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

Protected-area strategies seek to conserve biodiversity in tropical forests by restricting infrastructure development in legally-demarcated zones. However, conflicts can arise when forested areas have significant agricultural potential. Within conservation areas, regulatory enforcement may be weakened by dominant economic interests. And some areas with high biodiversity value may be left unprotected so that development can proceed. By implication, both development and conservation may be hindered by a dichotomous policy regime that treats large areas as either completely protected or completely open for development. In reality, valuable economic and ecological resources have non-uniform, overlapping spatial distributions. Policy makers in the Lao People’s Democratic Republic should therefore be equipped to weigh contextspecific trade-offs between development and conservation objectives. This paper seeks to contribute by focusing on the trade-offs associated with road improvement. We develop and apply a high-resolution spatial model of road improvement impacts that includes both ecological risks and the economics of forest clearing.

The remainder of the paper is organized as follows. Section 2 reviews prior empirical research on the economics of forest clearing globally. In Section 3, we motivate our exercise with a theoretical economic model of road improvement and deforestation. Sections 2 and 3 largely replicate the discussion in Dasgupta and Wheeler (2016). Section 4 describes our spatiallyformatted database for Lao PDR, while Section 5 specifies and estimates a deforestation model that incorporates the impact of road improvement. In Section 6, we prepare for the assessment of ecological impacts by developing a composite high-resolution ecological risk indicator for Lao PDR. Section 7 uses the results of Sections 5 and 6 to develop high-resolution estimates of potential ecological risks from a national road improvement program in Lao PDR. Section 8 summarizes and concludes the paper.

2
18 Table 3 Net Bank Credit to Non-bank Financial Institutions (Selected years, percent of GDP)

|Year|All Sample Countries|Latin|East Asia(5)|Middle East &|
|---|---|---|---|---|
||<br>with data|America (7)|<br>(excl. China)|North Africa (3)|
|1990|0.1|-1.0|1.2|0.9|
|1993|-0.1|-1.9|1.8|0.4|
|1995|0.4|-0.7|2.4|0.5|
|2000|1.1|-1.1|4.1|3.6|

In East Asia over the 1990s, banks were growing net lenders to NBFIs (except Korea), with credits in Malaysia and Thailand reaching the large figure of over 7 percent of GDP. In many cases, the initial growth of these credits reflected the development of bank-like NBFIs such as finance companies. Often these NBFIs were linked to the banks in a conglomerate structure. Toward the end of the decade, some banks in East Asia also became creditors of asset recovery institutions as part of the post crisis clean-up of their balance sheets.

In the rest of the countries the experience was mixed, with NBFIs accounting for relatively small amounts of bank credit even by the end of the decade. In the Middle East and North Africa, the whole growth in average credit to NBFIs is explained by the growth in bank credit to non-bank intermediaries in Morocco. In some countries, such as the Czech Republic and Russia, a small non-bank sector developed over the decade.

In many of the remaining countries, notably China, India, Korea, Hungary, Pakistan, Poland, reported net bank lending to non-banks was very small or zero but this may simply reflect a lack of separate reporting of these transactions. For example, bank credit other than to government may simply reported as a residual, as noted above, and non-deposit liabilities other than to the monetary authority may simply be aggregated into an “Other liabilities” category. However, this does not mean NBFIs and development banks do not exist in these countries. For example, in India, a large NBFI sector developed in the 1990s before suffering a crisis in 1997 (World Bank 2000b). Moreover, the three Indian development banks were among the largest financial institutions in the country in 2000 and the banks held bonds of these institutions.<sup>40</sup> In Pakistan, a number of small leasing companies, investment banks, Islamic banks, etc. exist, often linked to some of the banks.

# **IV.   Competition from On-shore and Off-shore Institutions and Markets.**

# **A.   Competition from Domestic NBFIs and Markets**

In many countries in the sample, a subset of NBFIs compete with banks in taking deposits and offering credit, albeit under somewhat different regulations than banks.

> 40  In 2002, one of these institutions merged into its associated commercial bank, becoming the second largest commercial bank in the country.
|**Table 16. Patterns of borrowing by Village**|**Fund comm**<br>Serv<br>|**ittee membe**<br>es on VF commit<br>|**rs**<br>tee<br>||
|---|---|---|---|---|
||Currently|No longer|Never|Overall|
|All VF borrowers|11.0|6.2|82.8|100.0|
|Expenditure per capita quintile|||||
|1 (poorest)|6.9|3.1|90.0|100.0|
|2|9.6|5.2|85.2|100.0|
|3 (middle)|11.7|7.1|81.2|100.0|
|4|14.9|8.3|76.8|100.0|
|5(richest)|15.0|9.7|75.3|100.0|
|Interest rate paid on VF borrowing (% p.a.)|||||
|Average|5.93|5.88|5.97|5.96|
|Excess over village average|0.015|-0.004|-0.002|0.00|
|Loan from VF (baht per loan per year)|||||
|Average|20,524|17,781|16,360|16,913|
|Excess over village average|2,366|749|-369|0|
|Proportion repaying on time (%)|||||
|Average|94.3|92.8|92.3|92.7|
|Excess over village average|1.5|0.1|-0.3|0.0|
|Expenditure per capita (baht per capita per month)|||||
|Average|4,115|4,696|3,507|3,647|
|Excess over village average|348|863|-129|-15|
|Source: Socio-economic Survey 2010.|||||

**<mark>Table 17.  Patterns of borrowing by expenditure per capita quintile</mark>**

||1(poorest)|2|3(middle)|4|5(richest)|
|---|---|---|---|---|---|
|Interest rate paid on VF borrowing (% p.a.)||||||
|Average|5.88|5.94|5.99|6.00|6.06|
|Excess over village average|0.014|-0.014|0.021|-0.019|-0.013|
|Loan from VF (baht per loan per year)||||||
|Average|15,954|16,000|17,011|18,292|18,634|
|Excess over village average|-503|-425|187|588|740|
|Proportion repaying on time (%)||||||
|Average|91.8|93.0|92.4|92.5|93.5|
|Excess over village average|-0.8|-0.1|0.1|0.9|0.2|
|Memo:||||||
|VF loansper adult|0.48|0.47|0.38|0.28|0.13|

Source: Socio-economic Survey 2010.

# **7. Assessing Village Fund Performance**

We now turn to the estimation of the determinants of the various measures of VF performance, starting with profit, and then moving on to the loan recovery rate, and the amount of funds available for lending.

# **Profit**

The “profit rate” measures VF income (mainly interest) less actual outgoings (interest costs on loans, dividends paid on shares, honoraria to VF committee members, administrative expenses), the imputed cost of unpaid time donated by VF committee members, and the interest that the VF would have had to pay on its 27
protracted litigation. Although there is a trend away from a regulatory approach towards a more market-orientated approach (e.g. public/private partnerships involving private developers and community-based organizations; use of fiscal instruments such as vacant property tax and/or luxury tax), progress to date has not generally been promising. For example, an experiment with a US-influenced model of Transferable Development Rights (TDRs) in Mumbai, which allows for negotiated land purchase by providing incentives to existing owners rather than compulsory acquisition, has done little to accelerate land acquisition for low-income housing or infrastructure development. Stakeholder analysis reveals opposition by those with formal land rights under the existing legal framework, and by municipal bureaucrats, to what they perceive to be the illegitimate acquisition of land rights by private sector developers on the one hand, and by

## **Table 4 Potential stakeholders in reform of land registration**

# **Relative degree of influence**

**High Low**

<!-- Start of picture text -->
*  Large landowners (owner-  *  Prospective buyers and<br>operator and absentee)  sellers of rural land<br>High  *  Revenue Inspectors  *  Lawyers?<br>*  Judiciary?<br>*  Potential buyers and sellers<br>of urban property<br>*  Housing development<br>corporations (public and<br>private)<br>0  Finance institutions<br>providing credit for housing<br>development<br>*  Rural credit institutions  *  Private sector surveyors<br>* NGOs & CBOs promoting<br>Low  interests of rural poor<br>* Agencies involved in R&R<br>(including donors)<br><!-- End of picture text -->

Note: Stakeholders shown in _italics_ expected to support proposed reforms; stakeholders underlined expected to oppose reforms 'squatters' in informal settlements on the other (Muttagi 1996). In this overall debate, it recognized that poor land records are one of several obstacles to progress. There are strong 33
As mentioned in Section **11,** since our selection criterion ziows a certain degree of arbitrariness in national specifications of R&D measurement, international comparison of the _levels_ of our proxies could be misleading. However, if national specifications have been timeinvariant, comparing the _growth rates_ of our proxies would be less problematic. This argument suggests that GSCP7085 or GSC17085 are better proxies than RNDIyY."4 At the same time, the results in Table 3 cannot be interpreted as demonstrating a causal relationship from R&D activities to economic growth. The positive partial correlation between GSCP7085 and economic growth may be due to simultaneous equation bias: Growth of R&D capital stock could simply follow economic growth. In order to control for this possible endogeneity, we estimated the relationship between initial R&D activities at the beginning of the sample period and subsequent economic growth, shown in Table 5.

Following Barro [1991] and other cross country studies, we regressed the growth rates of real GDP per capita between 1970 and 1985 on initial level of real GDP (GDP70), initial primary and secondary school enrollment rates (PRIM, SEC), and the ratio of government spending to GDP (GOV), together with a proxy for initial R&D capital stock,'<sup>5</sup> the average number of scientists and engineers per million population between 1970 and 1975 (SCI_P). We use the average value between 1970 and 1975 because some of our sample countries do '"We think this problem is more than a measurement error problem. First, international comparability of RND 1- Y is less problematic among _OECD countries,_ but we still have the same problem for OECD countries only. Second, the partial correlation between RND I -Y and GSCP7085 (GSC17085) is negative. The correlation coefficient between them are -0.48, -0.38, and --0.46 for the full sample, for OECD countries, and for developing countries, respectively. It suggests that both variables cannot be used as a proxy for the change of R&D capital stock. Section IV further discusses this negative finding.

15As shown in Mankiw, Romer, and Weil [19921, this specification can represen **t** he transitional dynamics of a neoclassical growth model with human and R&D capital.

10
The second type of sensitivity analysis compares estimation of govermnent size and growth regressions using Summers-Heston data with that using World Bank data. _Table I_ indicated the difference in the government consumption size measure that arose when using World Bank data in 1987 constant US dollar prices rather than international **prices.** In particular, government size in the low income countries of South Asia and Sub-Saharan Africa would be significantly reduced wvhen using 1987 constant dollar prices, while govenmment size in the high income countries would be increased relative to purchasing power parity values . To test the sensitivity of our results to the deflators, we now re-estimate the growth and size regressions but this time use the World Bank data in constant dollars. _Tables 11_ reports results of the size regression from the OLS and IV estimations for both constant dollar and international prices for the same set of countries.

While the explanatory power of the regression is very much lower using constant dollar values in the size regression (0.3675 versus 0.5791), the signs on the explanatory variables are generally the same. The main difference when using World Bank data is that we now find a significantly positive association bet. --en the income level and government size; an apparent confirmation of Wagner's LW<sup>4 6</sup> . One interpretation of growth - one or one and a half standard deviations greater than the sample mean of the per capita GDP growth rate -were removed. Fourth, observations with low or negative per capita GDP growth - one or one and a half standard deviations less than the sample mean of the per capita GDP growth rate - were removed.

- The greatest sensitivity -- as can be seen from Table **I** -- is in South Asia. To check the robustness of the estimates, we ran the same regressions without South Asia. The sign and size of the coefficients was not substantially affected.

- **46** A simple correlation of government size and per capita income is also positive and significant at the **I** percent level.

49
# **5 Results**

## **5.1 The impact of DSM on the quantity of seed purchases**

Table 4 presents the DID results for the impact of DSM on farmers’ purchases of maize and wheat seed, with different types of aggregation across cohorts (districts introduced to DSM within the same year) and survey years. The statistical test does not reject the assumption of pre-treatment parallel trends in DSM and non-DSM districts before the DSM intervention for both maize and wheat estimates. Results from cohort aggregation indicate that DSM led to a 15-percentage-point increase in the share of farmers purchasing maize seed across different cohorts, i.e., significant effects on the extensive margin. The impact of DSM on maize seed purchases is particularly strong in the 2017 and 2018 cohorts, showing a 27-percentage-point increase in the share of farmers purchasing maize seed. The results for the share of farmers purchasing maize seed are not statistically significant in the other cohorts.

The aggregation of the ATTs across districts under DSM in the two post-treatment survey years shows that the effect of DSM is positive and statistically significant in the 2019 survey year but not in 2016. Specifically, the impact of DSM on the percentage of farmers purchasing maize seed increased by 21 percentage points in the 2019 survey round. For wheat, while the estimates of DSM’s impact on the percentage of farmers purchasing seed are positive, none are statistically significant (Table 4).

15
Despite the sharp depreciation of the currency since July 1997, inflation has remained subdued (Chart P2), considerably below market expectations. This was mainly because of plentiful supplies of domestically produced rice -- a major component of the food, beverage and tobacco group which accounts for 60 percent of the basket. A significant slowdown in private consumption in response to the crisis was an additional contributing factor to the materialization of the lower than expected inflation.

<!-- Start of picture text --> Chart P1 Chart P2<br>INDEX OF INDUSTRIAL PRODUCTION EVOLUTION OF INTEREST RATES AND INFLATION<br>540-2<br>520_<br>500 . - I Z<br>480-<br>460f<br>420t<br>400-S<br>.0 3 40 960 5 9607 ' 59609' 904i 5701 90597b7 '7w03n 97-0.-I5 7 2400 2402 2.rI 9102 210 0 2100 0 0 0<br>II I neation (year-osn-year) - Reverse repo rate (weigthed)<br>-a--- D)iscount rate o--e---- Interbank cat loan rate<br><!-- End of picture text --> Chart P2 EVOLUTION OF INTEREST RATES AND INFLATION The lower than expected inflation performance, however, did not prevent interest rates from rising in the wake of the currency turmoil. As can be seen from Chart P2, interest rates have been increasing since July 1997.

<!-- Start of picture text --> Chart P3<br>REAL DOMESTIC CREDIT GROWTH<br>=a9 ee09 0 ba.Oth!) :209202 2<br>4<br>edor edOS o5 942.7 929 904 71 920 92 70 60 901<br><!-- End of picture text --> Chart P4 RATIO OF BANKS' RESERVES TO DEPOSITS The evolution of aggregate credit and liquidity conditions does not indicate excessive monetary tightening during the period under consideration. At the macro level, 28
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> 1 Also see Chaudhuri and Heller2003 for evidence on the highly positive impact of a campaign that empowered gram sabhas in the state of Kerala.

3
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