rafmacalaba/data-use-mentions-extended huggingface dataset:
34|18|9|
|**Memo:**<br>SEE-4(simple average)|20|10|27|16|7|6|

Note: SEE-4: Albania, Bosnia and Herzegovina, FYR Macedonia and Serbia and Montenegro Source: Computations based on EU-15 data as reported in UN COMTRADE Statistics.

Turkey’s performance contrasted rather sharply with that of other EU candidate countries and South East European countries (SEE-4, see note in Table 12), which are parts of the EU Stabilization and Association process. While the aggregate share of skilled labor and capitalintensive products in Turkish exports in 1996 was well below that in other comparator countries and groups except for SEE-4, the share in 2004 was the second largest after the EU-8. Between 1996 and 2004 it increased seven percentage points in exports from SEE-4, six percentage points in Romania’s exports and 1 percentage point in Croatia’s exports. It fell seven percentage points in Bulgaria’s exports triggered by the increase in exports of unskilled labor intensive products rather than natural resource intensive products, as one might expect given Bulgaria’s endowments in factors of production.<sup>12</sup> Another interesting feature of Turkey’s performance relative to other European transition economies—except for SEE-4 economies and Croatia—was that the levers of growth were skilled labor intensive products. They were responsible for 96 percent of the increase in the weight of these products in Turkish total exports between 1996 and 2004 as compared to 49 percent for the EU-8 economies and 25 percent for Romania. As for SEE-3 economies and Croatia the increase came also from exports of skilled labor intensive products accounting for 83 percent and 96 percent of the respective changes.

12 The share of unskilled labor intensive products in Bulgaria’s exports increased from 17 percent in 1996 to 31 percent in 2004.
owns telephone|**_21.5%_**|**_9.1%_**|
|Household has access to electricity|42.3%|41.6%|
|Household has no running water|56.1%|48.9%|
|Roads are bad|72.1%|74.5%|
|Roads are impassable most of the time|45.0%|42.6%|
|Household has pit latrine or no toilet|62.6%|**_85.1%_**|
|Household does not live in a permanent building|53.6%|56.9%|
|Household makes cash contribution to project|**_32.0%_**|**_53.5%_**|

Source: DLA Land Reform Beneficiary Survey Note: Bold coefficients are significantly different at 10% and those in **_bold italics_** at 5% or higher.

26
firms represent a smaller share of aggregate activity: high-risk firms represent only 7.1% of countrywide sales compared to the low-risk firms that represent 41.2% of aggregate sales (Column 2). As a result, the contribution of high-risk firms as a group to overall indebtedness is smaller, 0.11 percentage points, than the contribution of low-risk firms, 0.53 percentage points (Column 3). The small weight of riskier firms in the aggregate therefore mitigates the micro adverse selection documented in the previous sections. Summing across all risk groups shows that the indebtedness of firms that use the credit program increases by 1.35 percentage points. This change in debt corresponds to 2.9% of the GDP reported by macro statistics. Those formal firms experience an increase of 3.6% of GDP in guaranteed credit (Table 11) and a decline in non-guaranteed credit.

Next, we extend the analysis to include the non-program users.<sup>24</sup> The leverage of program users increases by 9.71 percentage points during 2020, while the leverage of nonusers actually falls by 1.06 percentage points (Panel B). However, given that program users have a low weight in the aggregate economy (13.9% compared to 86.1% of non-users), total indebtedness across all firms increases by only 0.44 percentage points. Last, partitioning all active firms (users and non-users) by risk confirms that the higher the risk, the larger the within-change in debt (Panel C). But the higher the risk, the smaller their weight in the economy, thereby attenuating the increase in aggregate risk.<sup>25</sup> This analysis shows why the micro results presented in previous sections can have a relatively small impact at the macro level.

# **7.2 Risk Sharing between Banks and the Government**

Banks and the government share, absorb, and mitigate the risks that the credit program entails by dealing with
|-0.04|0.60|1.66|1.06|
|Sri Lanka|3.02|1.52|4.74|-1.72|4.21|4.97|0.75|
|Tajikistan|3.08|0.41|3.24|-0.16|-0.38|2.93|3.31|
|Tanzania|3.68|1.65|5.94|-2.26|3.04|5.26|2.22|
|Togo|3.70|1.38|5.39|-1.69|-3.21|1.62|4.83|
|Uganda|3.26|1.09|5.44|-2.18|0.72|4.39|3.67|
|Ukraine|3.44|0.19|3.64|-0.20|-0.42|3.17|3.59|
|Uzbekistan|3.97|0.69|4.68|-0.71|-1.71|3.25|4.96|
|Vietnam|1.99|0.82|5.52|-3.53|-1.70|1.04|2.74|
|Yemen, Rep.|3.68|0.49|4.64|-0.96|0.67|3.84|3.17|
|Zambia|6.93|0.46|8.07|-1.14|0.75|7.35|6.60|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s own agricultural import tariffs.

30
The estimation period is from 1983 to 1992. The period is limited to ten years so that the model reflects characteristics of the most recent economic structure. The model has also been restructured from the original EPA model to some extent in order to capture the economic dynamics surrounding the creation and bursting of the bubble.<sup>2</sup> '

# _B._ **_Fall_** _in Land and Equity Pnces_

As discussed in preceding sections, the financial sector problem in Japan is deeply connected with the collapse in land and equity prices.22 Furthermore, in the absence of major publicly funded bailouts, possible bank runs would force the sale of land and equities held by these financial institutions, causing a further decline in asset prices and exacerbating the problem.' With this in mind, simulations were conducted for the cases of 25 percent asset price deflation in land and equities, respectively.

The land price is defined in the model as the land value index of residential sections in city areas. Its variation affects private consumption and money demand (cash and time deposits, in particular) through changes in the household sector's net worth at the outset. The equity price is defined as that in the Tokyo Stock Price Index, since that reflects the market condition better than other average prices (e.g. Nikkei 225). Its forward linkage starts from the price earnings ratio (PER), which influences investment decisions of the corporate sector and is one of the crucial factors determiniing the user cost of capital (relating to Tobin's q). The variation of the equity price also directly affects the household sector's net worth, and thus changes consumption pattems and money demand behavior.

Tables 15 and 16 collect the computation results from simulated declines in land and equity prices, respectively. Although the percentage decline in prices is the same in both simulations (25 percent decline from baseline values), the magnitude of the impacts seems to differ greatly in these two cases. This is due
of using this standard model as the starting point is to create a tractable setup for the effect of productivity on tariffs and then, having accounted for the economy-wide trade reform, to ultimately obtain structural equations for econometric analysis.

It is often argued that trade reform may be used as an exogenous change and this uniform shift in policy helps identify the effect of trade policy on productivity without worrying too much about its endogeneity. In order to account for this argument, I model a unilateral trade liberalization shock which is common across sectors. I find that under such a common exogenous shock, the reduction in tariffs is lower for the sectors that experience a higher productivity gain (or a lower productivity decline) as compared to the other sectors in the presence of political economy. Initially, I keep the additional channel simple making sure that it is clearly the political economy consideration that is driving the results. Next, I give more structure to the extra channel of protection and to the way the liberalization shock manifests itself by allowing for an infant industry argument.<sup>3</sup> This argument led to the widespread use of import substitution policies in most developing countries until the mid-1980s and its dismissal was the source of much unilateral trade liberalization since then.

In my two period model, the government initially believes that there exists a learning-by-doing (LBD) process and decides about the current tariffs by considering both the political economy effects and the effect of these tariffs on future welfare through LBD. In the second period, the government realizes that LBD does not actually exist and it is a false perception, and thus initiates a trade reform. Given that political economy forces still determine tariffs, the extent of the liberalization differs across sectors (due to political economy) despite this common shock across sectors. The implication from the models I present is that, assuming cross-sectional differences in trade policy to be independent of cross-sectional differences in productivity is incorrect.

I employ Colombian data for the empirical tests of my theory. Colombia has been used in
**Annex 4. Replications of Malaysia-focused analyses about the complementarities between digital, cognitive, and socioemotional skills for the occupational skills profile, Cambodia, Thailand, and Vietnam**

# **Table A.4.1. Correlations between skills (replications of Table 7)**

_Pairwise correlations based on the number of skills counts of skills categories_

# _A. Occupational skills profile_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|<br>Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate|1.00<br>**0.45*****|1.00||||||||
|Basic|0.05|**0.31*****|1.00|||||||
|Cognitive<br>Thinking|**0.55*****|**0.40*****|**0.39*****|1.00||||||
|Communication|**0.22****|0.11|**0.37*****|**0.65*****|1.00|||||
|Organization|0.14|**0.30*****|**0.44*****|**0.59*****|**0.60*****|1.00||||
|Socioemotional<br>Emotions|0.15
the poor’, and here the differences are minimal: 13 percent for the food and non-food index, 20 percent when using only food items, and a negative differential of 2 percent when using SIMA prices.

To summarize, these findings suggest that the spatial distribution of prices ostensibly reflected in the official PLs is highly questionable, and does not appear to represent the actual prices faced by the poor. Since poverty headcounts are calculated using these PL’s, our results suggest that the spatial distribution of poverty recorded in the official figures is a consequence of methodological weaknesses in the construction of the PLs themselves and does not accurately describe the prevalence or disposition of poverty in Mozambique.

> 20 The Paasche index has been computed for 1060 enumeration areas and 49 food and non-food items using the national price for every item as a base price. 21 SIMA is the Portuguese acronym for the Agricultural Marketing System of Mozambique. SIMA is a database of agricultural market prices, updated monthly, with reasonably comprehensive coverage.

> 22 A recent policy note by Sohnesen (2011), focuses on Mozambican spatial price distribution. The note examines price distribution at the local level by calculating product prices and spatial Paasche indices under different scenarios. The basic rationale is that all the scenarios proposed to compute unit prices should not impact overall prices considerably. The fact that some scenarios show a high impact on prices leads the author to argue that price data are not stable enough to support the relatively high number of PLs used by MPD-DNEAP (2010).

> 23 All these results are available upon request.

14
EU markets in 1996-2000. The share of footwear in EU imports was in 2003 38 percent higher than in 2000 and that of furniture products 37 percent higher. In both networks, producers of parts have outperformed suppliers of final products in terms of gains in market share. The same applies to TC network, with textiles registering much stronger gain (95 percent) than clothing (25 percent over 2000). Exporters of parts saw their share increase 73 percent between 2001 and 2003 (Table 28).

The shift towards parts is indicative that Bulgaria ceases to be mostly an assembly shop for EU firms taking advantage of available cheap labor force. While without the survey of firms in sectors or access to input-output tables it is impossible to give an unambiguous answer to this assessment, an examination of trade data tabulated in Table 15 offers some clues. Developments within each chain suggest slightly different answers. While data point to the development of backward linkages in footwear and furniture chains, no clear conclusion can be drawn in reference to TC value chain.

The TC at first involves cut-make-trim (CMT) tasks, possibly followed by a move to FOB, i.e., firm providing the fabric itself and charging for the final garment rather than earning only a processing fee. The ability to shift to FOB critically depends on the price and quality of domestically available fabrics. Their absence weakens competitiveness of domestic producers. The TC trade data do not suggest the fall in import intensity, the share of imported fabrics in total TC exports, which usually accompanies the shift to FOB operations. Yet, it would be impossible to conclude that (a) there is shortage of domestically available high quality fabrics and (b) that simple CMT operations continue to expand in the Bulgarian garment sector. Consider the following: The importance of imported inputs for final exports, as measured by imports of textiles as percent of exports of clothing and textiles, slightly increased in TC chain from 50 percent in 2000-01 to 55 percent in 2002-03. While no firm conclusions can
African countries.<sup>2</sup> To investigate SWF investments and economic growth, we use per-worker GDP data for 30 African countries over the period 2000-2013 taken from the WDI.<sup>3</sup> We also use private credit as a measure of the level of financial development, as well as stock market capitalization as an alternative measure to analyze the relationship between SWF investments and growth through financial development. The datasets of these variables are also taken from WDI.

**SWFs that invest in Africa mainly obtain their funding from extractive industries.** 47 percent of SWFs that invest in Africa obtain their finding from extractive industries, therefore 53 percent are from other sources (non-commodity).

**SWFs that invest in Africa mainly originate from Asia.** The investments of SWFs in Africa are dominated by Asian funds, with 49 percent of the total, followed by African funds with 38 percent (this includes Lybia and Algeria). However, the domination by Asian SWFs is due to China, which represents 48 percent. The share of investments of SWFs from the Middle East and the Americas in Africa is 6 percent, while only 1 percent of SWF investments are from Europe.

> 2Istithmar Investment Fund invested in Kenya, Djibouti, Rwanda, South Africa, and Tanzania. The Mubadala Fund invested in Guinea, and Nigeria. The Brazilian SWF invested in Mozambique, Democratic Republic of Congo and Angola. The Chinese SWF invested in Nigeria, Sudan, Guinea, South Africa, and Ethiopia. The Singaporean SWF (Temasek) invested in Nigeria, Tanzania, and South Africa.

> 3 The World Development Indicators are publicly available at `http://www.worldbank.org/` .

5
Figure 2: Average Job Quality Ratio by Employment Sector <!-- Start of picture text --> AgricultureFishingManufacturingMining UtilitiesConstructionHotels and RestaurantsCommerceTransportationPublic AdministrationFinanceReal EstateSocial and Health ServicesTeachingCommunity ServicesDomestic ServicesForeign Organizations<br>.8<br>.6<br>.4<br>.2<br>Average Job Quality Ratio<br>0<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data.

countries are Argentina, Brazil, Chile, Costa Rica, and Uruguay with over 40% of the quality dimensions satisfied on average. One way in which these countries stand out is that they employ a much smaller share of their labor force in the agricultural sector. For instance, only 4.7% of the workers in our sample of Uruguay are employed in the agricultural sector (which is partly due to the urban bias of the survey), compared to the over 40% employed in Guatemala. Agricultural jobs tend to have much lower job quality than other sectors of employment. This can be seen more clearly in Figure 2.

Figure 2 also shows that better quality jobs tend to be located in the public, utility, and finance sectors. Also, jobs in education and health services have high levels of job quality. Though we do not show how all dimensions of job quality 16
with the highest average number of application attempts is Durango, followed by Nuevo León, San Luís Potosí, and Aguascalientes ( **Figure 3** ), where the average number is just above 3, and for most states the average is 2-3.<sup>7</sup> These statistics illustrate that there is more demand for the program than the budget can allow, which is the basis for the regression discontinuity approach.

## _Selection Process of Applicants_

Applicant point scores are assigned by the national office of CONAFOR and used to prioritize the limited funds for the program. Applicants send in information about the property they wish to submit, and about their household or community. This is then combined with information from multiple geographic layers produced by different government agencies that reflect national conservation and social priorities. Because point scores are based on observable characteristics and are clearly defined, they are unlikely to be manipulated or influenced by subjective judgement.<sup>8</sup> After assigning point scores, CONAFOR admits properties into the PES program in the order of their total score until the available state budget for each sub-program has been allocated. This means that the score cutoff determining who receives payments differs by state, year, and program modality and is unlikely to be systematically correlated with characteristics that could influence outcomes. **Appendix Table A1** shows in detail the criteria used to award points for 2011-2014, including both land and applicant characteristics. **Appendix Figure A2** shows the percent contribution of the different elements of CONAFOR’s point system across time. In 2015, environmental value contributed approximately 40 percent, social elements 25 percent and deforestation risk 5 percent. Implementation capacity contributed the remaining 30 percent.

# **3. DATA**

This study uses two data sources: interpreted satellite data and survey data. Satellite-based data are used to study program impacts on land-cover change or avoided deforestation. For the land-cover analysis, we compiled data for the entire population of both private and common property
from two basic sets of regressions. In Table 2, the (natural log) of a country’s GDP per capita level is regressed on its level of urbanization. Meanwhile, in Table 3, a country’s level of urbanization is regressed on the share of its national GDP that is generated by agriculture. In both tables, columns (1) – (3) report results obtained when using the WUP data, which are based on national definitions, to measure levels of urbanization, while columns (4) – (6) report the corresponding results when instead using the AI with _LandScan 2012_ population data as input.

As can be seen, the tables confirm the results for the WUP data that were depicted visually in Figure 1. Hence, a country’s level of development is significantly positively correlated with its level of urbanization, while there is a significant negative relationship between a country’s urbanization level and the relative importance of agriculture to its economy. Furthermore, Latin American (LA) and Caribbean (C) countries depart significantly from these relationships, but in opposite directions. This is evident in the opposite signs on the dummy variables for Latin American and Caribbean countries in column (2) in both tables. On average, Latin American countries have significantly lower levels of GDP per capita than one would predict based on their levels of urbanization. Consistent with this, they also appear “over-urbanized” given the contribution that agriculture makes 13
# (24%).

Demographic and labor-force participation descriptive statistics for mothers and fathers are shown in Tables 2 and 3, respectively. These tables include variables such as parent’s age, age at first birth, years of education, household size, residential region and indicators for religion among others. The labor-force participation variables are based on questions concerning the employment status of the parent during the last 7 days and 30 days previous to the moment of the surveys. The four surveys (LSMS and DHS) contain labor supply indicator variables which measure whether the parents worked for somebody other than a household member ( _worked off-farm_ ), whether they worked on a farm owned by a household member ( _worked on-farm_ ) and whether the parents worked on own account ( _self-employed_ ). It is important to notice that these working activities are not exclusive, which means that each parent might be doing more than one. Thus, I define another indicator variable that combines these three variables and takes the value of 1 if the parents performed at least one of these working activities during the last 7 days ( _worked in last 7 days_ ) and 0 otherwise.

Just the LSMS surveys also contain other labor supply variables that measure whether the parents have a _second occupation_ , their usual number of _hours worked per week_ and also their amount of _labor income per month_ in old leks.<sup>13</sup> The latter two variables are set to zero for those parents who reported not working during the last 7 days.

The descriptive statistics in Tables 2 and 3 indicate that the mean age is 35 for the sample of mothers and almost 40 for the fathers (husbands or partners of mothers). On average, women become mothers at the early age of 23 compared to 28 for men. Parents in Albania complete a similar number of years of education, being 10 years for the mothers and 11 for the fathers. Half of the households in the sample live in urban areas and around 38% of them live in an extended
household receiving the transfer or subsidy (urban-rural).<sup>48</sup> For example, the Más Familias en Acción (MFA) program, one of the programs with the greatest number of beneficiaries, is the one with the greatest impact on reducing inequality and poverty (see table 1 in Annex A), although this effect is concentrated in rural areas. Thus, while the coefficient of the contribution to the reduction of inequality is 0.004 for urban areas, it rises up to 0.019 for rural areas. Similarly, it is found that the contribution in the reduction of extreme poverty of this program is 0.0199 in rural areas, while in urban areas, it barely rises to 0.0032. On the other hand, transfers delivered under the Colombia Mayor program for the elderly are the most progressive as shown by the Kakwani coefficient (Figure V-7).

<!-- Start of picture text --> FIGURE V-7: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF DIRECT TRANSFERS<br>(FROM MARKET INCOME + PENSIONS TO CONSUMABLE INCOME)<br>0.9000 0.0080<br>0.8000 0.0070<br>0.7000 0.0060<br>0.6000<br>0.0050<br>0.5000<br>0.0040<br>0.4000<br>0.0030<br>0.3000<br>0.2000 0.0020<br>0.1000 0.0010<br>0.0000 0.0000<br>Kakwani Coefficient Marginal Contribution<br>Source: World Bank Group staff estimates based on ENPH 2017<br>Kakwani Coefficient Redistributive effect<br>Regressive <---------> Progressive Later Life Unequalizing <---------> Equalizing<br>Early childhood School Meals Program Unemployment subsidy Más Familias en Acción Familias en su tierra Jóvenes en Acción Victimization transfer