rafmacalaba/data-use-mentions-extended huggingface dataset:
process[es]/negotiated deal[s]” under its transaction advisory work in PPP projects (World Bank 2020, p. 139).

These advantages explain why use of PPPs has been widespread across developing countries. For example, data from the World Bank Private Participation in Infrastructure (WBPI) database indicate that 134 developing countries implemented new PPPs in infrastructure between 2002 and 2011. Beyond infrastructure investments (which includes those in transport, energy, water and waste, telecommunications, and digital networks), PPPs have “increasingly moved into the provision of ‘social infrastructure,’ such as schools, hospitals, and health services,” (World Bank Group, 2014, p. 10), though infrastructure projects continue to dwarf those for social infrastructure in total volume (Inderst, 2020).<sup>9</sup> Overall, PPP investments averaged $30 billion per year in developing countries from 2002-2006 and $79 billion per year from 2007 to 2011 (World Bank Group 2014, p. 10). The report accompanying the 2022 update of the WBPI data noted that “The recovery of PPI that began after the COVID-19 pandemic has continued into 2022. In total, private sector investment commitments reached US$91.7 billion across 263 projects, equivalent to 0.25 percent of the GDP of all low- and middle-income countries” (World Bank, 2022c). While volumes have grown and total investment via PPPs is large relative to guarantees (though smaller than syndicated loans, as discussed below), they too fall far short of the trillions estimated to be necessary to achieve the SDGs. On the other hand, PPPs have been much more focused on the types of investments most likely to raise the marginal product of capital (those with high ‘A’ or ‘H’ in the stylized theoretical framework in section 1) than other financial structures discussed

> 9 For example, Inderst (2020) cites a PwC (2015) study which estimated that 15% of global infrastructure
The recent interest in commodity prices movements has led to additional suggestions as to their determinants. Svensson (2008) argues that global demand and supply shocks may be important for commodity prices. The importance of global demand as a determinant of commodity prices has also been highlighted by Wolf (2008). He emphasizes the increasing demand from emerging market economies such as China and India, as they become more prominent in world trade of commodities. Finally, according to Krugman (2008) as inventory holdings have not surged in recent years, speculation is a less convincing rationale for common or idiosyncratic movements in commodity prices. Instead, Krugman believes that a resource shortage is the main determinant of increases in the prices of primary commodities. Consistent with this view, the increase in oil prices may explain the contemporaneous increase in other commodities, such as foodstuff, via both cost effects on the energy intensive agriculture sector and substitution effects due to the increasing production of biofuels.

A recent empirical literature has investigated the determinants of fluctuations in commodity prices. This literature only partly relates to the above explanations. Akram (2009), for example, uses structural VAR models of quarterly data from 1990 to 2007 to separately look at the impact of real interest rates changes on the real price of crude oil, industrial raw materials, food and metals. Vansteenkiste (2009) extracts a dynamic principal component from a large set of monthly commodity prices for the recent period and tests its potential determinants using IV regressions. She finds that co-movement was highest in the 1970s and 1980s, declined in the 1990s and has recovered somewhat during the early years of this century. Also, she finds evidence that supply, global demand, exchange rate and real interest rate are important. Lombardi, Osbat and Schnatz (2010) also look at the quarterly commodity prices from the middle of the 1970s to 2008 and apply a FAVAR to assess the role of real 7
|
|Finance, insurance, real estate<br>and services to business|23.31|<br>5|3.60|<br>4|
|Community, social and|||||
|personnel services|14.07|9|3.30|5|
|Government services|47.85|3|2.02|7|
|**Economy-wide**|**9.40**|**-**|**100**|**-**|

Source: Authors’ calculations based on data from the INS.

in the mining sector, the country’s overall labor productivity would be multiplied by 2,230 times. Unfortunately, mining cannot absorb such large number of workers. Comparing sectors that have similar potential to absorb labor also reveals large gaps between sectors. For instance, productivity in construction is more than 10 times larger, and productivity in manufactures is 7.5 times larger than productivity in agriculture.<sup>5</sup> The productivity gap between agriculture and other sectors is also confirmed when comparing the situation in 2006 with that of 2015. The data also depict declining

> 5 For a discussion on the reasons for the productivity difference between agriculture and other sectors, see Mundlak _et al_ . (2012), and Gollin _et al_ . (2014a, b). In the specific case of Guinea, that difference could be due to weak and less efficient investment in the agriculture sector and the slow path of adoption of new technologies by that sector.

10
# 1 Introduction

Over 130 preferential trade agreements (PTAs) were formed in the last ten years—more than in the previous 50 years combined. Nearly all countries are currently members of at least one PTA and nearly a third of world trade is carried out under such agreements. Although most economists favor multilateral trade liberalization (MTL), there is no such consensus on the desirability of preferential liberalization. The original concern with PTAs was their ambiguous effect on welfare: positive if the preferential partner is more efficient than the rest of the world but negative otherwise (Viner [1950]). During the late 1980s and early 1990s, MTL was stalled while the United States and the European Union pursued PTAs, generating much debate on whether PTAs are a “building block” or a “stumbling block” toward MTL. (Bhagwati [1991]) This issue is also prominent in the current multilateral round with several developing countries fearing that MTL by the countries that provide them with preferential treatment will erode those preferences.<sup>1</sup> An important source of concern with PTAs is that they can hurt non-members. One direct channel by which this occurs is if the PTA members divert their import demand away from the non-members and that effect is large enough to reduce non-members’ export prices. There is evidence of trade diversion and also some direct evidence that PTAs do lower export prices for non-members.<sup>2</sup> This and other costs to the non-members due to discrimination disappear if the preference is fully eroded by MTL. Thus, it is crucial to determine if PTAs hold back MTL and entrench these costs, particularly given that two-thirds of world trade is not covered by any preferences. After much debate there is still no theoretical consensus about, and scant empirical evidence of, a “clash of liberalizations”. We provide evidence of such a clash during the last multilateral trade round by applying product-level protection data to estimate a model of the interaction of preferential and multilateral liberalization.

Most of the
WPS3762

# **Measuring the initial impacts on deforestation of Mato Grosso’s program for environmental control**

## Kenneth M. Chomitz<sup>1</sup> and Sheila Wertz-Kanounnikoff<sup>2</sup>

Rev 3.01

# **Abstract**

Although private forest use in Brazil has been regulated at least since the Forest Code of 1965, cumulative deforestation in the Brazilian Amazon reached 653,000 km<sup>2</sup> by 2003 (INPE 2004). Much of this deforestation is illegal. In 1999, the State Foundation of the Environment (FEMA) in Mato Grosso introduced an innovative licensing and enforcement system to increase compliance with land use regulations. If successful, the program would deter deforestation that contravenes those regulations, including deforestation of riverine and hillside forest (permanent preservation areas), and reduction of a property’s forest cover below a specified limit (the legal forest reserve requirement).

This study seeks to assess whether introduction of the program affected landholder behavior in the desired direction. Simple before/after comparisons are not suitable for this purpose, because there is considerable year to year variation in deforestation due to climatic and economic conditions. Nor is it valid to assess program impacts by comparing licensed and unlicensed landholders, even though the program focused its enforcement efforts on the former. This is because, first, landholders with no intention of deforesting may choose to become licensed; and second, unlicensed landholders may be deterred from deforestation by the mere existence of a serious program that aims for universal licensing.

To meet these challenges, the study applies a difference-in-difference approach to geographically explicit data. It looks for, and confirms, post-program declines in deforestation in high-priority enforcement areas relative to other areas; in more easily observed areas relative to less easily observed areas; in areas of low remaining forest cover (where further deforestation is probably illegal) relative to high remaining forest cover. Thus, even against a backdrop of higher aggregate deforestation (driven in part
much lower than the pre-war levels. Between 1920 and 1950, the country developed a more diversified economy and abandoned the mono-industry model of the silk era. Remittances continued to play an important role, but less than in 1880-1914. In the late 1950s, social and economic transformations drove growing numbers of the rural population out of their areas. Some of them emigrated, but the largest part settled in cities. The second generation of this new urban population had access to better education and was attracted by the booming economies of the oil rich countries of the Gulf, starting in the early 1970s. Since that period, remittances and foreign inflows have become once again a major element of GNDI. The war of 1975-1990 triggered a large outmigration and increased the reliance on foreign inflows, since large segments of the economy were severely damaged due to the direct and indirect consequences of the war. In the following paragraphs, I will be considering foreign inflows as a whole, since the development of the banking sector reduced the boundaries between remittances and capital inflows, making it possible to examine the economic impact of the two variables combined.

# **_1. Reliance on Inflows and Transfers in Modern Lebanon: A View through National Accounts_**

The National Accounts of Lebanon reflect the strong impact of the war on the Lebanese economy in terms of reduction in productive capacities and structural reliance on foreign inflows. Through extensive destruction of capital, the war reduced the productive capacities of the economy and increased the reliance on imports financed by substantial flows of income, transfers, and capital. Table 5 compares the structures of GDP and external accounts between the pre-war period (1972), the aftermath of the war (1994) and 2004. I chose 2004 because it is the year when the post-war reconstruction era ended. The champion of that era, former PM Rafik Hariri was assassinated in February 2005. Since then, no other economic and political project was proposed to the Lebanese society, and the rulers focused on extending the life of the post-reconstruction system they
(financial services). In fact, evidence increasingly suggests that service sector reform is a major potential source of gains in economic performance, including manufacturing productivity and the coordination of activities between and within firms. Efficient, low cost and high quality services generate economy-wide benefits. This is because goods production requires efficient services such as transport and logistics, telecommunications, business services, etcetera.

This paper explored for the Indonesian case, the role that service sector reforms, particularly in the form of reduced restrictions on FDI, played in the evolution of firms’ productivity in the manufacturing sector. We first constructed a service sector restrictiveness index for each manufacturing sub-sector by weighting the OECD restrictiveness index for FDI in the services sector in Indonesia with the intensities with which manufacturers relied on inputs from services sub-sectors – obtained from input-output tables. Then, we combined that restrictiveness index with firm-level data for over 20,000 Indonesian manufacturers, over the period 1997-2009, to understand the extent to which changes in service sector restrictiveness affected manufacturers’ TFP, and to shed light on which service sectors are particularly important for manufacturing productivity, which types of restrictions are particularly disruptive, and which types of firms are particularly harmed by these restrictions.

Our results reveal that the effects of service sector reform on Indonesian manufacturers’ productivity are economically meaningful. The changes in restrictiveness toward FDI that took place during the period under study explain about 8 percent of overall TFP growth in Indonesian manufacturing during the same period while the productivity gains were largely similar for domestic and foreign owned firms.

The gains do not accrue equally to all firms, however. More productive firms tended to benefit more from service sector reforms than less productive firms, although, controlling for the firm’s position in the distribution of TFP, those that carried out research and development investments, trained their workers, or produced more sophisticated goods tended to gain less from reform (because, perhaps, some sort of selection mechanism made them circumvent the existent restrictions in the first place).

Finally, not all reforms
into the system will require policy support actions that focus on the better integration of smallscale traders into regional supply chains.

In any case, the finding that the participation in group trade is skewed towards the betteroff traders limits the policy scope to use group trade as a broad-based formalization strategy. The current practice does not seem to be very inclusive and has made a limited contribution to poverty reduction. Other policy measures and strategies might be more effective in improving the wellbeing of poor small-scale traders and the livelihoods of border communities.

Our findings are to some extent limited due to the challenges of data collection in the Great Lakes region, a conflict-prone area, following the COVID-19 pandemic. Survey data were collected by interception of traders at official, secure border crossings during a two-week period in December 2021. The cross-sectional nature of the survey means that we might have missed seasonal variations in trading behavior. Also, as discussed earlier, we cannot rule out reverse causality.

Given the focus of the empirical survey and analysis in this study on determinants of group trade participation, the investigation of other policy actions to support border communities and the formalization process is left for future research. Complementary analysis seems also warranted on other potential determinants of group trade that were not covered by the Great Lakes region survey, as well as on any differences in marketing behavior related to the increased business volume. If participation in cooperative trading structures changes the sales partners, locations, and prices, then these marketing aspects are certainly policy-relevant and deserve further scrutiny. Finally, our study did not explore the reasons why poorer traders are underrepresented in group trade arrangements. Possible hypotheses could include higher risk aversion to engage in new activities due to fewer savings, concerns of being exploited by better25
(2020), respectively. “Baseline” indicates the variance decompositions of global inflation based on the global FAVAR model that consists of oil price growth, and the global factors of inflation, output growth, and interest rates extracted from the dataset of G7 countries. “Alternative narrative restriction” indicates that narrative restrictions are imposed on the historical contribution of oil price shocks on oil prices. “Additional narrative restriction” indicates that an additional narrative restriction is imposed such that the structural oil price shock is positive in March 2022 when the Russian invasion of Ukraine resulted in disruptions in global oil productions. “Alternative sign restriction” indicates that sign restrictions are imposed two months over impulse responses of interest rates following the global shocks. All the other restrictions are same as the baseline sign restriction.

35
69 1971‐72 1974‐75 1977‐78 1980‐81 1983‐84 1986‐87 1989‐90 1992‐93 1995‐96 1998‐99 2001‐02 2004‐05 2007‐08 2010‐11*<br><!-- End of picture text --> _Source:_ Authors using data from Directorate of Economics and Statistics, Dept. of Agric. and Cooperation With higher and more stable output, the government is compelled to procure increasing volumes of wheat at the pre-announced MSP. It acquires wheat early in the marketing season and then uses the accumulated stocks throughout the year to supply the PDS. Since the quantities

> 4 It is important to note that while production shocks, driven by the vagaries of the monsoons, are a fact of life in India, the largest production decline since 1950-51 was in 2002-03 at 7.01 million tons in absolute terms and 10% in relative terms from the production level of the previous year.  In terms of rainfall, 1972 and 2009 had the worst monsoon years over the past 110 years, each experiencing 22% less rainfall than the 110 year average. Yet wheat production fell in 1972 by only 1.7% but grew marginally (0.02% or 0.1 million tons) in 2009.

8
share. Such a situation implies that the negative effect of that particular channel is more than offset by the extension caused by the others.<sup>7</sup> Let me first look at the regional averages reported in the last row. I find that changes in the patterns of final consumption and trade created grounds for an industrial expansion. Shifts in trade shares have supported an extension of the industrial employment allocation and imply an average increase of 2pp. However, the amplifying force generated by the two channels is more than offset by that affected by shifts in labor market wedges. The exercise shows that the overtime changes in wedges, on average, shaved off 4pp from the industrial employment share.

Now, let's see how the inference drawn at the regional level compares with the experience of the constituent economies. Consistent with the regional finding, wedges generate contractionary pressure in each country. Still, its impact varies substantially. Implied shrinkage is comparatively much higher in Panama (8.7pp) and is also quantitatively above the regional average in El Salvador (5.2pp). On the other hand, the channel's impact is milder in Costa Rica and Honduras. Like for the region, the channel accounts for more than the total contraction in industrial share in three of the five economies that underwent deindustrialization and can explain 85 percent of the decline in Honduras. In contrast, though aiding the fall, it accounts for less than 15 percent of the 6.2pp decline in Costa Rica.

**Table 2: Actual and implied change in industrial shares**

||||**Actual**||**Implied c**|**hange (pp)**<br>**Final**|**Labor**|
|---|---|---|---|---|---|---|---|
|**Country**|*
supervision is included in the BCP’s but the data on BCP compliance (BCP 20) indicate that it is an area where many countries fail<sup>13</sup> . Indeed BCP 20 has the highest percentage of countries in either non-compliance or material noncompliance than any other Basel Core Principle and this area is singled out for special mention in a recent paper by the World Bank and the IMF on concerns regarding the pattern of BCP compliance<sup>14</sup> .

If we extend consolidation to the cross-border variety (BCP number 23), then the picture becomes even more worrisome. Cross-border here might include an “offshore bank” owned by a national bank or a retail or other type of bank operating in another jurisdiction. In the case of developing countries, especially those with a history of capital flight, cross-border consolidation can be important. In Latin America in particular such institutions have been the source of serious problems and regulators frequently do not have the appropriate authority, enforcement powers or information<sup>15</sup> .

The aim of Basel II is that banking supervisors will consolidate from the holding company of a banking group down and not just traditional consolidation, i.e., down from the bank within a holding group. This change is understandable given the changing face of financial group structures in many countries, but as many developing countries do not have traditional consolidated supervision, it represents a huge challenge<sup>16</sup> .

> 13 The wording in the new Accord does allow for a national supervisor not to consolidate all entities within a group (paragraphs 7 and 8 of the CP3).  However, the spirit of the Accord is clearly towards a deeper level of consolidation and in those instances where an entity within a group is not consolidated a) it must be subject to other (appropriate) regulation, b) it remains imperative that banking supervisors still have sufficient information on those entities and c) all investment in those entities must be deducted
the pre‐crisis upward trend for developing economy users, the 40% increase may have taken place even under more “normal” macroeconomic conditions had the 2008‐2009 crisis not occurred.

I address this question more closely in table 3. In addition to summarizing figure 3, table 3 also provides a breakdown, by policy‐imposing G20 economy, of the increase in the stock of product coverage of TTBs between 2007 and 2009 using the method defined in both equation (1) [column (2)] and equation (2) [column (4)]. The economies are ordered in the table by which had the largest percentage change in TTB product coverage between 2007 and 2009 using the _count_ method of equation (1). Three major emerging economies – India, Indonesia, and Argentina – lead the list with the largest increases in the stocks of products covered by TTBs during this period, again reflecting the information presented in figure 2.

# [TABLE 3 HERE]

The main piece of new data presented in table 3 are economy‐by‐economy _forecasts_ of the 2009 level of TTB coverage based on predictions from the historical data. Motivated by figure 3b, the thought experiment is simply to regress the 1997‐2007 import share data on a liner time trend, use the estimated coefficient from the regression to _predict_ the (out of sample) import share for 2009, and then to compare the prediction for 2009 with the realized data for 2009. I report in column (3) the prediction which uses the _count_ import share defined in equation (1), and I report in column (6) the prediction which uses the _value_ share defined in equation (2).

A table 3 comparison of column (2) with (3) and column (5) with (6) makes it difficult to conclude that the change in product coverage taking place between 2007 and 2009 is a substantial deviation from historical trends. First note that in columns (2) and (5),