rafmacalaba/datause-encoder-data huggingface dataset:
There are additional reasons for low fertilizer use tied to knowledge and information hurdles. Several authors note that levels of education are especially low in Africa which slows the dissemination of new ideas and techniques.<sup>11</sup> A related argument is that the institutions charged with dissemination are also weak, partly because of low stocks of human capital and partly due to governance. A second set of arguments are more subtle and focus not on the capacity to learn but on difficulties in discovering appropriate techniques. This argument goes that, because soil and growing conditions are heterogeneous in Africa, an identical technology identically applied can have different outcomes for neighboring households. Consequently, advice offered by extension agents gleaned from nearby field stations can be inappropriate for many farmers. For the same reason, the efficacy of learning by example is limited; instead, farmers must learn through experimentation, which is risky and comes with added costs.

As discussed, the empirical model we develop in the next section is motivated by the presumption that Ethiopian farmers decide on fertilizer input levels in a constrained environment. This distinguishes our model from earlier efforts to measure the determinants of fertilizer demand in Ethiopia. Most empirical studies to date, by relying on tobit-model estimates, implicitly characterize zero-valued observations of fertilizer demand as corner solutions – that is, zero-value outcomes are modeled as though they emerged from a self-truncating process.<sup>12</sup> Alternatively, when farmers who would use fertilizer are additionally constrained by poorly performing markets, an additional process is implied. We return to this topic in the next section.

# **The Empirical Framework**

The starting point for the empirical model is an input demand function, derived from the standard farm household profit maximization conditions via Hotelling’s lemma. As discussed, the subsequent choice of an appropriate empirical model depends critically on assumptions about the determinants of zero-value outcomes. In our case, we assume that, in some instances, farmers forgo fertilizer-using techniques because the opportunity costs of doing so are high. But we also allow for instances in which market imperfections further truncate implicit demand. In particular, we include in the estimation of fertilizer demand, an additional step, and we use a set of determinants to control for the potential effects of incomplete markets.<sup>13</sup>

> 11 See Sachs et al. (2004)

> 12 For example, see studies by Bacha et al (2001), Croppenstedt and Demeke (2003), Fufa and Hassan (2006), Wubeneh and Sanders (2006), and Alem et al (2008) have looked at different variants of this issue in Ethiopia.

> 13 The alternative is that zero-use outcomes are voluntary corner-solutions that are optimal from the household’s perspective.

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liarities, we focus on the SCP supply from public and private HEIs while taking the supply of SENA’s programs as given.

To open a new program, HEIs need an authorization (or operating license) from the Ministry of Education, which can take a year or two to arrive and must be renewed periodically. In contrast, they do not need an authorization to close a program.

# **2.2 Data Sources**

We leverage multiple Colombian data sources. First is the National Higher Education Information System (SNIES), which covers the universe of higher education programs (bachelor’s and SCP) and contains program-level information on institution, geographic location, field of study, length, and enrollment. We use SNIES data between 2003 and 2019. We define a program as a combination of institution, program code, and department; when an institution offers the same program in two different departments, it counts as two different programs. We focus on in-person SCPs located in metropolitan areas; we exclude online programs because we cannot assign them a geographic location. This yields an SCP sample that accounts for 73% of the national SCP enrollment.

Our second data source is the Labor Observatory for Education (OLE). For the 2007-2013 period, OLE tracks individuals who graduated from higher education beginning in 2001 and work in the formal sector. For each graduate, it includes program identifiers that allow us to find the program in SNIES, and also labor market information such as work location and economic sector of work. Third, we use use annual GDP data by department and economic sector from the National Statistics Agency (DANE) between 2003 and 2019. Fourth, as an instrument for competition we use information on SENA’s department-level budget, which quantifies SENA’s resources by department and year.) All monetary values are expressed in Colombian pesos (COP).

Overall, our data includes 13 departments, 279 HEIs, 3,463 SCPs, and 4,392 bachelor’s programs (the latter are included for some comparisons with respect to SCPs). Programs are classified into 24 fields of study. In some of our analyses, we aggregate these into four “field categories” (Table A.1 lists all fields and field categories). We analyze entry between 2004 and 2019 and exit between 2004 and 2018 (since our last available year is 2019, we cannot identify which programs closed that year).

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# **Table 4: Characteristics of Single-Shift Schools**

||**Earl**|**y Grades - FE**||**Fina**|**l Grades - FE**||
|---|---|---|---|---|---|---|
||||**Items in t**|**he house**|||
||Control|Treatment|Diff|Control|Treatment|Diff|
|Became Single-Shift in 2011|6.70|5.97|0.73**|6.73|6.86|-0.13|
|Became Single-Shift in 2012/2013|6.72|6.60|0.12|6.41|6.30|0.11|
||Pre-Treatment|Post-Treatment|Diff|Pre-Treatment|Post-Treatment|Diff|
|Became Single-Shift in 2011|5.94|5.97|-0.03|6.53|6.86|-0.33|
|<br>Became Single-Shift in 2012/2013|6.42|6.60|-0.18*|6.58|6.30|0.29|
||||**Mother E**|**ducation**|||
||Control|Treatment|Diff|Control|Treatment|Diff|
|Became Single-Shift in 2011|0.33|0.41|-0.09|0.39|0.39|-0.01|
|Became Single-Shift in 2012/2013|0.38|0.32|0.05***|0.45|0.43|0.02|
||Pre-Treatment|Post-Treatment|Diff|Pre-Treatment|Post-Treatment|Diff|
|Became Single-Shift in 2011|0.09|0.41|-0.32**|0.08|0.39|-0.32***|
|Became Single-Shift in 2012/2013|0.19|0.32|-0.13***|0.15|0.43|-0.28***|
|||**A**|**ge-grade**|**distortion**|||
||Control|Treatment|Diff|Control|Treatment|Diff|
|Became Single-Shift in 2011|19.79|24.97|-5.18|37.31|24.88|12.43***|
|Became Single-Shift in 2012/2013|17.73|16.52|1.21|35.73|26.38|9.35***|
||Pre-Treatment|Post-Treatment|Diff|Pre-Treatment|Post-Treatment|Diff|
|Became Single-Shift in 2011|29.40|24.97|4.43|35.34|24.88|10.47***|
|Became Single-Shift in 2012/2013|20.93|16.52|4.41***|40.80|26.38|14.42***|

T-test: * significant at 10%; ** significant at 5%; *** significant at 1% Notes: Items in the house refers to the sum of major household goods in the house of 5th and 9th grade students. The household goods considered are: TV, stereo, DVD, refrigerator, freezer, car, washing machine and the total number of bathrooms and bedrooms. Mother education refers to % of 5th/9th grade students of which their mother has Tertiary Education. The age-grade distortion is the proportion of students with more than 2 years of school delay.

Data sources: Brazilian Ministry of Education.

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generally unavailable. In other cases, microlenders may appear in our data set as subsidiaries or departments of state-owned banks or foreign-owned banks, although we cannot separately identify their microlending activities.<sup>9</sup>

# 3. **Data and efficiency issues**

We briefly discuss our data sources, variable definitions, and give some summary statistics. We then briefly outline how the efficiency ranks are computed.

# **3.1. Data sources, variable definitions, and summary statistics**

We employ both bank-specific and national-level variables in our analysis. The bank-specific data are taken from the BankScope database, with informnation on over 7,500 banks each year. The national-level data are taken from the Office of the Comptroller of the Currency, World Bank and Intemational Monetary Fund (IMF) data on World Development Indicators, IMF Intemational Financial Statistics, Wall Street Joumal and Heritage Foundation Govemance data, and several other public sources.

Table 1 shows definitions for most of the variables employed in the analysis, and also shows summary statistics for these variables for the developed nations (Panel A) and developing nations (Panel B). The developed nations correspond to the IMF definition for "high-income" countries, and the developing nations correspond to IME "middle-income" and "low-income" countries. Table 2 shows the names of each of the developed and developing nations and the numbers of banks and potential community banks in each nation in the sample, averaged over the sample period. As discussed, for developed nations, we use the conventional definition of community banks as small, private, domestically-owned institutions (SMALL banks), whereas for developing nations, we include state-owned banks (STATE banks) and foreign-owned banks (FOREIGN banks) as potential community banks as well. STATE and FOREIGN banks are defined as having 50% or more state and foreign ownership, respectively, although govemment and foreign owners may exert effective control with less than 50% ownership in some cases. One other difference is that for developed nations, SMALL banks include institutions with less than US$1 billion in assets, whereas for developing nations, SMALL banks are truncated below $100 million in assets due to the smaller bank sizes in developing nations.1<sup>0</sup> This should not create any difficulties of 9For example, Shorebank Advisory Services, a subsidiary of the U.S.-owned Shorebank Corporation has transferred its expertise in providing commercial and housing loans in underserved markets in the U.S. to international development initiatives around the world. It has ownership relationships with institutions in Asia, Eastern Europe, and Latin America that aim to increase financing to SMEs. For instance, Shorebank began operating in Azerbaijan in 1998 to finance new businesses. By 2002, their credit portfolio in Azerbaijan reached US$1 million and loan sizes increased from US$4,000 in 1998 to US$100,000 (Tumag 2003).

° The US$1 billion and US$100 million cutoffs for SMALL'banks are based on real 1992 dollars using the CPI.

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# **Figure 4 Sri Lanka Official and Unofficial Remittances (US$ Million)**

<!-- Start of picture text --> Figure 4.4 Sri Lanka. Official and Unofficial<br>Remittances (US$ millions)<br>1000<br>800<br>600<br>400<br>200<br>0<br>1985 1990 1995<br>Official Unofficial<br><!-- End of picture text --> _Source: World Bank (2005a)_ _In addition to cost and speed, informal remittance systems also fare more favorably for migrants, with respect to the formalities required to transfer money, reliability, and convenience._ The informality of _Hawala_ makes it attractive to many Sri Lankan migrants who find banks and other financial institutions uncomfortable to deal with. The lack of bureaucracy renders informal services accessible to less educated customers, many of whom live in rural areas where alternatives are not available in any case. Informal systems are also reliable. Trust and the cultural importance of one’s reputation and good name perform an effective self-regulatory function. Disputes and errors are settled among informal service providers who assume the risk of a counterpart’s default or law-enforcement actions.

_The convenience of door-to-door service is appealing to all customers, especially in the regions where transportation is poor and to female migrants in remote and traditional regions, where they may not be allowed to leave the house._ In some instances, as field surveys indicate, informal service providers visit the workplaces of expatriates to collect money for remittances. Private banks have opened branches in migrant-destination countries to assist Sri Lankan nationals remitting their earnings, and these branch offices have managed to attract a number of clients through personalized service. However, due to the constraints of work conditions, housemaids still prefer to use informal channels to remit their earnings. With the fluctuation of the dollar, many of these women are unaware that their informal contacts do not give them a fair exchange rate and that they are losing on the transaction. (See Malsiri et al. 1999.) _Areas of conflict are also more likely to be linked to informal methods of money transfers._ The northern parts of Sri Lanka, for example, have limited formal financial services. Migrants from that region resort to the _Hawala_ system and in-kind remittance services to make remittances. Informal remittance services are also attractive to the undocumented immigrants, who may not be permitted to have bank accounts or do business with formal institutions. This is said to be particularly important to Sri Lankan migrants based in Italy.

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# **I. Introduction**

An increase in sea surface temperature is strongly evident at all latitudes and in all oceans. The scientific evidence indicates that increased surface temperature will intensify cyclone activity and heighten storm surges.<sup>1</sup> These surges<sup>2</sup> will, in turn, create more damaging flood conditions in coastal zones and adjoining low-lying areas. The destructive impact will generally be greater when storm surges are accompanied by strong winds and large onshore waves. The historical evidence highlights the danger associated with storm surges.

During the past 200 years, 2.6 million people may have drowned during surge events (Nicholls 2003). More recently tropical cyclone Sidr<sup>3</sup> in Bangladesh (November 2007) and cyclone Nargis<sup>4</sup> in the Irrawady delta of Myanmar (May 2008) provide examples of devastating storm-surge impacts in developing countries.

Recent scientific studies suggest that increases in the frequency and intensity of tropical cyclones in the last 35 years can be attributed in part to global climate change (Emanuel 2005; Webster et al. 2005; Bengtsson, Rogers, and Roeckner 2006). Others have challenged this conclusion, citing problems with data reliability, regional variability, and appropriate measurement of sea-surface temperature and other climate variables (e.g., Landsea et al. 2006). Although the science is not yet conclusive (IWTC 2006: Pielke et al. 2005), the International Workshop on Tropical Cyclones (IWTC) has recently noted that “[i]f the projected rise in sea level due to global warming occurs, then the vulnerability to tropical cyclone storm surge flooding would increase” and “[i]t is likely

> 1 A sea-surface temperature of 28o C is considered an important threshold for the development of major hurricanes of categories 3, 4 and 5 (Michaels, Knappenberger, and Davis 2005; Knutson and Tuleya 2004).

> 2 _Storm surge_ refers to the temporary increase, at a particular locality, in the height of the sea due to extreme meteorological conditions:  low atmospheric pressure and/or strong winds (IPCC AR4 2007). 3 According to Bangladesh Disaster Management Information Centre (report dated Nov 26, 2007) 3,243 people were reported to have died and the livelihoods of 7 millions of people were affected by Sidr - <u>(http://www.reliefweb.int/rw/RWB.NSF/db900SID/EDIS 79BQ9Z?OpenDocument</u> ).

> 4 In Mayanmar, 100,000 people were reported to have died and the livelihoods of 1.5 million people were affected by Nargis (http://www.dartmouth.edu/%7Efloods/Archives/2008sum.htm )

2
financial market failures, imbalances in the tax regime or enforcement, or labor policy failures. There was a sharp decline in aggregate productivity in manufacturing, construction and commerce over the period 2010 to 2014. Other services and transport, storage and communications experienced increasing aggregate productivity between 2010 and 2012, but productivity declined thereafter.

In a dynamic setting (equation 2), Foster et al. (2001) decompose aggregate productivity growth. Their approach allows an analysis of the contribution of the reallocation of activity across individual producers in accounting for aggregate productivity growth. There are five components in the decomposition that help explain growth (each of these terms correspond to the terms in equation 2): (i) within-firm productivity growth holding employment share constant; (ii) a between-firm effect measuring the contribution to aggregate productivity growth of high-productive firms expanding shares and low-productivity firms shrinking shares; (iii) a cross-term capturing the contribution of firms that increase productivity and expand, and firms that decrease productivity and shrink; (iv) an entry effect that contributes positively to productivity growth if entering firms have higher productivity than the average in the base period; and (v) an exit effect that contributes positively to sector productivity growth if exiting firms have lower productivity than sector average in the base period.<sup>23</sup> Foster et al. (2001) demonstrate that a key determinant of productivity growth dynamics illustrated in equation 2 is the horizon over which the productivity growth is measured. For example, due to learning and selection effects, younger firms are likely to have on average a lower productivity than older firms. Therefore, the results presented here are sensitive to: (i) the length of period under examination because the contribution of any differences in productivity will likely be greater for changes measured over a longer horizon; and (ii) the specific period under examination because the where the economy is in the business cycle at the time will also be reflected in productivity dynamics.<sup>24</sup>

> 23 In this decomposition, the between term and the entry and exit terms involve deviations of plant-level productivity from the initial industry index (Foster et al., 2001).

> 24 For example, in Foster et al. (2001) productivity decompositions yield quite different stories for the periods that are roughly coincident with cyclical downturns. These findings suggest that these estimations should be updated as additional tax administrative data become available.

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# **Box 5: Unit Record Approach: Household Income and Occupational Choice Model A Microsimulation Study on Côte d’Ivoire**

Between 1978 and 1993, Côte d’Ivoire experienced a sharp deterioration in its terms of trade and a significant increase in its external debt. The average annual per capita growth of GDP became negative. Following the 1994 devaluation of the CFA franc and a series of structural reforms supported by both the World Bank and the International Monetary Fund, growth recovered, mainly in the export sector, agro-industry, manufacturing, and energy.

Grimm (2004) studied the inequality and poverty implications of macroeconomic adjustment in Côte d’Ivoire. The study focuses on the effects of three phenomena on income distribution: changes in returns to labor, changes in occupational choice, and sociodemographic changes. The analysis is based on the 1993 and 1998 household surveys. Monthly wage and profit functions are estimated in terms of these changes and unobservable effects (reflecting unobservable individual and household characteristics). Income for different sources is aggregated at the household level for 1993 and 1998.

The study concludes that income-inequality patterns were different across regions, but poverty trends were similar. In the case of Abidjan, the observed reduction in inequality and poverty is linked to a boost in employment in the formal wage sector and an increase in returns to observable determinants of wage earnings. Rural areas also experienced a strong growth in household income and a significant reduction in poverty. However, these developments were accompanied by a rise in inequality. The negative income growth in Abidjan and the positive income growth in the rural sector were both connected with increasing inequality. Moreover, while within-region inequality increased, between-region inequality declined. For example, while within-inequality increased from 44 percent to 54 by 1998, between-region inequality decreased from 7 percent to 3 percent at the national level by 1998 (see table below).

**Decomposition by Microsimulation of the Change in the Distribution**

|||**1992/3**|||**1998**||
|---|---|---|---|---|---|---|
|**National **|**Gini**|**dGini**|**E(0)**|**Gini**|**dGini**|**E(0)**|
|Initial values|_0.494_||0.512|_0.508_||0.563|
|Within-group inequality|||_0.441_|||_0.537_|
|Between-group inequality|||_0.071_|||_0.026_|
|Observed change||0.014|||0.014||
|Price observables|0.483|−0.011|0.497|0.540|−0.032|0.630|
|Returns to schooling|0.471|−0.023|0.473|0.547|−0.039|0.640|
|Returns to experience|0.476|−0.017|0.486|0.512|−0.004|0.573|
|Ivorian/non-Ivorian wage differential|0.495|0.001|0.515|0.508|0.000|0.562|
|Regional differential|0.484|−0.010|0.496|0.511|−0.003|0.574|
|Returns to land|0.489|−0.005|0.506|0.500|0.008|0.550|
|Residual variance|0.498|0.004|0.525|0.482|0.026|0.515|
|Total price effects|–|−0.007|–|–|−0.006|–|
|Occupational choice|0.496|0.003|0.505|0.515|−0.007|0.557|
|Price and occupational choice||−0.005|||−0.014||
|Population structure effect||0.019|||0.028||
|_Source_: Grimm 2004.<br>_Note_:E(0)is themean logarithmic d|eviation.||||||

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media market segments only. Anecdotal evidence points at both possibilities. In China, despite widespread media capture, the CCP can deem any broadcast or article offensive post publication. Such a “regime of uncertainty” induces effective self-censorship across the entire media spectrum, whether public or private, captured or not (Hassid 2008). In Peru, Fujimori and his proxy explicitly bribed some TV channels to fire investigative journalists, thus combining inducement-based capture with direct news suppression (McMillan and Zoido 2004). On the other hand, in Russia, blatant censorship has been used in the relatively independent, non-captured segments of the media market only when going too far. The many intimidation attempts and the brutal murder of wellknown journalist Anna Politkovskaya in 2006 are reminders of the lines that cannot be crossed.

Empirically, since Besley and Prat’s argument, “old-fashioned” censorship has increased dramatically as part of the democratic backsliding and security related trends worldwide. All existing metrics of media freedoms, including our data set described below, confirm this reversal empirically. Even established democracies, such as the United States and France, and countries once perceived as regional leaders of democratization, such as Poland and Hungary, have experienced a rapid deterioration of media freedoms since 2001. We collected an original data set capturing all the salient censorship worldwide between 2001 and 2015. Figure 1 plots the data trend and shows a dramatic increase in such events by 231%, from 219 global episodes in 2001 to 725 in 2014. Figure 2 considers three subjective _Freedom House_ indexes that proxy the main types of media capture tools: legislation that openly sets limits to freedom of reporting ( _Legal constraints_ ); political interference with information collecting, reporting and dissemination ( _Political constraints_ ); and economic media capture through positive inducements such as bribes, regulatory favors and market concentration de facto placing media into the hands of the state or government-connected oligarchs ( _Economic constraints_ ).

11