rafmacalaba/datause-encoder-data huggingface dataset:
Relative to information costs, the banking policies of the host country are expected to have a clearer impact on the share of local claims. Policies that limit cross-border activities such as capital controls on loans from abroad are anticipated to diminish this form of bank presence, in the same way that trade tariffs discourage trade. On the other hand, limits on “banking freedom” such as restrictions on bank activities and foreign bank entry; controls on foreign currency lending by banks operating in the local market; and high corporate taxes are expected to reduce the attractiveness for international banks of extending claims locally in a foreign country.

There are a number of reasons why governments might put in place banking policies that promote one type of foreign bank claims or the other. First, by deterring foreign banks from freely operating locally in a host country, governments might seek to protect the domestic banking sector from foreign competition. Second, governments might promote certain types of foreign claims over others because of their different implications for the balance of payments. In particular, while cross-border financing virtually always implies obtaining foreign currency, in the case of local claims, beyond the initial FDI investments, the local activity of foreign banks involves intermediating local savings. Third, as discussed above, governments might want to influence the type of foreign bank financing to the extent that this has an impact on volatility.

Finally, consistent with what other international banking studies on FDI and cross-border claims have found, we allow for measures of economies of scale, profit opportunities, as well as, default, price and expropriation risks to influence the share of local to total foreign claims extended to a country. While some of these variables have a clear expected impact on the share of local claims, the effect of others on the mix of foreign financing is an empirical question. In our view, this makes our empirical investigation all the more interesting.

6
from the government, while 28 percent of the schools also received support from the community, but there is no systematic correlation between the two sources of support – one is not a substitute for the other.

Most schools in Sierra Leone have very poor classroom conditions and still lack sufficient learning materials and adequately qualified teachers (World Bank 2007). The government and development partners have made efforts to provide textbooks, but significant challenges remain. One study reported that, in 2004, a ratio of 1 set of textbooks to 3 pupils in urban areas and 1 set to 6 pupils in rural areas had been reached, whereas the official policy was a ratio of two students for one set of four coresubject textbooks (World Bank 2007).

With the Local Government Act of 2004, the Government of Sierra Leone commenced a national decentralization process: Primary education stood among the first functions scheduled to devolve to the Local Councils. Under the policy of decentralization, local councils would have full control and supervision of all primary schools including such functions as the recruitment and payment of teachers, the provision of textbooks and teaching materials, and the rehabilitation and construction of schools. Our baseline data show that the process of decentralization was still ongoing in the education sector of Sierra Leone as of early 2008. District Education Officers (DEO) in the centralized system and local council officers in the decentralized system were both active, leading to some confusion on the exact chain of command and roles and responsibilities of different agents.

# **3. Experimental Design**

## **3.1 The Intervention**

This paper evaluates a basic textbook distribution program of the Government of Sierra Leone in the year 2008. Under the program, textbooks were provided by the Ministry of Education, Youth, and Sports (MEYS) to primary schools based on student enrollment numbers as captured by the Education Management Information System – or EMIS. The actual transportation of books from central warehouses to schools was undertaken by local service providers who were competitively selected by the Government. The main objective of the textbook distribution program was to provide a set of core textbooks for every child in the treatment schools.<sup>1</sup>

> 1 A set of core textbooks is made up of English, Mathematics, Integrated Science and Social Studies textbooks.

5
Table D2: Probability of Responding to the Online Survey, Logit Coefficients and Marginal Effects

||Coeff.|Marg. Eff.|
|---|---|---|
|Treatment Group|1.498|0.274|
||(1.111)|(0.180)|
|Birth Weight (grams)|0.100<br>(1.076)|0.018<br>(0.196)|
|Weeks Pregnant at 1<sup>st</sup>Prenatal Visit|‐0.594<br>(0.648)|‐0.109<br>(0.121)|
|1<sup>st</sup>Visit before Week 13 of Pregnancy|‐3.590<br>(9.026)|‐0.657<br>(1.670)|
|% of Pregnant Women who are Plan Nacer Beneficiaries|1.620<br>(4.359)|0.296<br>(0.774)|
|Tetanus Vaccine During Prenatal Visit|3.350<br>(3.817)|0.613<br>(0.646)|
|Number of Prenatal Visits|‐0.099<br>(0.559)|‐0.018<br>(0.101)|
|Constant|7.644<br>(18.248)||
|Observations|36|36|

Notes: This table reports the coefficients and marginal effects from a logit regression that estimates the probability that a clinic responded to the May 2015 online survey.

57
representing the long-run equilibrium, the single-most important determinant of capital expenditures appears to be cash flows.

These results based on the comparison of alternative models of investment are indicative of the relative importance of the determinants of capital expenditures at the firm-level. The results suggest that managers care more about cash flows and cost of capital than the level of output and stock market signals (q ratio). In other words, managerial perceptions of fundamentals facing the firm are more important than market perceptions with regard to capital expenditure decisions at the firm-level.24 These results also suggest that contrary to the presumption in the literature, the Q model of investment performs the worst among competing models of investment in empirical specifications, even though it is superior to other models in terms of theoretical elegance. This finding regarding the poor empirical performance of the Q model is consistent with the results of Bischoff (1971), Clark (1979), and Bemanke et al. (1988) based on aggregate data.

Given this overall lackluster performance of the Q model, it would seem that mangers are not primarily guided by the stock market with regard to investment decisions. In other words, the stock market may be a sideshow as far as capital expenditures at the firm-level are concerned. Therefore, these models are consistent with the findings of Morck et al. (1990) and Samuel (1996b) regarding the limited implications of stock market activity for the resource allocation process in the economy. This conclusion then raises two related issues: (i) if the stock market is really a sideshow, market volatility may not really be an issue from the point of view

**24** See Samuel (1996b) for a more detailed analysis of the relative importance of managerial and market perceptions with regard to capital expenditure decisions at the firm-level. See Blanchard et al. (1993) for a similar analysis at the aggregate level.

27
# **Table 5 Regressions results on lagged Altman’s Z” SOE score as a predictor of fiscal injections**

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|
|---|---|---|---|---|---|---|---|
|_Item_|_Diffe _<br>_Subsidies +_<br>_government_<br>_equity injections +_<br>_Δ government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans +_<br>_Δ DTL_|_rencefrom chan_<br>_Subsidies_|_ge in total assets(_<br>_Government_<br>_equity_<br>_injections_|_as % of total asse_<br>_Subsidies +_<br>_government_<br>_equity_<br>_injections_|_ts), country and ind_<br>_Δ Government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans_|_ustry xyearfixed effe _<br>_Government_<br>_equity injections +_<br>_Δ government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans_|_cts_<br>_Government_<br>_equity injections +_<br>_Δ government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans +_<br>_Δ DTL_|
|Z<sup>′′</sup>score (_t_– 1)|–0.562***|–0.372***|–0.053***|–0.561***|–0.017***|–0.226***|–0.052***|
||(0.107)|(0.053)|(0.012)|(0.080)|(0.006)|(0.048)|(0.016)|
|Observations|608|843|795|788|734|691|608|
|Number of SOEs|92|118|114|112|110|106|92|
|_R_-squared|0.236|0.285|0.229|0.291|0.142|0.317|0.144|
|Adjusted_R_-squared|0.160|0.232|0.168|0.234|0.0682|0.255|0.0584|
|F-statistic|3.104|5.395|3.773|5.148|1.925|5.064|1.685|
|Log likelihood|–2,671|–3,317|–1,919|–3,392|–1,238|–2,587|–1,524|
|Mean of dependent<br>variable|5.035|3.519|0.412|4.793|0.266|2.545|0.756|
|Standard deviation of<br>dependent variable|22.40|14.66|3.083|21.27|1.411|12.38|3.209|

_Note_ : The dependent variables are variations of the fiscal injections variable that measure the change in fiscal injections, when they are nonnegative, minus the change in assets, all relative to assets in period _t_ . The regressions have random effects and no clustering, in order to let the financials of the firms rather than variation that affects firms define the fiscal events. Regressions include industry times year fixed effects and country fixed effects to try to capture variation that affects all firms in an industry/country the same way. Robust standard errors in parentheses. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

25
Figure 4. Telephone Service Prices and Penetration Growth in Africa, 1995-2005 <!-- Start of picture text --> MRT<br>50<br>SOMMLI<br>NGA<br>GHA<br>40 SDN CMR TCD UGA<br>TZA<br>GINRWACIVTGOBFA SENLSO<br>30 BWA KENAGO BEN MOZ GAB<br>GMBMWISWZ MDGNER<br>CAF<br>BDI<br>20 ZWECPVMUSZMB ZAFSYCSTPCOM<br>NAM<br>ETH<br>ERI<br>10<br>0<br>0.0 0.1 0.2 0.3 0.4 0.5<br>Cost of three minute local call (U.S. dollar)<br>International Telecommunication Union .<br>MRT<br>50 SOM ZAR<br>MLI NGA<br>40<br>GNQTCD UGACMRGHATZASDN<br>LSO<br>30 NERBDIGINRWAGMBSWZTGOCOGMWIBENBFAMDGGABMOZAGOBWASENCIV KEN<br>20 STP COM CAF SYC CPV ZMBNAMETH ZWEMUS ZAF<br>ERI<br>10<br>GNB<br>0<br>9 11 13 15 17<br>Ln(average telephone subscribers in 1996-2005)<br>Annual average growth rate (percent)<br>Annual average growth rate (percent)<br><!-- End of picture text --> Source: _International Telecommunication Union_ .

# **_Network externalities_**

As expected in the traditional telecommunications sector literature, network externalities seem to play a certain role in explaining network expansion. There is a significant correlation between the absolute size of telecommunications network and its growth rate. To account for the skewness of the country-size distribution, Figure 5 takes the number of total telephone subscribers in logarithm on the horizontal axis.

Figure 5. Network Externalities of Telecommunications in Africa, 1995-2005 Source: _International Telecommunication Union_ .

# **_Demographics_**

The relationship between demographics and telephone access improvement appears to have been insignificant in Africa. Intuitively, urbanization or high population density is 11
both Saddam Hussein and the subsequent period of instability and insurgency was the country's informal economy. In the Afghanistan context, Ciarli et al. (2015) show that the probability that a household engages in self-employment activities with low capital intensity, or in subsistence agriculture, increases with surges in conflict.

The impact of conflict could even be positive in the case of illicit activities. Stepanova (2012) argues that the war of the US-led coalition on the Taliban did not significantly reduce Afghanistan’s opium economy. Lind et al. (2014) note that conflict increases the perception of lawlessness while destroying infrastructure that is crucial to the production of alternative crops. In interviews conducted by Keohler and Zuercher (2007), farmers confirm that opium poppy is one of the few available cash crops. And Goodhand (2008) even argues that in parts of Afghanistan drugs have contributed to some degree of political order.

The goal of this paper is to offer an integrated assessment of the change in formal, informal and illicit economic activity in response to surges in conflict. The analysis is for Afghanistan, a country that has been repeatedly at war. The focus is on the period starting in 2012, at a time when the intensity of conflict increased dramatically, after a decade of relative stability and rapid economic growth.

The analysis relies on high-frequency data from non-traditional sources of information to measure economic activity. Satellite imagery allows us to assess nighttime light intensity for “tiles” measuring just one square kilometer. Remote sensing by satellites is also informative on the land surface devoted to the cultivation of poppies in every season. And anonymized cell phone traffic data from several thousand registered firms have been made available by a major telephone operator.

These non-traditional sources of information make it possible to conduct rigorous empirical analyses despite the obvious shortcomings of official statistical systems in conflict-affected countries. It has been shown that nighttime light intensity is highly correlated with GDP in developing countries (Donaldson and Storeygard 2016, Henderson et al. 2012). In South Asia, this high correlation holds even at the subnational level (Beyer et al. 2018). It has also been documented that data from mobile phone records can be exploited to understand a large array of social phenomena from land use to wealth and asset distribution to traditional measures of firm activity (Pei et al. 2014, Blumenstock et al. 2015, Blumenstock et al. 2018), among others. And the land surface devoted to poppy cultivation has already been used as a direct measure of the size of illicit activities (Lind et al. 2014).

While there is no readily-available data source to assess economic fluctuations in the informal sector, these can be inferred from the other high-frequency series considered. Aggregate output is the sum of output by the formal, informal and illicit sectors, and there is a sense of the relative weight of these sectors 3
Table 2: Balance across control and treatment and across treatment arms

|||Mea<br>Any<br>|n (SD)<br> demo in <br>|village<br>|C|oef. (SE) [p|]|
|---|---|---|---|---|---|---|---|
|||Regular|Shared|Decentral-||||
||Control|demo in<br>village|demo in<br>village|ized demo<br>in village||Diference||
||||||(2, 3, & 4)|||
||(1)|(2)|(3)|(4)|- (1)|(3) - (2)|(4) - (2)|
|Age of HHH (Years)|48.69|48.71|48.32|48.53|-0.18|-0.39|-0.18|
||(11.93)|(11.91)|(11.85)|(12.56)|(0.83)|(0.97)|(1.14)|
||||||[0.832]|[0.688]|[0.872]|
|Any pulses cultivation|0.203|0.240|0.246|0.160|0.012|0.006|-0.080|
||(0.403)|(0.428)|(0.431)|(0.367)|(0.064)<br>[0.850]|(0.068)<br>[0.934]|(0.056)<br>[0.153]|
|Veteran cultivator|0.117|0.100|0.126|0.117|-0.003|0.026|0.018|
|(Any promoted crop cultivation)|(0.323)|(0.300)|(0.332)|(0.322)|(0.061)<br>[0.965]|(0.046)<br>[0.573]|(0.041)<br>[0.667]|
|Any pulses improved seed|0.050|0.076|0.052|0.040|0.006|-0.024|-0.036|
||(0.218)|(0.266)|(0.223)|(0.196)|(0.027)<br>[0.834]|(0.028)<br>[0.392]|(0.027)<br>[0.181]|
|Any irrigation|0.463|0.367|0.406|0.419|-0.065|0.039|0.052|
||(0.499)|(0.483)|(0.492)|(0.494)|(0.111)<br>[0.561]|(0.118)<br>[0.740]|(0.119)<br>[0.661]|
|Total plot area (ha)|0.202|0.163|0.198|0.171|-0.024|0.035|0.008|
||(0.300)|(0.222)|(0.246)|(0.354)|(0.039)<br>[0.537]|(0.039)<br>[0.369]|(0.050)<br>[0.873]|
|# of connections|9.05|10.75|7.19|10.50|0.37|-3.56|-0.25|
||(6.65)|(6.43)|(5.04)|(5.76)|(1.62)<br>[0.817]|(1.57)<br>[0.024]|(1.75)<br>[0.888]|
|# of observations (Farmer)|281|341|382|375||||
|# of clusters (Farmer group)|15|19|21|21||||
|Omnibus F-stat [p]|||||0.15|1.33|0.96|
||||||[0.994]|[0.263]|[0.470]|

_Notes:_ Columns 1, 2, 3, and 4 present the baseline mean of the dependent variable and the baseline standard deviation of the dependent variable in parentheses, for farmers in control and regular, shared, and decentralized demo villages, respectively, and the total number of farmer and farmer group observations. Columns 5, 6, and 7 present differences, with robust standard errors clustered at the farmer group level in parentheses, and p-values in brackets.

for the promoted crops (Columns 3 and 5, Table 3). In Section 4.2, we use exogenous exposure to flood-saline shocks to present evidence that the new seeds increased productive resilience, and our point estimates imply the new seeds increase average yields by 2,900 BDT/ha, or roughly 7%. We note that our estimated impacts of demonstration on yields and profits cannot rule out effects consistent with these 24
Trade Database.<sup>1</sup> In Sub-Saharan Africa—the region that is of primary interest for this paper— the average is 7.65% of sales, but the range reported by firms runs from 0.01% to 124%. In Latin America, by contrast, the average is only about one-third as high (2.56%) and the range is much narrower (0.01% to 13.36%). A similar pattern is apparent in the last row of Table 1, which summarizes the data for textiles and clothing producers in all sample regions. The average cost is 2.73% of sales, but the range is once again very wide: 0.01% to 44.1%. It seems reasonable to expect that part of this variation is due to differences in firm size and productivity—with the largest impacts being felt by the smallest and least productive.<sup>2</sup> Foreign product standards are therefore likely to be a particular constraint on small and medium-sized businesses, which makes this issue a vital one for developing countries seeking to stimulate that part of the economy via increased contact with world markets.

Even in the presence of significant compliance costs for exporters, however, the trade policy question in this area cannot simply be one of “rolling back” product standards, as if they were protectionist tariffs or quotas. Rather, the emphasis should be on limiting—where present—the negative spillovers that legitimate product standards can have for exporters in other countries. This is the difficult line that the WTO _Agreement on Technical Barriers to Trade_ traces. One way the Agreement attempts to do so is by encouraging the use of de facto international standards: Article 2.5, for example, creates a rebuttable presumption that technical regulations aligned with international standards do not constitute “unnecessary obstacles to international trade”. The idea is relatively simple: complying with one “international” standard—and there can

> 1 This firm-level survey database is described in detail by Wilson and Otsuki (2004), and is publicly available at <u>http://www1.worldbank.org/wbiep/st-db/Criteria.asp. For an econometric analysis using these data, see Maskus et al.</u> (2005) and Chen et al. (2006).

> 2 Jaffee and Henson (2005) show through detailed case studies that management capacity and strategic choices also play an important role.

- 2 -
**Table A1 Data sources**

|**Indicator**|**Years**|**Source**|
|---|---|---|
|Number of physicians and<br>nurse/midwives per 1000<br>population|1990‐2013|WHO Global Health Observatory|
|Total population, Population aged<br>65 or over|1980‐2030|United Nations, Department of Economic<br>and Social Affairs,Population Division|
|Real GDPper capita(2011 PPP$)|1995 ‐ 2030|See Annex B,estimation byPatrick Eozenou|
|Total health expenditures per<br>capita (PPP constant 2011<br>international $), Percent of total<br>health expenditures spent out‐of‐<br>pocket|1995‐2013|World Development Indicators, extracted<br>from the WHO Global Health Expenditure<br>database|

Data on worker density from the WHO Global Health Observatory database for 193 countries were first cleaned to remove obvious outliers due to misreporting. In each of the country‐year observations listed in Table A2, outliers were replaced with missing data so that estimated growth rates would not be unduly influenced by arbitrary substitution.

**Table A2 Workers per 1000 population data outliers**

|**Country**|**Years**|
|---|---|
|Bhutan|2012|
|Saint Lucia|1999|
|Swaziland|2000|
|Bahrain|2005,2011|
|Bolivia|2001|
|Cabo Verde|2004|
|Cameroon|2004|
|Central African Republic|2004|
|Congo|1998|
|Ghana|2004|
|Guinea‐Bissau|2004|
|Guyana|2004|
|Micronesia|2009|
|Samoa|1999|
|Sierra Leone|2004|
|Zambia|2004|
|India|1991|

Missing data points for physicians and nurses/midwives per 1,000 population between any two real data points were then linearly interpolated. The resulting data set of physicians and 25
In January 2007, the government of Nicaragua implemented a demonstration project aimed at extending the Nicaraguan Social Security Institute’s (INSS) health insurance program to the population of informal sector workers. In addition to potentially improving healthcare access and overall health, enabling middle-class informal-sector workers to seek care at EMPs has the potential to alleviate the overburdened MINSA healthcare system, leaving more resources for the poor. Coverage and cost was designed to be as similar as possible to the standard insurance for formal sector workers, and enrollment into the program was voluntary. Both this paper and Thornton et al. (2010) report results from this evaluation. In 2007, a baseline survey was conducted among randomly selected uninsured informal sector workers in the three largest open-air markets in central Managua.<sup>5</sup> The survey asked detailed questions about utilization and spending, conditional on being ill 12 months prior to the survey, for the adult respondent and for any child in the household under the age of 16.

At the end of the baseline survey, respondents were either given an informational brochure about the insurance product or the brochure plus a six-month subsidy for insurance worth approximately USD$100, nearly half of the sample’s median household income.<sup>6, 7</sup> Respondents could enroll in the insurance plan at the INSS or at local micro-

> 5 Respondents were selected with the following two methodologies: in the first phase of the survey, prior to the baseline survey a census of market booths was conducted to define the sampling frame of possible respondents. Participants deemed eligible through the census were selected randomly (stratified by gender, marital status, and micro-finance client status) and administered the baseline survey. In the second phase of the survey, interviewers went door to door and sampled each market booth with eligible respondents. Individuals who were between ages 18 and 54, had a government ID, were an owner of the market booth, and lacked health insurance coverage were eligible. Overall completion rates were 51 and 53 percent in the two phases.

> 6 The study design also assigned respondents into 2-month subsidy group during the first phase of the project; these individuals were not included in the follow-up survey. The baseline survey included respondents in four other smaller markets but because these respondents were not followed over time, they are not included in the analysis (Hatt et al. 2009).

7