rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Regression Results: Determinants of the log of consumption per adult equivalent in Uganda, 2005/6**<sup>**a**</sup>

|||**Rural**|**Areas**|||**Urban**|**Areas**||
|---|---|---|---|---|---|---|---|---|
||**1(a)**||**1(b)**||**2(a)**||**2(b**|**)**|
|**Variable Description **|Coeff|S.E|Coeff|S.E|Coeff|S.E|Coeff|S.E|
|**Household demographic**<br>**characteristics**|||||||||
|Household size|-0.113***|0.009|-0.113***|0.009|-0.169***|0.016|-0.169***|0.017|
|Household size squared|0.004***|0.001|0.004***|0.001|0.007***|0.001|0.007***|0.001|
|Male headed household|0.019|0.018|0.023|0.018|0.014|0.036|0.018|0.037|
|Age of household head|0.007**|0.003|0.008**|0.003|0.031***|0.006|0.030***|0.006|
|Age of household head squared|-0.0001**|0.000|-0.0001**|0.000|-0.000***|0.0001|<br>-0.0001***|0.000|
|**Household education characteristics**<sup>**b**</sup>|||||||||
|Prop. 15+ with some primary education<br>Pro 15+ with comlete rimar|0.132***|0.025|0.130***|0.025|0.243***|0.068|0.261***|0.07|
|p. p. py<br>education<br>P 15+ ith d|0.387***|0.031|0.384***|0.031|0.542***|0.072|0.567***|0.073|
|rop. w some seconary<br>education<br>|0.557***|0.039|0.559***|0.039|0.697***|0.074|0.734***|0.075|
|Prop. 15+ with complete. secondary or<br>degree|1.088***|0.075|1.089***|0.074|1.378***|0.089|1.421***|0.088|
|**Household sources of income**|||||||||
|Has farm non-wage income|-0.091***|0.027|||-0.05|0.037|||
|Has non-farm enterprise income|0.134***|0.015|||0.209***|0.034|||
|Has agric wage income|-0.124***|0.017|||-0.212***|0.059|||
|Has non-farm wage income|0.113***|0.02|||0.123***|0.036|||
|Receives remittances|0.041**|0.016|0.036**|0.016|0.047|0.033|0.045|0.033|
|**Livelihood category (Base category – F**|**arm non-wag**|**e)**|||||||
|Has nonfarm wage income only|||0.308***|0.059|||0.277***|0.076|
|Has farm wage income only|||-0.072|0.054|||0.088|0.147|
|Has household enterprise income only|||0.236***|0.048|||0.334***|0.073|
|Has farm non wage & farm wage income<br>only<br>|||-0.117***|0.023|||0.073|0.135|
|Has farm non wage & household<br>enterprise income only|||0.119***|0.02|||0.372***|0.081|
|Has farm non wae & non-farm wae|||||||||
|g g<br>income only<br>Has farm non wage non-farm wage &|||0.054*|0.029|||0.170**|0.082|
|,<br>household enterprise income only<br>Has non-farm wae & household|||0.276***|0.04|||0.468***|0.083|
|g<br>enterprise income only|||0.322***|0.069|||0.448***|0.081|
|Other livelihood category|||0.014|0.024|||0.228**|0.082|
|**Household location**<br>Resides in an Internally Displaces|||||||||
|<br>People's Camp|-0.225***|0.033|-0.216***|0.032|-0.217*|0.113|-0.231*|0.132|
|Constant|10.516***|0.068|10.410***|0.068|10.202***|0.145|10.052***|<br>0.161|
|Observations|5536||5574||157|5|160|2|
|Adjusted R -Squared|0.35|5|0.35|5|0.43|7|0.43|0|

**Notes** : (a) ;  * p<.1; ** p<.05; *** p<.001, S.E – robust standard errors. Results based on unweighted regressions and includes regional fixed effects (not shown).(b) Education variables are based on the proportion of household members aged 15 and above who are not currently in school.

23
rural farmers moved to rural non-agricultural jobs and almost two-thirds of these exited poverty in the process. The potential for the non-farm rural economy to be a stepping stone out of poverty is clear. Here, the panel results add great emphasis to the SUSENAS-based analysis.

# 3. **Non-agricultural activities in urban areas may be somewhat more stable:** As Table

5 shows, 77.6 percent of the non-poor in this category remained non-poor, nonagricultural and in urban areas. At the same time, 44.6 percent of the urban nonagricultural poor remained in that category. Both rates of “stability” are the highest of the four categories. This stability may arise because earnings are much higher in urban areas, so the poor prefer to stay in the hope of a good job; or it may be difficult for poor urban workers to move or return to rural areas. The distinction should not be overdrawn, however, as 61.5 percent of the rural, agricultural non-poor remained in this category and 62.2 percent of the rural, nonagricultural non-poor had similar stability. Indeed, when agricultural and nonagricultural activities are combined, a common occurrence for households in rural areas, 78.0 percent of rural agricultural households and 79.3 percent of rural nonagricultural households (in 1993) remained stably out of poverty. Importantly, only the panel results can bring a clear picture of how much “churning” there is near the poverty line. Clearly, there is a small but significant probability of falling below that line for rural workers, whether farm or non-farm.

In summary, urbanization has been rapid, but only a small part is due to actual physical migration of workers and households. In rural areas, there is a gradual diversification of economic activities taking place, characterized by greater reliance on non-farm sources of income. This process of rural diversification reflects greater opportunities for growth in a dynamic non-farm economy than in agriculture _per se,_ although increases in agricultural productivity have remained a very important path out of poverty, at least as judged from the panel data.

These results are only a glimpse into the power of panel data. Many of the transition paths described above are strongly influenced by the individual characteristics 27
# Appendix E: Examples of Stakeholder Input

_Table E.1. Weights provided by each DM for the short-term criteria of this study_

||**A.1.1**|**A.1.2**|**A.1.3**|**A2.1**|**A2.2**|**A2.3**|**A2.4**|**A2.5**|**A2.6**|**A2.7**|**A2.8**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**DM1**|0.016|0.016|0.031|0.007|0.006|0.006|0.008|0.005|0.014|0.004|0.012|
|**DM2**|0.006|0.019|0.059|0.003|0.017|0.003|0.004|0.004|0.015|0.012|0.025|
|**DM3**|0.005|0.014|0.014|0.007|0.017|0.048|0.048|0.011|0.008|0.002|0.025|
|**DM4**|0.007|0.007|0.007|0.003|0.007|0.005|0.016|0.011|0.035|0.035|0.035|
|**DM5**|0.006|0.041|0.036|0.004|0.021|0.008|0.007|0.008|0.010|0.012|0.013|
|**DM6**|0.254|0.254|0.254|0.019|0.005|0.043|0.014|0.005|0.005|0.002|0.002|
|**DM7**|0.005|0.005|0.005|0.005|0.006|0.004|0.055|0.003|0.009|0.015|0.012|
|**DM8**|0.002|0.009|0.009|0.004|0.004|0.021|0.006|0.021|0.021|0.021|0.004|
|**DM9**|0.001|0.006|0.007|0.005|0.011|0.032|0.032|0.008|0.005|0.002|0.017|
|**DM10**|0.008|0.042|0.017|0.009|0.013|0.009|0.021|0.010|0.008|0.031|0.031|

_Table E.2. Weights provided by each DM for the long-term criteria of this study_

||**B.1.1**|**B.1.2**|**B.1.3**|**B.1.4**|**B2.1**|**B2.2**|**B2.3**|**B2.4**|**B2.5**|**B2.6**|**B2.7**|**B2.8**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**DM1**|0.136|0.136|0.229|0.081|0.033|0.033|0.039|0.023|0.021|0.016|0.055|0.073|
|**DM2**|0.019|0.039|0.263|0.096|0.014|0.045|0.047|0.017|0.046|0.013|0.132|0.105|
|**DM3**|0.022|0.158|0.158|0.063|0.034|0.077|0.058|0.038|0.030|0.018|0.074|0.071|
|**DM4**|0.009|0.009|0.009|0.076|0.027|0.012|0.012|0.119|0.174|0.038|0.174|0.174|
|**DM5**|0.017|0.135|0.135|0.130|0.012|0.044|0.047|0.015|0.043|0.020|0.102|0.134|
|**DM6**|0.019|0.025|0.029|0.052|0.002|0.002|0.002|0.001|0.002|0.003|0.003|0.001|
|**DM7**|0.191|0.191|0.191|0.191|0.004|0.004|0.018|0.018|0.010|0.007|0.023|0.026|
|**DM8**|0.006|0.023|0.023|0.073|0.016|0.168|0.168|0.168|0.037|0.106|0.016|0.071|
|**DM9**|0.027|0.027|0.027|0.027|0.064|0.064|0.064|0.064|0.064|0.064|0.191|0.191|
|**DM10**|0.076|0.185|0.116|0.266|0.019|0.023|0.021|0.020|0.015|0.007|0.019|0.032|

_Table E.3. Application of PROMETHEE II: Evaluation scores for short-term criteria by DM1_

||**Env**<br>**A.1.1**|**ironmenta**<br>**A.1.2**|**l**<br>**A.1.3**|**A2.1**|**A2.2**|**Ec**<br>**A2.3**|**onomic/**<br>**A2.4**|**Social**<br>**A2.5**|**A2.6**|**A2.7**|**A2.8**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**M1**|0.025|0.149|4.000|0.000|7.935|4.000|4.000|5.000|4.000|3.000|4.000|

57
**Table A3 (continued): Conflict in Irrigated Regions after the Arab Spring in the G5 Sahel and Rest of  Africa**

|Dependent Variable:|||Du<br>|mmy=1 if anyB<br>|ATTLE in a cell-y<br>|ear<br>|||
|---|---|---|---|---|---|---|---|---|
||(9)|(10)<br>Rest of SSA (|(11)<br>excluding G5)|(12)|(13)|(14)<br>North Afric|(15)<br>a (MENA)|(16)|
|ln(Irrigated Area) X Post|0.00483|0.00483|-0.00417|-0.00396|0.0504***|0.0504***|0.0255***|0.0254***|
||(0.00322)|(0.00322)|(0.00344)|(0.00345)|(0.00651)|(0.00651)|(0.00682)|(0.00681)|
|Observations|127800|127800|127580|127580|40560|40560|40540|40540|
|Number of Clusters (Grid-cells)|6379|6379|6379|6379|2028|2028|2028|2028|
|R-sq|0.136|0.369|0.372|0.372|0.115|0.389|0.407|0.407|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Cell FE||Y|Y|Y||Y|Y|Y|
|Controls X Time|||Y|Y|||Y|Y|
|Climate Controls||||Y||||Y|
|Mean Conflict|0.0635|0.0635|0.0635|0.0635|0.0233|0.0233|0.0233|0.0233|
|Each observation corresponds to a 50km x 50<br>in a given year, as reported in the ACLED data<br>include log of population at the start of the p<br>agricultural livelihood strategies. Standard er<br>p<0.05, *** p<0.01.|km grid-cell observe<br>set. Irrigated area is<br>eriod as well as du<br>rors in parentheses|d in a given year bet<br>measured as the per<br>mmies to indicate the<br>clustered at the grid-|ween 1998 and 2017.<br>centage share of an a<br>presence of wetland<br>cell level. Climate con|The outcome variable i<br>verage plot of land that<br>s, croplands, pasture l<br>trols include mean te|s a dummy variable in<br>is equipped for irriga<br>ands, rivers and the h<br>mperature and log of t|dicating that the gr<br>tion, as documente<br>omelands of ethnic<br>otal rainfall in the|id-cell experience<br>d by Siebert et al (<br>groups that histo<br>wet and dry seaso|d a battle event<br>2015). Controls<br>rically relied on<br>ns. * p<0.1, **|

26
Polish unemployment rate would be by about one-fourth lower.<sup>7</sup> Some examples of the differences in flows into and out of unemployment are shown in Table 5.

**Table 5.  Jobless workers in transition economies face low chances of escaping unemployment**

|Country|Year|Employment<br>to<br>Unemployme<br>nt|Employment<br>to Out of the<br>Labor Force|Unemployme<br>nt to<br>Employment|Unemployment<br>to Out of the<br>Labor Force|Out of the<br>Labor Force<br>to<br>Employment|Out of the<br>Labor Force<br>to<br>Unemployme<br>nt|
|---|---|---|---|---|---|---|---|
|_Central an_<br>|_d Eastern Eu_|_ropean Countr_<br>|_ies_<br>|||||
|Bulgaria|1994-1995|0.059|0.092|0.323|0.244|0.092|0.044|
|Czech. R|1994-1995|0.013|0.028|0.496|0.129|0.042|0.012|
|Czech. R|1996-1997|0.08|0.025|0.457|0.101|0.04|0.008|
|Czech. R|1998-1999|0.018|0.025|0.335|0.09|0.036|0.017|
|Poland|1992-1993|0.04|0.076|0.361|0.158|0.095|0.045|
|Poland|1993-1994|0.04|0.063|0.354|0.159|0.074|0.043|
|Slovakia|1994-1995|0.023|0.045|0.237|0.078|0.018|0.017|
|_Former So_|_viet Union_|||||||
|Estonia|1992|0.048|0.097|0.465|0.093|0.143|0.036|
|Estonia|1997|0.047|0.04|0.372|0.064|0.074|0.038|
|Russia|1992-1993|0.032|0.058|0.52|0.157|0.087|0.014|
|Russia|1995-1996|0.056|0.062|0.395|0.145|0.076|0.034|
|United||||||||
|States|1992-1993|0.028|0.053|0.659|0.288|0.043|0.161|

Source: Boeri and Terrel (2002) Data in Table 5 suggest that initially high unemployment in transition economies was the product of relatively high inflows into unemployment and limited outflows to jobs. Most of the economies were thus in the high unemployment/depressed (stagnant) labor market state.

As the restructuring process slowed down with the progress of the transition, the initially high inflow rates gradually declined, although they still remain higher than in a mature market economy, such as the US. This entailed a movement toward the moderate unemployment/stagnant labor market state, as the job finding chances hardly improved and thus the duration of unemployment remained high. At this stage unemployment is a stagnant pool: few workers enter unemployment but also few are able to exit it.

Recent examples of a substantial fall in unemployment in some CEE countries (e.g. Lithuania) suggest that these successful countries might have begun a movement toward moderate unemployment/dynamic labor market and eventually. It is obviously an open question if the movement will continue toward the low unemployment/dynamic labor market state.

> 7   Outflows from unemployment to jobs account for about two-thirds of all outflows from unemployment (the other being to inactivity), so doubling the outflows to jobs rate (holding the outflows to inactivity rate constant) results in shortening of the average duration of unemployment and thus lowering the unemployment rate by one-fourth.

23
financial services between high-income and developing economies: account penetration is close to universal (91 percent) in high-income economies, while only 41 percent of adults in developing economies, on average, report having an account at a formal financial institution.<sup>9</sup> Furthermore, we find that among developing economies, account ownership, on average, increases sharply with economic development (Figure 1): adults in upper-middle-income countries (58 percent) are almost three times as likely to have an account as adults in low-income economies (19 percent). In several countries around the world, including Cambodia, the Central African Republic, the Kyrgyz Republic, and the Republic of Yemen, more than 95 percent of adults do not have an account at a formal financial institution.

In addition to sharp differences in account penetration across countries, there are also important disparities in account use by individual characteristics (Figure 2). For example, among developing countries in our sample, those in the highest within-country income quintile are more than twice as likely to have an account as those in the lowest income quintile.<sup>10</sup> There are also significant disparities in the prevalence of accounts along gender lines: in developing countries in our sample, 46 percent of men report having an account at a formal financial institution, compared with 37 percent of women. In developing economies, adults with a tertiary education are, on average, more than twice as likely to have an account as those with a primary education or less. Furthermore, in both high-income and developing economies, adults between the ages of 25 and 64 are more likely to report having an account at a formal financial institution than younger adults and those aged 65 and over. Finally, the urban/rural divide also figures

> 9 All statistics aggregated above the country level (by income group, region, and the like) use population weights in addition to country-level weights.

> 10 The GWP provides imputed within-country (relative) income quintiles for all observations, but does not publish the imputed absolute income levels. In 2011, income data were imputed for 14 percent of income observations worldwide. Gallup uses published data on individual characteristics as well as proprietary data on each household member to impute income. For additional information see www.gallup.com.

8
level of approximately 5000-6000 Rupees. Direct reports of profits are much closer to this than the mean and median levels of reported revenue-expenses.

The large differences in levels between these two measures of profits and their weak correlation begs the questions of “which measure is more accurate?” and of “what causes differences between the two measures?”. The remainder of the paper serves to help answer these questions.

# **4. Adjusting for Unreported Categories**

The first explanation we consider for the difference between reported revenue-expenses and reported profits is that there are categories of expenses or forms of profit which are not captured in the basic questions above. After the first round of the SLMS we reinterviewed a subset of firms with large differences between reported revenue minus expenses and reported profits, and asked them to explain the difference between the two measures. These interviews revealed four unreported categories:

- (i) Business goods and materials used for home consumption, but recorded as business expenses

- (ii) Firm inputs which were given as gifts by non-household members, but which were recorded as business expenses.

- (iii) Firm owners who paid themselves a salary, but failed to include this when reporting profits (despite the profits question explicitly including this).

- (iv) Business revenues used to pay for household expenses, but not included as profits (unreported drawings).

We then included these questions in the KMS and in the second round onwards of the SLMS. The top panel of Table 3 reports the percentage of firms that report having expenses or profits in each category. The major unreported categories reflect fungibility of resources between the business and the household. This is particularly the case in retail firms, where a majority of firms use goods from the business for home consumption, but include these as business expenses. The tendency to “dip into the till” and use business revenues to pay for household expenses is greater in the Kandy firms, but still occurs in 8
administrative targeting variables (Kaboski and Townsend, 2012) or the error structure of a predictive model of entry (Mallick, 2012; Berg, Emran and Shilpi, 2015). Two of these studies, both based on data from Bangladesh, find a positive impact of MFI entry on informal lending rates (Mallick; Berg, Emran and Shilpi), though in the latter the effect is only significant when MFI coverage rates are high. Kaboski and Townsend, using data from Thailand, find no statistically significant impact on lending rates, but a small positive effect on the probability of default on other loans. A fourth study uses panel data from the Indian state of Jharkhand, and finds an inverse U- shaped relationship between SHG coverage and the rates charged by moneylenders, consistent with a model in which the SHG lender has superior information on borrowers’ creditworthiness and serves those with lower risk of default (Demont, 2016).

The identification of causal impacts in these studies relies on the assumption that community characteristics associated with the entry of new lenders do not affect informal credit rates directly. Given the multiple objectives of MFIs, which may include profit (or at least cost-recovery) as well as a social mission to assist the poor, it is impossible to sign the direction of potential bias in estimates from observational studies. The use of random assignment to a credit market intervention permits causal inference based on a much weaker set of assumptions. However, previous randomized evaluations of microcredit programs have not reported impacts on interest rates, presumably due to a lack of power on this outcome. The present study, which is based on the randomized roll-out of a government-led SHG program that offered microcredit and credit linkages to formal banks to the poor across 179 panchayats<sup>2</sup> in rural Bihar, overcomes this limitation.

Critical to the identification strategy, the SHG intervention had a strong direct effect on household use of informal credit. Just over two years after program initiation, households in panchayats selected for early roll-out were 51 percentage points more likely to include a member who belonged to an SHG than those in control areas. While borrowing from all sources increased overall during the study period, new borrowing from informal lenders was 18% lower in program panchayats compared to control areas, where households instead took advantage of the lowerinterest loans offered by SHGs. We find that this exogenous shock to informal credit markets 2 Village government units typically consisting of between two and four villages.
Assessment of the 'South-North Dialogue' Proposal. "Environmental Science and Policy" 2007 10(3):185-203 (2007). Pew Center 2002: Transportation in Developing Countries: An Overview of Greenhouse Gas Reduction Strategies. Pew Center on Global Climate Change. Arlington, USA. Accessed June 22, 2009 at: http://www.pewclimate.org/docUploads/transportation_overview.pdf Pointcarbon 2008a: Carbon Market Daily November 7th, 2008. Accessed June 22, 2009 at: http://www.pointcarbon.com/polopoly_fs/1.1000904!CMD20081107.pdf (2008). Pointcarbon 2008b: JI/AAU country ratings (October 2008). Accessed June 22, 2009 at: http://www.pointcarbon.com/trading/cpm/analysis/hostcountryratings/ji/ IPCC 2007: Ribeiro, Kahn, S. / Kobayashi, S. / Beuthe, M. / Gasca, J. / Greene, D. / Lee, D.S. /Muromachi, Y /Newton, P.J. / Plotkin, S/ Sperling, D. /Wit, R. / Zhou, P.L: Transport and its infrastructure. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA (2007). Samaniego and Figueres 2002: Samaniego, Joseluis / Figueres, Christiana. “Evolving to a Sector-Based Clean Development Mechanism”. Chapter 4 of the book Building on the Kyoto Protocol: Options for Protecting the Climate. World Resources Institute, Washington DC, (2002). Schmidt et al 2006: Schmidt, Jake / Helme, N. /, Houdashelt, Lee, Sector-based approaches to the post-2012 climate change policy architecture, Center for Clean Air Policy (CCAP), International Future Actions Dialogue (2006). Schneider 2007: Schneider, Lambert. Is the CDM fulfilling its environmental and sustainable development objective? An evaluation of the CDM and options for improvement. Berlin, Oko Institut (2007). Stehr 2008: Stehr, Hans Jürgen, Does the CDM need an institutional reform, Perspectives 2008. Sterk 2008: Sterk, Wolfgang. From Clean Development Mechanism to Sectoral Crediting Approaches- Way Forward or Wrong Turn?, JIKO Policy Paper 1/2008, Wuppertal Institute for Climate, Environment and Energy.

Sterk and Wittneben 2006: Sterk, Wolfgang / Wittneben, Bettinga, Enhancing the clean development mechanism through sectoral approaches: definitions, applications and ways forward. In: International Environmental Agreements: Politics, Law and Economics, Vol. 6, No. 3, September, pp. 271-287 (2006).

Streck 2007: Streck, Charlotte, The governance of the Clean Development Mechanism – the case for strength and stability, in D. Freestone and C. Streck (eds), “The Kyoto Protocol – current legal status of carbon finance and the flexible mechanisms”, Special Issue Environmental Liability Journal, vol. 15, Issue 2, p. 91 (2007).

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