rafmacalaba/datause-encoder-data huggingface dataset:
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE A similar increase was observed in funding for road maintenance and rehabilitation. Resources allocated for road maintenance and rehabilitation through the road fund doubled between 2005 and 2007 and grew an additional 50 percent in 2008, when they reached $54 million (table 10). Although this would be enough to cover maintenance costs of the existing network, approximately half of road fund’s resources have been allocated for rehabilitation. To remedy this situation, the road fund should have a clear mandate to provide resources only for maintenance, and rehabilitation should be financed out of the investment budget.

**Table 10. Existing resource flows to the road sector, US$ million per year**

|||Roa<br>|ds<br>|||Other transport<br>||
|---|---|---|---|---|---|---|---|
|Year|Capital exp.|O&M central<br>gov’t|O&M road<br>fund*|Total|Capital exp.|O&M central<br>gov’t|Total|
|2004|68|2|0|71|14|2|16|
|2005|67|1|16|84|42|2|44|
|2006|12|1|26|39|7|13|20|
|2007|208|1|35|245|91|8|99|
|Average,<br>2004–07|89|1|26|110|39|6|45|
|2008|276|3|54|333|73|7|80|

* 50 percent of road fund resources are used for rehabilitation.

An estimated $322 million of additional resources could be recovered each year by making more efficient use of the country’s existing resource envelope. Efficiency gains (improved capital budget execution, cost recovery, and operational efficiency) would account for $209 million of that total, and another $112 million could be gained by reallocating resources to better uses.

Reducing operating inefficiencies could reduce hidden costs by $79 million per year (or 1.3 percent of GDP). Distribution losses (particularly in the power sector) account for $51 million of that total, while the costs of overstaffing (primarily in the state-owned incumbent power utility) are $20 million per year. Finally, eliminating undercollection in the country’s power and water utilities could save an additional $8 million a year (table 11).

**Table 11. Potential gains from greater operational efficiency**

|Sector||US|$ million per ye|ar||
|---|---|---|---|---|---|
||ICT|Power|Transport|WSS|Total|
|Overemployment|--|17|--|3|20|
|Losses|--|49|--|2|51|
|Undercollection|--|7|0|1|8|
|Undermaintenance|--|--|0|--|0|
|Total|--|73|0|6|79|

_Source_ : Briceño-Garmendia and others 2008. — = data not available.

Infrastructure services are underpriced in the Republic of Congo, particularly in the power and water sectors. If tariffs were raised to cost recovery levels, a further $78 million could be recovered (table 12).

31
Table 4: Growth and net IIP assumptions for emerging and advanced economies, by scenario, 2010–25<sup>_†_</sup>

|||**Baseli**|**ne**|**H**|**igh gr**|**owth**|
|---|---|---|---|---|---|---|
||_2010_|_2025_|_Average_|_2010_|_2025_|_Average_|
|GDP growth (%)|||||||
|Emerging|6.2|3.9|4.7|6.2|4.4|4.9|
|Advanced|2.2|1.5|2.3|2.2|1.5|2.3|
|GDP (USD trillion)|||||||
|Emerging|21.6|43.7||21.6|44.8||
|Advanced|39.5|56.2||39.5|56.2||
|GDP per capita growth (%)|||||||
|Emerging|5.6|3.6|4.2|5.6|4.0|4.4|
|Advanced|1.7|1.3|1.9|1.7|1.3|1.9|
|Net IIP (USD trillion)|||||||
|Emerging|2.7|15.2||2.7|15.2||
|Advanced|-2.4|-9.8||-2.4|-9.8||

> * GDP levels are measured in constant 2009 U.S. dollars.

20 (9.0 percent in the high-growth scenario), and to an average of 6.7 percent annual growth between 2020 and 2025 (7.0 percent in the high-growth scenario).

Consistent with the past decade, the expansion of financial globalization, as measured by the rate of growth of cross-border deals, is expected to exceed that of real economic growth: Growth in cross-border deals is likely to outpace expected emerging-market GDP annual growth rates of 4.9 percent over 2010–20, and 4.1 percent over 2020–25. This expected growth in cross-border deals echoes a global trend of financial growth generally exceeding growth in real economic variables.

Preliminary evidence from M&A activity for 2010 and 2011 is supportive of this reasonably rapid recovery from the crisis. Indeed, as evident in Figure 1, deal activity by emerging market firms in 2010 has already exceeded pre-crisis peaks, and M&A activity in 2011 appears well on track for the full recovery, in 2013, implied by our projections. This is also supported by country-level evidence. Outbound cross-border M&A deals concluded by Chinese firms, for example, reached 744 deals in 2010 and 909 deals in 2011, levels comparable to (and in excess of) the pre-crisis peak of 760 deals, attained in 2007.

# **6 Conclusion**

In this paper, we have explored the factors associated with bilateral M&A activity by emerging market firms. Our main finding is that these firms’ decisions to pursue acquisition targets depend critically on whether they are investing in advanced or emerging market targets. Southern acquisitions tend to be located in countries with lower levels of per capita income, which likely reflects a desire to take advantage of lower wage costs in those countries. Acquisitions of 18
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mand generated in other sectors as a result of exports, and thus calculate the overall exposure index, we incorporate the 2011 input-output (I-O) GTAP tables. We chose 2011 I-O table instead of a more recent year available (2016) because it reflects the economy at the beginning of the studied period, which guarantees that all the shocks computed are ex-ante. An important underlying assumption with this decision is that there isn’t any significant changes in the sectoral structure. Admittedly, this is a major limit of the paper that we have to accept due to data constraint.<sup>1</sup> We begin by computing the input-output coefficients from the GTAP I-O tables, which capture the interdependencies between sectors in an economy. We match these coefficients with trade data the UNCOMTRADE data to compute the total export value for each sector, accounting for indirect changes in export demand through input-output linkages.

The next step is to link these total export data with the LFSs. We utilize concordance tables available from UNSD that translate International Standard classification (ISIC) rev 3.1. codes into HS codes. By leveraging this concordance, we merge the micro-data on labor force variables at the industry and area level in Viet Nam with total export data. Once the integrated labor and trade data is prepared, we are able to calculate the total trade exposure index based on provinces, as previously explained. The starting point for the analysis is the idea that the impact of a trade shock differs across regions, depending on each province-industry composition.

A fundamental principle for this approach is the existence of segmented labor markets. Existing labor mobility barriers or rigidities (such as commuting costs or lack of transport infrastructure) allow us to observe variations in local labor market outcomes and, as a result, to estimate the effects of differentiated exposure to trade. One heuristic method for assessing labor-market integration involves examining the standard deviation of wages across regions and over time. This heuristic measure is used because various factors can prevent wage equalization across regions. To investigate the level of labor-market integration in Viet Nam, we calculate province and industryprovince premiums, the existence of which can indicate segmented labor markets. Tables 1 and 2

> 1In future exercises, we will attempt to test this assumption using a later I-O table

10
The Student _t_ approximation is used for constructing confidence intervals and significance tests, where: with degrees of freedom: Finally, the fraction of information about that is missing due to nonresponse is: The results from an illustrative OLS regression that would have underlined MI in the absence of the bootstrapping option for the imputation model are reported in Tables 3 and 4. The models perform well in explaining the variance in the GPS-based plot area, with an R-square of 0.658 in Uganda and 0.688 in Tanzania. The coefficients on the farmerreported plot area are highly significant and large, with a similar order of magnitude (0.945 in Uganda and 0.866 in Tanzania). It is worth emphasizing that the imputation model neither intends to provide a parsimonious description of the data nor attempts to portray structural relationships among variables (Schafer and Graham, 2002). Instead, it attempts to be as comprehensive as possible in order to minimize any bias that could stem from omitting variables that might be relevant to the pattern of missingness or the subsequent analysis. “The possible lost precision when including unimportant predictors is usually viewed as a relatively small price to pay for the general validity of analyses of the resultant multiply-imputed database.” (Rubin, 1996: 479) Table 5 provides separately for the UNPS 2009/10 and the TZNPS 2010/11 the summary statistics on (i) GPS-based and farmer-reported plot area for plots with an observed GPSbased area measure, and (ii) multiply-imputed GPS-based and farmer-reported plot area for the entire plot sample. The mean and the associated standard error for the multiplyimputed GPS-based plot area are computed in accordance with Rubin’s rules. Looking at the _overall_ multiply-imputed mean GPS-based area and the associated standard error for the entire plot sample in Uganda and Tanzania, we observe that they are in line with the mean GPS-based area and the associated standard error for measured plots. While MI, in comparison to casewise deletion, leads to marginally lower mean and standard error estimates for GPS-based plot area, a simpler method of handling missing data, such as conditional mean imputation, would have understated the true variance in the variable of 14
There have been even fewer studies using data from Nigeria to examine the agriculture and nutrition linkage and the few papers that do exist are case studies or only use descriptive statistics (Babatunde et al., 2010; Okezie and Nwosu, 2007). Babatunde et al. (2010) examines the relationship between income and calorie intake for farm households in rural Nigeria using household data from 40 villages in Kwara state. Although, the authors find a significant positive relationship between income and calorie intake, the calorie-income elasticity was estimated as 0.181 suggesting that calorie intake does not increase substantially with income. They also find a positive relationship between farm size and calorie intake in Nigeria. Okezie and Nwosu (2007) examined the effect of agricultural commercialization on nutritional status of children in Abia state in Nigeria and found that children in households that are more commercialized recorded a higher prevalence of underweight and stunting.

Given the existing gaps in the literature, the present study contributes to the literature by using nationally representative data from Nigeria that contains information on household consumption, agricultural production, and geospatial variables to examine the link between agriculture production and dietary diversity. The econometric strategy of the paper is based on the nonseparable household literature to estimate the causal effects of production diversity on dietary diversity rather than associations. In previous cross-sectional studies, identification of the income and nutrition interlinkage may be confounded by the inability to distinguish the causal direction of the interlinkage, as higher income households may have increased nutrition, but households with better nutrition may also have higher productivity and higher incomes. By modeling this causal relationship using a nonseparable household model and using exogenous variation in degree days and rainfall and agricultural capital as instruments, 5
are approximately risk neutral (0 _._ 022, _se_ = 0 _._ 303). This is consistent with the strong positive correlation of risk tolerance with per capita GDP, since Ethiopia is significantly poorer than Vietnam. The findings are also consistent with data obtained with Ethiopian farmers by Doerr, Toman and Schmidt (2011) using a different elicitation method.<sup>5</sup>

**Table 1:** Summary measures of aggregate risk preferences by prospect

|prob.|median CE|mean CE|SD|test =EV|
|---|---|---|---|---|
|0.05|7.5|10.88|10.37|_z_ = 18_._21_, p <_0_._001|
|0.10|9.5|13.53|10.19|_z_ = 17_._84_, p <_0_._001|
|0.30|15.5|18.05|10.05|_z_ = 11_._51_, p <_0_._001|
|0.50|22.5|23.01|9.12|_z_ = 6_._26_, p <_0_._001|
|0.70|29.5|27.13|8.58|_z_ =_−_1_._49_, p_= 0_._136|
|0.90|34.5|32.01|8.51|_z_ =_−_7_._33_, p <_0_._001|
|0.95|37.5|34.38|8.17|_z_ =_−_6_._54_, p <_0_._001|
|mean|22.07|22.71|7.30|_z_ = 8_._19_, p <_0_._001|

We can now show how our data fit into different models in a purely nonparametric way. Finding a good model to fit the data is important inasmuch this will improve our econometric analysis of the determinants of preferences, reducing potential attenuation bias. We start with an expected utility ( _EU_ ) model. Since utility functions are unique only up to an affine transformation, we can arbitrarily fix the endpoints at _u_ ( _y_ ) _≡_ 0 and _u_ ( _x_ ) _≡_ 1. Plugging this into the general equivalence _u_ ( _CEi_ ) = _piu_ ( _x_ ) + (1 _− pi_ ) _u_ ( _y_ ), we now simply obtain that _u_ ( _CEi_ ) = _pi_ . The non-parametric mean utility function thus obtained is plotted in figure 1(a). This utility function resembles the one proposed by Markowitz (1952). Markowitz recognized that people may be risk seeking for some prospects while being risk averse for others, so that the utility function would have convex as well as concave sections. To accommodate this finding, he proposed to abandon initial wealth integration and to measure utility relative to a reference point given by current wealth.<sup>6</sup> This type of reference-dependence

> 5Similar results are also reported by Henrich and McElreath (2002), who found significant risk seeking in certainty equivalents over 50-50 prospects elicited from a Tanzanian tribe. They do, however, attribute this finding to the specific characteristics of the tribe. In contrast, Akay, Martinsson, Medhin and Trautmann (2012) found high levels of risk aversion eliciting CEs with poor farmers in Ethiopia. The latter finding, however, is driven mostly by subjects who consistently chose the sure amount for all choices.

> 6With initial wealth integration, convex and concave sections of the utility function might

9
pur 2014); and Russia, where 2.35 million individuals left between the 1990s and 2000, reflecting the alienation of professionals and entrepreneurs (Loiko, 2011).<sup>28</sup> Our findings have relevant policy implications. First, in order to prevent the use of forced displacement as a strategy for silencing political accountability, governments, election authorities, and the international community should facilitate out-of-country voter registration, consular and mobile voting, and secure diaspora voter rolls. Second, our findings imply that organized crime must be understood and confronted as a transnational system through coordinated cross-border intelligence and actions, since criminal organizations can migrate to areas with weak enforcement and institutions, and then expand and strengthen. Third, considering that autocratic governments derive illicit rents from organized crime activities such as human trafficking, actions that regulate displacement flows in host destinations might help reduce the rents derived from these networks.

We show that forced displacement has the power to transform cultural values, economic systems, and political landscapes in origin countries. As time passes and more data are available, future work should explore the medium- and long-term impacts of these flows and offer insights into their broad consequences for the development paths of affected countries.

# **References**

- Abadie, A. (2005). Semiparametric difference-in-differences estimators. _The Review of Economic Studies_ , 72(1):1–19.

Abramitzky, R., Boustan, L. P., and Eriksson, K. (2014). A nation of immigrants: Assimilation and economic outcomes in the age of mass migration. _Journal of Political Economy_ , 122(3):467–506.

> 28Yet, the “Kremlin couldn’t care less if the most talented, the most active Russians are emigrating, because their exodus lifted the social and political tension in the country and weakened the opposition.”

42
trips to origins ( _ji_ , and _ik_ ), giving us a total sample of 362,436 trips between 491 origin and 589 destination districts. The dependent variable in panel A of Table 4 is the log of value of trade flow and in Panel B, the log of total quantity hauled, both estimated from trip data. All regressions control for the log of distance but the sets of control variables vary across columns.

The first column in Table 3 reports the estimates when the log of the distance between OD pairs and interaction of crude price with average VAT rate between OD pairs are the controls in _Zij_ . The second column adds the log of diesel prices along the route to controls in column 1. The sample for this third specification is smaller as we have daily diesel prices in different districts only for 2019. The third column adds the log of night light luminosity along the route to the set of controls in the first column. It should be noted that both diesel price and night light luminosity are endogenous to trade flows and transport costs and could be argued to be bad controls in our context. These specifications are estimated to check the robustness of IV results later. The OLS estimate of trade elasticity with respect to the freight rate has the expected negative sign and is statistically significant in each of the three regression specifications. The estimates are remarkably consistent across specifications and fall within a narrow bound of -0.24 to -0.27. The estimates imply an average elasticity of trade with respect to the freight rate of around -0.25. The elasticity of volume of trade with respect to the freight rate is larger in magnitude (-0.79) than the average value of trade elasticity.

To take care of the potential endogeneity of freight cost in the trade gravity regression, we start with first stage estimates which are reported in Table 5. The instrument is the average VAT rate for the next trip from destination district _j_ ( _vj_ ) as defined earlier. The controls in each column are the same as in Table 4. The coefficients of the instrument in all three Specifications are positive and statistically significant at a 1 percent level. The estimate implies that an increase in the average VAT rate in the next possible destination increases the freight rate in the current route. An increase in the VAT rate in the next possible route ( _jk_ ) will decrease trade flow along that route, decreasing the probability of getting a match for the next trip. The estimates for the current route interaction term also suggest a positive pass-through of the 17
# **Table 5:  Significant Changes in WGI Estimates of Governance, 1998-2007 Cont'd**

||**Gov**<br>|**ernance S**<br>|**core**||||**Agree/**||||
|---|---|---|---|---|---|---|---|---|---|---|
||**2007**<br>**(Level)**|**1998**<br>**(Level)**|**Change**|**Agree**|**No**<br>**change**|**Dis-**<br>**agree**|<br>**(agree+**<br>**Disagree)**|**Sources**<br>**Added**|**Balanced**<br>**Change**|**Bal Chng/**<br>**Actual Chng**|
|**Government Effectiveness**|||||||||||
|MALDIVES|-0.19|0.96|-1.15|2|0|0|1.00|3|-0.88|0.77|
|COTE D'IVOIRE|-1.37|-0.36|-1.01|6|0|0|1.00|4|-0.96|0.95|
|ZIMBABWE|-1.48|-0.48|-1.00|5|1|1|0.83|7|-0.79|0.79|
|CHAD|-1.45|-0.61|-0.84|3|0|0|1.00|8|-0.59|0.70|
|TOGO|-1.48|-0.68|-0.80|3|1|0|1.00|6|-0.76|0.95|
|BOLIVIA|-0.83|-0.04|-0.79|4|1|0|1.00|8|-0.62|0.79|
|BELARUS|-1.26|-0.51|-0.75|2|0|2|0.50|6|-0.73|0.97|
|SPAIN|1.00|1.70|-0.70|6|1|0|1.00|4|-0.63|0.89|
|QATAR|0.06|0.70|-0.63|2|0|2|0.50|3|-0.84|1.33|
|ITALY|0.33|0.92|-0.59|4|1|2|0.67|4|-0.59|0.99|
|ETHIOPIA|-0.45|-1.09|0.64|4|1|0|1.00|7|0.70|1.09|
|ALGERIA|-0.52|-1.16|0.64|5|1|0|1.00|6|0.83|1.29|
|RWANDA|-0.37|-1.15|0.78|3|0|0|1.00|5|0.66|0.84|
|SERBIA|-0.34|-1.18|0.84|3|0|0|1.00|8|0.93|1.10|
|HONG KONG|1.80|0.92|0.88|5|0|1|0.83|4|0.90|1.03|
|KOREA, SOUTH|1.26|0.36|0.90|7|1|0|1.00|5|0.82|0.91|
|AFGHANISTAN|-1.33|-2.27|0.95|1|0|0|1.00|6|0.94|1.00|
|_Average_||||_3.82_|_0.47_|_0.47_|_0.90_|_5.53_|||
|**Regulatory Quality**|||||||||||
|ZIMBABWE|-2.24|-0.68|-1.56|8|0|0|1.00|4|-1.64|1.05|
|BOLIVIA|-1.18|0.30|-1.48|6|0|0|1.00|5|-1.58|1.07|
|VENEZUELA|-1.56|-0.15|-1.41|8|0|0|1.00|4|-1.63|1.16|
|ARGENTINA|-0.77|0.64|-1.40|8|0|0|1.00|3|-1.58|1.13|
|ERITREA|-1.95|-0.63|-1.32|3|0|0|1.00|3|-1.13|0.85|
|ECUADOR|-1.09|-0.05|-1.04|6|0|0|1.00|4|-1.18|1.13|
|COTE D'IVOIRE|-0.98|-0.07|-0.91|5|2|0|1.00|3|-0.95|1.05|
|GEORGIA|0.21|-0.77|0.98|5|0|0|1.00|7|1.03|1.06|
|Congo, Dem. Rep.|-1.35|-2.43|1.08|5|0|0|1.00|4|1.14|1.06|
|LIBYA|-0.98|-2.20|1.21|5|0|0|1.00|3|1.32|1.09|
|IRAQ|-1.35|-2.76|1.41|3|1|0|1.00|3|1.49|1.06|
|_Average_||||_5.64_|_0.27_|_0.00_|_1.00_|_3.91_|||

Note: Shaded countries correspond to increases in WGI estimates of governance, and non-shaded areas <u>correspond to declines.</u> 34
# Chemical &

# Pharmaceuticals

|_ijst_<br>_MA_<br>∆<br>×<br>|0.785<sup>_a_</sup>|0.378<sup>_b_</sup>|||
|---|---|---|---|---|
|Machinery and|(0.153)|(0.161)|||
|others|||||
|(<br>_M_<br>β )<br>_ijst_<br>_MA_<br>∆<br>×<br>|||0.570<sup>_a_</sup>|0.596<sup>_a_</sup>|
|_Medium Adv_|||(0.118)|(0.118)|
|_Low: comparativ_<br>_ijst_<br>_MA_<br>∆<br>×<br><sup>Paper</sup>|_e disadvantage_<br>0.658<sup>_a_</sup>|0.442<sup>_a_</sup>|||
|& Printing|(0.119)|(0.113)|||
|_ijst_<br>_MA_<br>∆<br>×<br>|0.226|-0.383|||
|Electrical &|(0.331)|(0.331)|||
|Electronics|||||
|(<br>_L_<br>β )<br>_ijst_<br>_MA_<br>∆<br>×<br>|||0.340<sup>_a_</sup>|0.302<sup>_a_</sup>|
|_Low Adv_|||(0.106)|(0.104)|
|Observations|4183|4183|4183|4183|
|0 <sup>:</sup><br>=<br>_H_<br>_L_<br>_H_<br>β<br>β(p-|||0.001|0.000|

# value)

Heteroskedasticity-robust standard errors clustered at the state of origin-year level appear in parentheses.<sup>_a_</sup> , _b_ and _c_ indicate significance at the 1%, 5% and 10% confidence levels. All regressions include the fixed effects _FEij_ , _FEst_ and _FEit_ & _FE jt_ . Columns 1 and 3 restrict the coefficient of sector-specific wage gaps to be the same across all industries. Column 2 and 4 allow the coefficient of the wage gap to vary across industries in the same way as market access. Wage coefficients are not reported for the sake of brevity. They are mostly positive and significant. For details on the industry classification see appendix S4.4. Source: Authors’ analysis based on data described in the text.

# EMPIRICAL RESULTS BY SECTOR AND EDUCATION

In this section, we distinguish between highly educated and low-educated workers. Figure A-1 displays differences in migrant shares between the two educational groups for each state for the years 1995 and 2003. Over the sample 22