rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 9: Actual Lights, Period 1 Figure 10: Actual Lights, Period 2<br>10 5 11<br>10 30 5 11 33<br>10 30 50 11 33 55<br><!-- End of picture text --> <!-- Start of picture text --> Figure 11: Illustrative Blooming, Period 1<br>1 3 1<br>1 7 10 4<br>1 7 19 20 9<br>1 4 10 7 8<br>1 4 9 8 5<br><!-- End of picture text --> <!-- Start of picture text --> Figure 12: Illustrative Blooming, Period 2<br>1 3 4 1<br>1 3 8 12 4<br>2 8 21 22 9<br>2 5 11 8 9<br>1 4 10 9 6<br><!-- End of picture text -->

**<u>Table 5:  Illustrative Blooming, Period 1</u>**

**<u>Table 6:  Illustrative Blooming, Period 2</u>**

||Actual|Observed||Actual|<br>Observed|Growth<br>Actual|Rates(%)<br>Observed|
|---|---|---|---|---|---|---|---|
|_AoL_(Pixels)|6.0|22.0|_AoL_(Pixels)|8.0|24.0|29|9|
|_R_(Digital Number)|23.3|6.4|_R_(Digital Number)|20.5|6.8|-13|7|
|_SoL_(Digital Number)|140.0|140.0|_SoL_(Digital Number)|164.0|164.0|16|16|
||||Growth rates are calculate|d using natu|ral logarithm|s.||

Sensor saturation at high radiance levels creates a problem for researchers interested in growth rates. The DMSP sensors tend to become saturated by very high radiances, meaning they cannot distinguish different radiance intensities above a certain level.<sup>31</sup> All such high radiances are recorded as _DN_ =63. This has consequences for the high radiance countries. While _AoL_ will not change, the observed values for _R_ and _SoL_ will be below their actual levels. This, in turn, has some implications for growth rates for _R_ . The observed growth rate for _R_ will be _less_ than the actual growth rate. The error will increase as more pixels within a given area reach _DN_ =63. Observed growth rates for _SoL_ will also be biased downwards for areas with a large share of pixels at _DN_ >=63 because _SoL_ = _AoL_ × _R_ .

Censoring pixels with low radiance creates a problem for _AoL_ in levels and in growth. Poor countries and settlements in rural areas tend to have many pixels with observations below _DN_ =6. Censoring these will reduce observed _AoL_ and increase observed average radiance, _R_ . This would tend to make observed growth in _AoL more positive_ than actual and growth in observed _R less positive_ than actual. As the actual average radiance increases, however, more and more pixels will take on _DN_ >=6, thus increasing the observed _AoL_ . It is therefore possible that observed growth in _AoL_ may be generated by a fixed area with growing radiance,

> 31 Elvidge et al (1999). See especially Figure 2.
Table 2 also presents separate statistics for single individual households (households with one adult and no children) as our main analysis will focus solely on these households (we discuss why below).

Table 2: Summary Statistics as of June, 2014

|Variables|Mean|Median|
|---|---|---|
|Per Capita Income (single individual households)|330.51<br>(324.30)|200.00|
|Per Capita Income (all households)|151.17<br>(184.97)|76.66|
|Number of Members Per Household|3.04<br>(1.46)|3.00|
|Observations (single individual households)|4,232,528||
|Observations (all households)|28,932,001||

_Note:_ This table shows summary statistics for households in the Cadastro Unico<sup> ́</sup> database as of June, 2014. Per-capita income denotes household, monthly, per-capita income and is measured in Brazilian reais. The PPP conversion from US dollars to Brazilian reais was 1.813 in 2014 (OECD, `https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm` ).

# _3.2 The Transfer Schedule and the June 2014 Reform_

At the beginning of our data (December 2011), the extreme-poverty threshold was equal to R$70 percapita, per-month and the poverty threshold was equal to R$140 per-capita, per-month.<sup>27</sup> The basic benefit was equal to R$70 per-month, while the variable benefits were based on the number and ages of the children in the household (see Appendix B.4 for more information on the variable benefits). In June 2014, the government increased both the benefits and thresholds by 10%. Thus, the extreme-poverty threshold was raised from R$70 to R$77 per-capita, per-month, the poverty threshold was raised from R$140 to R$154 per-capita, per-month, the basic benefit was raised from R$70 to R$77 per-month, and the variable benefits were also increased by 10%. This reform was announced on national television by the president in April 2014.

Our main analysis will focus on single individual households. These households are not eligible for the variable benefits as they do not have children. Thus, prior to June 2014, these households received R$70 permonth if their reported income was less than or equal to R$70 per-month and 0 otherwise. After June 2014, these households received R$77 per-month if their reported income was less than or equal to R$77 per-month and 0 otherwise. Thus, the benefit schedule for these households has a single notch which increased both in level and location as a result of the June 2014 reform; see Figure 2.

27. In 2003, the extreme-poverty threshold and the poverty threshold were set to equal one-fourth and one-half of the monthly minimum wage of R$200, respectively. These thresholds have since been periodically adjusted for inflation; the time betwen adjustments is _ad hoc_ and not linked to the minimum wage. Prior to June 2014, the last readjustment was in July 2009 (Gazola Hellmann, 2015).

16
exports per firm is highly skewed - as is the case in most countries as shown by Freund and Pierola (2012) - since the median exporter in Turkey exports just 50,000 USD in 2002, 100,000 USD in 2007, and 125,000 USD in 2011. The figures for the share of the top 1% or the top 5% of exporters are indicative of a high degree of concentration across exporters - in any given year between 2002 and 2011, just the top 1% of exporters (i.e., the largest 1% of exporters according to their export values) account for close to 60% of total exports while just the top 5% account for close to 80% of total exports. However this degree of concentration of the export sector is not particularly high from a cross-country perspective: data from the Exporter Dynamics Database described in Cebeci, Fernandes, Freund, and Pierola (2012) for year 2007 shows that concentration is much higher in Mexico, New Zealand, South Africa, or Spain.<sup>14</sup> Regarding diversification at the firm-level, the average number of HS 12-digit products exported per firm increased strongly and steadily from 9.2 in 2002 to 11.8 in 2011 while the average number of HS 6-digit products exported per firm increased strongly and steadily from 7.9 in 2002 to 10.4 in 2011. The average number of export destinations served per firm also increased continuously from 3.4 in 2002 to 4.4 in 2011.<sup>15</sup> Figure 1 shows Turkey’s aggregate exports in nominal terms and in real terms based on the exporter dataset. Turkey experienced very strong export growth, with aggregate exports in nominal terms quadrupling over the 2002-2008 period. Aggregate exports declined importantly with the global financial crisis in 2009, but then recovered strongly in 2010-2011. The average annual growth rate of aggregate nominal exports during the 2002-2011 period was 16%. Focusing on growth in aggregate real exports - the change in aggregate exports deflated by the U.S. producer price index in constant values of 1997 - the qualitative pattern is similar as shown in Figure 1. Interestingly, our sample period covers the period of explosive Chinese export growth subsequent to its WTO accession but that did not prevent Turkey from also experiencing a boom in aggregate exports, even if Turkish exports of textiles and clothing to the EU and the US were hurt by that accession.

Table 2 shows the composition of Turkey’s aggregate exports across broad sectors and across groups of destination markets, the numbers of exporters in each of those categories as well

> 14 The values for the share accounted for by the top 1% [ _top 5%_ ] of exporters in those countries are: 69%[91%] in Mexico, 72%[ _90%_ ] in New Zealand, 79%[ _92%_ ] in South Africa, and 63%[ _85%_ ] in Spain.

> 15 The median number of HS 6-digit products exported by firm was stable at 3 between 2002 and 2010 increasing to 4 in 2011, while the median number of destination countries by firms was stable at 2 between 2002 and 2011.

8
# **ABSTRACT**

Many societies have limited corruption through the broad-based mobilization of a diverse range of interests willing and able to defend themselves by making meaningful demands for accountability of, and limits upon, official power, and for an end to illicit advantages enjoyed by others. Historically such a process has taken place gradually, as political development has proceeded and the base of participation broadens. But today's highcorruption societies cannot wait for several generations to see such developments take place. We argue that _social action coalitions,_ linking public and private actors, are a way to mobilize these sorts of participation and advocacy. Such coalitions are neither a new idea nor a guarantee of successful reforms; indeed, in many instances they win out by default as an anti-corruption strategy. But we contend that if sustained by careful planning and a diverse set of incentives, they can reinforce political will and enhance the strength of civil society. Coalition-building efforts are underway in many societies. But too often they have focused only on anti-corruption tactics and pursuing their own growth, rather than looking at the coalition-building process in more general terms. We do this, in Part I, by employing Wilson's (1973) analysis of the incentives that motivate and reward participation in organizations. This approach helps us identify ways in which the anti-corruption goals can be augmented by other kinds of appeals, even when material incentives are scarce. We also identify four stages of the coalition-building process— formation, credibility, expansion, and transformation—in which differing combinations of incentives will be necessary to address the group's most important problems and opportunities. Part II examines two important coalition-building efforts in light of the discussion thus far—Ghana's Anti-Corruption Coalition, and the Bangalore Agenda Task Force in Bangalore, Karnataka State, India. In Part III we link those cases to our broader analysis, suggesting that while purposive incentives are common in the early phases of all coalitions, other varieties must be added to the mix. Wilson's scheme points to ways in which the imaginative use of incentives can aid the transition from one phase of coalition development to the next. We conclude with general strategic issues, suggesting ways in which our analysis can be applied to those questions given the important variations to be found among cases.
_(Table A1 continued)_

||Inside H<br>|omeland<br>|Outside H<br>|omeland<br>|
|---|---|---|---|---|
||1996|2011|1996|2011|
|Share Male|0.459|0.470|0.491|0.495|
||[0.034]|[0.032]|[0.055]|[0.040]|
|Share Connected to electricity grid|0.325|0.800|0.690|0.839|
||[0.347]|[0.271]|[0.325]|[0.210]|
|Share No refuse|0.176|0.098|0.050|0.045|
||[0.232]|[0.149]|[0.100]|[0.074]|
|Share Piped water|0.599|0.624|0.899|0.808|
||[0.399]|[0.347]|[0.201]|[0.291]|
|Total population|1,614.7|2,140.1|2,879.0|4,989.4|
||[2,759.5]|[3,761.2]|[4,944.0]|[6,885.0]|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Sample of communities within 30 kilometers of the former homeland borders (on both sides). Income expressed in December 2012 rand. Due to data availability, education variables are for total population and not household head in 2011. Employment by sector not available for 2011. The definition for connection to piped water changed between 1996 and 2011.
**<u>Table 6: Estimates of stochastic frontier production function</u>**

||**MWI**|**NER**|**NGA**|**TZA**|**UGA**|
|---|---|---|---|---|---|
||**Pa**|**nel A: Coefficie**|**nts for manag**|**er (entire samp**|**le)**|
|Operated area (log)|0.467***|0.153***|0.349***|0.333***|0.135**|
||(0.043)|(0.040)|(0.038)|(0.035)|(0.054)|
|Number of days worked by family labor (log)|0.129***|0.215***|0.074***|0.184***|0.364***|
||(0.011)|(0.026)|(0.008)|(0.017)|(0.031)|
|Value of days worked by hired labor (log)|0.022**|0.061***|0.034***|0.082***|0.055***|
||(0.009)|(0.012)|(0.008)|(0.011)|(0.012)|
|Value of used fertilizer (log)|0.090***|0.089***|-0.001|0.050***|0.149***|
||(0.005)|(0.013)|(0.006)|(0.010)|(0.019)|
|Value of agricultural assets (log)|0.128***|0.099***|0.020**|0.063***|0.084***|
||(0.008)|(0.027)|(0.008)|(0.011)|(0.022)|
|Joint manager||-0.116**|-0.225|0.050|0.145*|
|||(0.054)|(0.151)|(0.042)|(0.078)|
|Female manager|-0.039*|-0.323***|-0.277***|-0.126***|0.072|
||(0.021)|(0.106)|(0.040)|(0.049)|(0.080)|
|Constant|4.816***|5.035***|6.534***|5.328***|4.313***|
||(0.056)|(0.174)|(0.051)|(0.095)|(0.183)|
|lnsig2v|-2.607***|-1.116***|-2.385***|-2.231***|-1.611***|
||(0.069)|(0.083)|(0.104)|(0.115)|(0.125)|
|lnsig2u|1.676***|1.268***|0.726***|1.161***|1.146***|
||(0.016)|(0.045)|(0.037)|(0.038)|(0.050)|
|Observations<br>|9,891<br>|2,171<br>|2,723<br>|2,354<br>|2,004<br>|
|Loglikelihood|-16316.677|-3490.879|-3416.922|-3409.797|-3011.470|
||**P**|**anel B: Coeffic**|**ients for owner**|**(entire sampl**|**e)**|
|Joint owner|0.169***|-0.077||-0.036|0.131**|
||(0.030)|(0.057)||(0.043)|(0.055)|
|Female owner|-0.037|-0.413***|-0.266|-0.154***|-0.052|
||(0.023)|(0.107)|(0.261)|(0.048)|(0.060)|
|Observations|7,927|1,934|249|2,095|2,052|
|Loglikelihood|-12688.820|-3012.830|-315.977|-2932.613|-3083.917|
||**Panel**|**C: Coefficients**<br>|**for manager (**<br>|**married couple**<br>|**s only)**<br>|
|Joint manager||-0.103*|-0.239|0.032|0.111|
|||(0.054)|(0.146)|(0.043)|(0.092)|
|Female manager|0.044|-0.017|-0.229***|-0.190*|0.071|
||(0.036)|(0.148)|(0.061)|(0.099)|(0.100)|
|Observations|7,312|2,077|2,317|1,761|1,506|
|Loglikelihood|-11801.985|-3313.100|-2815.082|-2500.516|-2156.795|
||**Panel**|**D: Coefficient**|**s for owner (m**|**arried couples**|**only)**|
|Joint owner|0.146***|-0.070||-0.046|0.126**|
||(0.029)|(0.057)||(0.042)|(0.054)|
|Female owner|-0.052*|-0.196|-0.006|-0.129|-0.210**|
||(0.027)|(0.162)|(0.713)|(0.118)|(0.095)|
|Observations|<br>5,885|<br>1,843|<br>232|<br>1,571|<br>1,517|
|Loglikelihood|-9185.318|-2845.716|-280.104|-2146.969|-2164.665|

_Note:_ As other coefficients are similar to those in panel A, panels B through D report only coefficients on female owner or manager. Standard errors are in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

20
# **4. What Is behind These Stylized Facts?**

## **4.1 Drivers**

The SME market is becoming a strategic sector for Colombian credit institutions, primarily due to its perceived profitability and growth potential. All interviewed banks have SMEs among their clients, although their degree of involvement and the sophistication of their business models differ substantially. As can be seen in Figure 4.1, growth (perceived profitability) is a decisive factor in banks’ decision to target this segment, as they believe that the potential profits – which stem primarily from high loan spreads – more than compensate for the elevated costs and risks associated with this business. In addition, a few banks that had not previously focused on this market segment are discovering the cross-selling opportunities available to them in having a large number of SMEs as depositors but not as borrowers. Diversification (for banks heavily concentrated in other business lines) and market saturation (for banks focused on mature and less profitable market segments, e.g. corporate banking) were also cited as important drivers for increased SME involvement.

Figure 4.1: Drivers of Banks’ Involvement with SMEs <!-- Start of picture text --> 29%<br>Growth 43%<br>14%<br>14%<br>Cross Selling 29%<br>57%<br>0%<br>Diversification 29%<br>43%<br>0%<br>M arket<br>14%<br>Saturation 57%<br>Significant Very Significant Extremely Significant<br>Source: Authors’ analysis of bank questionnaires.<br>Note: The Figures do not add up to 100 percent because credit<br>institutions provided more than one responses.<br><!-- End of picture text -->

## **4.2 Market Environment**

Banks are optimistic about the prospects of this market segment, as it is still relatively immature and not saturated. Virtually all interviewed credit institutions believe that the SME financing market is big and its growth prospects are good. They expect the market to continue growing, albeit at a slower pace than recently since it is becoming increasingly competitive. In spite of its growth in recent years, there have been no significant changes in market structure with a few large domestic banks and leasing companies being the main players, although international banks and smaller domestic credit institutions have also been growing their operations. Most interviewed banks perceive that there is a first mover advantage, as SMEs tend to use fewer banks (1-3 on average), exhibit greater loyalty, and do less switching and shopping around than 19
stronger preferences for employment generating ‘active’ labor market policies, such as public employment centers and administration, labor market training, or subsidized employment. This is in contrast to labor market insiders, defined by the security of their contracts, who tend to prefer stronger measures of worker protection and lower tax-spending on employment policies (Rueda 2005, see also Rueda 2006).<sup>25</sup> Other studies, based on larger samples of countries, suggest that lower skilled members of the work force tend to report stronger support for government redistribution (Haggard et al. 2013, see also Blanchflower and Freeman 1997, Corneo and Gruner 2002, Cramer and Kaufman 2011).

Cross country comparisons of labor market policies suggest that these differences in behaviors and political preferences are reflected in actual political outcomes. Even among social democratic or labor-dominated governments there are well-documented examples of policies that favor protection of the interests of labor market insiders over active employment generating policies (Rueda 2005, 2006, Shayo 2009). These outcomes are attributed to higher levels of political activism and stronger aversion to costlier employment generating policies among the former group (Rueda 2005, 2006). In addition, outsider-friendly policies often fail to materialize because of the existence of social associations and alliances outside of the labor market.<sup>26</sup> In the literature on social exclusion similar arguments have been made about social policies towards low-skilled, temporary workers. In particular ‘workfare’ programs designed to restrict access to social benefits and to push recipients into the labor market are often blamed for the perpetuation of precarious low-paid work relations, as these interventions have the potential to interrupt individual employment histories and skills acquisition (King and Rueda 2008: 279, McKnight 2002). This can result in a ‘reserve army’ of workers in low cost, flexible and temporary jobs who meet the low-cost labor needs of the economy, but do not enjoy the prospects for upward mobility of more advantaged groups (Byrne 1999, King and Rueda 2008).

The practical lessons that result from these observations usually return full circle to the root causes of low inclusion and mobility in the labor market. Constructive long-term

> 25 These results are based on the 1996 Eurobarometer survey. 26 In particular religious beliefs, rural background, and nationalistic identities have been shown to reduce political positioning around work or status lines (Haggard et al. 2013, Shayo 2009, see also Kaufman 2009). Insider-outsider differences are often reduced when the vulnerability of labor market insiders increases due to regulatory reform or rising unemployment rates (Rueda 2006: 388f, Iversen and Soskice 2001).

24
by (p/2)R, a premium Juba would be willing to pay for this information if they were concerned about going to war (i.e. if they are not risk neutral).

The value of this information to Juba and the possibility that asymmetric information might contribute to war, even when neither actor’s first best solution is war, suggests that more complete information might benefit both actors. Unfortunately, in this one shot game (there will only be one referendum in 2011) a credible signal on the state of nature cannot be sent by Khartoum. With incomplete and asymmetric information, there is the possibility that the state of nature is δHigh and that Juba still chooses Partition, to which Khartoum responds with War. While both actors would prefer Unity to war in this case, Khartoum cannot credibly signal the state of nature, because it always has an incentive to signal that the state of nature is δHigh. The earlier quotation by President alBashir reflects the incentives for Khartoum to signal this strength.

The limitations on credible signals from Khartoum and the value of more complete information are both part of the dilemma of a continued peace in Sudan. Still, this analysis suggests an area for possible contribution by an international community interested in securing the peace between Juba and Khartoum. The game of incomplete information shown here is just one example of the many ways in which incomplete information could result in war when neither actor prefers war. More complete information provided to both actors could reduce the probability of unwanted conflict. To that end, the mapping exercises, explicit resolution of Abyei border issues and the CPA mandated census which has fallen behind schedule and is now planned for early 2008 could contribute to better informed strategy choices (policymaking) from actors in both the North and the South. All of these activities would contribute to a more explicit 26
<!-- Start of picture text --> Figure 12 Export farmers<br>Cumulative distribution functions<br>80<br>60<br>40<br>20 o 0<br>0<br>______ _- 1988 -- 1____1992_<br><!-- End of picture text --> In conclusion, social welfare worsened for all socio-economic groups in Accra. For Other Cities, a relatively large improvement benefited informal group and the betteroff food-crop farmers. The improvement for formal employees was slight and there was no improvement for poor food-crop farmers. It is noticeable that the living standard of the population in formal sector has declined in Accra but not in Other Cities. The downsizing of the public sector, especially the civil service, apparently had different impact on local economy in different localities. In rural areas improvement mostly took place for food crop farmers and non-farm group; there was some improvement for poor export farmers, but not for non-poor export farmers.

That the urban-formal group in Other Cities maintained its living standard could be because they were able to supplement their wage income with earnings from informal sector activities. This was probably conditioned on the fact that the informal sector was much larger than the formal sector in Other Cities. In Accra, most likely, because over 50 percent of population depend on formal sector employment, a sudden decline in formal sector dominated the local economy. The relatively small informal sector in Accra was not able to pull the economy out of the recession. It is apparent from the above graphs that within each locality, the changes of welfare are quite similar for different socio-economic groups. Therefore, an integrated regional development strategy may prove to be effective in improving the living standard of a population.

# _IV. Decomposition of the changes in poverty incidence_

The analysis reported so far has shown that over the periods of 1988-92 and 19891992 the degree of inequality in the distribution of expenditure moved in different directions in different regions and for the different socio-economic groups. At the same time the mean levels of household expenditure also changed in different degrees. The over-all distribution in the economy as whole would then be dependent on the shifts in 11