rafmacalaba/datause-encoder-data huggingface dataset:
**With regard to the ICT network, exports are low but have increased over the last years** . The USA used to be an important destination before the drop due to the crisis, but exports to the country have started to increase again in 2004 and 50-60 percent of these exports are composed by P&C. Brazil wasn’t an important destination of ICT until 2003, when exports to Brazil first exceeded those to the USA in value and kept increasing. Exports to the EU have also increased although from a low level, with a steep rate in the 2005-2006 led by an increase in exports of radio broadcast receivers.

**Figure 19. Volume of Exports and Imports to Partner Countries and Share of P&C in Network (in US$’000)**

<!-- Start of picture text --> Automobile exports Automobile imports<br>100 3,000,000 100 5,000,000<br>80 80 4,000,000<br>60 2,000,000 60 3,000,000<br>40 1,000,000 40 2,000,000<br>20 20 1,000,000<br>0 0 0 0<br>1996 1999 2002 2005 1996 1999 2002 2005<br>BR P&C % EU P&C % USA P&C % BR P&C % EU P&C % USA P&C %<br>BR EU USA BR EU USA<br>ICT exports ICT imports<br>80 50,000 60 1,200,000<br>60 40,000 50 1,000,000<br>30,000 40 800,000<br>40 30 600,000<br>20,000 20 400,000<br>20 10,000 10 200,000<br>0 0 0 0<br>1996 1999 2002 2005 1996 1999 2002 2005<br>BR P&C % EU P&C % USA P&C % BR P&C % EU P&C % USA P&C %<br>BR EU USA BR EU USA<br><!-- End of picture text --> _Source:_ Comtrade and author’s calculations (SITC version 2).

**Overall, it seems that also in producer-driven networks Argentina is likely to be a net importer.** Both in automobile and ICT, imports value has exceeded exports value. However, the producer-driven network has clearly contributed to the increase in Argentinean automobile exports, increasing its market share in world exports from 0.2 to 0.4 percent between 2003 and 2006, and it may help the country to become a net exporter in few years.

# **4. CONCLUSIONS**

**Despite a rich endowment of natural resources, Argentina’s export dynamism has not performed up to its potential as a contributor to economic growth** . Following the liberalization policies of the 1990s and the devaluation of 2001 exports have grown steadily, which has helped the country weather difficult economic times. However, the number and sophistication of exported products has remained stagnant. Most export growth has come as a result of increased existing flows and finding new markets for existing products.

**The composition of exported goods has not changed significantly in the past decade** . Argentina’s trade product concentration remains relatively low, but it is higher than important regional competitors such as Brazil, and has increased in the past 10 years. In 1997, 25 percent of exports were concentrated in 10 products, but by 2008 that share had risen to 30 percent. The main export products remain overwhelmingly natural-resource intensive, compared to highly technology-intensive imports. In 2006, exports contained on average 70 percent natural resources, compared to about 10 percent

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17 such as Article XIX, but what matters at the end of the day is the extent to which they are exploited. This is the subject of the next section.

# V. Initial **Commitments (Sectoral Offers) of Developing Countries**

At the time of writing, countries are still in the process of negotiating their specific commitments. Recognizing that final offers might be substantially expanded as the result of further negotiations, there is nonetheless value in undertaking an analysis of the _initial_ offers that have been made by participants. They provide an indication of the willingness of developing countries to provide significant national treatment/market access commitments, and also the extent to which such countries are willing to bind unilateral reforms at the multilateral level. As of early 1992, 28 developing countries had presented an initial offer. In the subsequent year an additional 12 countries submitted initial commitments, bringing the total to 40. All large developing countries including Argentina, Brazil, China, India, Indonesia, Korea, Mexico, and Nigeria have presented an offer.<sup>29</sup> A number of summary indicators can be constructed to compare country offers. The most straightforward is to simply count the number of subsectors mentioned in an initial offer and express this as a share of the total number of subsectors in the GATS indicative list of service activities. This list distinguishes 11 major service categories and _154_ sub-sectors.<sup>30</sup> The initial offers of developing countries as of the end of 1992 had "coverage ratios" ranging from I to 50 percent, the average being 18.5 percent. There was substantial geographic variance in the offers. Whereas Latin American countries on average had ratios of 20 per cent, Asian country offers averaged 22 per cent, and the offers of African countries represented only 11 per cent of all sub-sectors. Large developing countries as a group -- defined as those with a service sector representing over $100 billion in GDP in 1990 -- had above average coverage ratios of
most bank financing went to projects supported by the central government whereas the number of projects promoted by local governments has remained predominant.

Chart 4: Bank financing of PPI projects <!-- Start of picture text --> 25000<br>20000<br>15000 Regional<br>Central<br>10000<br>5000<br>0<br>1995 2002<br>Source: Dailami (2004)<br>KRW billion<br><!-- End of picture text -->

# **_B. Procurement of PPI projects in Korea_**

In Korea, the rules and procedures regulating the procurement of PPI projects are spelled out in the 1999 Act, and in its Enforcement Decree. This sub-section reviews these rules and procedures.

# _1. One-stage bidding process_

According to the Act, competent authorities are responsible for the designation of concessionaires in charge to develop PPI projects. To do this, local governments must organize competitive tenders according to the rules and procedures spelled out in the enforcement decree of the 1999 Act. The procurement of PPI projects follows an open sealed-bid tender even though competent authorities can implement a pre-qualification stage. Consequently, the number of potential bidders remains unknown until the submission of bids. Furthermore, bids are made of one proposal which includes technical and financial components. In fact, the procurement rules do not differentiate technical from financial proposals. Proposals are prepared according to article 12 of the enforcement decree, and should reflect the following key elements:

- contents of the project proposal including basic engineering plans

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when they again start teaching first grade students and new teachers are placed to replace old teachers and to ensure a constant student-teacher ratio.<sup>52</sup> We also simulate the impact of increasing the time teachers spend teaching, from the current average of 2 hours and 46 minutes per school day (see Table 1) to the OECD average (4 hours and 30 minutes of compulsory instructional time per school day); i.e. an increase of 63%. To estimate this effect, we interpret α as the contemporaneous effect of teacher content knowledge on student achievement of, on average, 2 hours and 46 minutes of instructional time per day. We then assume that the effect is linear in time spent teaching, implying that the marginal effect of teacher content knowledge, when teachers teach 4 hours and 30 minutes, is 1.63α. Finally, we consider the effect of combining the two reforms. The results of the policy simulations are summarized in Figure 7.

Four key results of this simulation stand out. First, even if all newly hired teachers have the required minimum content knowledge (a stark difference from the current system) the reform has only relatively small effects even after 10 years: effective years of student learning are simulated to increase by approximately 14 percent in the first decade. This relatively small effect, in turn, is caused primarily by the fact that it takes time to improve grade 4 outcomes, given that in the shorter run most students will still be taught by teachers already hired before the reform. For example, in the first year (2019), only 3 percent of the grade 4 students will benefit from having a fully qualified teacher.

Second, the simulation suggests that the reform would increase effective years of schooling by approximately 36 percent by 2050. We note that this is likely a lower bound, since higher subject content knowledge may also raise other aspects of teacher quality.

Third, increasing the time teachers teach raises student achievement directly (by assumption). But the magnitude of this impact is small (18 percent in the longer run), mostly because of the poor content knowledge of teachers. Note that in contrast to the reform aimed at improving new teachers’ content knowledge, this reform is assumed to influence all teachers.<sup>53</sup> Therefore, the simulation shows starkly how having teachers that know little teach more only marginally raises student achievement.

Fourth, combining the reforms leads to big short and longer run impacts. After 5 years, for example, students’ effective years of schooling increases by 26 percent and by

> 52 The simulations reported below are robust to various alternative assumptions about how new teachers are allocated to schools.

> 53 Here we project a reform that broadly increases the incentives to teach and thus a reform that affects all teachers.

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# **3.2 Hierarchical Clustering**

Using the resulting components from MCA, we conduct Hierarchical Clustering on Principal Components (HCPC). The hierarchical clustering algorithm is quite intuitive and relies on grouping together observations that are the most similar. Our measure of dissimilarity would be the Euclidean distance on the components from the MCA. The dissimilarity between landownerparcel _i_ and landowner-parcel _l_ is equal to where _m_ is a component. Each landowner-parcel observation has value i for component _m_ . Hierarchical clustering identifies the main groupings by building a tree-like structure. We will use xx Hierarchical clustering identifies the main groupings by building a tree-like structure. We will use an agglomerative algorithm to build the tree. The algorithm starts by treating all observations as a single cluster, at the roots of the tree. First, the two observations with the least Euclidean distance are identified and connected side-by-side. These two then are grouped together in a new cluster. For this new cluster and all the remaining observations, the Euclidean distance is recalculated again to find the two most similar groups, which are then linked as well.<sup>20</sup> The algorithm keeps repeating this step until all observations/clusters are linked, creating a hierarchical tree.

Once a dendrogram is created, one needs to partition the tree into smaller clusters. The HCPC method chooses the highest quality partition to determine the final clusters. A good quality partition ensures the minimum variance within a cluster (landowners within a cluster are the most similar) and maximizes the variance across clusters (landowners belonging to differing clusters are very different).

# **3.3 Clustering Results and Robustness Checks**

The MCA analysis has shown that the transfer rights contribute the most to the variation in the composition of a wider body of constructs related to self-reported rights to and control over land. Consequently, we further restrict our specification for hierarchical clustering to use only the five rights variables (bequeath, sell, rent, improve, and use as collateral). Focusing on these rights also improves the conceptualization and interpretation of the resulting clusters.

As a robustness check, we compare how the results change when we base the clusters on all landrelated variables versus only rights variables. The resulting tabulations are presented in Table 4. Very similar clusters result in either approach, with about less than 3 percent of individuals assigned to different clusters depending on the method. This confirms that rights variables are what drives the algorithm in choosing the cluster categories, even when other variables are fed into the algorithm. MCA results using only rights variables are consistent with MCA discussed in the previous section and are provided in the appendix.

> 20 For the previously new created cluster that encompasses two observations, one can use the mean of the two observation and find the Euclidian distance between this mean and the other observations.

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- Fedorov, V. V. & Leonov, S. L. (2013). _Optimal design for nonlinear response models_ . CRC Press.

- Ghatak, M. (2015). Theories of poverty traps and anti-poverty policies. _The World Bank Economic Review_ , 29(suppl_1), S77–S105.

- Haushofer, J. & Shapiro, J. (2016). The short-term impact of unconditional cash transfers to the poor: Experimental evidence from kenya. _The Quarterly Journal of Economics_ , 131(4), 1973–2042.

- Haushofer, J. & Shapiro, J. (2018). The long-term impact of unconditional cash transfers: Experimental evidence from kenya.

- Hussam, R., Rigol, N., & Roth, B. (2020). Targeting high ability entrepreneurs using community information: Mechanism design in the field.

- Jappelli, T. & Pistaferri, L. (2010). The consumption response to income changes. _Annual review of economics_ , 2, 479–506.

- Kaplan, G. & Violante, G. L. (2014). A model of the consumption response to fiscal stimulus payments. _Econometrica_ , 82(4), 1199–1239.

- Kraay, A. & McKenzie, D. (2014). Do poverty traps exist? assessing the evidence. _Journal of Economic Perspectives_ , 28(3), 127–48.

- McIntosh, C. & Zeitlin, A. (2018). Benchmarking a child nutrition program against cash: experimental evidence from rwanda.

- McKenzie, D. & Woodruff, C. (2008). Experimental evidence on returns to capital and access to finance in mexico. _The World Bank Economic Review_ , 22(3), 457–482.

- Meager, R. (2019). Understanding the average impact of microcredit expansions: A bayesian hierarchical analysis of seven randomized experiments. _American Economic Journal: Applied Economics_ , 11(1), 57–91.

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# **<mark>On the Structural Transformation of Rural Africa</mark>**

<mark>Christopher B. Barrett</mark><sup>a</sup> <mark>, Luc Christiaensen</mark><sup>b</sup> <mark>, Megan Sheahan</mark><sup>c</sup> <mark>and Abebe Shimeles</mark><sup>d</sup>

## **JEL classification** : O13, O18, Q18

**Key words** : agriculture, rural nonfarm employment, poverty, structural transformation, Africa

a Charles H. Dyson School of Applied Economics and Management and Department of Economics, Cornell University b World Bank

> c <mark>Precision Agriculture for Development</mark> d African Development Bank

Prepared for the African Economic Research Consortium’s Biannual Research Workshop, held in Addis Ababa, Ethiopia in November 2015. We thank Joanna Upton, Peter Timmer, Erik Thorbecke, Tom Reardon, Bart Minten, Ellen McCullough, Brian Dillon, Paul Christian, Michael Carter and two anonymous reviewers for helpful discussions on this topic. Any remaining errors are exclusively our responsibility.
# **4. Shift-Share (Bartik) Analysis**

The goal of this section is to assess how export expansion affected local labor market outcomes in Cambodia, exploiting variation in export expansion across provinces and industries between 2010 and 2020 (Bartik, 1991). To this effect, we combine export data from UNCOMTRADE data, and information on local labor market outcomes from CSES data. Details on each dataset and cleaning techniques are described below.

# **_Overview of the Approach_**

We use a shift-share model that assesses the effect of trade on Cambodia’s local labor market outcomes. Following Bartik’s (1991) approach, we estimate how increases in exports per worker (as a measure of exposure to trade) affects informality, wages, and hours worked at the district level. As noted, labor-market outcomes vary significantly across districts. Following many in the literature, we assume that the districts represent relatively independent local labor markets. The identification strategy under this assumption is that labor-market outcomes will vary in response to differential exposure to exports. The differential exposure to exports is calculated using _a priori_ district-level employment in different exporting sectors.

We estimate the following equation: 0 1 , (2) dd dd dd tt+nn tt tt,tt+nn cc cc dd In equation (2), yy −yy is the change in outcome of interest. Examples include the = ββ + ββ xx + XX ′ββ + εε employment rate, informality rate, female participation rate, average annual income average dd dd tt+nn tt annual wage, college premium, or gender wage gap, and so on, identified at district yy −yy _r_ over the period from time to + h. is the change in the export exposure index (change in exports from Cambodia to the United States (US) weighted by sectoral employment). The dd tt,tt+nn export exposure is measured as the growth in Cambodian exports in industry tt tt xx _i_ between multiple time periods captured by the term . This change is allocated to each district in Cambodia by dividing this expression with ii ii , which is employment in district d tt+nn tt in Cambodia in the initial period 2009. This index is further weighted by share of district d WW −WWdd tt in total employment in Cambodia in industry _i_ ( <u>t</u> ). LL LLii,dd dd LLtt i,d(Wt+ni −Wti) = �<sup>Lt</sup> LdLi,cambodia dd t t tt,tt+nn xx The key coefficient of interest is 1, which measures the impact of total trade exposure on the outcome, and ′ is the vector of control variables including individual demographic background taken from Cambodia Labor Force Survey (LFS). ββ cc XX 22 | P a g e
in the competition invHHI to increase firm exit in their study of gross entry and exit rates from 1984-89 in Morocco. Similarly, Fafchamps (2004), using exit aggregated to the location-sector level, found the number of firms in the location-sector and the competition invHHI to both have negative and significant influences on firm exit. Looking at exit at the firm-level in this study rather than aggregated to the location-sector level as in these previous papers, reinforces the strength of this result.

Consistent with evidence from Frazer (2005) for Ghanaian firms and Gebreeyesus (2005) for Ethiopian firms, productivity is found to have a strong influence on the probability of firm survival.<sup>23</sup> This is true whether one uses the level of TFP or the ‘distance-to-frontier’ measure. Both the distance to the nation frontier and the provincial frontier were examined. The effect of the former is twice as high. This is consistent with competitive pressures acting on a larger scale than purely local interactions with neighboring firms. This pattern becomes even stronger as the geographic unit used for the agglomeration exercise is narrowed.

# _4.2 Firm Exit (Commune Level)_

The commune-level results from 1998-2001 are shown in Table 5.<sup>24</sup> While the time dimension is much shorter, the benefit of the comparison is that we can test the significance of moving to much more narrowly defined geographic area.

> 23 Harding et al. (2006) found productivity to only have a significant negative influence on firm exit when interacted with firm size.

> 24 We also have information at the city level.  However, with the vast majority of provinces having only one city, there were few differences between the province and city results.

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# **6 Results**

## **6.1 The economic impact of rising temperatures**

Figure 1 and the first row of Table 2 show the baseline results from Equation 1 measuring the impact of temperature differences on firms’ revenues. To ease the visualization of results, we report nine charts in Figure 1, based on World Bank income classifications (high income, upper-middle income, and combining low income and lower-middle income, denoted LMCs) and firm size categories (small, medium, and large). For instance, the bottom-left panel reports the effect of temperature differences on firms’ revenues for small firms in low and lower-middle income countries; the top-right panel shows the effect on large firms in highincome countries. Each chart in Figure 1 plots the predicted response functions of the logarithm of firms’ revenues to a N-degree Celsius difference between the historical average temperature and the current fiscal year temperature at the mean value of each income group and size category, based on the coefficients from the estimation of Equation 1. In Table 2, we instead present the estimated effect of a 1 standard deviation increase in temperature, relative to a location’s historical average temperature, at the mean value of each income group and size category, based on the coefficients from the estimation of Equation 1. Each row shows estimated effects of a 1 standard deviation increase in temperature differences for a different measure of firm performance: revenue, revenue per worker, average wages, whether a firm makes any investments in the current fiscal year, the value of investments made in the current fiscal year, whether the firm has engaged in innovation, and the firm’s energy intensity.

Figure 1 shows that the economic costs of climate change from rising temperatures are large and significant for all firms in low and lower-middle income countries, which include the majority of firms (more than 80,000) in our sample. For small and medium-size firms, a 1 standard deviation increase in temperatures relative to the historical average in firms’ locations—corresponding to 0.75<sup>_◦_</sup> C—reduces corporate revenues by approximately 20-22% 16