rafmacalaba/datause-encoder-data huggingface dataset:
# **E.8 Alternative Functional Forms for Reported Cases**

**Table E.8:** Effect on Reported Cases (Alternative Specifications)

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Linear Probability Model|||||||
|Total|0.182|0.047|0.057|0.039|0.018|5,440|
||(0.386)|(0.026)*|(0.034)*|(0.031)|(0.038)||
|Confrmed|0.009|0.021|0.022|0.02|0.002|5,440|
||(0.096)|(0.007)***|(0.01)**|(0.008)***|(0.011)||
|Negative|0.164<br>(0.371)|0.038<br>(0.024)|0.038<br>(0.03)|0.037<br>(0.029)|0.002<br>(0.034)|5,440|
|Log(Ebola Cases + 1)|||||||
|Total|0.16<br>(0.363)|0.065<br>(0.033)*|0.074<br>(0.044)*|0.057<br>(0.039)|0.017<br>(0.05)|5,440|
|Confrmed|0.007<br>(0.077)|0.023<br>(0.008)***|0.027<br>(0.012)**|0.019<br>(0.009)**|0.008<br>(0.013)|5,440|
|Negative|0.139<br>(0.335)|0.045<br>(0.027)*|0.04<br>(0.034)|0.049<br>(0.033)|-0.008<br>(0.04)|5,440|
|Poisson|||||||
|Total|0.281|0.469|0.552|0.407|0.144|5,440|
||(0.727)|(0.213)**|(0.286)*|(0.25)|(0.322)||
|Confrmed|0.011|1.669|2.008|1.369|0.639|5,440|
||(0.129)|(0.513)***|(0.58)***|(0.569)**|(0.5)||
|Negative|0.238|0.342|0.276|0.389|-0.113|5,440|
||(0.648)|(0.201)*|(0.268)|(0.234)*|(0.3)||

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01 A40
**Table 13 Random Effects Estimator with Autoregressive Error Term**

||Regulations|Finance|Entry|Time to<br>Export|Infrastructure|Property|
|---|---|---|---|---|---|---|
|RealGDP(PPP)|1.007|1.044|1.088|1.035|0.984|1.031|
||(0.029)***|(0.039)***|(0.029)***|(0.030)***|(0.029)***|(0.029)***|
|Log(Area)|-0.123|-0.139|-0.185|-0.133|-0.108|-0.137|
||(0.031)***|(0.037)***|(0.032)***|(0.031)***|(0.030)***|(0.031)***|
|Remoteness|-0.408|-0.408|-0.491|-0.573|-0.542|-0.582|
||(0.161)**|(0.184)**|(0.177)***|(0.164)***|(0.154)***|(0.160)***|
|Export Growtht,t-1|0.034|0.038|0.026|0.034|0.042|0.045|
||(0.016)**|(0.019)**|(0.016)|(0.019)*|(0.016)**|(0.019)**|
|log(TTRI)|-0.104|-0.195|-0.203|-0.216|-0.162|-0.220|
||(0.052)**|(0.057)***|(0.053)***|(0.058)***|(0.050)***|(0.055)***|
|log(MATTRI)|-0.044|-0.170|0.060|0.174|-0.145|-0.202|
||(0.022)**|(0.130)|(0.063)|(0.152)|(0.067)**|(0.076)***|
|log(MATTRI)*Indicator|0.049|0.031|-0.011|-0.074|0.077|0.023|
||(0.025)**|(0.036)|(0.006)*|(0.045)*|(0.051)|(0.012)*|
|Indicator|0.310|0.115|-0.018|-0.214|0.938|0.110|
||(0.060)***|(0.092)|(0.010)*|(0.089)**|(0.144)***|(0.027)***|
|Constant|3.562|2.579|3.489|5.342|4.162|4.282|
||(1.585)**|(1.816)|(1.746)**|(1.638)***|(1.521)***|(1.579)***|
|Observations|205|175|204|203|193|189|
|Number of Countries|98|82|98|97|90|88|

Panel regression. Standard errors are in parentheses. All regressions control for year fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

23
# **Nonfarm Microenterprise Performance and the Investment Climate: Evidence from Rural Ethiopia***

**Josef Loening**<sup>**1**</sup> **, Bob Rijkers**<sup>**2**</sup> **and Måns Söderbom**<sup>**3**</sup>

1World Bank, Washington DC, USA 2Department of Economics, University of Oxford, UK 3Department of Economics, University of Gothenburg, Sweden

**__________________________________________________________________________________**

Keywords: Nonfarm Enterprise, Rural Investment Climate, Rural Labor Markets, Ethiopia. JEL Classification: O13, O14, O18.

**_*_** _Authors’ names are in alphabetical order. We would like to thank the Central Statistical Agency for being able to make extensive use of the survey data, seminar participants at University of Gothenburg, the 2008 CSAE Conference on Economic Development in Africa, the 2008 Oriel College Oxford Joint Academic Forum on Economics and Finance, the Ethiopia Ministry of Finance and Economic Development, the Ministry of Agriculture and Rural Development, as well as Magdi Armin, Kathleen Beegle, Mulat Demeke, Mary-Hallward Driemeier, Laketch Mikael Imru, Måns Nerman, Gbemisola Oseni and Francis Teal for useful discussions and comments. Financial support from the Research Committee of the World Bank is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Executive Directors, or the countries they represent._

Corresponding author’s email address: mans.soderbom@economics.gu.se.

-1-
# **Table 3. Correlation matrix**

This table reports Pearson correlation coefficients for the whole sample of banks and the sample of large banks. Large banks are defined as banks with total assets above 50 billion US $. See Table 2 for the definition of all capital ratios. RWA is risk-weighted assets and TA is total (un-weighted) assets. The balance-sheet data are all obtained from Bankscope and the market data (stock returns, price-earnings ratio, Beta, market to book value of equity) from Datastream. See Table A2 for a detailed definition of all control variables.

## **All banks**

||Stock return %|RWRt|RWRt1|RWRt2|LRt|LRt1|LRt2|Common<br>equity/RWA|Common<br>equity/TA|Other<br>capita/RWA|Other capital<br>/TA|Market to<br>book value<br>of equity<br>(PB)|<br>Price-<br>earnings<br>ratio (PE)|Beta|Loan Loss<br>Provisions<br>/TA|<br>Liquid<br>Assets/<br>TA<br>Total<br>Deposits<br>TA|/<br>Net<br>Loans/TA|log(TA)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Stock return %|1||||||||||||||||||
|RWRt|0.0378*|1|||||||||||||||||
|RWRt1|0.0534*|0.8341*|1||||||||||||||||
|RWRt2|-0.0370*|0.1893*|-0.3838*|1|||||||||||||||
|LRt|-0.0196|0.6268*|0.5231*|0.1465*|1||||||||||||||
|LRt1|-0.0054|0.6639*|0.7112*|-0.1681*|0.9093*|1|||||||||||||
|LRt2|-0.0274|0.0421*|-0.3592*|0.7090*|0.3204*|-0.1029*|1||||||||||||
|Common equity/RWA|0.0597*|0.5513*|0.6451*|-0.2311*|0.4498*|0.5567*|-0.1663*|1|||||||||||
|Common equity/TA|0.0293*|0.4303*|0.5608*|-0.1812*|0.6470*|0.7081*|-0.0380*|0.9130*|1||||||||||
|Other capita/RWA|-0.0326*|0.1540*|0.0577*|0.1540*|0.0456*|0.0360*|0.0094|-0.5563*|-0.5435*|1|||||||||
|Other capital /TA|-0.0385*|0.1474*|0.0562*|0.1457*|0.0947*|0.0977*|-0.0056|-0.5565*|-0.5335*|0.9712*|1||||||||
|Market to book value of equity (PB)|-0.0047|0.0311*|0.0154|0.029|0.0607*|0.0404*|0.0469*|0.0404*|0.0483*|-0.0838*|-0.0795*|1|||||||
|Price-earnings ratio (PE)|-0.0111|-0.0038|-0.0146|0.021|-0.029|-0.0317|0.0056|-0.0043|0.0177|-0.0062|-0.0098|0.0028|1||||||
|Beta|-0.0606*|0.0185|-0.1357*|0.2337*|-0.2023*|-0.2678*|0.1059*|-0.1812*|-0.1310*|0.1125*|0.0703*|0.0463*|0.0351*|1|||||
|Loan Loss Provisions/TA|-0.1112*|-0.1235*|-0.2441*|0.0940*|-0.0745*|-0.1119*|0.0974*|-0.1594*|-0.0793*|-0.01|0.0235|0.007|0.0268*|0.1792*|1||||
|Liquid Assets/TA|0.014|0.1719*|0.0909*|0.1469*|0.2804*|0.1867*|0.1729*|-0.0522*|-0.0229|0.2362*|0.2142*|0.0750*|-0.0148|0.1288*|-0.0677*|1|||
|Total Deposits/TA|0.0464*|-0.2415*|-0.1261*|-0.2012*|-0.1567*|-0.0638*|-0.1555*|0.0282|-0.0764*|-0.2041*|-0.1589*|-0.0614*|0.018|-0.1909*|0.0082|-0.1610*<br>1|||
|Net Loans/TA|-0.0629*|-0.2201*|-0.1188*|-0.1396*|0.2290*|0.2709*|-0.0242|0.0458*|0.0849*|-0.1366*|-0.0568*|-0.0646*|-0.0258|-0.1826*|0.1449*|-0.3534*<br>0.3252*|1||
|log(TA)|-0.0338*|-0.3106*|-0.5017*|0.2439*|-0.5538*|-0.6842*|0.2095*|-0.4060*|-0.5255*|0.0276|-0.0298|0.0131|-0.0027|0.1539*|0.1392*|-0.0297* -0.1593*|-0.3180*|1|

(*) denotes statistical significance at the 5 % level and above.

18
# **4) HOW DIVERSIFIED IS SADC TRADE?**

High barriers between countries reduce not only the volume of a particular product being exported to a particular destination, but also the number of different products being exported there (Melitz, 2003). Therefore, we would expect greater integration to manifest itself in the form of larger varieties of products being traded. To enhance our understanding of SADC integration, this section now assesses the diversity of products being traded; it first reveals the share of exports accounted for by the top 10 export products before presenting measures of the „thickness‟ of trade.

Figure 4 presents the share of total exports to SADC and the Rest of the World (ROW) made up by the top 10 export products by value to each region in 2008. What is striking is the high level of export concentration. The top 10 export products account for over 60 percent of exports for SADC members outside of SACU. In some cases (Malawi, Mozambique, Zimbabwe) the top 10 products account for over 90 percent of export volumes.

The concentration of SADC exports, however, is higher with the Rest of World than with SADC members. In most cases the export concentration ratio is 15 percentage points or more greater for exports to the rest of the world than it is for exports to other SADC members. This is consistent with the aggregate analysis of intra-SADC trade and corroborates the finding that SADC trade is regionalized.

**Figure 4: Share of top 10 export products in total exports to SADC and Rest of world (ROW), 2008**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Exports to SADC Exports to ROW<br>Zimbabwe Zambia Tanzania Seychelles SA Mozambique Mauritius Malawi Madagascar Namibia<br><!-- End of picture text --> Notes: Own calculations using HS 6-digit 2008 data obtained from UNComtrade.

Looking at the actual products exported in Table 3, the top 10 products to the Rest of World and SADC are comprised mainly of primary products, although Mauritius and Malawi are also exporters of clothing and textile products.<sup>4</sup> A further observation is

> 4 Not shown in this table is the relatively high level of apparel exports from Swaziland and Lesotho associated with preferences under the African Growth and Opportunity Act (Portugal-Perez 2008;

9
where his caste identity would be known (e.g. Gupta 2000, Srinivas 2009). A low-caste boy could not move up, a high-caste boy could not move down.

At the bottom of the traditional caste hierarchy are the castes whose members were traditionally marked as “unclean.” They were called untouchables and are today called Dalits. Untouchability has several dimensions: exclusion from public spaces and water sources, humiliation (including prohibition from all but menial occupations), and exploitation by the high castes (Desphande, 2011, p. 9). Untouchability is illegal under the Constitution of India, and attitudes towards Dalits are radically different today from what they were in the recent past (Kapur _et al._ 2010). But the social division persists. Bros and Couttenier (2011) use official Indian crime statistics for 2001 to demonstrate the systematic use of violence across India to enforce untouchability rules. Two surveys give some indication of how untouchability plays out in schools: “One common example of social prejudice in the classroom is the disparaging attitude of upper caste teachers towards Dalit children. This can take various forms, such as telling Dalit children that they are ‘stupid,’ making them feel inferior, using them for menial chores, and giving them liberal physical punishment.” (PROBE, 1999, p. 51) “In one out of four primary schools in rural India, Dalit children are forced by their teachers or by convention to sit apart from non-Dalits. As many as 40 percent of schools practice untouchability while serving mid-day meals, making Dalit children sit in a separate row while eating.” (Shah et al., 2006, p.168, based on a 2001-02 national survey) Participants in our experiment were 288 junior high school boys drawn from the top of the caste hierarchy (the “General Castes”, hereafter “high castes”), and 294 junior high school boys drawn from the bottom of the caste hierarchy (the Dalits, hereafter “low castes”). In groups of six, participants solved mazes under incentives under one of three conditions that varied the publicness and salience of caste.

In the first condition, caste identity, which is not discernible from natural physical markers,<sup>3</sup> was not made public in a session of three high-caste and three low-caste boys. Since population that had an incentive to change their identity from a non-agricultural to an agricultural caste member were able to do so.

3 See Deliège (1999) and Gupta (2000). In contrast, Hindu surnames do constitute a marker of caste. For that reason some low-caste groups have sought a constitutional amendment to abolish Hindu surnames ( _The Telegraph,_ October 15, 2005, “Slash Surname to Kill Caste”).

4
Subsidies on Commercial Fertiliser Demand and Maize Production : Panel Data Evidence from Malawi. _Journal of Agricultural Economics_ , _68_ (1), 70–97. https://doi.org/10.1111/1477-9552.12161

- Shigute, Z., Strupat, C., Burchi, F., Alemu, G., & Bedi, A. S. (2019). Linking Social Protection Schemes: The Joint Effects of a Public Works and a Health Insurance Programme in Ethiopia. _Journal of Development Studies_ . https://doi.org/10.1080/00220388.2018.1563682

- Slater, S., Baker, P., & Lawrence, M. (2022). An analysis of the transformative potential of major food system report recommendations. _Global Food Security_ , _32_ (January), 100610. https://doi.org/10.1016/j.gfs.2022.100610

- Smale, M., Thériault, V., Mason, N. M., & Mason, N. M. (2020). Does subsidizing fertilizer contribute to the diet quality of farm women? Evidence from rural Mali. _Food Security_ . https://doi.org/10.1007/s12571-020-01097-w ORIGINAL PAPER Does

- Snapp, S. S., & Fisher, M. (2015). “Filling the maize basket” supports crop diversity and quality of household diet in Malawi. _Food Security_ , _7_ , 83–96. https://doi.org/10.1007/s12571-0140410-0

- Sulaiman, M. (2016). Making Sustainable Reductions in Extreme Poverty : A Comparative Meta-Analysis of Livelihood , Cash Transfer and Graduation Approaches .

- Thome, K., Filipski, M., Kagin, J., Taylor, J. E., & Davis, B. (2013). Agricultural Spillover Effects of Cash Transfers: What Does LEWIE Have to Say? _American Journal of Agricultural Economics_ , _95_ (5), 1338–1344. https://doi.org/10.1093/ajae/aat039

- Thome, K., Taylor, E. J., & Filipski, M. (2014). _Local Economy Impacts of FISP. Discussion paper, Evaluation of the 2013/14 Farm Input Subsidy Program, Malawi_ . London.

- Tione, G., Gondwe, E., Maonga, B. B., Machira, K., & Katengeza, S. P. (2022). Improving wasting among children under-5 years in Malawi: The role of farm input subsidies. _Frontiers in Public Health_ , _10_ . https://doi.org/10.3389/fpubh.2022.862461

- Tirivayi, N., Knowles, M., & Davis, B. (2016). The interaction between social protection and agriculture: A review of evidence. _Global Food Security_ , _10_ , 52–62. https://doi.org/10.1016/j.gfs.2016.08.004

Tiwari, S., Daidone, S., Ruvalcaba, M. A., Prifti, E., Handa, S., Davis, B., ... Seidenfeld, D.

29
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satisfied. The first one is that the structural relationships underlying the assignment of employment to each sector remain constant, that is the conditions that determine employment in a specific sector are not affected by the crisis. Additionally, the model assumes that the growth rates of labor income and profits are the same as the aggregate product rates for each sector, the factors of production are immobile; and price changes occur only between food and non-food.

<u>The final step</u> of the micro-simulation exercise is to assess the impact of a possible crisis by generating new income distributions. These distributions allow one to compare between the crisis and no-crisis scenario. This step includes adjusting the poverty line to account for changes in food prices, constructing measures of per-capita household income, and evaluating the poverty and distributional impact of the crisis using the distribution of income under both scenarios.

Household level data as well as macroeconomic data are needed. The major data needs for the exercise are, at the household level, labor force surveys with detailed information on the distribution of the working age population by sector and economic status as well as earnings. At the macro level, ideally, good projections for the key macro variables under the two scenarios (benchmark and post crisis/post policy reform) are needed (projections on population growth, labor market composition and earnings, and international remittances).

# **3 Country context**

# **3.1 Macroeconomic developments**

Uruguay was hit hard by the 2001/02 Argentinean crisis, but recorded a strong macroeconomic performance over the following years. Uruguay’s economy experienced a deep recession with negative growth rates of real GDP of up to 7.7 percent in 2002, high inflation and significant debt accumulation. Following the crisis, however, the economy recovered and real GDP growth averaged more than 5 percent per year since 2003, marking one of the longest growth periods in the country’s history. Prudent macroeconomic policies, improvements in structural areas and favorable external economic conditions, such as buoyant demand for main export products and a booming regional economy, have contributed to the strong economic performance of Uruguay and helped protect the economy during the 2008/2009 crisis.

**Figure 2: CPI, food price inflation (yoy), 2000-2012**

**Figure 1: Real GDP growth rates, 2000-2012**

<!-- Start of picture text --> 10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>-4%<br>-6%<br>-8%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>CPI Inflation Food Inflation<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: BCU Source: INE 3
Policy Research Working Paper 8145

# **Abstract**

This paper examines the determinants of the productivity of Nigerian firms, using three waves of Enterprise Surveys from 2007, 2009, and 2014 and 7,670 firms. The paper uses three alternative measures of productivity, which are found to be highly correlated: labor productivity, value added per worker, and total factor productivity. The more notable trends in the data show: a rise in productivity, with the output of exporting firms decreasing; increasing concentration of production, reflected in the rise of the Herfindahl-Hirschman index by a factor of three; increasing costs of crime, power outages, lack of security, and bribery; significant heterogeneity of these costs along several dimensions, such as firm size, age, location, and the exporting or domestic nature of the market it serves. These costs are inversely related with investment. Regardless of the measure of productivity, its main determinants are the education of the worker, size of the firm, availability of credit, and business climate variables. When labor productivity is used, the stock of capital is also a major determinant of productivity. Within the investment climate variables, power outages and the corruption index are the more significant ones. Power outages are negatively associated with productivity. Bribery is positively related, supporting the “greasing the wheels” hypothesis of bribery as a factor that reduces transaction costs. The impact is nonlinear, as it decreases with firm size. The results also show a positive association between productivity and exporting, but the causality is reversed when the analysis controls for endogeneity: productivity is a weak determinant of the likelihood of a firm becoming an exporter.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group and a background paper to the Nigeria Growth and Competitiveness Report, entitled Towards Sustainable Growth in Nigeria: Empirical Analysis and Policy Options. Vols. 1 and 2. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sherrera@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team