rafmacalaba/data-use-mentions-extended huggingface dataset:
and ES); electricity/basic infrastructure (ES and DB); internet/modern infrastructure (ES and GC); and human capital (DB or World Bank HLO, ES, and GC). We are less sure about the negative effect of corruption (given that findings from the Enterprise Survey and the Global Competitiveness data are contradictory).

# **Importance of contract enforcement**

The systematic differences between the effects of the de jure and de facto BE indicators suggest that enforcement might play an important role in determining BE effects. We thus turn to how the _Doing Business_ BE indicators depend on contract enforcement, as measured by the Contract Enforcement indicator from Doing Business. The results of interacting _Doing Business_ BE indicators and the Contract Enforcement indicator are presented in table 6.

The _Doing Business_ indicators tend to be more effective for improving firm performance 17
||%|45.6|11.6|19.6|3.6|13.6|6|100|
|---|---|---|---|---|---|---|---|---|
|Turkey|n|121|17|23|22|36|31|250|
||%|48.4|6.8|9.2|8.8|14.4|12.4|100|
|**Total**|**n**|**1,253**|**134**|**237**|**246**|**279**|**275**|**2,424**|
||**%**|**51.69**|**5.53**|**9.78**|**10.15**|**11.51**|**11.34**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

_Age:_ Respondents are generally fairly established in their respective markets. The average respondent has over 17 years of experience operating in its respective host market (Table 6). On average, respondents have over 10 years of experience across all 10 countries.

_Trading behavior:_ Respondents are active in global trade, both in terms of importing into and exporting from host countries. On average, respondents import 46 percent of their inputs, and exports account for 34 percent of their sales (Table 6). There is wide variation in average trading behavior across host countries, with average export share of sales ranging from 15 percent (Brazil) to 53 percent (Vietnam) and average import share of inputs ranging from 33 percent (China
# **I-** **<mark>Introduction</mark>**

1. **The Democratic Republic of Congo (DRC) has one of the world’s highest degrees of export concentration, which exposes the country to exogenous shocks.** According to the UNCTAD database, the concentration index in DRC reached 0.80 in 2014 (measured by the Herfindahl index). This concentration is high both in terms of products and of markets. Indeed, exports depend heavily on minerals, with more emphases on copper and gold, and on few destinations, China being the most important. In a volatile and uncertain world, the high export concentration level and low diversification make DRC very sensitive to changes in the international market. Those changes can have severe consequences on DRC’s growth and foreign balances, but also on revenue mobilization, employment, and poverty. Several papers and case studies (Hesse, 2008; Misztal, 2011; Cadot, Carrère, Strauss-Khan, 2011) have shown that a decrease of the level of export concentration (increase in the level of export diversification) leads to an increase of GDP per capita growth. Hence, these papers find an empirical positive relationship between export diversification and GDP per capita. Based on these findings, this paper assesses the degree of export concentration in DRC and the potential impact on the economy. Given the price and demand of commodity exports are exogenously determined, the paper analyzes the potential impact of a change in trade partners’ GDP on DRC’s income. Section II discusses the role of trade in DRC’s economy; Section III examines export dependence and export concentration, and Section IV concludes.

# **IIThe Role of Trade in the Economy of the DRC**

# 2. **Exports in DRC have been volatile, and the dynamics of GDP and exports correlate**

**closely.** Indeed, in the past 20 years, DRC has experienced 3 years where the growth rate of exports
<br>10.0<br>0.0<br>Ethiopia Malawi Nigeria Uganda<br><!-- End of picture text --> Source: Authors’ calculations from the ESS 2018/19, IHPS 2019, GHS-Panel 2018/19, & UNPS 2019/20 For the Ethiopia HFPS, to obtain representative estimates at the national, urban, and rural level, the target sample size for the HFPS was 3,300 households: 1,300 in rural and 2,000 households in urban areas. To account for non-response and attrition all of the 5,374 households with contact information in the ESS were called in round 1 of the HFPS. In rural areas 1,413 households owning a phone and 771 households with reference phone were contacted. Whereas in urban areas 3,213 households owing phone and 24 households with a reference phone numbers were contacted. A total of 3,249 households (2,271 urban and 978 rural households were fully interviewed yielding an overall crude response rate of 60 percent (67% for urban and 50% for rural households).

> 8 The variables considered in the cube sampling approach are the same variables included in the bias correction calibration below (displayed in Table 3.2).

> 9 Response and contact rates reported here are “crude” in that they do not cleanly correspond to AAPOR standard definitions. This crude calculation is retained for the sake of simplicity.

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institutional set-up and other determinants unrelated to trade (e.g. geography or fractionalization of the population).<sup>8</sup> Perhaps less emphasized is the finding that natural riches engender institutional weaknesses as groups attempt to capture rents (Mehlum, H., K. Moene and R. Torvik (2006)). This curse-via-politics is largely endogenous to the political environment and not subject to improvements by governments in power who have a vested interest in blocking institutional change. Resource riches can also block the trade reforms that would reduce distortions and improve the management of natural resources. For example, drawing on the Doing Business data, in a large sample of 133 countries, Amin and Djankov (2008) find that the proclivity to undertake micro-reforms that reduce regulation is much less in countries whose exports are concentrated in abundant natural resources.<sup>9</sup>

# **3.2 Transport-Related Emissions**

The elasticity of trade to world income has grown from around 2.0 in the 1960s to 3.7 in the 2000s (Freund (2009)). For environmentalists, this is evidence that international trade is a direct contributor to deterioration of the environment.

> 8The early literature mistakenly used resource dependence as a measure of resource abundance. When this distinction is made the ‘resource curse’ effects found in the early literature are dampened (Brunnschweiler and Bulte (2008)).  Evidence shows that resource dependence and institutional quality are negatively correlated (Isham et al.). The outcome of natural resource abundance and performance also depends on the institutional setting (Bhattacharya and Hodler (2008)).

> 9 Collier and Hoeffler (2004) were the first to give evidence that conflicts were more likely to be driven by greed to get hold of the rents than by grievances due to ethnic or religious conflicts. Bulte and Brunschweiler (2009) contest this result suggesting that conflict increases dependence on resource extraction (captured by the share of primary exports) while resource abundance (measured by
we find similar results with respect to IPA assistance with business setup (Table 46, p<0.05) and with operational issues and grievances (Table 47, p<0.10). Both of these results are also robust to alternative specifications. Importers may value IPA services more insofar as IPAs help them navigate issues related to obtaining import licenses and addressing grievances concerning import processes, although further research is necessary to fully explain these results.

> 7 Assistance with resolving operational issues and grievances was the most valued IPA service in the 2017 GIC Survey, compared to the fourth-ranked service in the 2019 survey. Nevertheless, the 2019 survey continues to show a sizable majority (67 percent) valuing such services.

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on Trade and Development. The tariff data are generally available for the period of analysis, but the extent of time coverage differs considerably across countries. The product-level trade and tariff data were also mapped into the 16 sectors we use in the analysis, as detailed in Table A1 in the Appendix.

# **4.2 Descriptive statistics**

To set the stage for the analysis that follows, we first document trends in robotization. Robot utilization varies widely not only across sectors, but also across countries. Figure 2 plots the evolution of robot adoption by country, and shows that robotization is most advanced in highwage countries such as Germany, Denmark and Belgium. It also shows that robotization remains limited in lower wage countries such as Romania and Bulgaria, though these countries are adopting progressively more robots. Figure 3 provides evidence that robot adoption across countries is closely aligned with GDP per capita. This evidence is consistent with the prediction of our theoretical model that incentives for robot adoption are greater among countries with higher productivity and labor costs.

Figure 4 shows the evolution of the stock of robots per million hours across sectors. Automotives, rubber and plastics, and metal are the sectors in which robotization is most pronounced and has been advancing most rapidly. By contrast, the adoption of industrial robots in education, agriculture, construction, utilities and other non-manufacturing sectors remains limited. These differences in the extent of robot adoption across sectors reflect in part the technological feasibility adoption. Figure 5 plots average adoption of robots across sectors over our entire sample period against the share of jobs in a sector that are replaceable. The association between adoption and replaceability is clearly positive, with sectors where a larger share of tasks can potentially be performed by robots indeed using more robots, as also shown by Graetz and Michaels (2018).

In sum, robot adoption has increased significantly over time, although at different speeds in different countries and sectors, which is convenient for identification. Figures A1 and A2 in the Appendix display clearly the extent of variation in
**Table 1: Stages of Export Diversification**

<u>(Country FEs estimation; data from 1992-2003)</u>

||`Herfindahl`<br>`Index`|
|---|---|
|`GDPper capita`|`-.0000117`|
||`(2.41)**`|
|`GDPper capita`<sup>`2`</sup>|`2.60e-10`|
||`(2.46)**`|
|`Diversification/Specialization`<br>`Transition Point`|`$22,500`|
|`Observations`|`1140`|
|`Number of Countries`|`130`|

`Absolute value of t statistics in parentheses` ** significant at 5%

**<u>Table 2: The Changing Nature of Innovation</u>**

||`Inside-the-Frontier:`<br>`Discoveries`|`On-the-Frontier:`<br>`Patents`|
|---|---|---|
|`ln(GDPper capita)`|`8.667`|`-16.237`|
||`(6.53)***`|`(2.96)***`|
|`ln(GDPper capita)`<sup>`2`</sup>|`-0.514`|`1.059`|
||`(6.67)***`|`(3.43)***`|
|`Constant`|`-32.289`|`65.872`|
||`(5.71)***`|`(2.74)***`|
|`Observations`|`73`|`68`|

``` Absolute value of z statistics in
if we change the definition from population-based to administrative. All KHDS locations were matched with their census ward-level classification, which distinguish between urban, mixed and rural areas. All urban and mixed areas are classified as towns (or cities if in Mwanza, Dar or Kampala); all rural areas as rural. The KHDS CAPI application had a series of conditional drop-down menus where the interviewer chose the region, district, ward and village in which the household was located. These were pre-populated with the exact census locations and codes, so that the matching exercise is perfect. The census classification is based on 2002 data and the average migrant respondent in our survey moved in 2003.

The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and controlling for seasonality. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing/personal items, transfers out, and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Conservatively, rent is also excluded from the aggregate to avoid large differences in prices for similar quality housing being the driver of any measured urban-rural disparities. The aggregates are temporally and spatially deflated using data from the price questionnaires included in each survey round. As household size may differ between urban and rural households, consumption is expressed in per adult equivalent units rather than per capita. The poverty line is set at 326,474.2 Tanzanian shillings (TSh),<sup>5</sup> calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2007 National Household Budget Survey estimate for rural areas (37.6 percent).

Unless otherwise indicated the analysis below is conducted at the individual level.

Decomposition analysis by Christiaensen et al. (2019), illustrated again in Table 2, shows that moves to cities are more lucrative for those who
of pre-treatment and post-treatment waves:

1. _Over what horizon is a pooled treatment effect relevant and of interest?_ The analysis in this paper has assumed a common treatment effect γ. Of course if the treatment effects vary over individuals, researchers are usually content to estimate an average treatment effect, and the analysis here will still hold. However, in some experiments the effect of the treatment may also vary over time. In this case estimation of γ by pooling multiple post-treatment survey waves will yield an average treatment, where the average is over multiple waves. If the survey waves are relatively close together in time (e.g. monthly or quarterly), then combining several post-treatment waves and getting the average

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_i_ = 1 _,_ 2 _,_ 3 _,_ 4 _..._ denote time periods after export entry and ( _Yt_<sup>0</sup><sup>_′−Y_</sup> _t_<sup>0</sup> _−_ 1<sup>) denotes the change in the outcome of interest between the two periods in the untreated</sup> state.

The treatment on the treated is given by Here _N_ refers to the number of firms that start to export, _T_ refers to the treated group i.e., the export starters and _C_ refers to the control group of never exporters. Each firm _i ∈ T_ is matched to firms in the control group and _wij_ is the weight on each firm _j_ in the control group as a match for firm _i_ .<sup>18</sup> _Yit_<sup>1</sup><sup>_′−Y_</sup> _it_<sup>1</sup> _−_ 1<sup>isthechangeintheoutcomevariablefortheexportentrantbetween</sup><sup>_t′_</sup> and _t −_ 1 and _Yjt_<sup>_c′−Y_</sup> _jt_<sup>_c_</sup> _−_ 1<sup>isthechangeintheoutcomevariableforthecontrolfirmsbetween</sup><sup>_t′_</sup> and _t −_ 1.

The outcomes of interest are ( _α_ � _χχit_ + � _ωit_ ) as well as the individual components _α_ � _χχit_ and _ω_ � _it_ , to examine whether overall productivity improves on export entry as well as the relative importance of the two channels.

# **5 Data**

The firm-level variables are from the Prowess dataset provided by the Center for Monitoring of the Indian Economy (CMIE). This is a panel data set of Indian firms in the organized sector. It includes both listed and unlisted firms. The dataset has been used in several
In the next stage, in each of the 60 villages that were randomly selected to receive a cash transfer program, all poor eligible households were present for a participatory lottery to randomly assign those households to either receive or not receive the particular type of transfer allocated to that village. To determine whether a household was eligible (based on their poverty status) to take part in the lottery, immediately prior to the baseline survey, we conducted an extended household census in every village to collect information from each household about household living structure (cement or mud brick walls, metal or straw roof, flooring, access to latrine), household asset ownership (plow, cart, draft animals, motorcycle, radio), whether the head of household ever attended school, whether the household grows cotton, and whether there was a weekly market in the village. We combined this information with a Burkina Faso nationally representative household survey to calculate a predicted consumption level for each household and compare that with the national poverty line to determine whether a household is considered poor and subsequently is eligible to receive the cash transfer. Given the government transfer program’s limited budget, in consultation with village leaders, we decided that randomization was the fairest way to determine which poor eligible households receive a transfer, and everyone was aware that not all poor households in a given village would be receiving a transfer during the pilot program.

We conducted three rounds of surveys (baseline, one-year follow-up, two-year follow-up) in June 2008, June 2009, and June 2010, interviewing all poor households eligible to receive a transfer in each of the treatment villages and who had been randomly selected to receive the transfer. In each of these four groups of 15 villages, we interviewed approximately 540 poor households randomly selected to receive transfers. The control group consisted of 615 randomly 6
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# Information

In the literature on voting behavior in the United States, the extent to which voters are informed is assessed directly through surveys of their political knowledge (see, e.g., Bartels 1996). Such information is, unsurprisingly, unavailable for cross-country research. Three proxies for voter information that are available, however, ranging from voter education to government control of media, should capture the same underlying phenomenon tracked in US political research.23 Following Brunetti and Weder (1998) and others, the regressions first employ two variables collected by Charles Humana (1985, 1992), one evaluating the independence from government control of radio and television networks, and the other the independence of newspapers. Since they exhibit significant differences (they are correlated at 0.69), both are used. The 1985 value of each variable is applied to crises of the 1980s and the 1992 value to crises of the 1990s. The highest (most independent) score goes to countries in which there is “unqualified respect” for the independence of the respective media. Occasional breaches of independence earn a country the next highest score, followed by countries in which there are frequent intrusions on independence and finally countries that exhibit a constant pattern of violations of independence.24 23 Measures of levels of voter information range from interviewer assessments of respondents’ level of information, to respondent answers to factual questions. These turn out to be highly correlated. One would expect that voters exposed to slanted or limited media would be less informed according to these criteria, and that less educated voters would exhibit less political knowledge than more educated voters. 24 Notes in many of the country entries provide greater insight into the criteria used. Government ownership and control of all radio and television networks (as in Ghana in the early 1980s) earns countries the lowest score; countries score the next to lowest score if they exhibit some limited private ownership or, as with newspapers in Chile in the 1980s, evidence of seizures of private newspapers and mandatory prior approval of
*

This table shows the collected exogenous variables in different categories and the transformations we apply (feature engineering). The order of transformations and aggregations follow the vertical structure of the table. Spatial aggregations are applied to the district-level data to compute country-level values. Temporal aggregations are computed on a monthly frequency and we then select the values that are contemporaneous with the reported IPC values.

|Category|Argonomic stress and weather|Confict|Economic|
|---|---|---|---|
|Variable|Rainfall<br>Normalized diference vegetation<br>index (NDVI)<br>Evapotranspiration (ET)<br>Evaporative stress index (ESI)|Number of violent events<br>Number of fatalities|Consumer price index (CPI)<br>Gross domestic product (GDP)<br>Staple food price index|
|Transformations|Average<br>Anomalies (anom)|Count<br>Inverse-distance weighted (IDW)||
|Spatial aggregation||Average over regions<br>Standard deviation over regions||
|Temporal aggregation|<br>Rollin|Percentage changes (%Δ)<br>Rolling average over 3- or 6-months<br>g standard deviation over 3- or 6-mon|ths|

6
220***|0.000|420||||||
|Proportion of protocols and<br>clinical guidelines<sup>f</sup>|0.565|0.086|0.254***|0.000|420|0.408|0.100|0.261***|0.001|278|

Note: data from health facility assessments. All regressions are the difference-in-differences specification with facility fixed effects and controlling for exposure to the collaborative quality improvement and citizen scorecards interventions. Standard errors are clustered at the district and survey round level with wild bootstrapping. Availability of consumables was not measured at the health houses.

a Improved source of water includes piped water, public taps, tube wells, protected dug wells, protected springs, rainwater and bottled water. b Proper sterilization procedure includes autoclaving, boiling, steam sterilization, chemical sterilization, and outsourcing c Proper decontamination procedure is scrubbing, or cleaning followed by use of a disinfectant d Proper biowaste disposal method includes burning or outsourcing e Because of error in the questionnaire skip pattern, the variable is missing in the baseline survey data for many facilities.

f Availability was defined as existence of at least one unit, confirmed by direct observations of the survey enumerators. The items included in the calculation of availability of equipment, and consumables are listed in appendix Table A3.

Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 24
24). A similar pattern is observed among men.

Figure 23. Percentage of women and men who own the house they inhabit by internal migration status <!-- Start of picture text --> 80 73.5 73.6<br>70<br>60<br>50 39.0 40.8<br>40<br>30<br>20<br>10<br>0<br>Men who migrated Men who did not Women who migrated Women who did not<br>migrate migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

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