rafmacalaba/datause-encoder-data huggingface dataset:
Change, forthcoming (a previous version is available as _OECD Environment Working Paper_ 4, ENV/WKP(2008)3. Paris: OECD).

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**Figure IV.3 Real local stock prices: July 1997 - February 1998 (July 1 = 100)**

<!-- Start of picture text --> The oc. -40 f A piona X been a = a _ oa ia a wr a aA a a , a a hA 4 -_ A MA tal ,~~I A -A , a - A-M rices fel sh a<br>1 0 --------<br>Source:. IF, Emrgn Makt DatBas<br>Bewe mi-aur and Apri 1998 maret reoude stonl in mos Eas<br>conris wit gan in domesti curec bewe 30 to4%n vnmre ndlaes<br>makt in East Asi hav in loa 5 sOO curec term s delie XandQb bewe 23 b i (th Phlppns<br>5 i_a -a_ _a_ a -__a a-aa -a a- ;s} i ss;<br><!-- End of picture text --> e Asia Source: IFC Emerging Markets DataBase Note: Stock prices in local currency have been deflated by using CPI-indexes. 44%~ (Kra. ~ T0,he xetin av en hn an Tia,,RC Between mid-January and April 1998 markets rebounded strongly in nost East Asian countries, with gains in domestic currency between 30% to 43%, and even more in dollar terms. Refecin _=the=_ stogcmven _1£_ durin ceti perod, corltos bet4we _._en_ stoc The exception has been Indonesia, where, after an initial gain, prices fell sharply after early February. Cumulative over the period since July 2, 1997 to mid-February, however, stock markets in East Asia have in local currency terms declined between 23% (the Philippines) and 44% (Korea). The exceptions have been China and Taiwan, ROC.

Reflecting the strong comovement during certain periods, correlations between stock prices in East Asia and in other emerging markets were at times very high. While before July 2, the average correlation among 22 major emerging markets was about 10%, during the crisis period the average correlation increased to about 20% over the period July 1997 to February 1998. During the October to mid-November period, correlations among these 22 emerging markets were on average close to 30%, and within regions correlalions amnong stock prices were even higher. Between the Philippines and Malaysia, for example, the correlation was 16% in the year before the crises, but rose to 29% during the crisis, and was _59%_ during the October to minNovember period. In case of Argentina and Brazil, the correlation was 44% in the year before the crises, but rose to 75% during the crisis, and was 84% during ithe October to mid-November period.

52
obtained on, for example, gross regional output and incomes, but survey data on the inter/intraregional trade flows of this output may be far less detailed. Inter-industry transactions may only be available at the national level, and international trade data may be very detailed, but based on a different product classification system. The notion of conservatism, both in the information theoretic sense and in terms of computational burden, should be the primary guiding principal in reconciling this information.

Robinson et al. (2001) interpret conservatism by the rule of using ‘only, and all’ information in the estimation problem. Considering this rule in the present context, the fact that a component such as gross regional outputs are available from highly detailed and reliable statistics suggests all this information should be used. However, if the associated intra/inter-regional trade flow account has more general product aggregations than the output account, it appears that one is faced with an ‘only **or** all’ decision. Although the specific situation often guides the approach one takes, it is worth noting that there are usually many opportunities to introduce all information available into the estimation process.

In practice, conserving on computational burden may also become an issue. When employing a more general estimation framework such as the model presented in this paper, the use of iterative techniques that diminish computational burden may not be readily available.<sup>13</sup> Both computer hardware and software available to the researchers may become binding in many such instances. For example, access to special solvers or greater programming finesse becomes a more prominent issue when computational burdens grow tremendously as model dimension increase. In addition, while conventional personal computers have improved dramatically, limits on current 32-bit operating systems to manage sufficient memory on PC’s may become a binding constraint for very large models. Solutions to these issues can become expensive.

# **_Add Additional Constraints to Use All Available Information_**

The greatest opportunities to use all relevant information are in the form of additional binding linear constraints, beyond the adding up and consistency requirements, on any selected 22
In order to reduce the subjectivity associated with ordinal scales, we use binary measures of each practice – the firm is either doing the practice or it is not. For many practices this involves imposing a time frame on the frequency of the practice. For example, under marketing practices, we measure whether or not a firm has visited at least one of its competitor’s businesses to see what prices its competitors are charging within a period of the last three months. A firm which only checks on the prices of the competition less frequently would then be coded as not employing this practice. Appendix 1 details each of the 26 practices.

We then define our main measure, the _business practices score_ , as the proportion of these 26 business practices used by a firm. This method of aggregation has several attractive features. It naturally lies between 0 and 1, so coefficients can be interpreted easily as the effect of employing none of the practices to employing all of the practices. It also allows to compare practices even when not all 26 questions are asked in a particular country or answered by a given firm, since we can scale by the proportion of questions answered.<sup>3</sup> Nevertheless, for robustness purposes we also considered the first principal component of the individual practices, as well as the average of standardized z-scores for each practice. The correlations between all three of these aggregates range between 0.965 and 0.997. As such, our results are robust to these alternative methods of aggregation, as we show in Table A1.

# **2.2 Data Collection**

These questions were included in surveys of micro and small enterprises conducted in seven countries between 2008 and 2014. These samples vary in their representativeness and size, since they were in most cases conducted as part of impact evaluations of particular programs. The surveys conducted in Bangladesh, Kenya, Mexico, and Sri Lanka provide representative samples of firms of particular size cutoffs, while those in Ghana and Nigeria come from applicants to business plan competitions. The Chile survey was administered to a sample of applicants to a government microenterprise training program. Appendix 2 provides more detail on each sample. A consequence of these differences in sample frames in different countries is that we will not be able to compare the levels of business practices across countries, as Bloom and Van Reenen (2007) did for management practices. Instead, this sample of over 20,000 small firms provides rich data

> 3 Four of the questions on financial planning were not asked in Kenya and Nigeria.

5
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# **Introduction**

Biodiversity conservation is essential for sustainable development, poverty reduction, and a healthy planet. It supports ecosystems that are fundamental to human livelihoods by providing food, clean water, and climate stability. Healthy ecosystems ensure the availability of natural resources critical to agriculture, fishing, and forestry, helping to alleviate poverty and promote sustainable economic growth. Additionally, biodiversity preservation supports industries like ecotourism and pharmaceuticals, which generate revenue and create jobs, further contributing to shared prosperity. Maintaining biodiversity is also a key to a livable planet, as it strengthens the resilience of ecosystems that regulate the climate by sequestering carbon and mitigating the impacts of climate change. Ecosystems such as forests, wetlands, and oceans act as natural buffers, stabilizing global temperatures and sustaining life on Earth. Thus, investing in biodiversity conservation is crucial not only for environmental health, but also for human well-being and global sustainability.

However, biodiversity is declining at an alarming rate. A recent UN report by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services revealed that 1 million species are near extinction, with the rate of extinctions accelerating despite increasing awareness of the interconnectedness between human life and nature (IPBES 2019). This report, based on a systematic review of approximately 15,000 scientific and governmental sources, aligns with the findings of the Living Planet Index,<sup>1</sup> which has recorded a 68% decline in global biodiversity since 1970. The primary drivers of this crisis include habitat destruction, overexploitation of resources, pollution, illegal wildlife trade, invasive species and climate change.

In response to these unprecedented losses, immediate and coordinated international action is required. Every country and territory must actively engage in biodiversity conservation to halt further biodiversity decline. Nations need to implement effective policies and conservation strategies tailored to local ecosystems, while leveraging global frameworks such as the Convention on Biological Diversity (CBD) and the United Nations Sustainable Development Goals (SDGs).

One of the significant barriers to effective conservation is the impact of human conflicts, particularly in weakly-governed areas. These regions often experience destructive effects on biodiversity, spanning terrestrial, marine, and freshwater ecosystems. Physical damage to landscapes, habitat destruction, pollution, and overexploitation of natural resources are common consequences of such conflicts. Military dimensions of conflicts can further exacerbate these challenges by diverting resources from environmental protection and disrupting natural food webs and migratory patterns (Rist, Norstrom, and Queiroz 2024; Parkinson and Cottrell 2022). The academic literature extensively documents the challenges of biodiversity conservation within

> 1 <u>htps://www.livingplanetndex.org/</u>

2
behavior. It starts with the development of an active mindset through action principles (Glaub et al., 2014), which is then refined and routinized with active practice and feedback during the training (Mensmann & Frese, 2017). The BSED training focuses on more traditional business skills but also seeks to develop a creative mindset _._ The entrepreneurship training programs offered by DOT are called StartUp! and ReachUp! and take an innovative approach to entrepreneurship development, with a mission to “help entrepreneurs learn basic technology and business skills, and to foster the self-esteem and entrepreneurial spirit needed to build sustainable livelihoods”. The DOT training focuses on building the life skills and mindset shift required of aspiring entrepreneurs to set and reach their goals.

The results from two randomized controlled trials (RCTs) that evaluate the effectiveness of these mindsetoriented trainings are mixed. The impact results reveal that only the DOT training in Mekelle achieved a positive statistically significant impact on monthly profits, measured one and two years after the training. For the PI and BSED trainings in Addis Ababa we find no evidence of an impact on profits or other measures of business performance, one and a half years after the training. The key channel of influence on profits in the DOT study seems to be through a mindset shift since we find evidence of higher average levels of entrepreneurial self-efficacy, personal initiative and entrepreneurial locus of control among the women who were trained one year post the training, relative to a control group. Entrepreneurial self-efficacy refers to one’s own belief in their entrepreneurial competences; and entrepreneurial locus of control refers to a sense of control over one’s business and the business environment in which they operate. We find no evidence of an impact on business practices such as improved book-keeping or marketing; or knowledge outcomes. In the PI and BSED trainings this mindset channel link is missing since we find no evidence of an average impact on the psychological outcomes included in the survey.

The lack of impact of the PI and BSED trainings in Ethiopia on psychology we hypothesize to be related to the implementation of the program. In Ethiopia existing skills development opportunities for entrepreneurs are offered mainly through government TVET colleges which provide classroom-based training on fixed schedules to large groups. The TVET system in Ethiopia is designed as a low-cost/high-
between hygiene, water quality, and sanitation. Overall, sanitation interventions were the focus of studies in only three regions: North America, South Asia, and Sub‐Saharan Africa. Finally, at the country level the most common intervention in India was sanitation, whereas in Bangladesh, handwashing evaluations prevailed, along with trials of point‐of‐use devices for water treatment.

**Figure 5. Different Regions Focus on Different Types of Interventions** (WASH impact evaluations by subgroup and region)

<!-- Start of picture text --> HYGIENE SANITATION WATER SUPPLY WATER QUALITY ALL MIXED INTERVENTIONS<br>100.0<br>80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>East Asia East Europe Latin America Middle East South Asia Sub‐Saharan Multiple<br>and Central and the and North Africa Country/<br>Asia Caribbean Africa Regions<br><!-- End of picture text --> _Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database.

# **_Research Methods_**

Most studies included in this review (71 percent) were experimental, mainly RCTs (Figure 6). In such a design, the intervention is offered randomly to a subset of the eligible population (the treatment group), while assigning the rest of that population to the control group.

**Figure 6. Randomization Far Exceeds Quasi‐Experimental Approaches**

(Identification strategy for WASH impact evaluations) <!-- Start of picture text --> 96<br>Number of Quasi‐experimental methods<br>evaluations<br>15<br>11<br>7<br>3 4<br>Randomization Matching Double Double Instrumental Other/<br>differences differences & variables Combined<br>matching<br><!-- End of picture text --> _Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database.

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- 23 -

As shown above, one likely results chain from road improvements to household income is that improved main roads contributed to farmers’ access to input markets, particularly for fertilizer. How about access to output markets? Equation (5) is estimated using the truncated regression model. Although the improvement of feeder roads was found to have little influence on farmers’ access to input markets, feeder roads do play an important role in providing access to the output market. The coefficient of _D_<sup>_URRAP2km_</sup> is estimated at 0.36 to 0.39, which is significant at least at the 10 percent level ( **Table 8** ).

By contrast, major roads do not have any impact on farmers’ output sales. This can be interpreted to mean that farmers are selling extra produce at their neighboring markets. This is in line with the above observation that most farmers travel fairly short distances to market (see **Figure 3** ). Therefore, feeder roads are far more critical than main roads, allowing the consolidation of produce which can then by distributed to larger market centers.

|**Table 8. Truncated**|**regressio**|**n of market**|**crop sales**|**with fixed-e**|**ffects**||||
|---|---|---|---|---|---|---|---|---|
|Dependent var.|ln_SALE_||||||||
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|D<sup>RSDP2km</sup>|-0.183|(0.245)|||-0.244|(0.243)|<sup>-0.548</sup>|<sup>(0.415)</sup>|
|D<sup>URRAP2km</sup>|||0.366|(0.220) *|0.392|(0.223) *|0.326|(0.236)|
|D<sup>RSDP2km</sup>•D<sup>URRAP2km</sup>|||||||0.539|(0.494)|
|ln_L_|0.040|(0.074)|0.051|(0.076)|0.052|(0.077)|0.048|(0.077)|
|ln_R_|0.022|(0.036)|0.026|(0.037)|0.028|(0.036)|0.027|(0.037)|
|ln_I_|0.007|(0.042)|0.003|(0.043)|0.005|(0.043)|0.005|(0.043)|
|ln_P_|-0.009|(0.019)|<sup>-0.007</sup>|<sup>(0.020)</sup>|<sup>-0.007</sup>|<sup>(0.020)</sup>|<sup>-0.008</sup>|<sup>(0.020)</sup>|
|ln_F_|0.031|(0.019) *|0.024|(0.019)|0.025|(0.019)|0.025|(0.019)|
|ln_SIZE_|-0.090|(0.246)|<sup>-0.106</sup>|<sup>(0.246)</sup>|<sup>-0.103</sup>|<sup>(0.245)</sup>|<sup>-0.095</sup>|<sup>(0.247)</sup>|
|ln_MALE_|0.240|(0.301)|0.226|(0.293)|0.237|(0.295)|0.217|(0.294)|
|ln_AGE_|0.241|(0.326)|0.239|(0.322)|0.231|(0.321)|0.234|(0.321)|
|EDU2|0.082|(0.251)|0.079|(0.249)|0.062|(0.250)|0.068|(0.249)|
|EDU3|-0.064|(0.363)|<sup>-0.060</sup>|<sup>(0.365)</sup>|<sup>-0.077</sup>|<sup>(0.365)</sup>|<sup>-0.065</sup>|<sup>(0.364)</sup>|
|EDU4|0.875|(0.504) *|0.936|(0.524) *|0.961|(0.517) *|0.900|(0.523) *|
|EDU5|-0.036|(0.511)|<sup>-0.007</sup>|<sup>(0.513)</sup>|<sup>-0.006</sup>|<sup>(0.510)</sup>|<sup>-0.012</sup>|<sup>(0.510)</sup>|
|EDU6|0.078|(0.578)|0.093|(0.600)|0.137|(0.575)|0.076|(0.598)|
|EDU7|0.070|(0.328)|0.146|(0.334)|0.155|(0.337)|0.136|(0.335)|
|COOK|-0.128|(0.467)|<sup>-0.111</sup>|<sup>(0.477)</sup>|<sup>-0.120</sup>|<sup>(0.471)</sup>|<sup>-0.140</sup>|<sup>(0.467)</sup>|
|LIGH|0.390|(0.228) *|0.448|(0.223) **|0.442|(0.224) *|0.476|(0.220) **|