rafmacalaba/datause-encoder-data huggingface dataset:
conflict intensity across the provinces which suggests the presence of a precautionary motive in the household wealth accumulation behavior. Taking our investigation further, we now formally investigate the presence and the strength of this precautionary motive in the country. We source our econometric specification from previous studies which we discuss in more detail below. We note again that in our investigation of the household wealth accumulation behavior, we are also concerned with whether conflict interacts with this behavior. For this purpose, our specification needs an extension of the standard specification followed in the past literature.

The data that we use in our analysis are of a cross-sectional nature. Hence, our strategy is to exploit the cross-sectional variation to test whether higher wealth accumulation is associated with higher income uncertainty. To this end, we use the following regression specification: where _Wi_ g _p_ denotes the wealth accumulated by a household _i_ belonging to an occupational group g living in a province _p_ . The precautionary motive dictates that this accumulated wealth rises with an increase in income uncertainty which is captured by _V ARLY_ g. The variable _V ARLY_ g is the variance of the log of income which varies with the occupational group of a household. Using a Deaton (1991) type buffer-stock model of wealth accumulation and data from the US, Carroll & Samwick (1998) show that this theoretical measure of uncertainty fits the data relatively well compared to a theoretical measure of relative uncertainty. We follow the same specification. The coefficient β1 is the above specification and hence becomes the first object of interest in our study.

To bring variation in conflict under analysis, we extend the standard specification by introducing a dummy for conflict intensity _CONFp_ . This dummy classifies the Afghan provinces into two groups – high- and low-conflict. Our objective is to check how the precautionary behavior changes with conflict and for this purpose we interact the conflict dummy with the measure of income uncertainty _V ARLY_ g. The parameter β3 captures the variation in wealth accumulation across high- and lowconflict provinces at the same level of income uncertainty. In our benchmark specification, we define a province to be a high-conflict province if the total number of terrorist attacks in the province exceeds that of the median province (189 attacks). We perform several alternative variations around this classification to ensure robustness. We note that it is straightforward to use conflict intensity as a continuous variable compared to constructing a dummy. We prefer the dummy classification because it allows for a simpler interpretation of the coefficient β3.

We complete our specification by controlling for a host of factors that are considered important 10
- 23 -

# Table 2.2. Percent of Manufacturing Establishments Without Private Generators or Boreholes In Each Employment Size Category

|ExpLoVmant|Ni9|rim|Ind|ones ia|The|llrand|
|---|---|---|---|---|---|---|
||Private<br>GCnerators<br>**_**_____<br>_____<br>_ _ _ _ _<br>_ _ _ _ _|Private<br>loraholns<br> <br>_ _ _ _ _ _<br>_ _ _ _ _|Private<br>**_GCnerators_**<br> <br>_ _ _ _ _ _<br>_ _ _ _ _|Private<br>Artesian<br> <br>WetLs|Private<br>GCnerators<br>_ _<br>_<br>_<br>_<br>_<br>_|Private<br>**Artesian**<br> <br>W etls|
|1- 19|68.8|100.0|40.0|80.0|100.0|87.9|
|20- 49|8.6|85.7|52.1|48.9|97.3|82.4|
|50- 99|0.0|63.6|32.1|42.9|98.5|82.4|
|100 - 199|0.0|31.4|29.0|30.7|98.2|70.4|
|200 - 499|0.0|34.6|13.6|34.1|83.0|63.8|
|500- 9°9|0.0|26.7|31.6|31.6|81.3|56.3|
|1000&over|0.0|25.0|10.0|20.0|62.5|50.0|
|All|7.8|55.9|34.5|40.0|94.0|75.7|

NOTE: From Appendix TabLes 12, B4, C2, C4, D2, and D4.

Source: World Bank Nigeria infrastructure Project Establishment Survey 1988. World Bank Research Project CRPO 676-71) Estabilshment Survey for Indonesis nd ThaiLand 1992.
for poverty predictors in both base and target surveys. However, even if this requirement is fulfilled, substantial differences may still exist between base versus target surveys regarding length, thematic scope, and complexity of questionnaires. These differences may lead to considerable differences in interview duration and respondent burden, which can affect measurement in diverse ways that are ultimately context- and subject-specific (Kreuter _et al._ , 2011; Eckman _et al._ , 2014). In our case, the understudied topic is whether poverty imputation accuracy can be affected by the fact that the target survey questionnaire, by design, would be _lighter_ and less burdensome than its older, base survey counterpart – even if the requisite questions underlying the poverty predictors are _identical_ across base and target survey questionnaires. The only available evidence regarding this question comes from a randomized experiment that was implemented in Malawi but not replicated elsewhere, and that shows the measurement of poverty predictors can indeed be affected by the length of the target survey in a way that can also impact predicted poverty estimates (Kilic and Sohnesen, 2019).

The second and related issue is whether _shorter_ consumption modules included in a target survey (e.g., with reduced or aggregated item lists vis-à-vis the base survey) can provide cheaperto-collect but reliable predictors that can further boost the accuracy of poverty predictions under marginal additional costs of data collection. In this case, the requisite questions underlying the poverty predictors may be _non-identical_ across base and target survey questionnaires – relaxing the aforementioned traditional requirement for survey-to-survey imputation. In this respect, only two studies exist, and they offer inconclusive evidence. While <mark>Christiaensen</mark> _<mark>et al.</mark>_ <mark>(2022) suggest that using consumption sub-aggregates for poverty imputation only works under certain stringent conditions, Dang</mark> _<mark>et al.</mark>_ <mark>(forthcoming) analyze 14 s</mark> urveys from various <mark>countries and demonstrate that adding household utility expenditures to a basic imputation model with household</mark> 3
2 provide an update of the estimate for Hungary. Previous estimates have inid'cated that the Soviet subsidy changed significantly from year to year, and our estimate of the Soviet subsidy is more than double the Marrese-Wittenberg estimate for 1987; (2) We introduce a new data set which is based on our extensive interview survey of experts in important Hungarian enterprises who engage in trade with the Soviet Union; the frequently cited estimates of Marrese-Vanous (1983), which were based on unit values, have been criticized for excessive adjustment for quality. We believe that our method, which .mplicitly adjusts for quality based on the expert opinion, is at least as reliable; (3) we analyze the previously unnoted methodological question of how to assess the terms-of-trade change for the Eastern European countries who have trade surpluses in TR. There is a problem in assessing the terms-of-trade change, given that in 1989 and 1990, all Eastern European CMEA countries except Romania had a surplus with the Soviet Union denominated in an inconvertible unit of account, the transferable rouble (TR);<sup>3</sup> (4) we show that contrary to the conventional wisdom on the subject, the majority of Hungarian firms exporting to the Soviet Union have been disfavored by the combination of the payments mechanism and exchange rate, tax and subsidy policies; and (5) we compare and evaluate the methodology of other recent estimates of the terms-of-trade loss due to the switch-over in Eastern Europe.

Based on data for 1988, 1989 and 1990, this paper presents estimates that Hungary will suffer a terms-of-trade loss as a result of the switch-over to hard currency pricing with the Soviet Union in 1991. The estimate is presented using two measures: the loss of income to Hungary (the income terms-of-trade) and the 3The uncertain settlement of the TR balance between the Soviet Union and Hungary is described below.
What do **we** know about theze schools that can give **us** any lndlcatlon of why they have performed exceptLonally well or exceptLonally poorly? We shall take **the** positlve outllero for Mathematics and English first. The poitLve outliers **are** drawn from three different schooltypess there are two former Group A schools, one high fee-paying school and three mLssion schools. Schools number 2 and number 15 **were** ldentlfled as positlve outllers for both subjects. **FLve** out of the six schools **are** boarding schools, and four out of the **slx are** sLngle **sex** schools, wlth two of each type. There is a high percentage of trained teachers ln all the **six** schools, between 86 and 92% of the teachers belng traLned. The teacher pupil ratios at the schools range from 12 to 33 pupils per teacher. The teachers at all the schools have on average at least two years of teaching experience and have been at **the** schools in questLon at least two years. Four of the six sehools have sufficLent textbooks for each chlld to have her/hli own, the remaLnlng two schools have thelr students sharlng between two students. Five of the **six** schools **are** at least 90% Afrlcan, one havLng only 23% Afrlcans in the student body. School sizes vary from 528 to 929 _pupilo._ FLnally, class sLzes vary from between 29 pupils to a class up to 40 per class.

Aside from wantlng to Lnvestlgate further the fact that the positive outllers **are** primarily boarding and sLngle sex schools, there is nothlng overwhelmlngly indLcatLve of factors llkely to set these schools apart from the rest Ln the sample, particularly as one of the other two sLgnLfLcant features has already been controlled for in the final model, namely, the ratio of textbooks to students.

44
Burtraw, D., K. Palmer, & D. Kahn (2010). A Symmetric Safety Valve. _Energy Policy_ , 38(9): 49214932.

- Chai, S., Liu, Q., & Yang, J. (2023). Renewable Power Generation Policies in China: Policy Instrument Choices and Influencing Factors from the Central and Local Government Perspectives. _Renewable and Sustainable Energy Reviews_ , 174, 113126.

- De Jonghe, C., Delarue, E., Belmans, R., & D'haeseleer, W. (2009). Interactions Between Measures for the Support of Electricity from Renewable Energy Sources and CO2 Mitigation. _Energy Policy, 37_ (11), 4743-4752.

- Fischer, C. (2001). Rebating Environmental Policy Revenues: Output-Based Allocations and Tradable Performance Standards. RFF Discussion Paper 01–22.

- Fischer, C. (2010). Renewable Portfolio Standards: When Do They Lower Energy Prices? _The Energy Journal. 31_ (1): 101–120.

- Fischer, C., & Fox, A. K. (2007). Output-Based Allocation of Emissions Permits for Mitigating Tax and Trade Interactions. _Land Economics, 83_ (4), 575-599.

- Fischer, C., Hübler, M., & Schenker, O. (2021). More Birds than Stones – A Framework for Second-Best Energy and Climate Policy Adjustments. _Journal of Public Economics, 203_ , 104515.

- Fischer, C., & Newell, R. G. (2008). Environmental and Technology Policies for Climate Mitigation. _Journal of Environmental Economics and Management, 55_ (2), 142-162.

- Fischer, C., & Preonas, L. (2010). Combining Policies for Renewable Energy: Is the Whole Less than the Sum of Its Parts? _Resource for the Future Discussion Paper_ , (10-19).

- Fischer, C., Preonas, L., & Newell, R. G. (2017). Environmental and Technology Policy Options in the Electricity Sector: Are We Deploying too Many? _Journal of the Association of Environmental and Resource Economists, 4_ (4), 959-984.

- Fischer, C., & Springborn, M. (2011). Emissions Targets and the Real Business Cycle: Intensity Targets versus Caps or Taxes. _Journal of Environmental Economics and Management, 62_ (3), 352-366.

- Flues, F., Löschel, A., Lutz, B. J., & Schenker, O. (2014). Designing an EU Energy and Climate Policy Portfolio for 2030: Implications of Overlapping Regulation under Different Levels of Electricity Demand. _Energy Policy, 75_ , 91-99.

- Goulder, L. H., Hafstead, M., & Williams, R. (2016). General Equilibrium Impacts of a Federal Clean Energy Standard. _American Economic Journal – Economic Policy, May 2016._

- Goulder, L. H., Long, X., Lu, J., & Morgenstern, R. D. (2022). China’s Unconventional Nationwide CO2 Emissions Trading System: Cost-Effectiveness and Distributional Impacts. _Journal of Environmental Economics and Management, 111_ , 102561.

- Goulder, L. H., Long, X., Qu, C., & Zhang, D. (2023). China's Nationwide CO2 Emissions Trading System: A General Equilibrium Assessment (NBER working paper No. w31809). Cambridge, MA: National Bureau of Economic Research.

- Holland, S. P. (2012). Emissions taxes versus intensity standards: Second-best environmental policies with incomplete regulation. _Journal of Environmental Economics and_

44
Concentration analysis, based on the Herfindahl-Hirschman ( _HHI_ ) and Dominance ( _DI_ ) indexes are probably the guideline’s best known aspects. The guideline’s concentration criterion establishes the following index classifications:<sup>5</sup>

1. Where the post-merger _HHI_ index increases less than 75 points or is less than 2,000 points, a merger has a low probability of being blocked. Thus, a _HHI_ index less than 2,000 is a safe harbor; mergers falling below that level will rarely be blocked.

2. If the post-merger _DI_ reduces or is less than 2,500 points, a merger has a low probability of being blocked. Thus, a _DI_ index less than 2,500 is a safe harbor.

The Law states that any factor that the Commission deems appropriate, given the nature of the relevant market, e.g. sales indicators, number of customers or productive capacity and so on, will be used as the input while constructing the concentration indexes. Furthermore, the FECL sets out a list of factors that, in addition to concentration indices, should be used to determine whether competition has been lessened substantially. Factors listed are:<sup>6</sup>

1. Actual or possible effective import competition;

2. Availability of substitutes; 3. Any barriers to entry including financial costs, amount of the required investment, and regulatory control over entry, and any effect of the merger on such barriers to entry; 4. Share equity of the firm(s) or agents involved in the merger on other firms participating directly or indirectly in the relevant market or in related markets;

> 5 See Official Journal of the Federation (OJF), July 24th 1998. The _HHI_ index, which is equal to the sum of the squares of the market shares, is well known in the literature and extensively used in many antitrust authorities around the world as the first screen for merger approval. For example, the _Horizontal Merger Guidelines_ of the Department of Justice (DOJ) and the Federal Trade Commission (FTC) establish a presumption of illegality when a post-merger _HHI_ exceeds 1,800 _and_ the merger-induced change in the index exceeds 100 points. Expressing the shares in percentage terms, the maximum value the HHI can attain is 10,000 in a situation where all supply is concentrated in a single firm, and the minimum value is 10,000/N when all firms have equal market shares. In Mexican merger control the FCC also relies on the so-called Dominant Index ( _DI_ ) as a first screening device. For a description of _DI_ index, see Appendix 1.

6 These factors are not mutually exclusive nor arranged in order of importance.

6
countries, irrigation levels are very low, typically less than five percent.

Panel B presents summary statistics on the primary crop production (millet in Niger, maize in all other countries). The patterns present in our cross country comparisons of total farm production are mostly borne out in our comparison of primary crop production. Nigeria has, on average, the largest harvests, highest yields, and the highest use of purchased inputs like fertilizer, pesticide, and herbicide. On average, Niger has the largest land area dedicated to the primary crop and Ethiopia, on average, expends the most labor per ha. Uganda has, on average, the smallest harvests and yields and applies the least amount of fertilizer.

Since total farm values are given in constant USD and primary crop values are in kg, we cannot directly compare most values. However, we can compare land area for the farm with land area dedicated to the primary crop. In Ethiopia, Nigeria, and Uganda, land area to the primary crop makes up around 30% of the total farm area, implying fairly diversified production practices. By contrast, the primary crop in Malawi, maize, commands 92% of total farm-land, suggesting Malawian agriculture is highly specialized. Niger (44%) and Tanzania (58%) are in between these two extremes. The relative degree of specialization (mono-cropping) compared to diversified agricultural production has implications for the use of remote sensing weather data to predict agricultural production as well as serve as an instrument for other economic indicators. In Malawi, where 92% of the farm is dedicated to producing a single crop, it may be fairly easy to settle on a single rainfall and temperature variable that do a good job at predicting yields. However, in highly diversified agricultural settings, like Ethiopia, Nigeria, and Uganda, it may be difficult to find a single set of variables that predict outcomes, since the rainfall and temperature metrics that are highly correlated with maize production may not be correlated with teff, cassava, or bean production. The need to account for numerous crop-weather relationships when choosing a weather metric to proxy for economic outcomes is an issue not adequately addressed in the literature.

# **3 Analysis Plan**

The following analysis, and the associated results, was pre-specified in our pre-analysis plan (Michler et al., 2019) and was registered with Open Science Framework (OSF). If methods, approaches, or inference criteria differ from our plan, we highlight these differences. Results arising from these deviations in our plan should be interpreted as exploratory.

## **3.1 Estimation**

Our basic model specification follows Deschˆene and Greenstone (2007): 17
incentive to export products back to Europe. The main purpose of their investment in Tunisia is cost saving, from both cheaper labor and tax incentives in natural resource–related industries, or from outsourcing a relatively simple part of the process, like textiles, in the production value chain. In fact, research on EPZs in developing countries found that foreign firms operating in EPZs mostly engaged in process trade based on cheap unskilled or semiskilled labor available in the host country and did not generate linkages with the local economy. This situation might explain the lesser likelihood of innovation investment for FDI firms.

The negative effects of FDI on technological upgrading in domestic firms have been documented in previous literature. Strong competition from foreign subsidiaries may reduce local firms’ R&D efforts (OECD 2002). Foreign subsidiaries may remain enclaves in developing countries, lacking effective linkages with the local economy. However, these negative effects are more relevant for competitor firms or other firms in the domestic market, rather than those firms that receive FDI. Therefore, there are many preconditions, such as a firm’s absorptive capacity and business environment, necessary for an effective technology transfer process to occur through FDI.

The findings above are consistent with previous literature investigating determinants of innovation in Tunisia. Surveys on selected industries, such as ICT and pharmaceutical industries, confirmed that innovation efforts are lacking in those so-called high technology industries (Harbi, Amamou, and Anderson 2009, 2012; Yacoub 2013). Also, a study using the innovation survey confirms that firm size has a positive effect on innovation (Ayadi et al. 2009).<sup>15</sup> Moreover, labor quality has a positive effect on innovation, whereas FDI has a negative impact on innovation in Tunisia (Gabsi, Mhenni, and Koouba, 2008; Karray and Kriaa 2008). Their findings are consistent with the abovementioned results.

_Which source of innovation is the more efficient way to increase productivity?_

> 15 The innovation survey used a small sample of firms that were highly likely to innovate, and therefore, cannot provide information for general firms in Tunisia. Also, the studies that use the innovation survey analyze the determinants of innovation output, not innovation efforts. Therefore, I cannot directly compare my results with those studies; however, they still provide valuable information to cross-check the characteristics of Tunisian firm behavior related to innovation.

37
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Tanzania now has one of the most vibrant domestic air transport markets in Africa, offering competing services on all routes.

**Table 4. Air transport**

|Country|Angola|Zambia|Tanzania|Botswana|Namibia|Zimbabwe|Mozam-<br>bique|
|---|---|---|---|---|---|---|---|
|TRAFFIC (2007)||||||||
|Domestic seats<br>(millions per year)|1.20|0.44|1.87|0.24|0.08|0.24|1.14|
|Seats for international travel within Africa<br>(millions per year)|0.48|1.46|1.27|—|—|1.11|0.58|
|Seats for intercontinental travel (millions<br>per year)|0.59|0.11|0.59|—|0.24|0.18|0.09|
|Seats available per capita|0.13|0.17|0.9|—|—|0.11|0.09|
|QUALITY||||||||
|% of seat-km in older aircraft|0.1|19.8|17.1|0|1.1|15.5|16.3|
|% of seat-km in newer aircraft|59.6|63.8|79.3|100|79|71.4|57.0|
|% of seat-km in aircraft of unknown age|40.2|16.4|3.6|0|19.9|13.0|26.7|

_Source:_ Bofinger, 2009. Derived from AICD air transport database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

# Water supply and sanitation

## **Achievements**

Zambia already has relatively good access to high-end water and sanitation solutions (table 5). About 34 percent of Zambia’s population has access to utility water, whether from private taps or standposts; compared with only 24 percent in other resource-rich countries of Africa. Access to septic tanks, at 18 percent, is far ahead of peer countries.

As for trends in recent years, Zambia – along with other African countries – is moving fastest with intermediate options (figures 5 and 6). The real action in WSS has been in expanding access to wells or boreholes and traditional latrines, for an additional 1 percent of the population each year. By contrast, coverage of high-end solutions such as piped water and flush toilets has declined slightly in Zambia, while growth in standposts and improved latrines has been modest. This pattern is consistent with elsewhere in Sub-Saharan Africa.

12