rafmacalaba/datause-encoder-data huggingface dataset:
Exports to Canada are on the rise, doubling from 2000/2001 to 2001/2002<sup>57</sup> , whereas exports to Belgium declined from about 14% in 1999/2000 to about 8%. In 2001/2, about 50% of Dominican processed cocoa products were exported to the US, about 15% each to Puerto Rico and Holland, and about 5% each to Columbia, Honduras, and Jamaica. All cocoa exports in 2001/02 went through the port of Haina.<sup>58</sup>

## **<u>Table IV.6   Cocoa Exports by Destination  Oct 2000- Sept 2001</u>**

|<br>**Country**|<br>**Quantity (bags)**|<br>**Percent**|
|---|---|---|
|United States|25,551|72.3|
|Italy|3,185|9.0|
|Canada|2,294|6.5|
|Holland|1,537|4.3|
|Belgium|756|2.1|
|Other|2,037|5.8|
|Total|35,360|100|

**Source: CNC**

Some Dominican cocoa is considered to be high quality and is sold in special niche markets that pay price premiums. The major specialty cocoa is organic cocoa, which is primarily exported by CONACADO. On average, CONACADO receives a premium of US$100 per ton on _Hispaniola_ cocoa as well as premiums for the sale of organic cocoas that can be as high as US$250 per ton. However, despite the fact that about 90% of CONACADO’s members are registered organic producers, at this point in time only 5% of its production is sold into organic markets – and CONACADO is currently the largest exporter of certified organic cocoa in the world. Organic cocoa buyers are extremely quality conscious and efforts to increase market shares of organic cocoas must include quality control through the production and marketing chains. Achieving organic production together with high quality cocoa beans has been a constraint for CONOCADO expanding its sales of organic cocoa.

The CRMG survey of cocoa producers indicated that about 22% are selling at least part of their production through specialty markets. Organic cocoa was the most dominant type of specialty market for Dominican producers (19% of producers), followed by Fair Trade cocoa (2% of producers).

# **D. Structure of Production**

Smallholder producers dominate the cocoa sector in the Dominican Republic. A survey by SEA in 1980 indicated that cocoa was grown on about 115,000 hectares by about 35,000 farmers, and that 84% of the cocoa farms had fewer than 5 hectares in cocoa (World Bank, 1981, p.9). In 1998, cocoa was grown on about 153,000 hectares by about 40,590 producers (SEA, 1998). Over the period, land planted to cocoa increased by about 33% and the number of producers grew by about 17%. By 1998, about 68% of producers had less than 5 hectares. Despite this trend towards larger cocoa landholdings, in 1998 almost one-half of all cocoa producers had less than 3 hectares of land planted to cocoa (see table IV.7). The 4% of cocoa producers with planted areas greater then 20 hectares account for about 20% of cocoa land area. In comparison to coffee,

> 57 Secretaria de Estado de Agricultura, Departamento de Cacao “Informe Anual de las Estadísticas del Cacao 2001/2” 2002.

> 58 Secretaria de Estado de Agricultura, Departamento de Cacao “Informe Anual de las Estadísticas del Cacao 2001/2” 2002.

58
if they have an excess credit (on the VAT paid for the inputs). Also, there are various products that are exempted from this tax and for which firms cannot reclaim the VAT paid for the inputs.

- _Excise:_ Excise duties is an indirect tax per unit (per liter, kilogram) levied on manufactured and specific imported goods that are considered to cause “harm” or are luxury goods. The main categories of goods taxed are fuel, alcoholic drink and tobacco and cigarettes.

- _Social and Health Insurance Contributions:_ Employed persons and their employers shall be liable to pay social and health care insurance contribution. In calculating social insurance, Albania relies on a system of minimum and maximum gross salary boundaries (ALL 22,000 and ALL 97,030). The employee’s contribution rate is 9.5 percent while the employer pays 15 percent. The contribution rate for health care insurance is 3.4 percent on the monthly gross salary, with the employer and the employee paying equal shares, respectively 1.7 percent and 1.7 percent. On the other hand, the self-employed pay 23 percent for social security and 3.4 percent for health insurance, but for the latter the contributions are calculated on the double of the minimum wage. While these contributions are compulsory for all economically active persons, resident in Albania, other non-active persons can voluntarily pay the social and health contribution.

**On the spending side, social protection, education and health are among the largest categories** . Overall expenditures in Albania amounted to 32.8 percent of GDP in 2015, with a large share of spending under the social protection category amount 9.12 percent of GDP (Table 2).

**Table 2. General Government Spending in million leks and as a percentage of GDP, 2015**

||**Fiscal Data**<br>**(in million leks)**|**Fiscal Data**<br>**(% of GDP)**|
|---|---|---|
|**TOTAL SPENDING**|**4,726,966**|**32.79%**|
|General Public Services|329,629|2.29%|
|Defense|107,797|0.75%|
|Public Order and Safety|245,217|1.70%|
|Economic Affairs|359,213|2.49%|
|Environmental Protection|16,784|0.12%|
|Housing and Community Amenities|244,376|1.70%|
|Health|409,841|2.84%|
|Recreation, Culture and Religion|26,083|0.18%|
|Education|403,988|2.80%|
|Social Protection<sup>7</sup>|1,314,368|9.12%|
|Unclassified Expenditures|1,269,670|8.81%|

A summary of the changes of the tax laws for 2014, (in force as from January 1<sup>st</sup> , 2014 if not otherwise defined in the below provisions) Law No.9920.

7 Social Protection includes social security contributory pensions (old age pension, family pension, disability pension and short-term benefits), non-contributory benefits (i.e. _Ndihma Ekonomike_ , unemployment benefit, disability benefits, social pension, child birth grant, nursing benefits) and other social spending under “Special Programs of the Government”.

7
# **Does Mobile Money Use Increase Firms’ Investments? Evidence from Enterprise Surveys in Kenya, Uganda, and Tanzania**

# **1. Introduction**

Investment, or the acquisition of assets with the expectation of future returns, is one of the pillars of firm growth. This view has been widely accepted from earlier models of economic development. In the seminal Harrod-Domar model, for instance, the rate of growth of output is in tandem with the rate of growth of investment, assuming constant capital to output ratios (Harrod, 1993; Domar, 1946). That is, investment in fixed assets is directly associated with output growth. The role of investment as a driver for firm growth has been also at the core of a vast empirical literature. Several studies have highlighted how certain types of investments -including investments on IT, R&D, and innovation, can enhance firm productivity (see Syverson, 2011 for a review), increase exports (Liu and Lu, 2015 ), and promote overall economic growth and development (De Long and Summers, 1993).

The role of private investment for development is particularly important for Sub-Saharan African countries given the low rates of economic growth and high poverty rates in the region (Ouredraogo and Kouaman, 2014). Even for those economies where recent growth rates have picked up, initial low levels of development make the case for considering how to accelerate investment levels. Against this backdrop, several developing countries have witnessed a dramatic transformation in their financial systems due to the emergence of mobile-phone-based technology for the delivery of financial services (also known as mobile banking or mobile money). The dramatic rise of mobile money use has important implications for the private sector, potentially providing expansion opportunities. An important question that arises is whether the use of mobile money has any relationship with firm outcomes, specifically private investment.

The use of mobile phone financial services has rapidly increased during the past years spreading outside of the well-known and successful experience of M-Pesa in Kenya and besides the original person-to-person use. At the end of 2015, mobile money services were available in 93 countries - with a total of over 411 million registered accounts and 134 million active users (GSMA, 2015). Mobile money services are currently used by individuals to pay bills, by institutions to pay salaries or make social transfers (Heyer and Mas, 2011), and by firms to pay bills, suppliers, and employees 2
# Appendix Table 1. Descriptive Statistics

|Variable|Source|Mean|Standard deviation|
|---|---|---|---|
|Gini|Brueckner et al. (2015)|0.39|0.11|
|Gini|WDI (2017)|0.39|0.10|
|Net Gini|Solt (2015)|0.38|0.11|
|Market Gini|Solt (2015)|0.46|0.10|
|Ln GDP per capita|Heston et al. (2012)|6.82|1.09|
|ΔLn GDP per capita|Heston et al. (2012)|0.28|0.19|
|Investment/GDP|Heston et al. (2012)|0.23|0.09|
|Government Consumption/GDP|Heston et al. (2012)|0.09|0.05|
|Population Growth|Heston et al. (2012)|0.08|0.06|
|Relative Price of Investment|Heston et al. (2012)|0.76|1.36|
|Average Years of Schooling|Barro and Lee (2013)|6.45|2.67|
|Share of Pop. Secondary Education|Barro and Lee (2013)|0.32|0.17|
|Share of Pop. Tertiary Education|Barro and Lee (2013)|0.08|0.07|

36
12 Storage duration, marketing share and auto-consumption levels, the harvested quantities, and storage and crop protection technologies used as well as the climatic conditions (temperature and humidity) of the storage environment are also used by APHLIS to refine their within country regional PHL estimates and update them on annual basis. To shed preliminary light on the drivers of the self-reported PHL estimates presented here and further assess their internal validity, the estimated effects of the proximate determinants are briefly examined first (estimates in Appendix A2), before proceeding to a more reduced form and causal analysis using (more) exogenous explanatory variables (Table 4).<sup>20</sup> The following model is estimated: PHL(t)i = π f(t) + αYi + β(1 −s(t)i) + [γ1 + γ2θi]m(t)i + δTi + ξXi + vi + εit (1) where _PHL(t)i_ represents the proportion of maize harvest lost, reported by household at time _t_ of the interview, with _t_ equivalent to the number of months between the time of the survey and harvest completion(i.e. _unadjusted_ for the timing of the survey), _f(t)_ is a function of _t_ (either quadratic or logarithmic)<sup>21</sup> to control for differences across households in the timing of the survey, _Yi_ is the total maize harvest at time _t=0_ , 1 −s(t)i is the depletion rate of storage observed at time t, i.e. 1 minus the ratio of maize quantities still stored at the time of the interview over quantities harvested, m(t)i is the commercialization rate, and θi is a vector of average marketing conditions at the household level (transportation distance and frequency), T _i_ reflects the post-harvest storage and crop protection technologies used, Xi the agro-ecological conditions (proxied with AEZ indicator variables), and vi are indicator variables of the month of harvest and geographic region of the household to

> 20 The proximate determinants discussed above are all choice variables (except temperature and humidity), which may be correlated with unobserved household factors that also affect the amount of PHL reported (Wooldridge, 2002). As a result, the estimated effects of the proximate determinants on PHL may also capture the effect of these other unobserved factors they are correlated with and which also affect PHL directly, but which are not controlled for in the equation. Care must thus be taken in the interpretation of the findings and they cannot be seen as causal.

> 21 Results are presented with logarithmic specifications for expositional purposes, but the quadratic ones are preferred by the data. The size and sign of the other coefficients remains robust to each specification. See the appendix A1 for PHL adjustment using the coefficients of quadratic specifications.
# Monetary Policy under Flexible Exchange Rates: An Introduction to Inflation Targeting

Pierre-Richard Agenor* The World Bank Washington DC 20433

**JEL Classification Numbers:** E44, F32, F34.

*I would like to thank, without implication, Esteban Jadresic, Brian Kahn, and Murat Ucer for useful comments on an earlier draft, and Nihal Bayraktar for excellent research assistance.
As regards DCs, the literature is extremely scarce<sup>30</sup> . However, Wang (2006) found convincing evidence that unemployment had fostered start-ups in Taiwan, China over the period 1986-2001; in contrast, in the previously-cited work by Santarelli (2011), no significant impact of the unemployment rate on new firm formation in Vietnam was found.

# **<u>4.2.7 The role of ethnic minorities</u>**

While most of the empirical literature on entrepreneurship is biased towards evidence from the industrialized countries, a particular driver of new firm formation is instead mainly studied with regard to DCs<sup>31</sup> : the role played by ethnic minorities in generating above-theaverage rates of entry and better post-entry performance among newborn firms. The basic hypothesis is that alien minorities may have an entrepreneurial advantage based on their opportunity to exploit their minority community networks to overcome important hindrances to entrepreneurship (see Section 4.1), such as information asymmetries, high transaction costs, credit constraints and difficulties in accessing available inputs and technologies (see Kilby, 1983 and Biggs and Shah, 2006). In addition, from a sociological point of view, an ethnic minority, characterized by common traits such as language, culture and religion, generates trust, social cohesion and emulation, which are all factors that favor entrepreneurial behavior (see Greif, 1993; Hobday, 1995; Iyer and Schoar, 2010). Finally, a minority group may also be affected by a feeling of insecurity and frustration (in comparison with a dominant group), which encourages members to seek economic success and a better social status (see Elkan, 1988)<sup>32</sup> .

Empirical evidence is generally consistent with the hypotheses just discussed; for instance, Ramachandran and Shah (1999) - using firm level data from Kenya, Tanzania, Zambia and

> 30 This is unfortunate since, as discussed in the introduction, ‘defensive and necessity entrepreneurs’ appear to make up the bulk of self-employment in DCs, with activities ranging from street vending and small retailing to traditional personal services.

> 31 One exception are the studies showing how in the US black-owned firms show lower survival rates and worse performances than white- or Asian-owned firms (see Fairlie and Robb, 2007; Jarmin and Krizan, 2010).

> 32 This mechanism can work up to a given threshold; indeed to belong to a socioeconomically excluded group may decrease the likelihood of successfully found a new firm (this is the case, for instance, of the caste system in India, see Monsen, Mahagaonkar and Dienes, 2012).

27
assumption of price-taking in output markets seems unrealistic as well, because the loan markets in the five East Asian countries are not highly competitive.

Berger and Mester (1997) derive alternative profit efficiency from the following prespecified profit function, in log _form,_ _ln(Qr_ + S) _= f(w, y, z, e)_ + In _v,_ - In u, , where xr is the variable profits of the firm, 4 is a constant added to every firm's profit so that the natural log is taken of a positive number, _f_ denotes some functional form, w is the vector of prices of variable inputs, y is the vector of quantities of variable outputs, z indicates the quantities of any fixed inputs, _e_ is a set of environmental or market variables that may affect efficiency, _u,r_ denotes an inefficiency factor that may lower profits below the best-practice level, and v,f denotes the random error that incorporates measurement error and luck that may temporarily give banks high or low profits. The inefficiency factor u, incorporates both allocative inefficiencies from failing to react optimally to relative prices of inputs, and technical inefficiencies from employing too much of the inputs to produce y or setting sub-optimal output prices. As Mester (1996) points out, the profit concept assumes that all financial institutions use the same technology. This assumption seems to be a strong one, because it is likely that foreign banks use superior technology compared to domestic financial institutions.

Application of both techniques on the same banking data can lead to very different results as Ferrier and Lovell (1990) have shown. The choice of technique depends on the situation. Possible reasons to prefer SFA over DEA are plentiful. Firstly, as pointed out by Schmidt (1986), DEA estimates give only an upper bound to efficiency measures so that it is difficult to use DEA to compare efficiency among frms. Secondly, DEA does not assume statistical noise which means that all the error term in the estimation is attributed to inefficiency. This means that DEA will account for the influence of factors such as regional factor price differences, luck, bad data and extreme observations as "inefficiency". Therefore, as Schmidt (1986) has pointed out, one would expect that DEA produces greater measured levels of inefficiency than SFA. Thirdly, the estimates generated by using DEA are not very robust. As Button (1992) points out, measured DEA efficiency in small samples is sensitive to the difference between the number of firms and the sum of inputs and outputs used. This is because the small number of free dimensions remaining increases the chance of each firm being seen as efficient. Moreover, as Greene (1993) points out, a single errant observation can have profound effects on estimates of DEA frontiers.

6
narrow 95 percent confidence intervals illustrate that the small differences in attrition rates across questions are statistically significant at conventional significance levels, there was not a systematic difference in attrition rates between sensitive questions and the other questions in the survey. Some of the attrition rates of sensitive questions were higher than those of non-sensitive questions, and some of them were lower; the question with the highest attrition rate- the question asking about subjective assessments of income- is nonsensitive; and Figure 2 illustrates that the attrition rate was nearly identical between DFA and IRG-controlled regions at every question despite the potentially higher sensitivity of some of the questions in DFA-controlled regions.

Second, internet users differ from the general population and from the samples of other more commonly used survey modalities in important ways that can limit the representativeness of the results. By quantifying the differences between internet surveys, other survey modalities, and the general population, we can begin to caveat the results and to provide important robustness checks for analysis that relies upon internet-based surveys. Although it is difficult to fully quantify differences between the internet surveys and the entire population given the lack of traditional data in the country, we are able to compare difficult-to-adjust demographic variables in the internet survey to nationally representative data from before the conflict, including age, sex, highest education level achieved, and population by governorate.<sup>15</sup> The summary statistics from each survey are presented in Table 3 and suggest exactly what one might expect of internet surveys- respondents to the internet surveys are younger, less likely to be female, more concentrated in urban areas, and tend to be more educated than the general population. However, mobile phone surveys are biased in the exact same ways, with little difference between the mobile phone surveys and internet surveys in the share of the respondents that are female and the average age of respondents. But the respondents to the internet survey do tend to be even more skewed towards

> 15Despite the onset of a forced displacement crisis beginning in March 2015, official figures suggest that approximately 10 percent of the population was currently displaced at the time of the surveys analyzed here, there likely has been limited migration outside the governorate where households lived prior to the escalation of the conflict, and there has also been a high share of displaced households that did not move outside their district (e.g., TFPM 2017; OCHA 2021; D’Souza et al. 2022a; D’Souza et al. 2022b).

13
# **Data**

# <u>Output and inputs</u>

Output is measured as agricultural GDP in 1990 US dollars.<sup>25</sup> Inputs to agricultural production include land, capital, labor, and fertilizers. Hectares of agricultural area are used for the measure of land. This includes arable land, land under permanent crops, and permanent pastures. Agricultural labor is defined as the economically active population in agriculture. Fertilizer consumption is measured in metric tons. It is often viewed as a proxy for the whole range of chemical inputs.<sup>26</sup> The construction of the agricultural capital data has already been described.

# <u>State variables</u><sup>27</sup>

State variables are meant to represent the economic environment related to the choice of the implemented techniques. Since the motivation for including these variables have already been discussed in full in the previous papers, we review them briefly here.

# <u>Technology</u>

Three variables are included to account for differences in technology. The most commonly used variable to represent the level of technology in a country is a measure of human capital, which can be proxied by schooling years of the labor force (aged 15+).<sup>28</sup> The peak yield variable represents a measure of the frontier of implemented technologies. It is measured by country-specific Paasche indices, constructed from peak commodity yields weighted by land areas. The development indicator variable measures the overall level of development in a country, which would incorporate the effects of various public goods such as infrastructure, public health, research, and institutions. The hypothesis is that higher levels of technology would have a positive effect on agricultural productivity.

> 25 Various sources of National Accounts data were used to compile longer time series, including the United Nations, World Bank, OECD, IMF, and if necessary, country-specific sources.

> 26 Data on agricultural area, agricultural labor, and fertilizers were downloaded from the FAOSTAT database.

> 27 As previously mentioned, the list of possible state variables is vast. The variables presented in this analysis were chosen based upon our priors of what are important determinants of agricultural productivity, as well as for the availability of the data. This list is not meant to be exclusive, but rather suggestive. Indeed, there must be other determinants as the state variables in our model account for about half of the country effect (Mundlak, Larson, and Butzer, 2010).

> 28 Total education data from Barro and Lee (2001) are reported for every five years up to 2000 through the World Bank website (http://go.worldbank.org/8BQASOPK40). Data for other years are obtained through linear interpolations.

13
_COTE D’IVOIRE: Volatility, Shocks and Growth_

**Table 1: FMLS Regression Model of the Relationship Between Cote d’Ivoire Cocoa Production and World Real Prices; Parameter Estimates and Measures o Goodness-of-Fit**

|**FMLS ESTIMATORS**|
|---|
|**Model:**|
|_N_<br>_t_<br>_ldummy_<br>_CocoaPROD_<br>_COCOAPRICE_<br>_t_<br>_t_<br>_t_<br>_t_<br>,....,<br>1<br>ln<br>ln<br>=<br>+<br>+<br>+<br>=<br>ε<br>γ<br>β<br>α|
|**PARAMETER ESTIMATES:**|
|Constant: 13.241469|
|LnCocoaProduction: -0.744556|
|Dummy (value 0 after 1975): -0.860411|
|Dependent Variable: lnCocoaPrices|
|**STATISTIC OF THE JOINT SIGNIFICANCE TEST for ALL VARIABLES:**|
|18.429302 p-value: 0.000100|
|**STATISTIC OF THE INDIVIDUAL SIGNIFICANCE :**<br>**(distributed as Chi-squared)**|
|Regressor: Constant Statistic: 29.266227 p-value: 0.000000|
|Regressor: lnCocoa Production Statistic: 16.743357 p-value: 0.000043|
|Regressor: Dummy Statistic: 7.636649 p-value: 0.005719|
|**STRUCTURAL BREAK year for cocoa prices**: 1976, according to the Hansen-<br>Phillips test for structural break of unknown timing.|