rafmacalaba/datause-encoder-data huggingface dataset:
Krueger and Summers (1988).<sup>27</sup> The results show that the magnitude of the coefficient for the foreign dummy increased with the inclusion of industry variables. A potential explanation is that the jobs generated by foreign firms during the period reflected by the VHLSS 2004 are disproportionately represented by low-wage industries. Thus, controlling for industry affiliation, the estimated effect of foreign ownership may well increase as a negative omitted variable bias is removed.

Column 3 in Table 3 shows the results introducing the impact of location on wages. With the inclusion of urban and regional dummies, the effect of foreign ownership is reduced. The latter result is consistent with the view that higher wages by foreign firms are partly attributed to the relatively high-wage areas where foreign enterprises are typically located. Therefore, the coefficient for the foreign dummy might be reduced by attributing higher wages to locations rather than to foreign ownership. Using regression (3) as a basic model, foreign firms are found to pay about 46 percent<sup>28</sup> higher wages relative to similar workers in the informal wage sector. The wage premiums are highest for foreign firms followed by state and private organizations.

Columns 4-6 in Table 3 report the regression results for a subset of workers employed in the formal sector so that the sample is roughly comparable to the firm-level analysis (e.g., Ramstetter and Ngoc, 2007a). The domestic private sector is the omitted ownership category. Restricting the sample to workers in the formal sector, the foreign wage premium relative to the domestic “private” sector is estimated to be 30 percent.

Columns 1-5 in Table 4 report the regression results by subsets of workers with different educational attainments, namely, college/university graduates, workers who have diplomas for upper-secondary, lower-secondary and primary education, and those without any school degree.

> 27 Krueger and Summers (1988) use cross-sectional and longitudinal data and demonstrate that industry wage differentials are substantial after controlling for detailed individual characteristics. 28 0.38 in log terms.

13
# **C Heterogeneous Effects Analysis**

In our pre-analysis plan we proposed to investigate the heterogeneous effects of livelihood diversification for different population subgroups. Specifically, we planned to assess differences for maleand female-headed households as well as urban and rural households. We did not present these results in the main body of the paper since our primary results were not significant and because the subgroup analysis also produced null results. For completeness, we discuss the method used and results of the sub-group analysis here.

In Table D1 we present the distributions of all of the indices comparing urban and rural as well as male- and female-headed households. We only include the pre-COVID-19 data, even when post-outbreak rounds are available for that index. Urban households tend to be more specialized than rural households, a result that is particularly evident in Ethiopia. There are not notable differences in income diversification by head-of-household gender.

## **C.1 Method**

To estimate heterogeneous effects of livelihood diversification on welfare outcomes use the ANCOVA specifications discussed in section 3. We interact the sub-group indicator variables with livelihood diversification at baseline to understand the differential impacts for female headed households and rural households.

_subi_ is an indicator variable for population subgroups based on head-of-household gender or household sector for household _i_ . All other terms are as previously defined. The interaction term, _β_ 2, represents the differential impact of pre-COVID-19 livelihood diversification on household welfare outcomes for these population subgroups. Standard errors are clustered at the household level.

Similarly, we estimate heterogeneous effects using a standard difference-in-difference model, with out coefficient of interesting being on the triple difference effect of COVID-diversification-female headed/rural household ( _β_ 7): All other terms are previously defined and standard errors are clustered at the household.

49
_Table 6. Estimation results for females_

||||ln(C|A)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om eff|ects||OLS||Rand|om eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|can read|0.05|0.57|0.92|-0.03|0.77|0.97|-0.39|0.77|0.61|-0.46|0.94|0.63|
|education>2|0.42|0.59|0.48|0.43|0.80|0.59|-0.10|0.80|0.91|-0.13|0.99|0.89|
|age 30-39|0.23|0.19|0.22|0.16|0.25|0.51|0.23|0.25|0.36|0.18|0.31|0.55|
|age 40-49|0.08|0.22|0.70|-0.01|0.30|0.97|0.08|0.30|0.79|0.01|0.37|0.97|
|age>49|0.09|0.21|0.67|-0.01|0.29|0.98|0.09|0.29|0.76|0.00|0.36|0.99|
|age unknown|0.30|0.22|0.17|0.23|0.30|0.44|0.24|0.30|0.43|0.20|0.37|0.59|
|exp. 5-10|0.12|0.26|0.63|0.10|0.35|0.77|0.26|0.35|0.45|0.25|0.42|0.55|
|exp.>10|0.17|0.32|0.60|0.17|0.43|0.69|0.05|0.43|0.91|-0.01|0.53|0.98|
|couture|1.73|0.59|0.00|1.68|0.80|0.04|1.74|0.80|0.03|1.64|0.98|0.10|
|electronics|1.01|0.70|0.15|1.85|0.87|0.03|0.64|0.94|0.50|1.20|1.12|0.29|
|Abidjan|0.35|0.50|0.49|-0.57|0.52|0.27|0.70|0.68|0.30|-0.06|0.74|0.94|
|PB|-0.06|0.11|0.60|-0.04|0.12|0.75|0.15|0.15|0.33|0.20|0.16|0.21|
|PA|-0.01|0.11|0.93|-0.02|0.12|0.87|0.26|0.15|0.09|0.29|0.16|0.07|
|NPB|0.01|0.11|0.91|0.01|0.06|0.92|0.16|0.14|0.27|0.20|0.11|0.06|
|constant|9.49|0.32|0.00|9.57|0.43|0.00|9.25|0.44|0.00|9.35|0.53|0.00|
|PB-NPB=0|0.41||0.52|0.14||0.71|0.01||0.93|0.00||0.97|
|PA-PB=0|0.19||0.67|0.09||0.77|0.53||0.47|0.58||0.45|
|PA-PB=-NPB|0.16||0.69|0.08||0.78|1.68||0.20|3.32||0.07|
|B&P LM-test||||79.17||0.00||||20.77||0.00|
|Hausman-test||||0.00||1.00||||0.00||1.00|
|N|696|||696|||696|||696|||

# _4.3. Presentation of Results from Estimations by Sector_

In all the models estimated the sector indicators appear to be highly significant. Therefore the models have been estimated separately for the three observed sectors. In table 7 the models estimated for the agricultural sector are presented. Again, the OLS-specifications are rejected, so all conclusions will be made from the random effects model. In the model of ln(CA) the only significant explanatory variables are PB and PA, indicating that the CA of the participants were lower both before and after PAFPA compared to the CA of the nonparticipants after PAFPA. The test of PB=NPB actually shows that the CA of the participants were also lower than the CA of the nonparticipants before PAFPA. On the basis of the test of the DID-estimate it is concluded that no impact from the program can be found. When we consider the random effects model of ln(revenue) the coefficients of PB and NPB indicates that the revenue of both groups were higher before PAFPA than the revenue of the nonparticipants after PAFPA. The revenue of the participants, however, has not changed 18
Rosenthal, S., Strange, W. (2003). “Geography, Industrial Organization, and Agglomeration”, review of Economics and Statistics 85(2): 377-393.

Rosenthal, S., Strange, W. (2004). “Evidence on the Nature and Sources of Agglomeration Economies”, in Henderson, J.V., and Thisse, J.F. (eds), Hanbook of Regional and Urban Economics (4), Elsevier North-Holland.

Segal, D. (1976). “Are there Returns to Scale in Cities?”, Review of Economics and Statistics (58):339-350.

Shefer, D. (1973). “Localization Economies in SMSAs: A Production Function Analysis”, Journal of Regional Science (13):55-64 Stelting, A., Allanach, C., Loveridge, S. (1994). The Role of Agglomeration Economies in Firm Location: A Review of the Literature. Staff paper P94-14, Department of agriculture and Applied Economics, University of Minnesota.

Sveikaukas, L.A. (1975). “The Productivity of Cities”, Quarterly Journal of Economics (89):393-413.

Tabuchi, T. (1986). “Urban agglomeration, Capital augmenting Technology, and Labor Market Equilibrium”, Journal of Urban Economics (20):211-228.

Turkey Prime Ministry and State Planning Organization (2006). Ninth Development Plan.

World Bank (2004). Turkey Municipal Sector Review.

26
|||**ESPS**<br>|||**PSF**<br>||
|---|---|---|---|---|---|---|
||**Respondent**|**Number and list of items**|**Recall period**|**Respondent**|**Number and list of items**|**Recall period**|
|||motorcycle, bicycle...);<br>Jewelries.<sup>1</sup>|||||
|**Other non-**<br>**personal items**|Household<br>head|6 items: Soap and housing<br>cleaning products; Maid;<br>Housing maintenance and<br>repair; Small appliances<br>and cutlery; Motor fuels,<br>repair of transport means;<br>Other expenses.|Over the last 30<br>days (and if not<br>purchased, amount<br>usually spent over<br>a month).|Household<br>and cell<br>heads|7 items: Maid; Housing<br>maintenance and repair;<br>Furniture and electrical<br>appliances repair; Small<br>appliances and cutlery;<br>Motor fuels, repair of<br>transport means;<br>Recreational devices and<br>accessories; Other goods<br>and services.||
|**Education**|Household<br>head|5 items: School fees; Books<br>and school supplies; School<br>transportation; School<br>uniform; Other expenses.|Over the last<br>school year|Cell heads|5 items: School fees; Books<br>and school supplies; School<br>transportation; Private<br>lessons; Vocational training.|Frequency is<br>reported by the<br>respondent who can<br>choose among|
|**Personal items**|Household<br>head|8 items: Soaps; Perfumes<br>and cosmetics; Tobacco<br>and cigarettes;<br>Recreational services,<br>books and newspapers;<br>Clothing; Shoes; Cloth;<br>Tailoring.|Over the last 30<br>days (and if not<br>purchased, amount<br>usually spent over<br>a month).|Cell heads|6 items: Transport; Clothing<br>and shoes; Personal care<br>(soaps, perfumes and<br>cosmetics, tobacco and<br>cigarettes etc.); Personal<br>belongings (jewelries etc.);<br>Recreational services;<br>Books and newspapers.|twelve modalities<br>from every day to<br>every year.<br>Respondent is asked<br>about both usual<br>expenditure and last<br>expenditure.|

> 1 In the PSF survey, jewelry expenditures are covered in the personal item section of the questionnaire.

6
|**Table 4**<br>**What do grade 3 children**|**know in**|**langua**|**ge***||
|---|---|---|---|---|
|Percentage children correct|UP|MP|KA|Grade level for the<br>competency as specified<br>in MLL|
|Can read at least 3 of 5 words without matra|38|47|86|I|
|Can read at least 3 of 5 words with matra|22|34|81|I|
|Can read sentence|14|26|61|I&II|
|Can write at least 2 out of 3 words without matra|31|36|76|I|
|Can write at least 2 out of 3 words with matra|13|21|60|I|
|Can write short sentence|10|18|40|I&II|

|**Table 5**<br>**What do grade 3 children know in mathematics***|
|---|

|Percentage childrencorrect|Test item|UP|MP|KA|Grade level for the<br>competency as specified<br>in MLL|
|---|---|---|---|---|---|
|Number recognition,20-40||29|50|87|I|
|Number recognition,40-99||20|35|83|I|
|2 digit addition without carryover|62+35|35|51|87|II|
|2 digit addition with carryover|53+39|15|26|69|II|
|2 digit subtraction without borrow|45-23|28|35|77|II|
|2 digit subtraction with borrow|54-36|9|11|50|II|
|Multiply1 digit by1 digit|6x8|12|20|66|II|
|Multiply2 digit by1 digit|42 x5|10|14|50|III|
|Divide 2 digit by1 digit|64÷ 8|9|11|54|III|

*End of school year for grade 3 in MP/UP and start of school year for class 4 in KA

# **<u>Table 6 What do grade 2 children know in language</u>**

|Percentage childrencorrect|UP|MP|Grade level for the competency as<br>specifiedin MLL|
|---|---|---|---|
|Alphabet recognition|32|53|I|
|Can read at least 3 of 5 words without matra|22|31|I|
|Can read at least 3 of 5 words with matra|11|19|I|
|Can write at least 2 of 3 words without matra|21|28|I|
|Can write at least 2 of 3 words with matra|7|15|I|

|**Table 7**<br>**What do grade 2**|**children kn**|**ow in m**|**athemat**|**ics**|
|---|---|---|---|---|
|Percentage childrencorrect|Test item|UP|MP|Grade level for the<br>competency as specified in<br>MLL|
|Number recognition,11-20||21|45|I|
|Number recognition,21-40||14|29|I|
|Writing2 digit numbers,21-40||14|31|I|
|1 digit addition|5+3|22|38|I|
|2 addition without carryover|26+43|17|27|II|
|1 digit subtraction|8-5|12|21|I|
|2 digit subtraction without borrowing|25-12|12|18|II|
|Multiply1 digit by1 digit|6x 4|7|14|II|
|Which is largest of three 2 digit<br>numbers|56, 84, 69|8|16|I|

22
14 Just under 11 percent (74) of the 694 publications allocated for topic assignment were not in the event assigned to a topical group. The most-cited items amongst these were textbooks (led by Paul Feldstein) which, by definition, paint on a much broader canvas. Others included the quite extensive reference made by economists to the literatures of demography and epidemiology written by scholars of those disciplines. The most frequently cited excluded source that was not a textbook was Scott and Meyer‘s book on organization theory and application ―Institutional environments and organizations: Structural complexity and individualism‖: a pioneering and hugely influential sociological work whose impact on economists is indicated by its being the 63rd most cited source.

Some titles were found to be superficially misleading. For example, Kremer‘s excellent ―The Illusion of Sustainability‖ (Kremer and Miguel, 2007) may appear superficially to belong to category 5 (Health and the economy) but is in fact about the evaluation of public health options in Kenya (category 12, Economic evaluation). Some authors excel at the invention of intriguing but slightly baffling titles. Mark Kamstra‘s ―Losing Sleep at the Market: The Daylight Saving Anomaly‖ (Kamstra et al., 2000) turned out to about equity returns in international capital markets in consequence of sleep disruption, and so was excluded. Contoyannis et al. on ―The Dynamics of Health in the British Household Panel Survey‖ (Contoyannis et al., 2004) could on the face of it have been assigned either to category 1 (Health and its value) or 3 (Determinants of health and ill-health) but on closer inspection was assigned to category 3 given its concentration on the impact of health status in a previous period on the probability distribution of current health status, and on the impact of socio-economic determinants. Articles that were about both supply and demand were assigned according where the balance of emphasis seemed to lie or, where the balance was even, to category 11 (Markets in health care). Siciliani and Hurst‘s (2004) ―Explaining Waiting-Time Variations for Elective Surgery across OECD Countries‖ was thus assigned to category 11.

# IV. **RESULTS**

Figure 3 shows the cumulative growth of health economics publications in EconLit since 1969. Also shown is the cumulative growth of publications in the economics of education. The two fields ran neck and neck up the early 1980s, whereupon health economics started to grow
UPPER-MIDDLE-INCOME ECONOMIES

# 1. **lstiuates**

# and Pro4ections

|_1wr_|**PgMIaUS**|**nail.**|Pftrd|at.||WEm|TME|**ftm**|**Pmilm**|In|0=N|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965<br>1990<br>199<br>**2000**<br>**2005**<br>2010<br>2015<br>2020<br>205|**29482**<br>322M<br>33157<br>38470<br>4W370<br>40S325<br>4574<br>516502<br> <br>549026|67.1<br>65.4<br>64.4<br>63.5<br>61.<br>59.3<br>51.6<br>s6.7<br>56.2|1965-69<br>190-94<br>199-9"<br>2000-04<br>2005-09<br>2010-14<br>2014-19<br>2020-24<br>2025-49|1.61<br>1.79<br>1.72<br>1.63<br>1.52<br>1.42<br>1.32<br>1.22<br>0.66|26.5<br>26.0<br>24.6<br>23.2<br>22.0<br> <br>30.9<br>19.7<br>16.6<br>15..|3.5<br>7.9<br>7.3<br>6.6<br>6.7<br>6.6<br>6.5<br>6.4<br>7.0|3.50<br>3.36<br>3.20<br>3.04<br>2.9<br>2.75<br>2.59<br>2.47<br>2.17|64.6<br>66.1<br>6.4<br>66.6<br>69.6<br>70.5<br>71.9<br>73.1<br>75.6|69.9<br>71.3<br>72.'<br>74.4<br>75.3<br>76.4<br>77.5<br>73.7<br>61.6|53<br>46<br>39<br>33<br>29<br>24<br>19<br>1 4<br>7|70<br>60<br>50<br>41<br>36<br>30<br>24<br>1<br>9|
|2050<br>2075<br>2100<br>2125<br>21SO|_66459_<br>7645<br>**305364**<br>1963<br> 62132|55.5<br>63.3<br>70.4<br>72.7<br>73.!|2050-74<br>2075-99<br>2100.24<br>2125-49|0.4<br>0.19<br>0.07<br>0.0Q|13.3<br>12.3<br>11.9<br>I.(|6.7<br>10.4<br>11.2<br> <br>11.4|2.27<br>2.06<br>2.06<br>2.06|76.6<br>6.5s<br>1.r7<br>62.2|4.6<br>6.<br> *.4<br>69.0|4<br>3<br>2<br>2|5<br>4<br>3<br>3|

# 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-89<br>60.64<br>55-59<br>50.54<br>45-4,<br>40.44<br>z>>2o sBaa 30 34<br>omz maa 26 25 29<br>20.24<br>16- i9_<br>10-14<br>5-9<br>0-4<br><!-- End of picture text --> 10 8 6 4 2 0 2 4 6 8 10 <!-- Start of picture text --> to 8 6 * 2 C 2 * 6 * 10<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> woom cis 4m~~~ear c<br>WPW*Nlo Cs "ID-IOCS *<br>LOW CMi W)<br>LW-* S Cis U) LON Col. go<br>L-Io C14 m<br><!-- End of picture text --> 20
Precipitation in the Tibetan Plateau has increased in most of the eastern and central regions but decreased in the western region during 1961-2001 (Xu et al., 2008). On the Chinese side of the Everest region, Xu et al. (2008) report contrasting precipitation changes in the southern and northern parts: stations in the north show statistically insignificant increasing trends; in the south, a decreasing trend with a sharp drop starting in the early 1990s. In the Yarlung Zangbo River Basin, You et al. (2007) observe decreasing precipitation during 1960s1980s but increasing thereafter. However, they found mostly increasing trends in the long-term (1961-2005) for annual and seasonal precipitation (not statistically significant in two seasons). A higher rate of increase in annual precipitation was found in the Yarlung Zangbo River Basin (24.6 mm/decade; You et al., 2007) in comparison to the Tibetan Plateau as a whole (11.9 mm/decade; Wu et al., 2007) during 1971-2000. Wu et al. (2007) found a statistically significant upward trend in 69% of 77 observation series mostly from southern stations in the Tibetan Plateau. A recent study in southwest China using 1960-2007 data (Qin et al., 2010) also shows increasing trends in most stations in the Tibetan Plateau in annual, winter, and spring precipitation. Wang et al. (2008) investigated the increasing temperature and rainfall trend in Tibetan Plateau numerically with atmospheric general circulation models and suggested that enhanced warming led to increased rainfall.

As stated earlier, physical processes influencing precipitation are complex resulting in large variability in observed precipitation trends. While spatial variability in precipitation trends in the Himalayas was commonly noted in the literature, the following tentative conclusions may be drawn based on the reviewed literature. First, monsoon and annual precipitation is increasing in Jammu and Kashmir but precipitation is decreasing in the western Indian Himalayas. Second, winter precipitation is decreasing in the western Indian Himalaya but it is increasing in the upper Indus Basin (Pakistan). Third, there exists no spatially coherent trend in Nepal or Bhutan; and finally, there exists an increasing annual precipitation in the Chinese Himalaya with Yarlung Zangbo River basin showing predominantly upward trend in annual, winter, and spring precipitation.

# **2.3 Increasing evidence of extreme climatic events**

Few limited studies are available that assess trends in climatic extremes of temperature and precipitation in the Himalayas. These studies typically use several climatic indices to detect extremes. Temperature extremes are most commonly evaluated using warm days (percentage 3
WPS4117

# **Forest Cover Change in Space and Time:**

## **Combining the von Thünen and Forest Transition Theories**

By Arild Angelsen<sup>1</sup> CIFOR and UMB World Bank Policy Research Working Paper 4117, February 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Associate Scientist, Center for International Forestry Research (CIFOR), Bogor, Indonesia and Associate Professor, Department of Economics and Resource Management, Norwegian University of Life Sciences (UMB), Ås, Norway. Email: <u>arild.angelsen@umb.no.</u>

> This paper is prepared as a background paper for _At Loggerheads? Agricultural Expansion, Poverty Reduction, and Environment in the Tropical Forests,_ a World Bank Policy Research Report. I would like to thank Ken Chomitz and William Sunderlin (author of a second background paper) for a number of clarifying discussions throughout the process of writing this paper. Together with Baikuntha Aryal, Ronnie Babigumira, Atakelty Hailu, David Kaimowitz, Marty Luckert, Alexander Mather, Tom Rudel and Sven Wunder they also gave very useful suggestions to draft versions of the paper.

1
Policy Research Working Paper 10828

# **Abstract**

This paper reviews the main methods for small area estimation of welfare indicators. It begins by discussing the importance of small area estimation methods for producing reliable disaggregated estimates. It mentions the baseline papers and describes the contents of the different sections. Basic direct estimators obtained from area-specific survey data are described first, followed by simple indirect methods, which include synthetic procedures that do not account for the area effects and composite estimators obtained as a composition (or weighted average) of a synthetic and a direct estimator. The previous estimators are design-based, meaning that their properties are assessed under the sampling replication mechanism, without assuming any model to be true. The paper then turns to proper model-based estimators that assume an explicit model. These models allow obtaining optimal small area estimators when the assumed model holds. The first type of models, referred to as area-level models, use only aggregated data at the area level to fit the model. However, unit-level survey data were previously used to calculate the direct estimators, which act as response variables in the most common area-level models. The paper then switches to unit-level models, describing first the usual estimators for area means, and then moving to general area indicators. Semi-parametric, non-parametric, and machine learning procedures are described in a separate section, although many of the procedures are applicable only to area means. Based on the previous material, the paper identifies gaps or potential limitations in existing procedures from a practitioner’s perspective, which could potentially be addressed through research over the next three to five years.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted atisabelmolina@ucm.es.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team