rafmacalaba/datause-encoder-data huggingface dataset:
transformation towards modern sectors such as manufacturing and services, the country continues to depend largely on the agricultural sector for national production and above all employment. Agriculture accounts for about a quarter of the GDP and provides work for over 70% of the labor force. Around 8 in 10 rural households are smallholders primarily engaged in subsistence agriculture.

Mozambique is also a risk-prone country. It ranks third among African countries exposed to risks from multiple weather-related hazards such as flooding, epidemics, cyclones and droughts (IFRC 2014). Nearly a quarter of the population lives in areas with high probability of experiencing a natural disaster (World Bank 2018a). Intense droughts are experienced in 7 out of 10 years in the Southern Region, and in 4 out of 10 years in the Central Region. The Mozambican coastline borders one of the most active basins of tropical cyclones. Floods generally occur every two or three years, mostly during the rainy season and along the nine major international river systems that cross the country or across the low-lying, densely-populated coastal areas (World Bank, 2012). Climate change is expected to intensify these trends. The average likelihood that a tropical storm will impact an enumeration area included in the Mozambican Integrated Agricultural Survey during the country’s nine-month storm season increased by a factor of four between 1968-1990 and 1991–2015 (World Bank 2018a).

We employ a triple-difference strategy that exploits variation in the shocks across space, time and cropping cycles using detailed information from household surveys, remote sensing data on weather outcomes and the geographic distribution of the main cropping cycles across the country. The shocks analyzed include 1) the floods that occurred between late 2006 and early 2007 along the Zambezi River Basin, 2) a category 3 cyclone (Jokwe) that struck northeastern Mozambique between March 8 and 18, 2008, and 3) a drought that affected parts of central and southern Mozambique between May 2005 to January 2006.

The findings demonstrate high levels of vulnerability. Irrespective of the type of weather anomaly, food security is systematically undermined among affected households. Experiencing any of the shocks leads to a drop of up to 25-30% in per capita food consumption and 0.4 fewer meals per day per person. Conservative back-of-the envelope calculations suggest that the shortfall in food consumption may be equivalent to a daily caloric reduction in the order of 150-200 calories 3
**Table 6: Heterogeneous impact of the intervention – interaction of treatment and tenure**

|Type of training|Reven<br>|ues from main a<br>|ctivity<br>|Value of<br>|
|---|---|---|---|---|
||Typical month<br>(ln)|Previous<br>month (ln)|Previous year<br>(ln)|household<br>assets(ln)|
|Basic (B**)**|-0.548**|0.451|-0.743**|-0.00391|
||(0.276)|(0.803)|(0.371)|(0.123)|
|Enhanced (E)|-0.340|-0.380|-0.589|-0.242|
||(0.312)|(0.940)|(0.365)|(0.256)|
|Basic (B)*tenure 2nd quartile|-0.258|-2.193*|-0.148|-0.212|
||(0.578)|(1.192)|(0.753)|(0.201)|
|Basic (B)*tenure 3rd quartile|0.312|0.730|1.964***|0.290|
||(0.334)|(1.189)|(0.754)|(0.186)|
|Basic (B)*tenure 4th quartile|0.924**|-0.833|1.593**|0.135|
||(0.442)|(1.317)|(0.762)|(0.226)|
|Enhanced (E)*tenure 2nd quartile|-0.470|-0.983|-0.764|-0.0506|
||(0.577)|(1.272)|(0.836)|(0.398)|
|Enhanced (E)*tenure 3rd quartile|0.362|0.983|1.414|0.361|
||(0.375)|(1.346)|(0.874)|(0.283)|
|Enhanced (E)*tenure 4th quartile|1.231**|1.278|1.807**|0.707**|
||(0.495)|(1.295)|(0.766)|(0.326)|
|Constant|11.08***|4.671***|14.25***|10.06***|
||(0.750)|(1.207)|(0.543)|(1.208)|
|Observations|678|680|645|700|
|R-squared|0.106|0.126|0.060|0.209|
|Impact for B - tenure 4th quartile|38%|-38%|85%|13%|
|Impact for E - tenure 4th quartile|89%|90%|122%|47%|
|F test - 4th quartile -<br>H0:_lincom_for B =_lincom_for E|0.0574|0.211|0.218|0.142|

Notes: The omitted variable is the first quartile of tenure All effects measured using ANCOVA specifications. All regressions also control for stratification dummies (geographical district, sector of activity, whether participants received a business gift). Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5, and 1 % levels, respectively 24
**Box 2. Applying CDR-Based Poverty Models to Fill Data Gaps**

Much of the discussion surrounding using CDRs for socioeconomic research ignores the practical conditions in which they will be applied. CDR data are almost always available in addition to conventional data, such as household surveys or censuses. There is little to be gained in showing that CDR data can be used to predict this existing survey data; instead, the fitted model must be applied to new, unseen data to answer new questions.

There are at least three different ways in which CDR data can complement conventional data sources:

**1. Spatial infill: generating small-area statistics.** Given the relatively high spatial resolution of CDRs, spatial infill likely offers the most added value as a socioeconomic research method. A particular household survey with a limited sample size may only support estimates at a relatively coarse spatial resolution, such as the _departamento_ level. The high-resolution behavioral signals in CDRs can enhance the statistical strength of survey data, enabling more detailed and accurate estimates. Ideally, the CDR collection period should coincide with the period in which the survey was conducted. This approach entails the risk of ignoring the role that space and geography might play in the prediction of poverty rates.

**2. Time interpolation/extrapolation.** The potentially high frequency of CDRs can also complement conventional data sources. Household surveys are generally updated at 2-to-5-year intervals. CDRs can be used to update these estimates, providing officials with current information on policy-specific subjects. A predictive model can be constructed for a survey year using contemporaneous CDR data. This model can then be applied to more recent CDR data for which corresponding survey data are not available. There is a risk, however, that the relationship between CDR behavioral signals and the target variable, in this case the poverty rate, could change over time.

**3. Spatial extrapolation.** This is the most ambitious potential application of CDR data. In countries or regions in which no recent survey data are available, such as conflict-affected areas or countries that have experienced severe political instability, estimates can be generated by using survey data and CDRs for a similar location and then applying this model to CDR data from the target location. This approach requires significant assumptions, but it may be useful in cases where no stronger data source exists. To date, research into the practical applications of spatial extrapolation has been limited.

# **_Evaluating the Model_**

One major liability of machine-learning models is a phenomenon known as “overfitting.” Training data always reflect both meaningful “signal” and random “noise.” Overfitting occurs when the model being fit has so many free parameters that it fits to both the signal and the noise. In such a case, the model will appear to be an excellent fit for the input data, with high R<sup>2</sup> and similar values, but it will perform poorly when applied to previously unobserved data.

**13**
# **5.  Outdoor vs. Indoor Air Pollution**

Policy researchers often view indoor and outdoor air pollution as separate problems in developing countries. Although a rationale is seldom provided, this distinction reflects two beliefs: that indoor pollution is a more serious problem for poor households, particularly in rural areas, and that outdoor pollution is distinct because it comes from multiple sources that may be distant from the household. Our 24-hour monitoring data for a Dhaka household with an extremely clean fuel – piped natural gas – provide us with some insight in this context, because we have also monitored the ambient concentration at five locations in urban Dhaka.

Figure 4 displays the 24-hour concentrations separately and overlaid. The kitchen readings for the natural-gas household closely resemble the ambient readings. The ambient readings, in turn, appear related to the daily cooking cycle for the great majority of households that use biomass fuels.<sup>3</sup> Because outdoor air rapidly replaces indoor air, the indoor concentration in a clean-fuel household closely reflects the ambient concentration. In urban Dhaka, our MiniVol readings at five outdoor locations yield a mean 24-hour concentration of 89 ug/m<sup>3</sup> .<sup>4</sup> The mean daily concentration for the natural gas household monitored with a PDRam unit is 101 ug/m<sup>3</sup> . For a broader group of 10 urban households monitored by our MiniVol sampler, the mean daily outdoor 24-hour concentration is also 101 ug/m<sup>3</sup> . By implication, burning piped natural gas adds negligible PM10 pollution to cooking and living spaces.

> 3 The 24-hour cycle of  ambient PM10 concentrations is very similar to the pattern of average hourly residuals from a panel regression that controls for differences in average PM10 concentrations for households that use biomass fuels.

> 4 For comparison, we cite PM10 concentration measured by the Bangladesh Air Quality Management Program monitor situation at the Parliament building in Dhaka.  From March, 2002 to February, 2003, the mean daily concentration was 137 ug/m<sup>3</sup> .  Our thanks to our colleague Paul Martin for this contribution.

15
# **Table 6: Cointegrating Vector for Savings Adjusting for Domestic Capital Gains**

**Part I: OLS** for _(s_ **_Iy) =,_** + **fl,** In **_ya_** + **_62r,_ +** ±3J[i + _fl4 lr_ + _f 5goVSa_ + **U**

||CHL|GHA|IDN|KOR|MYS|MEX|TUR|ZWE|
|---|---|---|---|---|---|---|---|---|
|Constant|-2.109|-1.539|-0.77|-2.438|-0.294|-0.631|0.097|-1.819|
||(-3.946)|(-3.015)|(-1.960)|(-7.682)|(-0.518)|(-2.821)|(0.085)|(-0.705)|
|Inyt|0.173|0.148|0.087|0.188|0.077|0.103|0.008|0.283|
||(4.174)|(3.363)|(2.734)|(8.441)|(1.131)|(3.586)|(0.097)|(0.757)|
|r,|0.000|-0.195|-0.619|-0.225|-0.927|0.093|-0.058|0.203|
||(0.007)|(-0.747)|(-4.009)|(-1.342)|(-2.644)|<br>(2.741)|(-1.071)|<br>(0.421)|
|flit|0.000|0.005|0.004|(0.016)|0.005|(0.009)|0.017|(0.009)|
||(0.085)|(1.246)|(0.962)|(-2.591)|(0.806)|(-4.862)|(4.014)|(-0.123)|
|n,|-0.038|-0.186|-0.700|-0.075|-1.031|0.140|-0.151|0.033|
||(-0.679)|(-0.717)|(-3.788)|(-0.548)|(-2.583)|(3.281)|(-2.155)|<br>(0.058)|
|govst|0.059|-0.425|-1.356|-0.432|-1.005|-0.792|-0.274|-0.029|
||-0.331|(-1.539)|(-2.906)|(-2.406)|(-6.370)|(-5.623)|(-1.105)|(-0.057)|
|NOBS|25|23|24|24|25|24|25|17|

48
# **4.2 Innovation function: Product, process and organization**

Innovation inputs generate innovation outputs and outcomes. The World Bank Enterprise survey contains information about whether firms implemented technological innovation outcomes (product & process innovations) and non-technological innovation outputs (organizational innovations).<sup>9</sup> For product and process innovation, firms are asked whether they introduced any new or improved product or service, or any innovative new process. For these technological innovation outcomes, there is also information on the degree of innovation in terms of what is defined by “new”; specifically, whether the innovation was new to the firm, local market, national market or international market, respectively.

Organizational innovations are another dimension of innovation that rely less on technological progress but can nonetheless bring about significant improvements in firm performance. We measure organizational innovation through a series of questions that capture whether a firm has undertaken any new organizational method in its business practices, workplace organization or external relations. Specifically, we consider a firm as having introduced an organizational innovation if it engaged in any of the following: 1) outsourced tasks, made changes to external relations, 2) made changes in its management structures, integrated departments/units, or 3) acquired management systems for information and knowledge (proxied by whether it made any changes to its hiring practices of employees or managers). Organizational innovations can improve firm performance such as its productivity by lowering costs, improving the workplace environment, and acquiring knowledge from external sources.

Questions on organizational innovation, however, were only implemented to medium and large size firms, which reduces the estimation sample by more than half. Therefore, our preferred model includes only product and process innovations.

Specifically, the three innovation outputs can be defined as:

|PDit= β3’X3it+ ε3it|(2)|
|---|---|
|PCSit= β4’X4it+ ε4it|(3)|
|ORGit= β5’X5it+ ε5it|(4)|

Where X3 to X5 include innovation input variables from equation (1). As with some of the ICT inputs, levels of generated knowledge are latent, or unobserved. In this case, we only observe whether a firm had a certain type of innovation or not. Therefore, we define L(.) as an indicator function which takes the value 1 if the condition is true and zero otherwise. Formally:

|PDt= L(PD<sup>*</sup>t>0) = L(β3’X3t> ε3t),|(5)|
|---|---|
|PCSt= L( β4’X4t> ε4t),|(6)|
|ORGt= L( β5’X5t> ε5t)|(7)|

One important element in the decision to innovate is that firms decide simultaneously what innovation outputs to produce based on existing knowledge capital investments. As a result, one should expect some

> 9 The survey also has information on marketing innovation, as well as other innovation outputs such as whether the firm has applied for a patent or a trademark.

10
40

# **Appendix 3: A comparison of Gini Coefficients and E(2) for the distributions excluding and including households with zero total incomes**

|**Year**|**Population share**<br>**of households with**<br>**zero incomes in**<br>**the sample**|**Gini**<br>**(excluding**<br>**zero incomes)**|**Gini**<br>**(including**<br>**zero incomes)**|**E (2)**<br>**(excluding zero**<br>**incomes)**|<br>**E (2) (including**<br>**zero incomes)**|
|---|---|---|---|---|---|
|**1981**|0.9%|0.574|0.577|1.447|1.461|
|**1982**|0.9%|0.581|0.584|1.552|1.566|
|**1983**|1.1%|0.584|0.587|1.515|1.534|
|**1984**|0.9%|0.583|0.586|1.464|1.477|
|**1985**|0.5%|0.589|0.591|1.622|1.631|
|**1986**|0.5%|0.578|0.580|1.637|1.645|
|**1987**|0.7%|0.592|0.594|1.791|1.803|
|**1988**|0.7%|0.609|0.611|1.742|1.752|
|**1989**|0.7%|0.625|0.627|2.212|2.225|
|**1990**|1.0%|0.604|0.607|1.767|1.786|
|**1992**|1.5%|0.573|0.578|1.876|1.905|
|**1993**|1.3%|0.595|0.600|2.308|2.337|
|**1995**|1.4%|0.591|0.596|1.627|1.654|
|**1996**|2.0%|0.591|0.598|1.609|1.645|
|**1997**|1.7%|0.593|0.598|1.739|1.771|
|**1998**|1.6%|0.591|0.597|1.672|1.701|
|**1999**|1.5%|0.585|0.590|1.530|1.556|
|**2001**|1.8%|0.586|0.592|1.661|1.696|
|**2002**|1.4%|0.580|0.585|1.522|1.545|
|**2003**|1.6%|0.575|0.580|1.474|1.498|
|**2004**|1.2%|0.564|0.568|1.618|1.638|

<u>Source: Authors' calculations from the PNADs.</u> 40
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE norm on rehabilitation and higher though not significantly higher than the norm in maintenance. While the comparison suggests that maintenance spending in recent years is over the norm, the experience in the country indicates that maintenance was neglected for very loing leading to much higher spending on rehabilitation to compensate. At the same time it is important to note that since Botswana’s road network is expected to expand considerably, its needs for maintenance spending will also increase in the future. So, if the quality of the road is to be maintained a further look at allocation to road maintenance might be necessary though higher public expenditure management.

**Figure 7. Botswana’s spending is commensurate with maintenance and rehabilitation needs**

<!-- Start of picture text --> 150<br>100<br>50<br>0<br>-50<br>-100<br>Maintenance Rehabilitation<br>Spending as % of requirements<br><!-- End of picture text --> _Source_ : Gwilliam and others 2008.

Botswana has prioritized the expansion of its road network. Over the 2005–07 period, the country spent an average of 1.3 percent of GDP on capital roads investments, which is an increase in comparison to previous periods and more than the 2003–09 investment planned under the 9th National Development Plan (NDP) (table 3). The country has thus consistently increased the resources invested in road expansion.

**Table 3. Botswana’s governmental investments in roads**

|Period|Period total, current pula|Constant 2007 $US per<br>year|Constant 2007 $US per<br>year per capita|GDP %|
|---|---|---|---|---|
|1966–86 (actual)|260,000,000|15,103,940|13|0.6|
|1991–97 (actual, NDP 7)|870,746,559|63,258,385|40|1.0|
|1997–2003 (actual, NDP|||||
|8)|661,274,384|27,119,970|16|0.4|
|2003–09 (planned, NDP<br>9)|2,467,660,000|72,944,329|39|0.6|
|2004 (actual)|315,433,705|73,784,049|41|0.7|
|2005 (actual)|604,046,771|125,494,297|68|1.2|
|2006 (actual)|791,362,633|139,454,757|75|1.2|
|2007 (actual)|1,157,844,594|188,610,310|100|1.5|

_Source:_ Botswana’s Department of Roads for spending under NDP;<sup>3</sup> AICD annual data 2004–07; authors’ calculations.

> 3 www.gov.bw/en/Ministries--Authorities/Ministries/Ministry-of-Communications-Science-Technology/Departments/Department-of-Roads/Divisions/#de53b8ebc29748f6af6959fd7b5fab33.

12
likely to default again in the future. As discussed in Reinhart and Rogoff (2005), history matters and one possible explanation is the inertia of institutions. Adopting this interpretation, past defaults should be seen as symptoms of deeper institutional failures and not just contemporaneous policies or external shocks. The market penalizes countries with default records by charging them a higher risk premium, which will in turn exacerbate the vulnerabilities of these countries.

Taking into account these issues, I include as explanatory variables log of real GDP per capita, debt as percent of GNI and default history in addition to ICRG, regulation of participation and government polarization. Table 4 reports the OLS cross-country regression analysis. Column 1 includes as explanatory variables log of real GDP per capita, debt as percent of GNI and default history. All three have the expected signs, although only GDP is significant. Countries with higher incomes have lower default probability; countries with higher debt as a percent of GNI and that have been more years in default are likely to default more often. Column 2 uses ICRG as a measure of countries’ institutional quality. Not surprisingly, the coefficient is negative and highly significant, indicating that countries with better overall institutions are less likely to default. As argued before, these institutional quality measures tend to improve with income level. Note that real GDP per capita is not significant in this case. When using regulation of participation (parreg) in column 3, real GDP per capita becomes significant and with the correct sign. Rich countries are associated with lower default probability. Countries with better regulation of participation, capturing how much limits are set to control the influence of powerful groups in government decisions, are less likely to default. In column 4, government polarization is entered alone. Although the coefficient is positive, it is not significant, consistent with Figure 3.

# **Fact 2: High government polarization is associated with high default probabilities conditional on weak institutions.**

Column 5 includes both ICRG and polarization to assess whether polarization is associated with default probability once the analysis controls for the overall quality of institutions. Indeed, government polarization is a relevant determinant of default probability. Given a certain level of institutional quality, a higher degree of government polarization is 10
_Size._ The large fixed costs associated with setting up an international distribution or service network will generally make exporting easier for large enterprises. Further, large enterprises generally have better access to finance than small enterprises—especially in developing countries—making it easier for them to finance these costs.<sup>21</sup> Several studies have found that large enterprises are more likely to export than smaller enterprises in low and middle-income countries. For example, Clerides _et al._ (1998) find evidence consistent with this for Colombia, Mexico and Morocco. Similarly, Grenier _et al_ . (1999) found that large Tanzanian enterprises export more than smaller enterprises. Finally, using data from several countries in sub-Saharan Africa from the mid-1990s, Bigsten _et al_ . (2004) and Söderbom and Teal (2003) found similar results. The results of this study are consistent with these earlier results. The positive and statistically significant coefficient on enterprise size indicates that large enterprises export more than smaller enterprises do. The point estimate indicates that a 1-point increase on the log-scale (e.g., from about 20 workers to about 54 workers) increases exports by about 4.3 percentage points.

_Ownership._ It is unclear whether state ownership will encourage or discourage exporting. On the one hand, state-owned enterprises might be less likely to enter export markets than similar private enterprises are. Many studies have shown that state-owned enterprises are less efficient on average than private enterprises, especially in competitive industries.<sup>22</sup> This might prevent state-owned enterprises from entering export markets—although barriers to trade allow inefficient firms to operate in domestic markets, it is more difficult for them to operate in international markets. On the other hand, state-owned enterprises might be better able to negotiate their way past regulatory barriers and might find it easier to access government programs designed to encourage exports. In practice, the evidence for Africa is mixed.<sup>23</sup> The results from this study suggests that state-owned enterprises are significantly less likely to export

> 21 See Schiantarelli (1996) for a review of the literature on firm size and financial constraints.

> 22 Shirley and Walsh (2001) and Megginson and Netter (2001) survey the large literature on this topic, finding that private enterprises are more efficient than similar state-owned enterprises on average in low and middle-income countries.

> 23 Using data from the early 1990s, Grenier _et al_ . (1999) find that state-owned enterprises in Tanzania were more likely to export.  They note, however, that Bigsten _et al._ (1997) did not find similar results

16
49 which reflect both the volume of output and economic policies in that year. On this basis, Nigeria dominates the region with a share of almost 50 percent. If 1987 exchange rates had been used following Nigeria's 86 percent current devaluation - the weights given to Nigeria would be less than 20 percent. Rather than show stagnation in Sub-Saharan Africa from 1980-87, these alternative aggregate statistics would show cumulative CDP growth of about 13 percent", "Africa's Adjustment and Growth in the 1980s" World Bank and UNDP. 1989.

Even if we assume that the problem of selecting an appropriate base year has been resolved, the use of official exchange rates is not appropriate. It is now widely recognized that the official exchange rates do not measure the relative domestic purchasing powers of currencies. The United Nations International Comparison Program (ICP) has developed measures of real GDP on an internationally comparable scale by using purchasing power parities (PPPs) instead of exchange rates, as conversion factors. These measures also sutffer from a variety of methodological issues.<sup>8</sup> Moreover, their coverage of countries is still limited.

To measure the sensitivity of total growth rate with respect to exchange rate, we derive the elasticity of We with respect to e; from (11.3): <!-- Start of picture text --> el at _ - rXt)<br>Re E X (12.5)<br><!-- End of picture text --> 8For an illuminating discussion of these issues see "Per Capita Income: Estimating Internationally Comparable Numbers," a World Bank document prepared by the International Economics Department, January, 1989.