rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Exploratory Analysis of the Mechanisms Underlying the Trade Impacts**

Our results to this point indicate that preferential trade agreements containing elevated standards of IPRs protection generate reasonably consistent changes in the trade of IP-intensive goods on the part of member countries with third markets, netting out trade with _demandeur_ partners. While the effects overall are modest, there are identifiable impacts on sectoral trade, especially where the major partner is the United States, the EU, or EFTA, and in IPAs in which IPRs standards are legally enforceable as measured by the World Bank database. These agreements tend particularly to expand exports of biopharmaceuticals and medical equipment, both in the aggregate and on a bilateral basis.

Such findings raise the question of what mechanisms may drive the trade expansion. One important channel is the responses of global firms to what they may perceive as improved investment climates associated with IPAs. It may be, for example, that increased third-country exports of high-IP products reflect increases in domestic production capacity, which may be the result of increased inward technology flows. In the absence of data regarding firm-level responses of both affiliates and domestic firms, this is a difficult question to answer. Here we explore this issue on a preliminary basis by bringing in additional data from the U.S. Bureau of Economic Analysis that may capture the role of FDI, via affiliate activities in IPA-member countries. We also employ data on U.S. related-party trade to explore the effects of IPAs on trade in intermediate inputs.

Specifically, we study first the impact of IPAs on the sales of local affiliates of U.S. majority-owned affiliates in broad manufacturing sectors that most closely track our high-IP products. The closest aggregate sectors in the BEA data are chemicals, computers and electronic products, and electrical equipment, appliances, and components. Our approach is to use affiliate sales as the dependent variable in the basic regressions above, with these sectors constituting a set of high-IP sensitive goods.<sup>21</sup> Given our prior results, we consider just three definitions of treatment IPAs: those with the United States as a partner, those with the United States, EU, or

> 21 We also considered affiliate employment as the dependent variable, with similar results.

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there are strengths and challenges by indicators. Table 8 shows the most prominent strengths/challenges identified by standardized scores being above/below 1.5.

# **Table** **<u>8: Strengths and Challenges by Development Partners</u>**

|Strengths|**Name of Development Partner (Indicator)**<br>IADB(Ind7, 9,10b);IFAD(Ind5a,10b);IMF(Ind5a, 5b)|
|---|---|
|Challenges|GAVI(Ind 8); Global Fund (Ind 8);IFAD(Ind 8); UN (Ind 6)|
|_Source: Appe_|_ndix 4_|

IADB performs well on indicators 7, 9 and 10b; IFAD performs well on indicators 5a and 10b and the IMF performs well on indicators 5a and 5b. While IADB and IMF operated in limited numbers of FCS<sup>15</sup> , it is nonetheless worth exploring efforts and implementation at their institutional levels.

This analysis helps development partners to identify areas of relative strengths and challenges, and encourages peer learning, and therefore FCS may in turn wish to learn from specific indicator examples by development partners from other FCS. If we pick up the top 5 percent of standardized scores for each indicator, we can obtain 5 examples of superior performance for each indicator (Table 9).

# **Table** **<u>9: Good Examples</u>**

|**Country**|**Ind 3**|**Ind 4**|**Ind 5a**|**Ind 5b**|**Ind 7**|**Ind 9**|**Ind 10a**|**Ind 10b**|
|---|---|---|---|---|---|---|---|---|
|Afghanistan||||||||Canada,<br>Germany|
|Bosnia-<br>Herzegovina|||WB|||GAVI|EU|WB|
|Burundi|||||||||
|Central<br>African Rep.||UN|IMF|IMF|||||
|Chad|||IFAD|IFAD||Switzerla<br>nd||EU|
|Comoros|||||||||
|Congo<br>Dem.Rep.||Belgium,<br>Netherlan<br>ds|IFAD|IFAD|Japan||IFAD||
|Guinea-<br>Bissau|AfDB|||WB|AfDB||AfDB||
|Haiti|||||||||
|Kosovo|WB|WB|||||||
|Liberia|EU|||||AfDB|||
|Sierra Leone|||||WB|GAVI|EU|WB|
|Solomon<br>Islands|||||||||
|Sudan|UN||WB|UN|UN|Sweden|||
|Timor-Leste|||||Australia||||
|Togo|EU|AfDB,<br>WB|||UN||Germany||
|West Bank &<br>Gaza|||||||||

_Source: Author_ For example, if FCS would like to learn how to improve performance on indicator 7 (aid is more predictable), the operations of Japan in Democratic Republic of Congo, AfDB in Guinea-Bissau;

> 15 IaDB operates in Haiti, and the IMF operates in Central African Republic and Haiti.

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# 1 Introduction

Current investment trends in emerging market and developing economies (EMDEs) are not enough to meet the needs of their growing populations. Existing infrastructure requires critical maintenance, and there are massive additional needs for transportation, clean water, and sanitation (Gaspar et al. 2019). The pandemic has only further increased these needs. Infrastructure gaps are estimated at about $3 trillion (approximately 3.4 percent of 2030 world GDP) to achieve the Sustainable Development Goal related to human and physical capital development (Carapella et al. 2023). Moreover, to achieve significant cuts in greenhouse gas emissions, boost the clean energy transition, and limit the rise in global temperatures could require between 1 and 8 percent of GDP in additional investment per year, with higher needs in low-income countries (figure 1A; World Bank 2022).<sup>5</sup>

**Figure 1** : Investment trends in emerging and developing economies

<!-- Start of picture text --> A. Additional investment for a resilient and low- B. Investment growth<br>carbon pathway, 2022-30<br>Percent of GDP Percent<br>10<br>10<br>2011-21 2000-10<br>8<br>8<br>6<br>6<br>4<br>4<br>2<br>2<br>0<br>0<br>Low income Lower middle Upper middle EMDEs EMDEs excluding<br>China<br>income income<br><!-- End of picture text --> _Source:_ World Bank.

_Note:_ EMDEs = emerging market and developing economies.

A. Annual investment needs to build resilience to climate change and put countries on track to reduce emissions by 70 percent by 2050. Depending on availability, estimates include investment needs related to transport, energy, water, urban adaptations, industry, and landscape. In some Country Climate and Development Reports, especially those for low-income and lower-middle-income countries, estimated investments include development needs, especially those linked to closing the infrastructure gaps—such as solar mini grids to provide energy access—and cannot be considered entirely “additional” to pre-existing financing needs. B. Arithmetic annual averages. GDP-weighted averages for the period indicated.

Despite these large infrastructure needs, sluggish global investment growth continues. Between the decades of 2000-10 and 2011-21, fixed investment growth in EMDEs effectively halved from 9.4 to 4.8 percent (figure 1B). The slowdown in investment growth is geographically widespread, affecting all EMDE regions. Moreover, prevailing economic conditions, including rising global interest rates and high debt levels, have made borrowing

> 5 See also, for example, Gaspar et al. (2019); and IEA (2021).

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Montserrado is used as a baseline. This follows the classification in a recent poverty analysis (LISGIS 2009). Poverty data are not available at a more disaggregated level. Therefore, no poverty measurement can be included in _X_ .

# **<u>Table 3 Summary Statistics</u>**

|<br>Variable|Abb.|Obs.|Mean|Std.<br>Dev.|Min|<br>Max|
|---|---|---|---|---|---|---|
|Number of firms newly registered at each<br>district in 2016|_dN_|136|52.62|407.03|0.00|4264.00|
|Number of firms registered as of 2015|_N15_|136|320.36|2463.31|0.00|25819.00|
|Rural access index at each district<br>|_RAI_|136|18.38|20.83|0.00|<br>94.60|
|Road density (km/100 km<sup>2</sup>)|_RDDEN_|136|15.84|17.23|0.00|<br>143.25|
|Share of roads in good condition (%)|_GDRD_|136|6.82|9.65|0.00|<br>58.33|
|Transport cost to bring one unit of goods to<br>|_TCMK_|134|5.78|3.88|0.00|<br>19.98|
|large city with population > 15,000 ($/ton)|||||||
|Transport cost to bring one unit of goods to<br>|_TCPT_|134|27.11|14.71|0.63|<br>51.39|
|Freeport of Monrovia ($/ton)|||||||
|Market access index from each district center<br>|_MAI_|134|0.87|8.65|0.01|<br>100.00|
|Population density (1,000 per km<sup>2</sup>)|_POPDEN_|136|0.10|0.52|0.004|<br>5.92|
|Share of informal employment (%)|_INFO_|136|47.36|15.45|20.93|<br>69.70|
|Unemployment rate (%)|_UNEMP_|136|40.11|20.38|12.12|<br>91.95|
|Share of population who attained at least|_EDU_|136|17.79|7.11|7.00|<br>48.00|
|junior high school<br>Instruments:|||||||
|Slope at each district center (degree)|_SLOP_|136|0.45|0.53|0.03|<br>3.62|
|Elevation (m)|_ELEV_|136|153.86|126.46|5.00|<br>563.00|
|Distance to nearest historic port (km)|_DIST_|136|93.60|55.38|0.02|<br>214.39|

# **IV. MAIN ESTIMATION RESULTS**

First of all, ordinary least squares (OLS) regression is performed. The results are shown in table 4. Although the results may be biased because endogeneity and spatial autocorrelation are not addressed, they are largely consistent with economic theory. Population density, which is an indication of the size of the local market, has a significant effect. Agglomeration economies are also always strongly positive; firms establish themselves where other firms already exist. For transport connectivity, the coefficients are largely consistent with prior expectations, except for road density, which has a significant negative coefficient. Market access seems particularly important; the coefficients of _TCMK_ and _MAI_ are statistically significant, although the results are not conclusive, as discussed above.
be actually teaching. TOT estimates suggest that the program dramatically increased inclass teaching, to about 90 percent (Table 7 Column 2).

However, we find no program impacts on learning outcomes as measured by the literacy and mathematics exams (Table 8). As noted above, impacts on student performance are measured by comparing student test scores in literacy and mathematics at endline between control and treatment schools, controlling for baseline school-cohort-subject average scores. Standard errors are clustered at the school level.<sup>5</sup> Therefore, on the whole we find that the textbook provision program had some positive impacts on teacher behavior (increased presence of lesson plan and increased likelihood of being in class _and_ teaching) but no discernible impacts at the student level (either on attendance or performance).

# **8. What explains the lack of program impacts on students?**

In order to fully understand the reasons for absence of program impacts at the student level, we exploit student survey data to examine how the program impacted textbook use by students. These data come from interviews conducted with students in Grades 4 and 5 at endline.

We find that the government textbook provision program did not significantly impact students’ use of textbooks. Table 9 summarizes program impacts on indicators of student textbook use and related behaviors. We see that the program did not increase the likelihood that students in treatment schools were using a textbook in class for a core subject, have a new textbook, or use a textbook given to them by a teacher. Likewise, the program did not impact the number of students with whom textbooks were shared, or the likelihood of receiving pamphlets (informal notes or an exam guide) from teachers.

Information was also collected on materials used by students for exam preparation, including books, pamphlets, and notes (Table 9 Columns 6-8). On these indicators as well, the program had no discernible impacts.

> 5 The test estimates do not use matched scores for individual students between baseline and endline because of extensive difficulties in matching, mainly because certain first and last names occurred with high frequency and students used inconsistent placement of first, middle, and last names. As a result, less than one-third of students could be matched, and we cannot rule out selective attrition in the matched sample. Nevertheless, we estimated program impacts on test scores even in that sub-sample and found no evidence of impact.

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are necessary but not sufficient for better provision of public goods and better local infrastructure, which ultimately will improve human welfare. What matters is whether the increased revenues are used in an efficient manner to deliver these public goods, and this is not a given in the developingcountry context. For example, Caselli and Michaels (2013) show that in Brazil, revenue windfall to oil-rich municipalities significantly failed to improve public good provision due to corruption and rent-seeking. Also in the same vein, Monteiro and Ferraz (2012) show that a revenue windfall to the same Brazilian oil-rich municipalities affects spending levels in education and health only through an increase in payrolls.

In this section of the paper, we focus on the political economy issues of translating more revenues into better outcomes in gold mining communes by investigating the relationship between governance (trust in public institutions, responsiveness or accountability to citizens, and corruption) and quality of public services. We test hypotheses related to the two most important factors—that is, political responsiveness and the technical capacities of local government that may undermine the positive effect of revenue windfalls on public good provision, and local living conditions. In the absence of objective measures of these factors at the commune level, we leverage the Afrobarometer<sup>15</sup> surveys to investigate at the individual level the effect of the proximity to gold-mining activities on perceptions of governance and quality of public services.

We use the 2005, 2012, and 2014 rounds of the nationally representative Afrobarometer survey in Mali. We focus on variables of political responsiveness, corruption, and quality of public services that are similar across the three rounds. Geocoded enumeration areas are combined with location of industrial mine sites to construct our treatment variable, which is the cumulative gold production within 20 kilometers of the mine at the time of the survey. This assumes that both distance to mines and intensity of production matter for the effect. We then estimate the following logistic regression of the form:

# _Pr_ ( _Outcomeij_ | _Production2_ 0 _kmij ,_ **Xij** ) = _logit_<sup>−1</sup> ( _α_ + _β *Production2_ 0 _kmij_ + λ* **Xij** )

Figure 17 shows the location of the enumeration areas on a map of Mali. We restricted our sample to the three regions were industrial and artisanal mining are taking place. The outcomes variables and their coding are defined in Annex 3. Our individual-level control variables _Xij_ include the respondent’s opinion of their own living conditions ( _Povertyij_ ), their education ( _Educationij_ ), age ( _Ageij_ ), gender ( _Maleij_ ), and living area ( _Ruralij_ ). We also include year dummies for the survey rounds, and the standard errors are clustered at the enumeration area level.

> 15 Data and codebook are available at www.afrobarometer.org/data/data-by-country/mali.

21
Table A7: Initial factor prices and other calibrated parameters

|Initial wage in South|||5.70||
|---|---|---|---|---|
|Initial wage in Other|||26.7||
|Initial wage in North|||39.5||
|Initial rental rate of robots|||59.0||
|_θ_|||5.00||
|_ν_|||9.00||
|||sector|X<br>sector Y|sector NT|
||South|0.48|0.31|0.00|
|_α_<sup>_m,i_</sup><br>_M_|Other|0.34|0.23|0.00|
||North|0.34|0.21|0.00|
||South|0.20|0.30|0.26|
|_α_<sup>_m,i_</sup><br>_T_|Other|0.38|0.45|0.41|
||North|0.36|0.49|0.40|
||South|0.20|0.47|0.33|
|_γ_<sup>_m,i_</sup>|Other|0.10|0.49|0.42|
||North|0.13|0.59|0.28|

_Notes_ : Initial wages are calibrated with data on per capita GDP and expressed in thousands of USD. The initial effective rental rate of robots is _<u>φ</u>_<sup>_i_</sup> _<u>R</u>_<sup>=59.</sup> The _φ_<sup>_i_</sup> _L_<sup>_wR_</sup> parameters _θ_ and _ν_ govern the trade elasticity and the elasticity of substitution between robots and workers, respectively. The Cobb-Douglas share parameters are calibrated using WIOD data.

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26

**Figure 7. Funding of the DI Scheme, 2013**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50% funded jointly<br>40%<br>funded by government<br>30%<br>funded privately<br>20%<br>10%<br>0%<br>High Upper Lower Low<br>income middle middle income<br>income income<br><!-- End of picture text -->

**Figure 8. Coverage Increased During Crisis and Remains Above Pre-Crisis Levels**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>Coverage limit / GDP per<br>10<br>capita, 2003<br>8<br>Coverage limit / GDP per<br>6 capita, 2010<br>4 Coverage limit / GDP per<br>capita, 2013<br>2<br>0<br>High Upper Lower Low income<br>income middle middle<br>income income<br><!-- End of picture text -->
of the high-tech industries tend to concentrate in the relatively developed coastal provinces.

**Table 3 – The geographic concentration of some industries in the coastal region: Gini coefficients**

|**Category**|**Code**|<br>**Industry**|**Gini**<br>**(1988)**|**Gini**<br>**(1997)**|
|---|---|---|---|---|
|A|8|Electronic and telecommunications equipment<br>manufacturing|0.27|0.42|
|A|12|<sup>Instruments, meters and other measuring equipment</sup><br>manufacturing|0.33|0.31|
|A|7|Electric equipment and machinery manufacturing|0.25|0.22|
|A|16|Medical and pharmaceutical products|0.16|0.21|
|**Average**|**of cat**|**egory A**|**0.25**|**0.29**|
|C|28|<sup>Timber processing, bamboo, cane, palm bibber and straw</sup><br>products|0.41|0.31|
|**Average**|**of cat**|**egory C**|**0.52**|**0.49**|
|D|<br>3|Chemical material and products manufacturing|0.24|0.15|
|**Average o**|**f cate**|**gory D**|**0.26**|**0.28**|
|**Average**|**of cat**|**egory B**|**0.34**|**0.34**|

However, table 3 shows that industries are less concentrated in the coastal provinces, which are at the 3<sup>rd</sup> stage of development, than in the entire territory. In the coastal region, the high-tech industries are not highly concentrated – the value of the Gini coefficient was 0.25 and 0.29 in 1988 and 1997 respectively. The concentration of the industries “electronic and telecommunications equipment manufacturing” and “medical and pharmaceutical products” increased, but that of the industries “instruments, meters and other measuring equipment manufacturing” and “electric equipment and machinery manufacturing” slightly decreased. At the national level, the concentration of the non high-tech industries increased (Gini of category B increased from 0.40 to 0.44); in the coastal region, it stayed at 0.34. Some industries, such as the “timber processing, bamboo, cane, palm bibber and straw products” and the “chemical material and products manufacturing” became much less concentrated in the coastal region.

In the following sections, we will empirically study whether the relative concentration of the high-tech industries in the coastal provinces results in a relocation of some labor intensive activities to the provinces that are at a lower stage of development.

# **3.2. Description of the variables**

We will study the determinants of the concentration of different industries at the national level and at the regional level (coastal / inland). We measure the relative concentration of industry _i_ ( _i_ =1...30) in province _j_ ( _j_ =1...30) in time 13
# **TAxi OF CONTW8**

|**I.**Introduction .. .. .. . ...,,,,,.,,,,.,<br>.........<br>....<br>v.<br>.1<br>.|
|---|
|II. Methodology ...................................<br>..<br>..<br>.<br>**3**<br>The Full Discounting or Elaborate Method**................**<br>3<br> <br>|
|The Earnings Function Method**...............................**<br>**6**<br>|
|The Short-cut Method .<br>.........<br>. .....<br>.<br>,......<br>. ...<br>8|
|III.Empirical Findings<br>.....<br>.............<br>.....<br>. **8**<br> <br>|
|Basic Earnings Functions ...................................<br>9<br>|
|Overtime Changes in Relative Earnings<br>**..**<br>14<br>|
|Overtime Returns to Education**.......................**<br>**.....**<br>20|
|Extended Earnings Functions ....<br>..<br>.............<br>......<br>.<br>.<br>25<br>|
|SecondaryGeneral<br>versus**Vocational**<br>**Education**<br>**...**<br>**.**<br>**...........**<br>28<br>Private Returns to Education<br>**...................**<br>29<br>|
|Social Returns to Education**....**<br>**...................**<br>**......**<br>31|
|IV. Conclusion ...<br>..<br>.<br>..<br>.<br>.<br>.<br>.<br>.......<br>.<br>37|
|References .......<br>39|
|Annex<br>1. Survey Data Description**.0.00000.....0.0.0.41**|
|Annex2. Variable Definitions ..<br>..<br>....<br>0.0......<br>.44|
|Annex3.BasicEanings Functions .....<br>.............<br>0...<br>48|
|Annex 4. Extended Earnings Functions**.....**<br>**.**<br>**............**<br>**77**|
# **5. Conclusion**

In this paper, our ultimate objective was to uncover the impact of weather shocks and aid receipt on household consumption outcomes. To do so, we first looked at where weather shocks had the largest impacts on maize yields, as we expect that negative impacts will occur primarily through lower agricultural outcomes. Point estimates give substantial negative impacts of droughts ranging from 33 to 40 percent across the two samples, and even greater negative impacts of floods ranging from 47 to 54 percent across the samples. We next considered the relationship between predictors of maize yields and WFP distribution rates, since we expect that well-targeted distribution would occur where maize yields suffered most. There are a number of different GIS-based data sources and specific variables on which to construct measures of droughts and floods, and our analysis suggests that using different variables from different sources matters when defining droughts and floods. This is important because geographic targeting of aid distribution is also based in part on specific GIS-based weather shock variables. Our results show that weather shock variables that explain maize yields do not explain aid distribution. Even more disconcerting is the fact that two rainfall variables that explain aid distribution also have significant impacts on maize yields, but exactly in the opposite direction vis-à-vis the expected impacts. These results suggest that there were significant geographic targeting errors.

Subsequently, we estimated the households’ probability of aid receipt, and aid receipt interacted with a weather shock. Those living in areas subject to a weather shock and with higher WFP distributions were more likely to receive food aid, as we expect. However, the interaction between aid distribution and the weather shock was not significant. The targeting errors also explained household aid receipt, indicating errors of exclusion (negative impact on aid receipt for households subject to weather shocks in poor districts with limited aid distribution) and errors of inclusion (positive impact on aid receipt for households not subject to weather shocks in non-poor districts with positive aid distribution). At the household-level, we documented that households headed by women and those with all elderly members 42