rafmacalaba/datause-encoder-data huggingface dataset:
etation cover, and Section 6 explores the role of the characteristics of forest communities surrounding the targeted forests in moderating the overall impact. Section 7 concludes.

# **2. Study context of Burkina Faso and the FIP project**

# _2.1. The role of fires in forest degradation_

Despite the Burkinab ́e government’s efforts to mitigate deforestation and forest degradation, the annual rate of deforestation is considerable. In the period between 1990 and 2010, deforestation rates were, on average, about 1 _._ 1% per annum (FAO, 2014). This rate of forest loss is especially worrisome as natural regeneration is hampered by the country’s very low levels of rainfall (on average about 600–1000 mm per year, concentrated on between 50 and 100 days in the rainy season; MECV (2014)); for more detailed information see Figure 1 (and then especially the grey bars therein). The rate of natural regeneration is thus low, and hence Burkina Faso’s forests are threatened by forest fragmentation as well as by a loss of resilience to extreme climate conditions (Miles et al., 2006).

The main proximate causes of forest cover loss in Burkina Faso are land conversion (especially for agriculture and cattle herding), logging (especially for the production of firewood and charcoal), and bush and forest fires; see Pouliot et al. (2012) and CIFOR (2016). Expansion of agricultural and pastoral activities is driven mainly by population growth (Pouliot et al., 2012; Ouedraogo et al., 2009) combined with very limited improvements in land productivity (Goldstein and Udry, 2008; Etongo et al., 2015). Population growth is also the main driver of the increasing demand for firewood and charcoal, the two most affordable energy sources for low-income, rural households (Ouedraogo, 2006; Bensch et al., 2015; Ouedraogo et al., 2011).

Forest fires, the third cause of forest degradation, can occur because of natural causes, but the vast majority of fires in Burkina Faso are man-made (Menaut et al., 1991; MECV, 2007; Devineau et al., 2010; Le Page et al., 2010; CIFOR, 2016). This is the case even 6
# 2011).

A related literature has tested whether a high expected return to human capital abroad affects potential migrants’ educational investment at the country level. Positive impacts on human capital formation in origin countries have been reported in a variety of contexts. A 10 percentage-point rise in the probability of future migration has been shown to cause a four percentage-point rise in the probability of intermediate school completion before migration in Cabo Verde (Batista, Lacuesta, and Vicente 2012). In Malawi, twenty years after migration shocks to South Africa took place, human capital was 4.8-6.9 percent higher among cohorts who were eligible for schooling in communities with the easiest access to migrant jobs (Dinkelman and Mariotti 2016). In Nepal, an increase in education requirements for Nepalese army recruits into overseas British Army service caused an increase in the average education of men in Nepal (Shrestha 2017b). Abarcar, Theoharides, et al. (2020) show that an increase in the emigration of nurses from the Philippines led to a rise in the number of nurses in the country, enabled by an increase in both the local demand for nursing programs and the domestic supply of nursing programs. In Fiji, an increase in exit opportunities led to a net increase in educational attainment of workers in the country, as only a fraction of prospective migrants ultimately migrated (Chand and Clemens 2019). In ten migrantsending countries in Eastern Europe, the education incentive channel, combined with return migration, has been shown to turn the drain on human capital into a brain gain (Mayr and Peri 2009). In Romania specifically, temporary emigration was found to have positive long-run effects on skill levels at home (Ambrosini, Mayr, Peri, and Radu 2015). Khanna, Theoharides, and Yang (2019) report similar findings for the Philippines.

The literature on endogenous human capital investment in response to migration opportunities has largely focused on high-skilled migration. The effect of temporary migration opportunities on human capital investment at home, however, depends on the skill-specific wage premium earned overseas. In contexts where low-skilled emigration predominates, one could argue that emigration opportunities may undermine investment in education, due to a large wage premium earned by low-skilled migrants as opposed to high-skilled workers. Evidence on this question from contexts were emigration is mostly low-skilled, however, is quite sparse. One of the very few studies looking at this question is McKenzie and Rapoport (2011) in the context of migration from Mexico to the US. They report a significant negative effect of migration on schooling attendance and attainment in Mexico: living in a migrant household lowers the chances of boys completing junior high school and of boys and girls completing high school. In the same context, the findings of Amuedo-Dorantes and Pozo (2010) point into the same direction: migration negatively impacts the school attendance of children and eliminates the positive effect of remittances once children in households with 25
unobservable factor is labor demand – when the demand for labor is increasing, more women are likely to join the labor force; at the same time, economies may be more likely to remove restrictions that prevent women from participating in the workforce to facilitate the provision of additional workers. To deal with this issue, we adopt the novel approach of Freyaldenhoven, Hansen and Shapiro (2019), FHS henceforth, who outline a methodology to identify causal effects in a panel data study when a time‐ varying unobservable confound η�,� is present, through the use of a covariate which is only related to the policy in question through the unobservable confound. To implement their approach, it is necessary to identify a covariate x�,� that is affected by the confounding variable η�,� but unaffected by the WBL index. Once a covariate has been identified, the authors show that β , the causal effect of the policy in question, can be estimated via two‐stage‐least squares (2SLS) regression of the outcome Y�,� on the policy variable of interest (in our case, WBL�,��� ) and the covariate x�,� using leads of the policy variable as an instrument for x�,� . As FHS explain, the covariate x�,� is a noisy proxy for the confound η�,� . If x�,� were a perfect proxy for the confound, simply adding it is a control variable would allow for causal identification of the parameter β .

Thus, the first model we estimate is a modified version of equation (1) in which we directly control for the covariate x in an OLS model: �,� Next, we proceed to implement the FHS procedure by estimating the following equation via 2SLS, using the lead of the policy variable (i.e., WBL�,��� ) as the instrument for x�,� : FHS note that the choice of the covariate x�,� should be based on economic reasoning; we employ as our covariate regional non‐agricultural value added, interacted with country i ’s oil rents as a percentage of GDP.<sup>7</sup> We hypothesize that this variable is an imperfect proxy for labor demand because, as economies in a region move away from agricultural production and towards manufacturing and services industries, this should cause the demand for labor to increase. We interact regional non‐agricultural value added with oil rents for several reasons – oil production has been tied to slower economic growth (for a discussion of

> 7 Data on oil rents as a percentage of GDP are from the World Bank’s WDI data set. Per unit oil rents are the difference between the world price of the commodity and the costs of producing it (the estimated extraction and harvesting costs). Per unit rents are multiplied by the physical quantities that countries extract and are presented as a share of GDP.

17
**AIO Indonesia**

**Table A10: Regional disparity** trends **in Indonesia**

|Year|Max/Min<br>(MMR)|Simple<br>coefficient<br>**of**variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CV|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>(Gu )|d Weighted<br>gini index<br>(Gw)|Theil<br>index<br>(T)|
|---|---|---|---|---|---|---|---|
|1983|22.5847013|1.278928412|1.045353512|0.524525937|0.498090815|0.327522886|0.291769|
|1984|23.5151753|1.259632203|0.986509155|0.510085267|0.495885587|0.321188141|0.271792|
|1985|23.456313|1.225443527|0.92956683|0.486817897|0.484608404|0.308012365|0.24848|
|1986|22.1419003|1.197745028|0.914691083|0.478128349|0.476612204|0.30382535|0.242335|
|1987|20.2619614|1.177594199|0.912915883|0.475944777|0.472133642|0.303754213|0.242334|
|1988|18.3373762|1.121082092|0.870920124|0.461382878|0.459062777|0.298613708|0.227941|
|1989|17.6853777|1.08754064|0.84622963|0.456606685|0.452021238|0.297733037|0.220859|
|1990|16.5142032|1.004046548|0.786066848|0.439602924|0.426054577|0.283251414|0.200953|
|1991|15.6473778|0.969170008|0.758619413|0.429445677|0.416552159|0.278166675|0.191102|
|1992|14.691083|0.935834451|0.730877648|0.419450668|0.406777244|0.272740377|0.180907|
|1993|12.0836414|0.854120074|0.724461673|0.408882556|0.386250383|0.27373039|0.179321|
|1994|12.0691998|0.856486923|0.725320312|0.40647926|0.384708797|0.273958672|0.179087|
|1995|11.3204885|0.832031106|0.714357197|0.402331558|0.378927738|0.272279979|0.175069|
|1996|11.2585855|0.829683054|0.715114513|0.401136398|0.378888842|0.272752728|0.175245|
|1 997|111.0479421|0.826971859|0.71556985|0.400533444|0.378321467|0.274007153|0.175501|
|1998|11.436067|[0.831810458|0.721908624|0.415908131|0.381198154|0.277069913|0.177892|

**Figure A1O: Regional inequality trends in Indonesia**

<!-- Start of picture text -->
1.4<br>1.2<br>4.a  . . ,  .;  . ,,  -,<  ..  _<br>E 0.8<br>E  .-.<br>O  ,<br>0.4<br>0.2<br>1983 1984 1985 1988 1987 1988 1989  1990 1991  1992 1993 1994 1995 1996 1997  1998<br>Year<br>Simple coefficient of variation (CVu)  '  Weighted coefficient of variation (cvw)  *  Relative mean Deviation (Rw)<br>4 Unweighted gini index (Gu)  +Weighted gini index (Gw)  -Theil  index (T)<br><!-- End of picture text -->

45
to a deficit of 1.4 percent of GDP in 1996. Oil revenue was generally higher than expected, however primary expenditure also exceeded budgetary allocations by very large margin, including large unrecorded extra-budgetary spending.

Oil revenue increased from below CFAF 2 billion in 1994 about 12 percent of oil export proceeds to CFAF 31 billion in 1998 around 13 percent of oil export proceeds. The ratio of government oil revenue to oil exports was small, as the production-sharing contracts initially signed by Equatorial Guinea were based on model contracts from a 1981 petroleum law designed to attract investors to an underdeveloped area with uncertain oil prospects.

In 1998, the government succeeded in renegotiating the Mobil contract. The most significant changes were (i) an increase in the royalty rate from the initial flat rate of 10 percent of gross revenues to a sliding scale ranging from 12 percent to 16 percent, (ii) the imposition of limits on cost deductions of an annual maximum of 80 percent pf gross export proceeds, (iii) a reduction in the uplift rate (margin added to cost carried forward) from 30 percent to 19 percent, and (iv) the assignment to the government of 5 percent share in equity. The increase in the royalty rate had an immediate positive impact on the government oil revenue, which increased to CFAF 53 billion in 1998, about 20 percent of oil export proceeds.

<!-- Start of picture text --> Equatorial Guinea: Fiscal Position 2000 - 2005<br>30<br>20<br>10<br>Primary non-oil Balance<br>0<br>Overall non-oil balance<br>2000 2001 2002 2003 2004 2005<br>-10<br>Overall Balance (including<br>Oil revenue)<br>-20<br>-30<br>-40<br>Years<br>Percentage of GDP<br><!-- End of picture text --> Source: IMF; Equatoguinean authorities.

Macroeconomic developments<sup>11</sup> continued to be dominated by the hydrocarbon sector, with oil and gas accounting for about 90 percent of GDP, 98 percent of exports and over 90 percent of government revenues. Real GDP grew by about 34 percent in 2004, driven by the sharp expansion in hydrocarbon production. Non-oil GDP increased by about 13 percent, owing to continued strong performance in the construction, infrastructure and service sectors simulated by government spending. In 2005 due to a slowdown in hydrocarbon production, the overall GDP growth decelerated sharply. Large

> 11 IMF – Republic of Equatorial Guinea: 2005 and 2006 Article IV Consultations.

9
teristics that may explain concentration (e.g., market size, consumer preferences) and δi,t is a product-year fixed effect that controls for global supply and demand factors in the product market that do not vary across countries (e.g., technological fixed costs, per-unit good value, logistics network requirements). The independent variable of interest is ExpComc,t defined as either the commodity share of exports or the export commodity price index, measured as differences from the sample median. Xc,t is a vector of country-year controls used in some specifications, also measured as differences from the sample median, and εc,i,t is an error.

This specification has several differences compared to Figure 1a. First, this specification focuses on the association of commodity exports with the level of HHI rather than the log. This approach is selected so results can be compared to a benchmark of how large an increase in HHI is presumed to increase market power. United States Federal Trade Commission (FTC) and Department of Justice (DOJ) guidelines presume that an increase in the HHI of 100 will increase market power in a highly concentrated market.<sup>7</sup> A market with HHI in excess of 1,800 is considered highly concentrated. Second, compared to Figure 1a, the regression weighs product markets equally rather than by value, so as not to focus primarily on highest value products (e.g., autos) and instead characterize the average product market.

Results for all products. Table 1, Panel A reports estimates of Equation 1 on a sample pooling across all imported products. Column 1 reports the raw correlation of the commodity export share (less its median) and HHI, with no fixed effects or controls. The mean dependent variable is 4,219, indicating on average highly concentrated markets according to FTC and DOJ guidelines. Here, β1 = 901 (standard error = 132). At the median, a one standard deviation increase in the commodity export share of 0.24 is associated with an increase in HHI equal to 901 × 0.24 = 216, more than sufficient to increase market power according 7See United States Federal Trade Commission and Department of Justice (2023). Previous FTC and DOJ guidelines presumed an increase in HHI of 200 would increase market power. Nocke and Whinston (2022) conclude that an increase in HHI between 100 and 200 can reduce consumer welfare given small efficiency gains from a horizontal merger that increases concentration.

10
15 Romania's exports. Among the CEE-5, Romania used to be the most important supplier of dtese products to the EC. However, Romanian exports collapsed in the 1988-91 period, with its share in EC imnports falling from 1.9 to 0.4 percent. The shre of the CEE-5 fell, however, from 2.3 percent to 1.6 percent.

Table 4: The "Four-Year-Delayed" Free Trde Group: Exports to the EC and Pre-fTAs Mirket Access

||Exports<br>1991|Share in Industria<br>Exports. 1991|l Index, 1991<br>1988=100|NTB Coverage<br>Ratio.|Simple Average<br>Tariff Rate|Range of<br>tariff rates<br>Max-Min|
|---|---|---|---|---|---|---|
||(in USS million)||(i|percent)|||
|Bulgaria|1|0.1|132|20.0|3.3|6.2-0.0|
|FCSK|2|0.1|312|100.0|3.1|6.2-0.0|
|Hungary|17|0.5|107|40.0|3.6|6.2-0.0|
|Poland|49|0.8|593|13.3|3.3|6.0-0.0|
|Romania|<br>27|1.6|21|0.0|3.2|6.0-0.0|

## Sources: See Table 2.

During the first year of the lTAs, exports of these products fell (with the exception of those from Poland which continued to expand by 18 percent). Despite a 20 percent reduction in tariffs, the value of exports from the FCSK fell by 38 percent and from Hungary by 35 percent The contraction in exports from Bulgaria (-52%) and Romania (-77%) was even larger.

# C. The "Quota/Five Year-Delayed" Free Trade Grou_p

The trade liberalizing measures in the ITAs for this group of industrial products are a mixtur of cuts in custom duties and increases in tariff quotas and ceilings. Custom duties are suspended within the limits of tariff quotas to be increased annually. The ITAs contain different stipulations concerning the annIua growth rate of tariff quotas: 15 percent for imports from Hungary, and 20 percent for imports from other CEE-5 countries. Simultaneously, custom duties on imports in excess of quotas are to be reduced progressively to zero by the end of the fifth year. The schedule of reduction in these customs duties, beginning on the day of entry into force of ITAs, calls for annual cutbacks of 15 percent on imports from Bulgaria, the FCSK, Poland and Romania and 10 percent on imports from Hungary. ITAs share the same stipulation that all duties be scrapped by the end of the fifth year, however.
If the first two ratios above are relatively high, the bank shows high dependence/reliance on short-term money market borrowings rather than on own sources of funding (like core deposits). If the third ratio is also high and it implies contractual commitments to disburse funds, the bank is also exposed to higher liquidity risks on the asset side.

As mentioned earlier, it is important to be cautious about balance sheet indicators which can be backward looking and can potentially give a false sense of security-implicitly suggesting that ‘the future is likely to be like the past’ when it is obvious that there are many surprises and ‘innovations’ around the corner. Certainly, it would be extremely risky to drive a car while just looking through the back mirror! For this reason, a lot of effort must be put into developing _prospective_ (as opposed to retrospective) approaches to liquidity requirements and management, while combining the absolute ratio analysis with a review of liquidity trends and peer analysis as suggested above.

- _A useful, but hard to estimate, forward looking bounded liquidity ratio_ : **1.0 < [Projected cash-in-flows/Projected cash-outflow] Bucket i < 1.20** (or pre-defined ‘comfort’ level).

- **_Liquidity Index (I )_ -** which compares ‘fire sale’ asset prices (Pf) with ‘fair market prices’ (Pm) to capture the degree of liquidity of a given asset as a function of how different these prices are.  I= ∑ [(wi) (Pf/Pm), where (wi) is the percentage of each asset in the bank’s portfolio and the ∑ (wi) =1.

47. The above list of indicators is not exhaustive and, as usual, they should be used with care. However, it points at the importance of banks having careful liquidity planning and a good understanding of the volatility of the different items in the bank’s balance sheet, particularly good probabilistic models of the degree of stability of different types of deposits.

**Recommendation 2** :  FSAPs should look more closely at the current position and especially to trends (3-5 years) and peer comparisons of changes in liquidity of the largest (5) banking institutions. At least the following ratios should be looked at: (i) Loans to Deposits (L/D); (ii) Short-term Liabilities to Short-term Assets (STL/STA); (iii) [(Market Funds- Liquid Assets)/ Total Assets]; (iv) Cash-in-hand; and (v) Wholesale Funds to Total Liabilities; as recommended by the OCC to its bank examiners. A more demanding test would be to require larger banks to provide their liquidity gap profiles (in different currencies), concentration of deposits, flow of funds analyses, and contingency funding plans (CFP). It is highly recommended to look at the CFP for the largest bank in the system.
2. In practical terms it implies that the change in poverty rates over time is attributable to changes in the explanatory variables _x_ rather than the returns to characteristics (or economic structure) and the unexplained characteristics (or random shocks)—which are respectively represented by β 1 and<sup>ε</sup> 1<sup>. In other words, given the same observed characteristics</sup><sup>_x_, households</sup> would be subject to the same level of poverty regardless of the time period the data were collected. While this assumption may seem counterintuitive, it may be especially relevant to economies where the returns to characteristics do not change or simply change little over time. Clearly, this is a testable assumption if household consumption is available for both of the periods under consideration.

As discussed earlier, previous studies commonly assume that the distributions of the household consumption parameters β 1 , μ 1 , and<sup>ε</sup> 1<sup>in equations (1) and (2) based on the data in</sup> survey (or period) 1 remain the same for the data in survey (period) 2. Assumption 2 is less restrictive since it allows for the estimated parameters to change over time, as long as the changes in the distribution of the variables _x alone_ can correctly capture the change in poverty rate. Technically speaking, Assumption 2 only requires that, overall the parts of the consumption distributions below the poverty line for both periods (that can be explained by the changes in _x_ in our model) be equal and not all the percentiles along the consumption distributions be equal as implied by the assumption made in existing studies; this result is formally stated in Corollary 1.2 below.<sup>12</sup>

> 12 Assumption 2 is also more general in the sense that, it practically allows for the estimated parameters to change even in different directions, as long as the changes in the _x_ variables can capture the net changes in poverty given the estimated parameters in period 1. Another difference between Assumption 2 and the stricter assumption of constant parameters related to model checking, is that the backward imputation (i.e., using the predicted coefficients from the later survey round to impute backwards on the data in the earlier survey round) may not necessarily yield the same results as the forward imputation. The difference in terms of prediction accuracy between the two would also depend on the changes in these predicted coefficients, in addition to the changes in the _x_ characteristics over time. The

15
<!-- Start of picture text --> 1 300<br>Incidence of Receiving Remittances Average Remittances<br>95% confidence interval Average Positive Remittances<br>95% confidence interval<br>200<br>.35<br>100<br>.12<br>0 0<br>3 6 9 12 14 3 6 9 12 14<br>Log of Lagged Asset Index Log of Lagged Asset Index<br>Rs 2003, 000's<br>Probability of receiving remittances<br><!-- End of picture text --> Figure 2: Non-parametric regression of the incidence of migration and amount of remittances by lagged asset index, NLSS 2004 40