rafmacalaba/datause-encoder-data huggingface dataset:
32 standard errors in panel regressions from Table 4 and to look at _R_<sup>_2_</sup> measures of goodness of fit of year-specific cross-sectional regressions of fiscal decentralization on the same variables as in Table 4 (column _A_ ). Convergence is likely to result in declining mean standard error of the between panel residuals and in increasing _R_<sup>_2_</sup> measure of goodness of fit for individual crosssectional regressions.

However Figure 2 demonstrates that there seems to be no clear increasing or decreasing pattern in the variance of residuals or _R_<sup>_2_</sup> coefficients.<sup>28</sup> the explanatory power of the chosen determinants of fiscal decentralization neither improves nor deteriorates over time, which also points toward the absence of fast convergence to an “equilibrium” degree of fiscal decentralization.

**Figure 2. Analyzing convergence**

<!-- Start of picture text --> 0.65<br>0.6<br>0.55<br>0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>1996 1997 1998 1999 2000 2001<br>inverse of the s.e. of between panel regression residuals<br>R2 for cross-sectional regression<br><!-- End of picture text -->

# **3.7. The flypaper effect revisited.** Economists have long been puzzled by a so-called

flypaper effect. Following a one dollar increase in regional income regional government spending normally increases by about 10 cents. However should an increase in regional income come in the form of intergovernmental transfer, between 50 and 100 cents of every dollar is spent by the

> 28 For visible comparability the graph shows the _inverse_ of the mean standard error scaled down by the factor of 20 (since better fit corresponds to _higher_ R<sup>2</sup> and _lower_ mean standard error).
**Table 5: Capital account openness and policy mix**

<!-- Start of picture text --> Central bank rate policy<br>Pro- Counter-<br>A-cyclical Total<br>cyclical cyclical<br>Pro- cyclical<br>Reserve<br>A-cyclical -0.14 0.14 0.30 0.09<br>Requirement<br>policy<br>Counter-<br>-0.05 0.48 0.22<br>cyclical<br>Total -0.14 0.09 0.35<br><!-- End of picture text --> Note: The Chinn-Ito index of capital account openness is a standardized index of de jure capital account openness. The index is based on the binary dummy variables that codify the tabulation of restrictions on cross-border financial transactions reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). The index varies between 2.44 (most financially open) and -1.86 (least financial open). See http://web.pdx.edu/~ito/ChinnIto_website.htm for a detailed explanation regarding the construction of such index.

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percent of exports, and close to 39 percent of employment. The agro‐industry is the country’s second‐largest industrial subsector. It represents about 27 percent of industrial GDP and 3 percent of total GDP. The sector’s value‐added is estimated at around MAD 30 billion (US$3 billion). Agribusiness employs up to 143,000 people and is composed of 2,050 industrial units (mainly small and medium‐sized companies). Agribusiness output is mostly destined for the domestic market, with exported goods accounting for only 12 percent of total industrial exports.

For a long time, the agricultural sector was the main driving force of Morocco’s economic growth. Between 2000 to 2015, agricultural GDP was the fastest growing sector in Morocco. Although its share in production has fallen over time, it remains today a significant source of growth. Morocco’s GDP growth fluctuates in line with the volatility of weather‐ driven agricultural activities.

Agricultural productivity has been rising since 2001 as shown by the positive trend of agricultural value added per worker observed in Figure 3.2. This reflects large and sustained public investments made by the government to spur agricultural productivity and production, including mechanization of agricultural processes, improvement of agricultural workers’ conditions, and training provided to farmers.

**Figure 3.2 Morocco: Agriculture value added per worker, 1991‐2017 (1991 = 100)**

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>Agriculture, value added per worker (constant 2010 US$) index<br><!-- End of picture text --> _Source_ : WDI.

Yet, the agricultural sector has not been able to reap its full potential. Increase in production has mainly been the result of _factor accumulation_ and the _extensive margin_ of production. The World Bank (2016) points out that the sector has not transformed deeply enough to compete on international markets and remains a sector in transition. Value chains remain limited. This calls for a new model that will favor innovation (adoption of new innovative technologies, management of production factors, and regional and worldwide networks) to enable Morocco’s agricultural sector to face the challenges of a rapidly changing international environment.

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The other main decomposition we use works with net flows to a country (growth in total assets net of returns, ) by isolating the contribution of the growth in weights net of returns (relative flows) and injections to net flows, in the following manner, A nice feature of the decomposition in Equation (8) is that both terms have a very clear economic interpretation. The first term is the change in weights net of relative returns, which corresponds to relative flows of managers to a country, and the second corresponds to injections/redemptions into the fund. The flows of fund money to country increase either because the fund has injections by the underlying investors that are proportionally allocated to all countries, or because the fund manager is investing relatively more money into the country. Appendix 2 shows two other possible decompositions of gross and net flows.

The results of the decompositions in Equations (7) and (8) are reported in two separate panels of Table 8 and offer a good picture of the role of managers and investors in explaining gross and net capital flows to countries. Each panel reports two sets of results: the average contribution of each of the two components to the level and variance of each type of flow.

To illustrate what Table 8 reports, take the example of gross flows in Panel A. The calculation for the left side of the panel (the “shares”) is as follows: for each country and time period we compute the share of each component (growth in weights and growth in fund assets) in the growth of the country’s gross assets. We then compute for each country the average over time of each of these components, and finally take their average across all countries in each of the groupings in the rows.<sup>36</sup> The right side of each panel (“variance decomposition”) reports a standard variance decomposition exercise, where we assess the share of the total variance of gross flows that can be attributed to each component. We again first conduct the variance decomposition at the country level and then average across countries to reach the reported estimates. Because the two terms are not orthogonal, we follow Klenow and Rodriguez-Claire (2005) and impute the covariance term equally to each component (see Section 3).

Table 8, Panel A shows that both components of Equation (7) have roughly a similar impact on the level and fluctuations in gross assets (around a 40%-60% split depending on the

> 36 We use both a geographical grouping (Asia, Eastern Europe, and Latin America) and another one (developed, emerging, and non-emerging developing countries) taken from MSCI. Non-emerging developing countries are the ones considered frontier markets by MSCI.

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Policy Research Working Paper 7218

# **Abstract**

This paper uses a unique data set of panel firms from the World Bank’s Enterprise Surveys in 47 economies, to provide estimates of the patterns of firm exit, and analyzes various firm characteristics and conditions under which firms leave the market. Firms’ labor productivity and age are robustly associated with a lower likelihood of exit, consistent with conceptions of creative destruction and findings elsewhere in the literature. These findings are robust across several specifications. However, the effects are mitigated by other factors, such as use of bank financing and the presence of limited liability. Although firm size does appear to matter, its effect is lessened after accounting for labor productivity. The paper also provides basic estimates of job loss attributable to firm exit, estimating that on average 3 to 4 percent of private sector employment is lost per annum due to firm exit. Because of the challenges of data collection, the analysis relies on a necessarily conservative definition of exit and provides a framework for future work on utilizing such periodic survey panels to estimate the relative patterns of firm attrition and the associated job loss.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dfrancis@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Šauer _et al_ apply the model to an ideal case on the basis of river pollution levels and emission charges in the Czech Republic, with the goal of achieving the best distribution of Fund’s money to polluters. In the first negotiation round, the authority sets the vector of unit payments for each pollutant _j_ , denoted by _pj_ . These unit charges are taken from Czech regulations. The authority also computes the amount of payments to the fund in period 1, equal to ∑ ∑ _p j zij_ , where _zij_ is the emission of pollutant _j_ by _i j_ polluter _i_ . In the second stage, polluters compute the minimum subsidy requirement for the abatement investment, _Di_ , and provide information about the abated level of emission, _ei_ . Authorities are now in a position to rank proposed projects, and establish which ones will be funded given their budget constraint. If some funds remain unused, they are brought forward to the next time period. Polluters evaluate the subsidy support and, in the last round of negotiation, the outcome of the proposed solution is evaluated in terms of its environmental impact. If the minimum environmental quality is met, binding agreements are signed, and the negotiated plan is implemented. In the simulated results, the environmental constraints were met in round one of the negotiation.

This model presents an offer-counteroffer bargaining procedure, where agreement is sought over the distribution of subsidies for “environmental technologies”, which improve the quality of river water. The central authority bargains individually with polluters, but the strategic behavior of polluters is not analyzed in depth. In addition, there is no bargaining over the desired level of environmental quality – which is instead set by legislation. Yet, it is shown in the theoretical models (see Negotiation Theory – Part 1) that the issue space matters in determining the negotiated outcome. It is possible to envisage two different solutions to the game: either polluters cooperate on some or all environmental improving projects, and agree on how to share the costs and benefits of cooperation; or each polluter acts individually, in a non-cooperative fashion, without considering possible synergies with other operators.

Similarly, Kerschbamer and Maderner explore the implications of information asymmetries on the equilibrium compensation payments required by an upstream country to reduce river pollution. As illustrated in the models by Ambec and Sprumont (2000), Kilgour and Dinar (1995), and Kilgour and Dinar (2001), in the case of shared river the 30
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ 36% of Vietnam’s electronics exports<sup>10</sup> in 2009 were comprised of local value added.<sup>11</sup> Vietnam ranks low relative to its ASEAN counterparts in this regard: next to last among the eight ASEAN countries for which TiVA data are available (See Figure 3). Thus, while Vietnam’s ICT hardware export growth in recent years has been impressive; a closer look suggests that it has depended heavily on imported components. In fact, the foreign content of Vietnam’s hardware exports has grown relative to domestic content since 1995 (See Figure 4). While TiVA data are only available since 2009, our field research suggests that local content remains a small portion of Vietnam’s electronics hardware exports today, and that TiVA estimates exaggerate local value added. The consensus among the respondents interviewed in Vietnam was that FDI has grown markedly since liberalization, but that linkages between foreign investors and local Vietnamese firms remain very weak.

**Figure 3. Electronics Domestic Value Added as a Percentage of Exports, 2009**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Percent<br><!-- End of picture text --> _Source:_ OECD-WTO TiVA Dataset _Note:_ Myanmar and Lao are not included in the TiVA dataset

# _Global Lead Firms in Vietnam_

A number of global lead firms are directly active in Vietnam, most prominently Samsung, LG, Panasonic, and Nokia (now Microsoft). Samsung is one of the largest foreign investors in Vietnam with $9 billion invested to date, and an additional $3 billion smartphone factory under development. Its existing investments include a smartphone production facility in the northern Bac Ninh province, a smartphone and tablet display assembly facility, an electromechanical assembly operation for camera modules, and the Samsung Vietnam Mobile R&D Center. The Bac Ninh

> 10 The TiVA data set defines industries using the International Standard Industrial Classification (ISIC) system, revision 3. The electronics category is titled “electronics and optical equipment” and includes manufacturing codes for office, accounting and computing machinery; electrical machinery and apparatus; radio, television and communication equipment and apparatus; and medical, precision and optical instruments, watches and clocks. Thus, the definition of the electronics industry used by TiVA differs from the definition we are using for the UN Comtrade data presented above. The two data sources have not been harmonized, and therefore should not be compared directly.

> 11 “Trade in value-added describes a statistical approach used to estimate the source(s) of value (by country and industry) that is added in producing goods and services for export (and import).” For more information, see http://www.oecd.org/sti/ind/TIVA_FAQ_Final.pdf.

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- Ralston, Laura, Colin Andrews, and Allan Hsiao (2017). “The Impacts of Safety Nets in Africa: What Are We Learning?” _World Bank Policy Research Working Paper Series_ No. 8255.

- Ranchhod, Vimal (2006). “The Effect of the South African Old Age Pension on Labour Supply of The Elderly.” South African Journal of Economics 74(4): 725-744.

- Shah, Anju, Sendhil Mullainathan, and Eldar Shafir (2012) “Some Consequences of Having Too Little.” _Science_ 338(6107): 682–85.

- Woodruff, Christopher and Rene Zenteno (2007) “Migration networks and microenterprises in Mexico”, _Journal of Development Economics_ 82: 509-28.

- World Bank (2017) “Trends in Migration and Remittances 2017”, April, <u>http://www.worldbank.org/en/news/infographic/2017/04/21/trends-in-migration-andremittances-2017</u> [accessed 10 October, 2017].

- Yang, Dean (2008) “International Migration, Remittances, and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks”, _Economic Journal_ 118: 591-630.

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from the following regression: where _Share In f ormali_ is the informal budget share of household _i_ , _expenditurei_ is its total expenditure per person. We use household weights from each survey.

In Figure 3, we plot the aggregate informal budget share (Panel A) and the estimated IEC slope (Panel B) against countries’ per capita GDP. Panel A reveals a large drop in the aggregate informal budget share, from over 90% in the poorest countries to 20% in income countries. This decrease in the size of the informal sector over upper-middle development, obtained using our novel approach based on consumer shopping behavior, is consistent with patterns observed using alternative informality measures, based on labor markets (La Porta and Shleifer, 2014; Morrow et al., 2019) or money demand (Enste and Schneider, 2000). In Panel B, we see that the negative IEC slope first increases in magnitude, between low-income and lower-middle income countries, and then decreases, between lower-middle and upper-middle income countries. The average IEC slope is 9.8, implying a nearly 1 percentage point reduction in informal budget share when household expenditure increases by 10%.

**Robustness** Our results are robust to using the alternative formality assignment rules outlined in section 2.2. We re-estimate the informal shares and IEC us- budget slopes ing these two alternative rules and present results in Figure A2. Our key findings are unchanged: over development, the aggregate informal budget share decreases steeply and the IEC slope first increases then decreases in magnitude. In Figures A4 and A5, we show that this robustness is driven by the fact that those expenditure categories with least uncertainty surrounding formality status are also those with the steepest Engel slopes (including non-market purchases, large stores, and institutional services).

# **3.2 Understanding Differences in Informal Consumption across Households**

do households consume a share of their from the informal Why poorer higher budget sector? This question is important, for at least two reasons. First, by attempting to answer it we investigate the mechanisms behind the shape of IECs. Second, if differences in informal consumption across households can be explained by characteristics that governments can easily observe and target tax reductions or exemptions on, then de jure redistributive policies and de facto informality exemption may in practice have very similar redistributive impacts.

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# **Summary**

This paper reviews and analyzes the status of public debt management performance in small states, defined as states with a population of less than 2 million. The focus of the paper is on small states classified by the World Bank as low- or middle-income.<sup>1</sup> It draws on the findings of the Debt Management Performance Assessment (DeMPA) reports of 17 small states to assess the current state of debt management performance, identify strengths and weaknesses and contrast debt management performance in small states with that of other low and middle income countries for which DeMPA reports have been produced. Based on the 17 sample countries the broad conclusions are;

- Small states lag well behind other developing countries in most dimensions of debt management performance;

- Small states that issue domestic debt have better capacity to manage domestic debt as compared to their capacity to manage external debt despite the fact that, in most small states reliance on external sources of financing is, on average, much greater than on domestic markets;

- The debt management capacity of small states is striking similar, with relatively little difference observed between the strongest and the weakest performers or between small states in one geographic region and those in another;

- Debt management capacity in small states does not correlate to income levels with small states classified as low income exhibiting, on average, better debt management performance (in terms of the DeMPA scores) than small states classified as upper middle income.

Empirical evidence supports the view that the higher the quality of a country’s policies and institutions, the better is its capacity to carry debt and withstand exogenous shocks.<sup>2</sup> Borrowing for productive purposes can be an important element in boosting GDP growth but, conversely, excessive or indiscriminate borrowing can quickly offset the positive impact, deter new foreign and domestic investment, compromise reform programs, depress GDP growth and exacerbate the challenge of meeting debt service obligations. Poorly structured debt in terms of maturity,

> 1 The World Bank classifies countries by income groupings on their GNI per capita, according to Atlas methodology.  For FY13 low income countries: $1,025 or less, lower middle income countries: $1026-$4,035, upper middle income countries: $4,036-$12,475, high income: $12,476 and above.

> 2 Kraay, A and Nehru, V, 2006, _When is External Debt Sustainable?_ , World Bank Economic Review, Vol. 20, No. 3, pp 341-365.  See also, WB-IMF (May 2007), Strengthening Debt Management Practices - Lessons from Country Experiences and Issues Going Forward - Background Paper (Chapter III prepared by A. Prasad and F. Rowe).

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