rafmacalaba/datause-encoder-data huggingface dataset:
24

**Table 7. African Trade Barriers Compared with those in Non-OECD Countries with the Highest Non-Oil Export Growth Rates.**

||1992-94<br>OECD Imports|1962-6.4 to<br>1992-94 OECD<br>Import Growth|Export<br>(unwe|ing Country's Tr<br>ighted averages f|ade Barriers<br>or tariffs)|
|---|---|---|---|---|---|
||($million)|Rate (%)**||||
|Exporting Countries*|||Tariff<br>Level (%)|All Import<br>Charges (%)|NTB Coverage<br>Ratio|
|ALL SUB-SAHARAN AFRICA|15,146|5.41|26.8|33.4|34.1|
|Low Income Africa|11,433|5.21|28.6|34.3|40.6|
|Middle Income Africa|3,713|6.08|20.9|30.1|12.5|
|FAST GROWING EXPORTERS|271,157|16.77|8.7|11.1|3.7|
|of which:||||||
|Republic of Korea|44,839|24.61|11.1|12.3|2.6|
|Singapore|28,064|22.66|0.4|0.4|0.3|
|<br>Saudi Arabia|2,239|22.17|12.1|12.1|3.9|
|Bahrain|471|20.62|7.1|7.1|1.5|
|Taiwan, China|56,046|20.47|9.7|9.7|11.2|
|Thailand|25,171|16.74|8.5|8.5|5.5|
|Qatar|130|16.30|4.2|4.2|1.3|
|Malaysia|26,336|16.26|12.8|17.6|2.1|
|Indonesia|17,689|14.97|17.0|20.1|2.7|
|Jordan|184|14.23|13.8|28.0|12.9|
|Mexico|42,635|13.83|13.4|16.9|3.9|
|Hong Kong|26,178|13.65|0.0|0.0|0.5|
|<br>Kuwait|179|12.93|4.2|4.2|3.5|
|Papua New Guinea|996|12.50|7.0|14.2|2.6|
|HIGH-INCOME NON-OECD|105,364|18.83|3.4|3.4|4.0|
|OECD COUNTRIES***|1,394,252|12.39|6.1|6.1|3.8|

* Several small island countries like St. Pierre, Malta, and the Comoros achieved export growth rates in excess of 13 percent per annum but were excluded from the above list since it was felt their special characteristics did not provide a useful basis for comparisons with other countries. The Peoples Republic of China achieved an annual growth rate of 20.37 but was excluded for two reasons: (i) the US export ban against China in the earlier period which greatly depressed the 1962-64 trade base, and (ii) under its state planning system tariffs and NTBs are not of paramount importance as import controls. This latter point invalidates comparisons with the other countries.

** Over the 1962-64 to 1992-94 period world trade in all non-oil products, measured in current prices, grew at a compound annual rate of 11.57 percent.

*** The 3.8 percent NTB coverage ratio is reported in Low and Yeats (1995) and reflects the dismantling of OECD countries nontariff barriers achieved in the Uruguay Round.

Source: UNCTAD, Directory of Import Regimes 1994, and Handbook of Trade Control Measures of Developing Countries, 1987 (Geneva: United Nations). Also, GATT/WTO, Trade Policy Review Mechanism Reports, various issues and various dates.
Before going to the results, we discuss briefly the first stage estimations for the three variables deemed endogenous. The choice of regulation appears to hinge mostly on the quality of institutions. Price cap regulation is less likely when the bureaucracy is more efficient, capturing perhaps the enhanced ability of bureaucrats to manage informationally demanding schemes like rate of return regulation. On the other hand, more generic measures like corruption and rule of law appear to have the opposite effect. One possibility is that price caps are less attractive for government in environments characterized by higher risk of bribery and weak enforcement. However, overall the bureaucratic efficiency effect seems to dominate.

As for exclusive private financing, it dominates in the transport sector (private operators may be less willing to assume alone the huge and ex ante uncertain modernization costs of obsolete water delivery systems). The existence of a regulator ex ante also increases the probability of some government participation in financing, maybe because it represents the implicit guarantee of better administrative control over the project. A better bureaucracy makes private financing more likely, probably capturing the fact that better bureaucrats find it easier to convince private investors of the convenience to invest. As for corruption, it has the opposite effect, which can be related to the effect commonly observed in FDI studies, showing that private investors prefer direct involvement in situation of weak governance for risk sharing purposes (Straub, 2005).

Finally, minimum income guarantee clauses are especially prevalent in transport projects, probably due to the high uncertainty surrounding future demand and as a way to make them attractive to private investors (see Engel et al. 2003 and INCO, 2004). Such clauses also appear to be more likely when a regulator is present.

# 3.4 Results

We present the main results for the determinants of government-led renegotiation below, making special mention of how they relate to similar estimations in GLS. We discuss the core variables.

- The existence of a regulatory body is again negative and significant most of the time. Its economic impact, however, appears to be smaller (about half) of what it is for firm-led renegotiations. Also, we again observe that it loses statistical significance when instrumental estimations are performed, which we interpret as being linked to the fact that this variable is used in the first

30
year on the sample of individuals 20 and older, the probability of being on the job 5–10 years on the sample of individuals 25 and older, and the probability of being on the job more than 10 years on the sample of individuals 30 and older.

To account for the effect of individual characteristics on job tenure, we include in **X** a dummy for gender, age and age squared, education-level dummies, a dummy if a worker is married, the share of children, and household size. Regarding job characteristics, we include a dummy for the nature of the job contract (temporary or permanent) and whether the job is full-time or part-time. A temporary job could capture the differential ability of firms to release workers <u>(Gregg and Wadsworth 2002). Part-time or self-employed workers are more likely to break their tenure than</u> full-time workers (Garcia-Cabo and Mudero 2019). We control for occupations by a set of dummies based on the two-digit International Standard Classification of Occupations (ISCO) classification (ILO 2021) and use five dummies corresponding to different firm sizes (Bryson, <u>Erhel, and Salibekyan 2021).</u><sup>8</sup> We also include a series of year dummies to capture the time trend.

To identify the time effects in equation (2), we assume that the coefficients of our control variables are constant over time. Burgess and Rees (1998) find little evidence of significant changes in the effects of the main control variables over time. We derive the marginal effects of the year dummies ( _πt_ ) estimated at the sample means. These marginal effects could be interpreted as conditional sample-year average proportions of each tenure duration.<sup>9</sup> To investigate the presence of a secular trend in the probability of having each tenure duration, we proceed to the second-stage estimation, which consists of linear regression with the marginal effects of each year dummy ( _πt_ ) as a dependent variable and a linear time trend as the single independent variable (equation 3). We bootstrap the standard errors of the second-stage estimations to account for the small sample error heteroskedasticity:<sup>10</sup>

> 8 We use the two-digit occupation classification because in 2010, the labor force surveys transitioned from the ISCO 88 classification to the ISCO 08 classification. A correspondence between both classifications is possible only at the two-digit level.

> 9 For the second-stage regression, we could also use the probit estimated coefficients (πt). However, using the marginal effect simplifies the interpretation of our results.

> 10 We estimate the standard errors of the second-stage regression using 500 bootstrapped sample draws (Imbens and Kolesar 2016).

12
2003). Prior to turning to this discussion, we outline the range of possible factors that might be associated with the likelihood of local conflict.<sup>3</sup>

# **_2.2. Potential Factors Affecting the Likelihood of Local Conflict_**

The likelihood that conflict escalates into violence may be affected by economic factors including poverty, inequality, and income shocks/uncertainty; frictions between groups (e.g., across religious and ethnic group cleavages); and institutional factors (e.g., the extent to which violence is effectively mediated or, conversely, sanctioned by the state apparatus, including the police, and the legitimacy and effectiveness by which power/leadership is exercised in a community) (cf Humphreys 2003). The exact mechanisms by which these factors might affect the likelihood of conflict escalating beyond a certain threshold may be complex and highly context specific, as may be the exact pathways toward resolution, stalemate, and escalation (Barron _, et al._ 2004b).

Observers have emphasized different factors that may be associated conflict, and/or which are important to mediate tensions, in the Indonesian context. They include, but are not limited to, unemployment and inequitable development (Barron and Madden 2003), weak legal systems (Welsh 2003), the effect of industrialization (Miguel _, et al._ 2003), decentralization (ICG

> 3 A growing literature has examined the incidence of crime and various associated economic factors at the international (cf Fajnzylber _, et al._ 2002) and at the local level (cf Demombynes and Ozler 2002).  Crime and conflict are often strongly associated.  For example, robberies may trigger conflict; incidents such as murder or rape may occur in the context of conflict.  Some of the factors identified in the crime literature, including inequality, may also be associated with conflict, although often through different mechanisms.  Whereas crime could be argued to be largely based around individuals (with varying degree of organization), instances of local conflict appear to be best defined in group/communal terms (Barron _, et al._ 2004b).  The measurement of crime also faces issues of potential underreporting/reporting bias, although phenomena such as property or personal crime incidents can often be defined as more discreet events.  While the Indonesian data from PODES also contains evidence of local crime (it asks whether stealing, robbery, plundering, torture/violence, arson, rape, use of narcotics and murder took place in a village in the past year, and whether levels increased, decreased, or remained the same) we consider an examination of this to be beyond the scope of this paper.

6
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Index of Unmarried<br>Autonomy (Standardized)|-|Core<br>Respondent|No|4|Standardized index of autonomy, measured for<br>core respondents who are not married at the<br>time of the household survey. Includes<br>questions on whether the core respondent<br>needs permission to do certain activities,<br>whether the core respondent can travel alone,<br>and whether the core respondent is allowed to<br>have money set aside for her own use.|
||||||Standardized super index of married<br>empowerment, measured for core respondents<br>who are married at the time of the household<br>survey. It includes 8 components described<br>below: index of decision-making; index of|
|Super-Index of Married<br>Empowerment<br>(Standardized)|IV|Core<br>Respondent|Yes|4|marital satisfaction; index of women's divorce<br>prospects; index of fertility empowerment;<br>index of self-determination in marriage; index<br>of frequency of social contact; index of spousal<br>abuse; and age difference between husband<br>and wife. All variables are constructed so that<br>higher values are an indicator of increased<br>empowerment.|
|Index of Decision-Making<br>(Standardized)|-|Core<br>Respondent|No|4|Standardized index on a series of questions on<br>who makes decisions in the household related<br>to food, clothing and children.|
|Index of Marital<br>Satisfaction (Standardized)|-|Core<br>Respondent|No|4|Standardized index on a series of questions<br>related to how satisfied the core respondent is<br>with her marriage.|

68
content measures for each of the four occupation categories included in the ELE survey. Appendix B reports details on the exact PIAAC survey questions used to compute each task content measure (Table B2).

Table 1 provides a schematic summary of the intensity of each task content measure for each of the occupation categories in the ELE survey.<sup>8</sup> Table 1 shows, for a given occupation, whether the use of each task is above (+) or below (-) the average use of that same task across all occupations. The shaded fields indicate the most important tasks for each occupation. The results show that the intensity of use of abstract and manual tasks has a clear correlation with the skill level of the occupations. Managerial and skilled production occupations are more intensive in abstract tasks as the task value is above the average across all occupations. Unskilled production occupations are more intensive in non-routine-manual tasks. Routine tasks, both cognitive and manual, are more important for administrative and unskilled production occupations. When analyzing which are the most important tasks for each occupation category separately, we find that abstract and non-routine-manual tasks are the most important for managers.<sup>9</sup> For administrative workers, the most important tasks are routine-cognitive and routine-manual. For skilled production workers, the most important tasks are abstract and routine-cognitive tasks – these are the set of tasks we expect to be potentially replaced by the complex software. Finally, for unskilled production workers the most important tasks are non-routine-manual and routine-manual. We define _firm-level task indexes_ as a weighted average of the task content measures across occupations, where the weights are given by the share of each occupation in the firm’s total employment: where _j_ designates a firm, _t_ a year, _c_ is one of the four occupation categories in the ELE survey (managers, administrative workers, skilled production workers, and unskilled production workers), _k_ is a type of task (abstract, routine-cognitive, routine-manual, and non-routine-manual),

> 8 The actual values of the task content measures for each occupation category are shown in Panel A of Table B3 (Appendix B). 9 The relative importance of non-routine-manual tasks for managers is in line with the finding by Messina et al. (2016) based on the STEP survey that occupations with a high content of non-routine-manual tasks include high-level occupations like database and network professionals, managing directors, and chief executives.

10
<!-- Start of picture text --> —— Trest<br>0.78 vy Selected spec<br>0.76 @ PMT simulation<br>x Policy baseline<br>0.74<br>3gFa 0.72<br>o 0.70<br>£<br>0.68<br>0.66 e<br>0.64<br>x<br>0.5 0.6 0.7 08 0.9 10<br>Share of households interviewed<br><!-- End of picture text -->
coverage increased significantly in recent decades. Access in the region is still low, however, at 2 percent of the population in Somalia, 19 percent in Ethiopia, 22 percent in Kenya, and 56 percent in Djibouti in 2019.

# **3 Data**

This paper uses household survey data that have been georeferenced, new spatial infrastructure data, and district characteristics in order to study links between access to infrastructure and the structure of local economies as well as the complementarities between types of infrastructure.

**Infrastructure** We collected new information on road network expansions, access to the electricity network, and access to Internet fiber backbone from various sources (Table 1).

Table 1: Summary of Infrastructure data

|Infrastructure|Country|Year|Source|
|---|---|---|---|
|Roads|Ethiopia<br>Kenya|1996, 2006, 2016<br>2003,2009,2016-18|Ethiopian Roads Authority (ERA)<br>Kenya Road Board|
|Electricity|All|varyacross countries|Nighttime lights / Population raster|
|Electricity grid|All<br>All|around 2006<br>most recent|Foster and Briceno-Garmendia (2010)<br>gridfnder.organd Arderne et al.(2020)|
|Internet(fber backbone)|All|2009-2019|Africa Bandwidth Maps 2009-19|

We gathered geospatial maps of road expansion using government sources as well as previously harmonized collections of road networks (Foster and Briceno-Garmendia, 2010; Jedwab and Storeygard, 2020). The quality of the network and associated features and the frequency of updates vary across countries. For Ethiopia and Kenya, we used a rich panel of data on road network expansions complemented by details on road conditions (a panel of GIS data and maps for 1996, 2006, and 2016 for Ethiopia and 2003, 2009, and 2018 for Kenya that rely at least partially on actual road surveys).<sup>3</sup> Panels of roads from the same source are rare. Figures A.2 and A.3 show the extensions of the paved road network for Kenya and Ethiopia. Related papers studying the expansion of the Ethiopian road network are Adamopoulos (2019), Gebresilasse (2019) and Kebede (2019), all of which focus on the feeder roads from the 2016 Ethiopian Roads Authority survey.<sup>4</sup> .

We used two methods to map access to the electricity network: nighttime lights and maps of power transmission grids. Nighttime light data are available for most years and locations but convey imperfect information on electrification. Historical maps of electricity grids are more

> 3Data for Ethiopia are used in Croke and Duhaut (2020)

> 4The study by Moneke (2020), which focuses on all-weather (gravel, asphalt, or bitumen surface) roads, is closer to this study

6
**<u>Table 8: Production function regressions</u>**

||<br>**20**|<br>**31**|**20**|**22**||**2021 and 20**|**22 combined**||
|---|---|---|---|---|---|---|---|---|
||||||**O**|**LS**|**Farm fix**|**ed effects**|
|Land (ha)|0.081***|0.114***|0.080***|0.114***|0.077***|0.095***|-0.031|0.014|
||(0.009)|(0.012)|(0.011)|(0.014)|(0.008)|(0.010)|(0.032)|(0.039)|
|Affected by war|||-0.111**|-0.122**|-0.098*|-0.114**|-0.129**|-0.122**|
||||(0.055)|(0.053)|(0.055)|(0.055)|(0.050)|(0.051)|
|Labor (US$/ha)||0.063***||0.068***||0.034**||0.019|
|||(0.016)||(0.019)||(0.014)||(0.033)|
|Fertilizer (US$/ha)||<br>0.081***||<br>0.084***||<br>0.069***||<br>0.050**|
|||(0.017)||(0.018)||(0.015)||(0.021)|
|Machinery (US$/ha)||<br>0.065***||<br>0.055***||<br>0.056***||<br>0.029|
|||(0.015)||(0.018)||(0.017)||(0.022)|
|Central # Year 2022|||||-0.184***|-0.137***|-0.232***|-0.197***|
||||||(0.020)|(0.021)|(0.018)|(0.023)|
|East # Year 2022|||||<br>-0.196***|<br>-0.118***|<br>-0.152***|<br>-0.114**|
||||||(0.043)|(0.044)|(0.055)|(0.058)|
|North # Year 2022|||||<br>0.010|<br>0.031|<br>-0.120***|<br>-0.088**|
||||||(0.042)|(0.042)|(0.039)|(0.043)|
|Southern # Year 2022|||||<br>-0.602***<br>(0.031)|<br>-0.486***<br>(0.034)|<br>-0.332***<br>(0.031)|<br>-0.285***<br>(0.036)|
|West # Year 2022|||||<br>0.007|<br>0.042|<br>-0.123***|<br>-0.072*|
||||||(0.049)|(0.050)|(0.037)|(0.039)|
|Constant|6.322***|5.165***|6.092***|5.012***|<br>6.341***|<br>5.497***|<br>6.803***|<br>6.134***|
||(0.043)|(0.136)|(0.049)|(0.146)|(0.040)|(0.118)|(0.138)|(0.332)|
|No. of farms|<br>954|<br>954|<br>959|<br>959|<br>1,913|<br>1,913|<br>1,913|<br>1,913|
|R-squared|0.073|0.170|0.055|0.133|0.221|0.270|0.278|0.289|

Note: Regressions are for monetary yield from main crops (wheat, barley, rapeseed, maize, sunflower and soybean) per hectare using the balanced panel of farms cultivating any crop in the 2021 and 2022 cropping season. Robust standard errors clustered at the farm level in parentheses: * significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include farm fixed effects.

27
2 mg/L. Following a similar methodology described in section 2.4, each VLSS commune is matched to its upstream pollution counterpart.

# _Data for Sub‐Saharan Africa_

We use data from 90 Demographic and Health Surveys (DHS) spanning a period of 23 years to account for all child and household variables presented in the analysis. Figure 2 shows the 31 countries included in the analysis from Sub‐Saharan Africa, as well as Morocco and the Arab Republic of Egypt. The dots represent the approximate locations of the communities where households in the survey live. We focus on anthropometric measures of children up to 5 years of age. We convert children’s heights into Z‐scores using the WHO growth standards (WHO 2006). Doing so allows us to assess child height relative to well‐nourished children of the same age and sex. For our main outcome variable, we use height‐for‐age Z‐score (HAZ) and low HAZ (i.e. HAZ below ‐2), which reflects stunting. Water quality data come from a machine learning algorithm presented in Damania et al. (2019). Each birth record is then matched to nitrate pollution flowing from urban centers that are farther upstream. These urban centers were identified using data of urban agglomerations from Africapolis (OECD/SWAC (2018)).

# 3. Empirical Methods

To estimate the long‐run health impacts of childhood exposure to nitrogen pollution, the research design exploits quasi‐random variation in exposure to nitrogen pollution experienced by different birth cohorts in different districts. Specifically, the analysis compares height outcomes between exposed and non‐exposed cohorts, controlling for average differences in these outcomes across birth years and across districts. The estimating equation for individual‐ level outcome Y of person i during time t and born in district d and state s is presented below.

Y��� �α�βN<sup>�</sup> ������,��� _3_ � � λX��� � γD��� �ρ�� �ρ�t�μ� �ε��� (1) ������, �� N<sup>�</sup> ��� , where superscript U denotes upstream, is the fraction of years from the time of birth to age 3 that individual _i_ was exposed to nitrate‐nitrogen levels from upstream areas that exceeded safe limits in their birth district _d_ . It serves as a measure of cumulative pollution exposure in early life during generally accepted critical periods for biological growth and development. These values are recorded from upstream districts exploiting the natural flow of rivers and the fact that pollution flows downstream even as the decision to pollute upstream is orthogonal to downstream health. In this way, we exploit quasi‐random variation in pollution that originates upstream and yet flows downstream to other districts. The analysis then uses these spillovers to ascertain how much of the health impact persists in the next district downstream of pollution incidence.

8