rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5: Results with Productive and Non-productive Expenditures (panel OLS)**

**_Dependent variable: Growth rate of GDP per capita_**

||Fast growing<br>countries<br>**(1)**|Comparison<br>group<br>**~~(~~2)**|Whole sample<br>pooled<br>**~~(~~3)**|Whole sample<br>with dummies<br>**~~(~~4)**|
|---|---|---|---|---|
|Private investment<br>(% of GDP)|0.071<br>~~(~~1.235)|0.111<br>~~(~~0.975)|0.154<br>(3.244)***|0.086<br>~~(~~1.629)|
|Initial human capital|0.007<br>~~(~~0.987)|-0.015<br>(-1.326)|-0.010<br>(-1.689)*|-0.005<br>(-0.747)|
|Tax revenue|-0.201|-0.038|-0.023|-0.037|
|(% of GDP)|(-1.877)*|(-0.313)|(-0.287)|(-0.438)|
|Other revenue<br>(% of GDP)|-0.531<br>(-4.051)***|-0.346<br>(-1.548)|-0.144<br>(-1.853)*|-0.242<br>(-3.143)***|
|Productive expenditure (-1)<br>(% of GDP)|0.664<br>(4.314)***|-0.051<br>(-0.574)|0.048<br>~~(~~0.698)|...|
|DFAST*Productive expenditure (-1)<br>(% of GDP)|...|...|...|0.219<br>(2.985)***|
|DCOMP*Productive expenditure (-1) <br>(% of GDP)|...|...|...|0.153<br>~~(~~1.3)|
|Non-productive expenditure (-1)|0.312|0.107|0.076|-0.060|
|(% of GDP)|(1.786)*|~~(~~0.944)|~~(~~0.904)|(-0.657)|
|Budget surplus<br>(% of GDP)|0.450<br>(4.182)***|0.275<br>(2.318)**|0.184<br>(2.571)**|0.231<br>(3.106)***|
|Inflation - consumer price index|-0.208<br>(-5.76)***|-0.013<br>(-1.015)|-0.023<br>(-2.332)**|-0.019<br>(-1.813)*|
|Growth rate of GDP per capita (-1)|0.377<br>(3.644)***|0.255<br>(3.181)***|0.284<br>(4.442)***|0.264<br>(4.169)***|
|No. of observations|120|167|287|287|
|Adjusted R2|0.454|0.101|0.312|0.327|

Note: The estimation method is the ordinary least squares for panel data. Annual data are used. (-1) indicates variables lagged one period. D-FAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

36
|LC Disaster Management Project II|Tiguist Fisseha|
|---|---|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|LC Disaster Management Project II|Tiguist Fisseha|
|FOR ASEM 2,TF052649|L. Richard Meyers|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|CN-Ningbo Water and Environment Project|Meskerem Brhane|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|VN-PriorityInfrastructure Investment Project|CuongDuc Dang|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
and has been updated yearly<sup>10</sup> . The underlying satellite imagery is a combination of Landsat (30m) and MODIS (250m resolution). We aggregate total deforestation within each polygon between 2005 and 2010, and assign an indicator equal to one for polygons where deforestation over this period was greater than 2 hectares, and that had a baseline forest cover of greater than 10 percent.

We estimate a probit model with the binary deforestation variable as an outcome and include only exogenous covariates (geographic features, ecosystem indicators, baseline forest, and distances to small and large cities) with coefficients shown in table A6. These variables are strongly correlated with returns to agriculture and livestock production, which are the main sources of deforestation in Mexico. These variables also correlate with accessibility, which may drive illegal timber extraction, a secondary source of deforestation. When households in our survey were asked what their enrolled PSA land might be used for, the most common potential uses (when they could be specified) were agriculture and livestock. These responses are consistent with other research on the causes of deforestation in Mexico (Kolb & Galicia 2012).

Table A6 shows two prediction strategies, without and with state fixed effects. We use the estimated coefficients from column (1) to generate a probability of deforestation risk for each parcel of land. In order to assign a single value to the submitted land from each ejido, we aggregate the parcels using the mean. Results are nearly equivalent using predicted risk from column (2), which includes state fixed effects and/or using the maximum risk for polygons within an ejido.

# 4 Measuring willingness to accept

We are interested in measuring communities’ underlying willingness to accept payment for enrolling common property land in the PSA program, in order to understand the program’s opportunity costs and to construct supply curves for predicting responses to counterfactual payments and enrollment criteria. Payments in the PSA program are set administratively, and so provide only an upper bound on applicants’ WTA. In lieu of an auction, we exploit a standard methodology in environmental and resource economics, whereby survey participants receive a referendum question on a program at a randomly chosen price, and are asked to vote yes or no for the program, at that

> 10This dataset has important limitations. It was created for global analysis, and the accuracy of the algorithm has been shown to vary across countries and ecosystems (Burivalova et al. 2015). In Mexico, it is thought to underestimate forest cover loss.

15
# App. Table 2: Correlation between industry traits and specialization/diversity values

||Spe|cialization in|dex|D|iversity ind|ex|
|---|---|---|---|---|---|---|
||Average|Initial|Change|Average|Initial|Change|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. Industry traits from ASI/NSS in 2000|||||||
|Log labor intensity|-0.169|-0.075|-0.179|0.227|0.236|-0.063|
|Log capital intensity|0.137|0.315|-0.139|-0.041|-0.166|0.062|
|Log materials intensity|0.104|0.222|-0.163|0.021|-0.126|0.212|
|Log average wage|0.022|0.124|-0.171|0.008|-0.068|0.055|
|Log financial dependency|-0.172|-0.032|-0.277|0.260|0.183|0.146|
|Log import dependency|0.430*|0.664*|0.072|0.235|0.338|-0.173|
|Share of establ. that are female-owned|-0.258|-0.468*|0.271|-0.247|-0.255|-0.163|
|Share of establ. that are household-based|-0.134|-0.604*|0.417*|-0.373*|-0.136|-0.408*|
|B. Industry traits from ASI/NSS across 198|9-2010||||||
|Log growth in establishments|0.617*|0.132|0.814*|-0.281|-0.158|-0.295|
|Log growth in employment|-0.601*|-0.235|-0.766*|0.075|-0.027|0.241|
|Log growth in output|-0.666*|-0.166|-0.830*|0.205|0.033|0.347|
|Organized sector share of establishments|0.426*|0.554*|0.179|-0.109|-0.081|-0.210|
|Organized sector share of employment|0.092|0.510*|-0.343|0.313|0.038|0.334|
|Organized sector share of output|0.415*|0.582*|-0.081|0.177|0.217|-0.004|

Notes: Table documents univariate correlations between industry traits and specialization and diversity index values. Specialization and diversity index values use the employment distributions of industries across districts as shown in Table 5. Average and initial index values are expressed on a ten-point scale representing deciles. Change values are expressed in unit standard deviations. Industry traits in Panel A are from the 2000 ASI/NSS, measured at the national level. Intensity measures are measured relative to industry sales. Industry traits in Panel B are calculated from the ASI/NSS data over the 1989-2010 period. An asterisk denotes a correlation is statistically significant at the 10% level.
||**Tab**|**le A.1.6 Panel A** (continued|)||
|---|---|---|---|---|
|**Country name**|**Does the deposit**<br>**insurance**<br>**authority make the**<br>**decision to**<br>**intervene a bank?**<br>yes=1 no=0|<br> <br>**Does the deposit**<br>**insurance authority have**<br>**the legal power to cancel**<br>**or revoke deposit**<br>**insurance for any**<br>**participating bank?**<br>yes=1 no=0|<br> <br> <br>**As part of failure**<br>**resolution, how**<br>**many banks**<br>**closed or merged**<br>**in the last 5 years?**<br>number|<br>**Were depositors**<br>**wholly compensated**<br>**(to the extent of**<br>**legal protection) the**<br>**last time a bank**<br>**failed?**<br>yes=1 no=0|
|Lebanon|0|0|6|1|
|Liechtenstein|0|0|0||
|Lithuania|0|1|4|0|
|Luxembourg|0|1|0|1|
|<br>Macedonia|0|0|5|1|
|Malaysia|||3|1|
|Malta|0|0|0|0|
|Mexico|0|0||1|
|Netherlands|0|0|0|No cases|
|Nicaragua|0|0|6|1|
|Nigeria|0|1|30|0|
|<br>Norway|0|0|0|1|
|<br>Oman|1|1|0||
|Paraguay|||7||
|<br>Peru|0|0|7|1|
|Philippines|0|1|2|1|
|<br>Poland|0|0|21|1|
|Portugal|0|0|0||
|<br>Romania|0|0|10|1|
|Serbia & Montenegro|0|0||1|
|Slovak Republic|1|0|7|1|
|<br>Slovenia|1|0|0|No cases|
|Spain|0|0|0|1|
|Sri Lanka|0|1|||
|Sweden|0|1|0||
|Switzerland|0|1||1|
|Taiwan|0|0|0||
|Thailand|0|0|7|1|
|Trinidad & Tobago|0|0|0|1|
|<br>Turkey|0|0|24|1|
|<br>Turkmenistan|0|0|2|1|
|Ukraine|1|1|46|0|
|<br>United Kingdom|0|0||1|
|<br>United States|1|1|||
|Venezuela|0|0|0|1|
|Zimbabwe|||2|0|

Notes: The countries which did not provide answers for any of the survey questions listed here are excluded. Blank spaces indicate no response.

42
fluctuations; and _Controls_ stand for additional control variables such as country trends, as above. In these regressions, net capital flows are calculated using the standardized versions of _CIF_ and _COD_ . The trade balance in goods and services is also scaled by trend GDP, demeaned and standardized by its standard deviations at the country level.<sup>20</sup> Our measure of business cycles is based on real GDP in constant units of local currency.<sup>21</sup> More specifically, we use the growth rates in real GDP, which should capture accurately the current state of the economy over the business cycle.<sup>22</sup> The results are reported in Table 3. They show that net capital inflows are strongly associated with capital inflows by foreigners for all income groups. For high-income countries, they are also strongly correlated with capital outflows by domestic agents. However, such association is not as strong in middle- and low-income countries, where larger coefficients are estimated for _CIF_ . Note that net capital flows are calculated as the difference between _CIF_ and _COD_ , and are thus, by construction, correlated with our dependent variables. To partly avoid this correlation, we use the trade balance in goods and services as an alternative measure of capital flows to the extent that it captures the other side of the balance of payments. The estimated coefficients confirm the previous results. The trade balance is strongly correlated with capital flows by foreigners, and more so than flows by domestic agents in middle- and low-income countries.

Regarding the dynamics of gross capital flows during the business cycles, we find that gross capital flows expand during good times, while during bad times, they decline. In other

> 20 The data on the trade balance are from the IMF's _Balance of Payment Statistics Yearbooks_ .

> 21 Real GDP in constant units of local currency comes from the World Bank’s _World Development Indicators_ . This information was complemented with data from the IMF’s _World Economic Outlook 2009_ if the data from the original source were missing.

> 22 As an alternative measure of business cycles, we also considered a measure of output gap based on the HodrickPrescott filter. The results were qualitatively similar to the ones reported here.

17
module requires considerable resources and logistic arrangement, thus almost all small-scale surveys that are conducted on an _ad hoc_ basis would normally fall into this category.

Group B represents data situations that are less data-scarce but, on the other hand, cover a large number of cases. Consequently, it is useful to break this group into the following three smaller sub-cases i) the (cross-sectional) consumption data not comparable across survey rounds, ii) the (cross-sectional) consumption data unavailable in the current survey but available in another related survey, and iii) the (cross-sectional) consumption data unavailable at more disaggregated administrative levels than those in the current survey. Sub-case (i) thus concerns the data situations with most Sub-Saharan African countries and India as discussed earlier. Sub-case (ii) is relevant to all situations where we do not have consumption data in the survey under consideration, but have consumption data in other similar surveys that somehow can be linked to the former. An example of this sub-case is where we have a more recent LFS that has no consumption data but has a similar design to an older household consumption survey.<sup>9</sup> Given the significantly higher costs of implementing panel surveys, Group C represents situations with least data availability. This is especially true for developing countries. For example, two major household consumption surveys that are commonly employed to provide poverty estimates in China and India—the China Household Income Project (CHIP) survey and the National Sample Survey (NSS)—are both cross-sectional surveys.<sup>10</sup> For countries where panel surveys exist, few such surveys are likely to be representative of the population over a long period of time without a great deal of efforts. One major reason is that the surveyed household unit can

> 9 There is thus some overlap between this sub-case and the non-consumption surveys in Group A. But note that we focus on the _absence_ of consumption data in Group A, and the _similarity_ between consumption surveys and other surveys in Group B. Put differently, we consider non-consumption surveys on their own in Group A but highlight their relationship with other consumption surveys in Group B.

> 10 See also Gustafsson, Shi, and Sato (2014) for a detailed discussion of the various consumption surveys in China.

9
There remains some controversy about the relevance of the Rybczynski theorem in some cases. Cohen and Hsieh (2000) focused on the very large immigration of Russian Jews into Israel in the early 1990s and found results more in line with the single-sector neoclassical model: a short-run fall in the wages of native Israelis and a rise in the return to capital. Equilibrium was restored through an increase in the capital stock associated with increased external borrowing. This was, however, something of a special case. Investigation of the response of the output response to this shock, and the response itself, was complicated by the ambiguous skills endowment of the immigrants. While they were much more highly educated than the native population, they suffered substantial occupational downgrading which made it difficult to assess whether the output response should have involved outputs intensive in skilled or unskilled labor.

If one accepts the potential validity of the Rybczynski theorem as a potential cause of structural change, a key question is whether there have been major changes in relative factor endowments that would cause changes in the composition of developing country output away from dependence on resources. Recent data on accumulation of human and physical capital suggest that there have been quite sharp changes both between developed and developing regions, and between different developing country regions. The most comprehensive such database of which I am aware is that by Nehru and his coauthors (Nehru and Dhareshwar 1993; Nehru, Swanson and Dubey 1995).

**Table 1. Annual Changes in Factor Endowment Ratios**

||K/L|K/Q|Edn/L|Edn/Q|Secondar|y Tertiary|
|---|---|---|---|---|---|---|
||||||Edn/L|Edn/L|
||%|%|%|%|%|%|
|Industrial|3.7|1.1|0.3|-2.3|2.2|4.9|
|Developing|||||||
|East Asia|5.1|2.3|4.2|1.4|9.2|3.4|
|South Asia|3.2|1.4|3.3|1.5|4.3|6.4|
|Latin America|2.4|1.4|2.0|1.0|5.3|6.7|
|Sub-Saharan Africa|2.1|2.1|4.2|4.2|9.7|12.6|
|M. East & N. Africa|3.4|3.2|2.3|2.1|1.9|6.3|

Source: Nehru and Dhareshwar (1993); Nehru, Swanson and Dubey (1995). Rates for physical capital refer to 1960-90 and for education to 1960-87.

18
# **6. Conclusions**

Expanding the number of schools, and thereby reducing the average distance to the nearest school, is a frequently advocated approach to increasing enrollment—especially in the poorest countries where school availability is low. This systematic analysis of data from 21 developing countries suggests that while school availability is often statistically significantly associated with the probability of enrollment, the magnitude of the association is small. Interpreting the associations as the impact of availability on enrollment and simulating the effect of increase school availability suggests that increasing the availability of schools is unlikely to have much of an impact on average enrollment, and will not typically reduce inequalities by gender or wealth in enrollment. In some countries, it may even exacerbate inequalities.

These results suggest that policy interventions to raise enrollments—especially if the goal is the substantial increase required to reach universal primary enrollment—should not rely excessively on school construction. While the availability of school places might be necessary for a minimum level of schooling once a child enrolls, other interventions, such as those that raise the quality of schooling, or those that affect the demand for schooling directly through incentives to enroll or indirectly through the expected benefits to schooling, should be included in any strategy for education for all.

# **References**

The word “processed” describes informally reproduced works that may not be commonly available through library systems.

- Alderman, Harold, Jere R Behrman, Victor Lavy, Rekha Menon. 2001. “Child health and social enrollment: A longitudinal analysis.” _The Journal of Human Resources_ 36(1): 185-205.

- Beegle, Kathleen, and Kathleen Burke. 2003. “Why children aren’t attending school: The case of Northwestern Tanzania.” World Bank, Development Research Group, Washington, D.C. Processed.

- Bommier, Antoine, and Sylvie Lambert. 2000. “Education demand and age at school enrollment in Tanzania.” _The Journal of Human Resources_ . 35(1):177-203.

- Duflo, Esther. 2001. “Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment.” _The American Economic Review_ ,. 91(4):795-813.

- Filmer, Deon. 2000. “The Structure of Social Disadvantage in Education: Gender and Wealth.” Policy Research Working Paper No. 2268. World Bank, Washington, D.C.

- Filmer, Deon, and Lant Pritchett. 2001. “Estimating Wealth Effects without Expenditure Data – or Tears: With an Application to Educational Enrollments in States of India.” _Demography_ 38(1):115-32.

- Foster, Andrew D., and Mark R. Rosenzweig. 1996. “Technical Change and Human Capital Returns and Investments: Evidence from the Green Revolution.” _American Economic Review_ 86(4):931-53.

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# **6. BIBLIOGRAPHY**

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Akresh, Ilana Redstone. 2008. “Occupational Trajectories of Legal US Immigrants: Downgrading and Recovery.” Population and Development Review, 34(3): 435-456.

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Borjas, George. 1987. “Self-Selection and Earnings of Immigrants.” American Economic Review 77(4): 531-553.

Borjas, George. 1989. “Immigrant and Emigrant Earnings: A Longitudinal Study.” Economic Inquiry 27(1): 21-37.

Borjas, George. 1992. “National Origin and the Skills of Immigrants in the Postwar Period.” In R.B. Freeman (ed.): “Immigration and the Work Force.” Chicago: University of Chicago Press.

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