rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In principle, place-based development programs can bring substantial advantages to firms (Duranton and Venables, 2018). The expectation is that firms in these zones would perform better because they would operate under better institutional arrangements, superior infrastructure, and benefit from other fiscal and non-fiscal incentives than their peers outside. Moreover, the urban economics literature emphasizes the returns from ”sharing, matching, and learning” brought about through agglomeration effects (Duranton and Puga, 2004) and improved matching between workers and firms through denser labor markets (Moretti, 2010).

In practical terms, policymakers find it attractive to adopt zone programs, under a variety of names, for understandable reasons (UNCTAD, 2019). Firstly, initiatives targeting subnational regions or discrete local areas are easier to implement than national-level enabling environment reforms, especially in countries with weak institutional capacity. Secondly, they are less costly to adopt than upgrading industrial infrastructure on an economy-wide scale, and if their financing is shared with private developers, the government budget burden is reduced. Thirdly, they can serve as an effective tool to attract international investors, who typically shop around globally for better investment incentives.

Despite the solid theoretical foundations supporting the advantages of spatial development programs, their real-world effectiveness is not assured. Global experiences with such programs have revealed potential pitfalls in their planning, development, and operation. These include costs stemming from inadequate strategic planning, a failure to adopt a demand-driven approach, neglect of critical market and government failures, a poor policy and regulatory environment, weak implementation capacity, and an inability to address environmental and social risks effectively (Zeng, 2021). As a result, the effectiveness of any given zone programs remains an empirical question.

The contribution of this paper is that it provides some answers to this question drawing from a comprehensive and unique firm-level survey conducted across South Asian countries. While there has been ample empirical work focused on the efficacy of zone programs in economically advanced countries (Neumark and Simpson (2015) provides an extensive 2
Montalvo and Reynal-Querol, 2005). Finally, in line with the literature we consider the effect of democracy, measured with the level of democracy using the Polity IV data set score for general openness of the political institutions, transformed into a dummy variable that takes value 1, if the score is greater or equal to 4, and 0 otherwise.

As military interventions for humanitarian and peacekeeping purposes are implemented once civil wars have intensified, we focus only on non-humanitarian and non-neutral military interventions, which may occur before an armed conflict begins and are likely to alter the balance of power and the winning probabilities of potential warring groups leading to armed conflict or the intensification of an existing one, as discussed in the theory section of this paper. We use the data set of International Military Intervention (IMI)<sup>28</sup> to define the intervention variable _Int_nhis_ . This data set records interventions that are purposeful, are the result of conscious decisions of national leaders, and involve “the movement of regular troops or forces of one country inside another, in the context of some political issue or dispute” (Pearson and Baumann, 1993). The data set excludes interventions that involve paramilitaries, government backed militias, private security forces, and other military units that are not part of the regular military of the state. The IMI data set contains a total of 1,243 cases of military interventions which meet these criteria for the period 1946-2005; they have been further classified as neutral, supportive of government or rebels, humanitarian, and other types.<sup>29</sup> This enables us to define external military intervention as a binary variable, _Int_nh,_ which takes the value 1 if there has been at least one intervention in the target country during the four years preceding the current period and the intervention was not neutral and

> 28 The IMI project was established in the late 1960s by Frederic S. Pearson and Robert A. Baumann. Under their guidance, 667 cases of international military interventions spanning the years 1946 to 1988 were coded.  Emizet N. Kisangani and Jeffrey Pickering expanded the IMI collection to 2005. Many studies have been done using the IMI data set, among others are Peksen (2012), Koga (2011), Sullivan and Koch (2009), Pearson et al. (2006), and Pickering and Kisangani (2006).

> 29 For the full list of variables consult the International Military Intervention, 1989-2005 notebook at http://www.researchconnections.org/ICPSR/studies/21282.

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# **Appendix B: Data Construction (NOT FOR PUBLICATION)**

# **B.1: List of Variables**

|**Variable**|**Description**|**Source**|
|---|---|---|
|**Political Connections**|||
|_Ben Ali Firm_|Dummy variable taking the value 1 if the firms is owned, fully or in part, by a<br>member of the Ben Ali clan|CC and<br>MoF|
|**Firm Characteristics**|||
|_L_|Number of salaried employees (annual average over 4 quarters)|INS|
|_Age_|The age of the firm defined as the difference between the current year minus the<br>year in which it first registered|INS|
|_Foreign_|A dummy variable taking the value 1 if a firm has majority foreign ownership, and 0<br>otherwise.|INS|
|_Offshore_|A dummy variable taking the value 1 if a firm operates in the tax regime ‘totalement<br>exportatrice’, commonly referred to as the offshore sector. Firms in this tax regime<br>do not have to pay output tax, provided they export at least 70% of their output or<br>sell it to other ‘offshore’ firms|INS|
|_Y_|Output as reported in firm’s annual tax declaration|MoF|
|_Profits_|Profits as reported in the firm’s annual tax declaration||
|_Gross Profits_|Profits for firms reporting positive profits and 0 for those reporting losses (The<br>maximum of 0 and profits as reported in the firm’s annual tax declaration)|MoF|
|_Gross Losses_|Profits for firms reporting losses and 0 for those reporting positive profits (The<br>minimum of 0 and profits as reported in the firm’s annual tax declaration)||
|_Profitable_|Dummy variable taking the value 1 if a firms reported positive profits and 0<br>otherwise|MoF|
|_Market Share_|The firms output divided by the sum of all output of firms operating in the same<br>five digit sector|MoF|
|**Regulation**|||
|Highly Regulated (de<br>Jure)|Dummy variable taking the value 1 if the 5 digit sector in which the firm is operating<br>is subject to both prior authorization and restrictions on FDI as stipulated in the<br>Tunisian Investment Code, and 0 otherwise|IC|
|Highly Regulated (de<br>Facto)<br>**Competition**|Dummy variable taking the value 1 if the 5 digit sector in which the firm is operating<br>is subject to an authorization which is de facto difficult to obtain, and 0 otherwise.<br>This measure is based on the expert judgment of one of Tunisia’s leading lawfirms<br>and inherently subjective.|Leading<br>lawfirm|

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<mark>hit by a major disaster, dependent firms experience an average drop by 2–3 percentage points in sales growth after the event. Given that suppliers represent a small share of firms’ total intermediate inputs, they conclude that these estimates are strikingly large.</mark> The vulnerability of firms depends on their sector and their position in the supply chain. Colon et al. (2019) show that, after the 2016 Morogoro flood in Tanzania, the supply chain amplification of disasters is higher for nonprimary products. Agriculture products, which as primary products are less dependent on other supply chains, are less affected by disruptions than secondary products (such as processed food), which rely on the supply of primary agricultural products. <mark>Mainly due to a lack of data, most research on supply chain propagation of disaster effects has focused on industrialized countries.</mark> Firms have a range of coping measures to mitigate losses, speed up recovery, and smoothen expenses to ensure business continuity. This study uses survey data to estimate whether Tanzanian firms implement similar coping measures to those identified by Dormady et al. (2017) in a case study of firms affected by Hurricane Sandy in the United States. The most common coping mechanisms relate to a firm’s decisions about its capital, assets, labor, inputs, and production technology (figure 2). But a lack of access to credit, inadequate governmental support, or limited cash can make certain coping measures unaffordable or inaccessible, particularly for informal and small firms (Rentschler et al. 2019a).

**Figure 2: Coping measures firms can use to mitigate the adverse effects of infrastructure disruptions**

|**Capital**|**Labor**|**Inputs**|**Technology**|
|---|---|---|---|
|- relocate|- hire/fire|- switch suppliers|- adjust|
|- replace<br>production assets|employees<br>- work overtime|- hold excess<br>inventories|- increase efficiency|
|- backup<br>machinery||- substitute<br>- use more/less||
|- reduced profit|**Finance**<br>- borrowing|**d through...**<br>- insurance|- governmental|
|margin|||support|

_Source: Rentschler et al (2019a), Dormady et al. (2017)_ Disasters not only cause damages in their aftermath, as background risk can suppress positive risk taking and investments. The mere possibility of a disaster can also prevent firms from hiring staff or investing in productive assets, such as machinery. Tanner et al. (2015) argue that the possibility of a disaster and its associated assets losses can reduce planning horizons and make business investments less attractive, causing firms to stay below their production possibilities and underperform. These inefficiencies should be accounted for as disaster losses that are incurred before the occurrence of a disaster. However, in the absence of dedicated surveys, the lack of data has made the quantification of this effect difficult.

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country that has been justly praised for some aspects of its education system, the 2000 PISA international assessment showed that 48 percent of Chilean 15-year-olds scored at Level 1 or below on a 5-level scale; by contrast, only 6 percent of Finnish children scored that poorly (Vegas & Petrow, 2007).<sup>15</sup> Taken together, these factors will tend to lower ω substantially, perhaps driving the excess return from schooling π into negative territory. They also drive a wedge between the ω and λ terms in (8), by in effect decreasing the efficiency with which λ is converted into ω _._ Both of these effects will increase the welfare losses for the current generation of the poor.

The notion of a low ω is not merely theoretical. One indication of the poor quality of public education is the defection of even poor households to private schools in some countries, a trend documented by recent work in South Asia and elsewhere. For example, Kingdon (1996) presents evidence that by 1993, 10 percent of 6- to 10-year-old students in rural India already attended private schools, and that among rural students in the large state of Uttar Pradesh, the share had reached 30 percent. Some parents are doubtless making this choice for reasons unrelated to expected learning gains – for religious reasons, for example. But the evidence suggests that many poor parents are choosing private schooling for their children primarily because of the low quality of education in the public schools, and that they are willing to pay more out of pocket to do this. In terms of the model of this paper, this corresponds to a situation where, under perfect capital markets, the following inequality holds: where ω<sup>pri</sup> represents the wage gains from and c<sup>_pri_</sup> represents the marginal costs of private schooling.<sup>16</sup> What this suggests is that the distributional implications of investments in educational quality may be even more ambiguous than the discussion in Section 2 indicated. Poor families may gain little from the additional public spending, and in the worst case may find themselves paying higher taxes even as their children remain in private school because of low public-school quality. Thus the welfare effects of higher taxes to finance educational improvements will depend heavily on whether those taxes translate into increased quality and on whether poor students attend public schools.

# **4. Implications for policy and analysis**

The model in the previous sections has shown that once we allow for a world with capital market imperfections and inefficiencies in public service delivery, the standard approaches to incidence analysis give a distorted view of who is being helped by education improvements. In particular, they fail to capture the very real intergenerational tradeoffs for poor households. Improvements in educational quality benefit future generations of poor people but not today’s adults, who often have to share the cost. As a result, 12
feasible. We discuss the existing definitions of SMEs below and explore to what extent the difference in definitions is associated with observed variation in the level of SME financing. In addition, we supplement the information on SME definitions available from the _Financial Access 2010_ survey with the information available from public sources.<sup>9</sup> The most common definitions used by regulators are based on the number of employees, sales and/or loan size. The most common among the three is the number-of-employees criterion. Sixty-eight countries provided information on the SME definition criteria used by the financial regulator. Fifty of them use the number-of-employees criterion, and 29 out of these 50 also use the other two criteria. A total of 41 regulators use maximum sales value criteria and 15 use maximum loan value criteria to define an SME (Table 2).

Number of employees and sale volumes are probably the most accurate parameters to define SME, but these data are not always available from lenders. Banks may collect this information at the time of evaluating loan applications, but often do not keep it in their systems and as a result they are not able to report lending volumes based on these criteria. As a result, some countries choose to rely on loan size as a proxy when collecting information on SME finance from financial institutions. Extracting information on loans to firms below a certain size and loans to individual entrepreneurs can be a reasonable approximation for SME lending volume. In _Financial Access 2010_ only 15 countries stated that they use loan size as a proxy for defining an SME. Similar to the definitions based on the number of employees and sales, there is a substantial amount of variation among countries.

Data availability and quality for SME lending depend on the way financial institutions and credit registries handle firm level data from loan applications, as they are the primary data collectors. Financial regulators collect these data from institutions and aggregate them. In this respect, there seems to be a need to harmonize SME definitions within each country even though such a task might be unfeasible across countries. In the long run, encouraging financial institutions to collect and maintain information on the number of employees and sale volumes in their systems will

> 9 Additional data were used from the following sources: UNDP country studies, MSME Country Indicators (WBG/IFC), and central bank websites.

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level, no industry reveals a systematic employment decline during the same period. These patterns of employment are consistent with Kien and Heo’s (2009) study which investigates formally the impacts of trade liberalization on employment in Vietnam using a system generalized method of moments (GMM) model.<sup>16</sup> Figure 6 and Appendix Table A.6 show how Vietnam’s industrial employment spread across Vietnam’s two big cities and eight regions. Of these regions, Ho Chi Minh City (HCMC), the Southeast region (excluding HCMC), and the Red River Delta region (excluding Hanoi) are host to a majority of Vietnam’s industrial employment, accounting for about 62 percent of total industrial employment in 2004. Growth in industrial employment has been faster in the rest of the Southeast region than in HCMC and in the rest of the Red River Delta than in Hanoi, showing that industrial development has spread beyond Vietnam’s two big cities. In contrast, the North Central Coast, the Central Highlands, and the Northwest regions are clearly lagging behind in terms of industrial development.

Vietnam’s establishment of normal trade relations with the United States appears to have induced a shift of its labor toward sectors that use the abundant factor more intensively. Using the _Enterprise Survey_ data, Figure 7.1 shows the proportion of workers employed by “laborintensive” industries in total industrial employment.<sup>17</sup> The ratio rose from 50.7 percent in 2001 to 53.6 percent in 2003 and then remained relatively unchanged, suggesting that Vietnam’s labor

> 16 Estimating a labor demand equation derived from the Cobb-Douglas production function for the period 19992004, Kien and Heo (2009) find that, holding the output level constant, the rising export intensity (measured by export-output ratio) in an industry increased Vietnam’s derived labor demand. This implies that an increase in exports may create more job opportunities per unit of output because export-oriented goods are labor-intensive in Vietnam. In terms of imports, whereas intensified import penetration would likely have destroyed some jobs, it may also have stimulated labor demand positively when domestic production depends on the importation of raw materials and capital goods or imported inputs are assembled in Vietnam for exports (e.g., in the electronics industry). The coefficients of import penetration (measured by import-output ratio) in Kien and Heo’s regressions turn out to be positive but statistically insignificant suggesting that the increase in imports did not necessarily negatively impact Vietnam’s employment level.

> 17 By the “labor-intensive industries”, I refer the industries whose employment coefficients in 2004 are above median, which is 6.5 persons per billion dong of output.

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Next, in Table 4, we analyze the impact of economic growth on the individual components of the MPI to identify potential heterogeneity and determine which components drive the current and lagged impacts.<sup>4</sup> All significant coefficients follow the expected direction, indicating that economic growth reduces deprivation in each measured form.

Table 4: MPI components and GDP

|VARIABLES|(1)<br>Nutrition|(2)<br>Child<br>mortality|(3)<br>Years of<br>schooling|(4)<br>School<br>attendance|(5)<br>Cooking<br>fuel|
|---|---|---|---|---|---|
|Change in GDP (current)|-0.390|-0.153|-0.365|-0.762**|-0.920|
||(0.325)|(0.299)|(0.382)|(0.296)|(0.775)|
|Change in GDP (lagged)|-0.644*|-0.837*|-0.191|0.222|-1.022***|
||(0.361)|(0.483)|(0.480)|(0.416)|(0.358)|
|Constant|-0.035***|-0.055***|-0.055***|-0.051***|-0.026|
||(0.010)|(0.011)|(0.015)|(0.011)|(0.026)|
|Observations|114|117|125|126|122|
|R-squared|0.052|0.044|0.008|0.041|0.078|
||(6)|(7)<br>Drinking|(8)|(9)|(10)|
|VARIABLES|Sanitation|water|Electricity|Housing|Assets|
|Change in GDP (current)|-1.805**|-0.502|-0.580|-0.948|-1.142***|
||(0.818)|(0.516)|(0.711)|(0.724)|(0.436)|
|Change in GDP (lagged)|-1.150**|-1.765***|-2.340**|-1.291***|-0.757*|
||(0.477)|(0.609)|(0.929)|(0.478)|(0.438)|
|Constant|-0.005|-0.046***|-0.043**|-0.030|-0.065***|
||(0.026)|(0.016)|(0.021)|(0.023)|(0.015)|
|Observations|126|124|118|122|124|
|R-squared|0.164|0.121|0.115|0.087|0.086|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

The health components, specifically nutrition and child mortality, are presented in Models 1 and 2 of Table 3. Both components exhibit a negative lagged response to economic growth, indicating that higher economic growth reduces nutritional deprivation and lowers child mortality rates, albeit with a delay.

Economic growth only weakly influences the education components of the MPI (Models 3 and 4). Years of schooling show no significant coefficient, while school attendance is only significant for the current change in GDP. This suggests that economic growth immediately impacts the decision to enroll or withdraw a child from school, whereas years of schooling is a more rigid measure that requires considerable time to change. Even the lagged coefficient for years of schooling is insignificant, underscoring its rigidity. This rigidity is expected, as the years of schooling indicator includes all household members aged ten and older, encompassing adults. Consequently, economic growth is unlikely to significantly alter the educational attainment of older

> 4 For a table with the combined current and lagged impact refer to Table A 3 in the appendix.

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# **Weathering the Storm: Responses by Cambodian Firms to the Global Financial Crisis**

Stephane Guimbert and Remco Oostendorp<sup>1</sup> JEL: D22 Firm behavior – empirical; D92 Intertemporal firm choice; J23 Labor demand Keywords: Firms; credit; crisis; risks; Cambodia Sector Board: Economic Policy

> 1 World Bank, Washington DC, sguimbert@worldbank.org, VU University Amsterdam, Tinbergen Institute, Amsterdam Institute for International Development, r.oostendorp@vu.nl. This paper is a product of the Poverty Reduction and Economic Management Unit, East Asia and Pacific Region. 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 sguimbert@worldbank.org and <u>r.oostendorp@vu.nl.</u>