seller relationship is an important element in Global Value Chains development. Some products are associated with a higher degree of rigidity in the buyer–seller relationships, which is sometimes associated with a high degree of product customization to integrate within the same production process. Based on Martin et al.

23
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