rafmacalaba/datause-encoder-data huggingface dataset:
table 5. Therefore, in this instance, it seems that, if anything, the use of OLS leads us to under-, rather over-, estimate the strength of growth spillover effects.<sup>38</sup>

# **7. The Costs of Fragmentation for Sub-Saharan Africa's Landlocked Countries**

Having provided evidence of heterogeneous spillover effects across landlocked countries and related these to differences in the strength of integration, in this section we present the results of a simulation exercise which is designed to be suggestive of the welfare losses associated with a lack of integration for such countries. This exercise answers the hypothetical question: What would have been the cumulative loss in real GDP over the period 1970-2000 had Switzerland, a landlocked country which is fully integrated with both its immediate neighborhood and the world economy, been subject to spillovers of the strength that the Central African Republic experienced? Thus, our exercise is akin to the thought experiment of relocating Switzerland—with all its domestic human and physical capital—from the heart of Europe so that it takes the Central African Republic's place in the heart of SSA.

To implement this exercise, we draw on our results from section 5 and make a highly conservative set of assumptions. Hence, we assume that the only parameter which changes from Switzerland's viewpoint is  , i.e. the strength of the cross-country spillover effect. From the results of table 3, therefore, we assume that Switzerland shared the estimated value of  of 0.0430 with the rest of SSA rather than the value of 0.2350 estimated for it as part of the OECD sub-sample. Apart from this, however, we assume

> 38 The results from the application of this ML estimator are available upon request from the authors.

34
activities were falling down in overall income distribution before the peso crisis.

Table 2: Proportion of Inequality Explained by Factor Components

||1989|1992|1994|1996|1998|2000|
|---|---|---|---|---|---|---|
|_sm_|2_._43|4_._77|7_._35|7_._38|4_._66|8_._58|
|_ss_|10_._57|16_._84|36_._84|24_._44|25_._02|31_._76|
|_si_|3_._15|0_._92|3_._89|2_._74|1_._38|3_._81|
|_sa_|0_._33|0_._14|_−_0_._13|_−_0_._04|1_._21|0_._17|
|_so_|83_._49|77_._31|52_._04|65_._49|67_._72|55_._65|

Source: Own calculations with data from ENIGH The results in Table 2 can be used to address our main question, i.e. _what proportion of total household income inequality can be accounted for by incomes derived in the different sectors?_ Incomes derived from service, manufacturing, agricultural and informal sectors can account for almost half of total household income inequality.<sup>17</sup> The proportion of inequality that is attributable to incomes generated in these sectors, increases in periods of stabilization. Despite the significant sectoral redistribution documented in section 2, the proportion of inequality that is explained by earnings in the manufacturing and service sectors, reduced between 1994 and 2000.

The results from Table 2 made apparent how little we actually know about the causes behind the high levels of Mexican income inequality. Even using complex enough models able to fully parameterize and account for total variation within income components **_Y_** _m_ , **_Y_** _s_ , **_Y_** _i_ and **_Y_** _a_ , we still wouldn’t be able to account for more than half of total household income inequality in Mexico during the 1990s.<sup>18</sup> Our results show that much of the income variation is actually concentrated in the highest income cohorts, where very little is known about their income generating processes. To the best of our knowledge, there are no models trying to estimate structural relationships capturing the income generating process occurring at the very top of the distribution. This leaves more than 50 percent of total income disparities without any structural interpretation, opening a mandatory line for future research on the causes behind Mexican high levels of inequality.

> 17Taking _CV_ 2 as the inequality index, we can assert that this proportion of total inequality explained by sectoral differences is indeed the expected value of their contribution (the Shapley value).

> 18For example, De Hoyos (2005b) estimates a model that parameterizes income components **_Y_** _m_ , **_Y_** _s_ , **_Y_** _i_ incorporating behavior and labor market restrictions. This model would be able to account for less than half of total income inequality between 1994 and 2000.

17
The methodology, however, lends itself naturally to the one used in MFMod and allows for the estimation of parameters using the exact functional forms. Furthermore, the long-run (equilibrium) elasticities of different equations can be estimated (and the theoretical restrictions imposed on the long-run component of the ECMs in MFMod can be tested). The approach retains cross country heterogeneity in the estimation of short-run coefficients, including the speed of adjustment parameter in the ECM. Country-specific estimates of parameters can be used to calibrate parameters for countries where data constraints prevent more traditional time-series estimation, while the distribution of parameter estimates can be used to constrain outliers.

# 3. Econometric methodology

Most of the equations in MFMod<sup>3</sup> are written using a dynamic error-correction formulation (see Wickens and Breusch, 1988; Engel and Granger, 1987 and Johansen, 1991). This formulation lends itself naturally to test the theoretical restrictions of MFMod where the long-run conditions in the ECM are typically driven by theory. The standard ECM estimation methods used in MFMod allows a separate modeling of the short-run and long-run relationships between variables (for example allowing for short-run rigidities and larger long-run elasticities).

The methodology essentially separates two aspects of each equation; the equilibrium path of the dependent variable from the speed of adjustment of the model when it is away from equilibrium. In equation (1) below, the long run conditions are summarized by y� �ωx� �ε� where y� is a dependent variable or a vector of country dependent variables and x� resembles the explanatory variables. The long run expected value of the error term ε� is zero, the expression between the square brackets in �y��� �ωx��� ] reflects the deviation of the model from equilibrium. When the model is in equilibrium, i.e. y� �ωx� , the square bracketed component falls out. In the short run, the path of the dependent variable will reflect short-term influences ∑������ γ� ∆y��� �∑������ δ�∆x��� . The adjustment to equilibrium is determined by the speed of adjustment parameter θ and the distance from equilibrium in the previous period �y��� �ωx���� .

At a more technical level, (1) assumes that we have two nonstationary I(1) time series variables; the dependent variable is Y� and the explanatory variable is X� for every period t in history. Note that ∆��1 �L� is the difference operator and small case letters are the variables expressed in natural logs. The formulation in equation (1) exploits the equilibrium mechanism between Y� and X� , and summarizes the dynamic interplay. There exists a common stochastic trend if �1 �θ�0 and statistically significant. We interpret ω as the long-run elasticity of X� on Y� , while γ� and δ� are the short-run elasticities. ε�~iid and is the structural error term of equation (1).

> 3 World Bank (2019) provides an in‐depth discussion of the model’s structure and functional forms employed.

4
This is achieved through a binary variable that attaints a value of 1 if the response to the question is 100 percent, and 0 otherwise. Using both variables allows the analysis to make a distinction between firms that are entirely managed by family, and those that are partially managed by family. The average share of family managers is about 40.7 percent. Around 32 percent of medium and large businesses in the sample are entirely managed by family (Table 1). Figure 2 provides the country averages for the percentage of firms that are entirely family managed. The share of family management for each country is presented in figure 3.

Data for the control variables are also obtained from the Enterprise Surveys. These encompass standard firm-level characteristics and the operating environment including firm size, age, outward orientation, quality certification, access to finance, manager experience in the sector, and perceptions of labor regulations as a constraint. Whether the firm is owned by the same family, and the presence of political connections are also included as control variables and are not collected in the standard ES but were part of the extended questions in this round of the surveys. The specific information on family ownership is obtained from the survey question: “ _What percentage of the firm is owned by the same family? (If more than one family, refer to the one with largest ownership_ ).” The information on political connections is obtained from the following survey question: “ _Has the owner, CEO, top manager, or any of the board members of this firm ever been elected or appointed to a political position in this country?_ ” The rationale for the control variables is detailed in the empirical strategy section. Summary statistics are provided in table 1.

Table A1 provides some description of the nature of family-managed firms. Entirely family-managed firms have lower management scores than other firms. About 81 percent of fully family-managed firms are of medium size, compared to 72 percent for other firms. Around 12 percent of fully family-managed firms are large, compared to 24 percent of other firms. Entirely family-managed firms are also less likely to be foreign-owned (5.53 vs. 13.89 percent), and more likely to have female managers (17.57 vs 13.50 percent). Furthermore, fully family-managed firms have a larger share of family owners (96 vs 32 percent) and have more managerial experience (24 vs 20 years). They are however less likely to be politically connected (5 vs 9 percent). These differences are statistically significant at least at the 5 percent level. There are no statistically significant differences between entirely family-managed firms and the rest with regards to exporting status, access to finance (in terms of loans or bank accounts), firm age, quality certification, perceptions of labor regulations as a major or severe constraint, innovation, bribery, whether the firm started formally or whether they have one or no competitor.

6
# **Table A14: Reasons for not having been able to access desired amount of fertilizer.**

||BFA|ETH|TZA|UGA|
|---|---|---|---|---|
|Too|||||
|expensive|98.2|58.5|94.3|100.0|
||(0.96)|(4.68)|(2.12)||
|Not|||||
|available|0.9|78.9|5.4|0.0|
||(0.74)|(4.21)|(2.09)||
|_N_|189|234|162|33|

Note: Reasons for not having been able to access desired amount of fertilizer. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

35
CLICROP was run at a 1°×1° resolution (i.e., 111 square kilometer grids in a country measuring 945,000 square kilometers). Separate models were developed for the 9 major crops of Tanzania (i.e., cassava, groundnuts, maize, millet, potatoes, sorghum, soybeans, sweet potatoes and wheat). Predicted yields for each sub-national region are calculated as the sum of overlaid gridded results weighted by geographic area. CLICROP was calibrated to information on soil parameters from the FAO Soils Database (e.g., field capacity, wilting point and saturated hydraulic conductivity) (FAO-UNESCO, 2005). Regional information on crop growing seasons and planting dates were provided by Sacks et al. (forthcoming). Finally, crop locations were based on You et al. (2006) for the year 2000, and crop parameters were drawn from Allen et al. (1998) and Doorenbos and Kassam (1979). Overall, the process is detailed with more than 13 million potential simulation sets.<sup>3</sup>

# _Crop modeling results_

Although CLICROP analysis was conducted for nine crops, we focus on the results for maize for purposes of exposition. Maize is the principal food crop in Tanzania, representing 35 and 45 percent of calories consumed by poor urban and rural households, respectively (Pauw and Thurlow, 2010). Tanzanian farmers allocate about one-third of their crop land to growing maize, mainly without the use of irrigation. Deviations in dry-land maize yields therefore provide a first-cut indicator of food availability in Tanzania (Thornton et al. 2009). In addition, the implications of climate outcomes for maize are likely to be similar for sorghum and millet, making the coverage of caloric sources and land use, particularly for more vulnerable populations, even broader.

Table 2 summarizes the deviations in mean maize yields from a “no climate change” baseline scenario for the 10-year period 2041-2050. The administrative regions are grouped into similar agro-climatic zones based on Fan et al. (2010). There is a high degree of variation both across the four climate scenarios considered and across sub-national regions. For example, maize yields in the Northern Zone are projected to increase substantially in the WET scenario, but decrease by similar amounts in the HOT and DRY scenarios. Varied impacts also occur across regions but within the same scenario. For example, average maize yields in the WET scenario are projected to increase by 15 percent in Manyara in the Northern Zone, but decline by 12 percent in Tabora in the Central Zone. Nevertheless, a few regularities emerge. Maize yields are generally more favorable under the COOL and WET scenarios than under the HOT and DRY scenarios. In addition, yield declines are much more prevalent across regions and scenarios than are yield increases. In particular, under the HOT and DRY scenarios, yields rise in only a few regions and these increases are in all instances small. Finally, the coastal islands remain virtually unaffected in all climate scenarios.

> 3 The product of approximately 8500 grids times nine crops times 43 crop years times four climate scenarios.

6
**Table 5: Association between Political Connectedness and industry characteristics**

||**Aggrega**<br>**Suhart**|**te Market**<br>**o Family F**|**Share**<br>**irms**|**Aggregate Market Sh**<br>**Politically Connected Firm**|**are**<br>**s (Broad)**|
|---|---|---|---|---|---|
||1993–96|2000–09|Diff|1993–96<br>2000–09|Diff|
|Log output|4.36**|3.66**|-0.70*|2.09***<br>2.02***|-0.07|
|Log labor|1.29|1.13|-0.16|0.10<br>-0.03|-0.13|
|Import|-0.10|0.08|0.18|0.21*<br>0.26**|0.05|
|Export|-0.37***|-0.23***|0.13|-0.24***<br>-0.16***|0.08*|
|Foreign (MS)|-0.19|-0.10|0.09|-0.18***<br>-0.11|0.07|
|State (MS)|0.16|0.25|0.09|0.33**<br>0.24**|-0.10|
|Entry regulations|0.01|0.11|0.10|0.15<br>0.17|0.02|
|Entry|-0.10*|-0.07**|0.03|-0.05**<br>-0.04***|0.02|
|Exit|-0.06|-0.05*|0.01|-0.05***<br>-0.03***|0.02|
|PCM|0.09|-0.03|-0.12|0.05<br>0.10|0.04|
|PE|1.15*|-0.56|-1.70**|0.26<br>0.09|-0.17|
|HHI|-0.17|-0.10|0.07|0.11<br>0.11|-0.01|
|MS4|0.05|0.02|-0.03|0.21***<br>0.20**|-0.02|
|Log number of firms|0.20|-0.01|-0.21|-0.78<br>-0.90**|-0.12|
|Log prices|0.27|-0.07*|-0.34|0.13<br>0.01|-0.12|
|Z|-2.85|-0.25|2.60*|-4.16***<br>-3.77***|0.39|
|External finance dependence (EFD)|-0.|10||0.01||
|Natural entry|-1.55|***||-0.82**||
|Tangibility|0.20|**||0.09*||
|Observations<br>|80<br>|0<br>||2000<br>||
|Industries<br><br>|20<br><br>|0<br><br>||200<br> <br>|<br>|
|_Note_: The associations for 1993-96 and<br>whereYjiis an industry-level outcome<br>1996-97 output share of firms with con<br>column reports the differences betw<br>αPC (MS)j+ βPC (MS)j∗Pβt Sh<br>1999, where all variables defined as ab<br>Suharto’s resignation and 0 otherwise.<br>in which case standard errors are cluste<br>dependence, tangibility, and natural ent<br>5 percent level, and *** - at the 10 perc|2000-2009 a<br>variable repo<br>nections to S<br>een these co<br>YtPi+ θi+<br>ove andPβt<br>Standard error<br>red at the thr<br>ry are constan<br>ent level.|re the coeffi<br>rted in the<br>uharto in an<br>rrelations, t<br>εijestimate<br>ShYtPii<br>s are cluster<br>ee-digit leve<br>t over time.|cientsαfro<br>first colum<br>industry, a<br>hese are t<br>d over the<br>s a dummy<br>ed at the fi<br>l, the level<br>* indicates|m the regressionYji= αPC (MS)<br>n,PC (MS)jis a measure of the a<br>ndθiis a set year fixed effects. Th<br>he coefficients β from the regr<br>sample period 1993-2009 but excl<br>variable taking the value 1 in the<br>ve-digit industry levels, except for<br>at which prices are observed. Exte<br>significance at the 10 percent leve|j+ θi+ εij,<br>verage over<br>e difference<br>essionYji=<br>uding 1997-<br>period after<br>log of price,<br>rnal finance<br>l, ** - at the|

35
# **Table S4: Comparison to Traditional Credit Scoring in Developed Settings**

|**Other Settings**||Performanc|e (AUC)|D|efault<br>Feature|s||
|---|---|---|---|---|---|---|---|
|**Traditional Credit Scoring**||||R|ate (%)|||
||Within|Time|Out of|Time||||
||All Models|Best Model|All Models|Best Model||||
|_UK_|0.500-0.758|0.668-0.758|-|-|10-25 16-19 u|nspecified pre|dictors|
|(Baesens et al., 2003)||||||||
|_Belgium / Netherlands / Luxembourg_|0.696-0.791|0.776-0.791|-|-|30-33 33 unsp|ecified predic|tors|
|(Baesens et al., 2003)||||||||
|_Italian small and medium enterprises_|0.615-0.723|0.723|0.573-0.762|0.623-0.762|1-5 Firm|leverage,|liquidity,|
|(Calabrese & Osmetti, 2013)|||||profitab|ility||
|_Bosnia microfinance_|0.679-0.707|0.707|-|-|22 Demog|raphics, earni|ngs, capital,|
|(Van Gool, Verbeke, Sercu, & Baesens, 2012)|||||debt, lo|an||

AUC represents the area under the receiver operating characteristic curve. Each study presents AUC estimates from multiple specifications; we present the range as well as the best out of sample AUC for each sample. These best estimates will tend to overstate performance on independent samples because they are selected based on performance on the test dataset. (Baesens et al., 2003) also reports results from publicly available Australian and German data sets, but the outcomes are not specified so they have been omitted. Source: authors’ analysis from telecom data.

11
Figure lb Partial scotterplot of growth GDPPW and Educational Capitol ([IK) NSD data, 1960-87 <!-- Start of picture text --> co~~~~~~~~~~~~~~~~~H~<br>0<br>3 to<br>0)<br>Q) -SEN<br>cL<br>. 0 O MAR TZA<br>cl ~~~~~~~~RCMR<br>o0 O ZMB IRN IRO ETI1<br>NGA GI-ASLESGP TUNSR<br>0-<br>O ZAR KOR VEN KEN<br>L- - MOZ IND MYS TURMMR DZA<br>_o M BOLRWA<br>0. D SL PA WE y<br>I T ; MGHTI HTILKA N2 1 kMEX juTrIqL GTM ISL PAN 8% MUSs<br>o O UGA O'hL GRC CYP<br>c I - DNKtistt0NA ITA<br>I KI C SA<br>3 ~~~~~~~~~~~~~~~GOR FR"f4L<br>0 MWI CHE AUT<br>DEU<br>0<br>-0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02. 0.03<br><!-- End of picture text --> Growth of GDPPW I Growth capital per worker
Using historical and near real-time data from Google Trends, this paper provides fresh evidence on the immediate impacts of COVID-19 on selected sectors of economies. We quantify the impacts of the pandemic on consumer demand for services involving face-to-face interactions – such as travel and tourism, hotel and restaurant, retail trade – and services that require less physical interaction, such as ICT and delivery services. Google search data measure the search intensity or popularity of a search term submitted to the Google search engine. These search queries are submitted by billions of users around the world each day and capture consumer interests, perceptions, and demands for different services and products. With the increasing penetration of the internet across the world, the application of "big data" – such as search queries, social media, and web-scraping – to understand human behavior is becoming prominent in finance, economics, and many other disciplines.

Google search data accurately predict the present and future economic outlooks, including consumer demand (Vosen and Schmidt, 2011; Choi and Varian, 2012; Gilchrist and Sands, 2016). Google search data have been used to forecast macroeconomic indicators such as GDP (Narita and Yin, 2018); evaluate firm and stock market performances (Da et al., 2010; Bordino et al., 2012); predict demand for automobiles and consumer confidence (Choi and Varian, 2009); forecast house prices (Wu and Brynjolfsson, 2015); understand consumer behavior and quantify demand for information (Goel et al., 2010; Drake et al., 2012); predict unemployment rates (Pavlicek and Kristoufek, 2015); measure job search (Baker and Fradkin, 2017) and racial bias (StephensDavidowitz, 2014; Chetty et al., 2020). Google search data have also been used for predicting the outbreak and spread of epidemics (Ginsberg et al., 2009) and election outcomes (Metaxas and Mustafaraj, 2012). More recently, these data are used to assess the impact of COVID-19 on mental health (Brodeur et al., 2020), economic anxiety (Fetzer et al., 2020) and religiosity (Bentzen, 2020). Google search data also has some advantages because Google the dominant internet search provider with more than 87 percent of the global online search engine market share in 2019.

COVID-19 is expected to have a broader negative impact on economies while also inducing reallocation of activities across sectors, where some sectors gain, and others lose. The magnitude of the aggregate impact and the potential reallocation of economic activities is likely to depend on the structure and composition of economies. Sectors that are functionally dependent on the internet and offer remote work options are likely to be less affected, relative to those involving face-to-face interactions (Dingel and Neiman, 2020). The economic impact is also expected to 3
<!-- Start of picture text --> 30<br>25<br>Qn DSSI<br>5 |<br>6 20<br>&a) 1'<br>5<br>B ' ;<br>3<br>g<br>6<br>©<br>51<br>5 10<br>E<br>=) '<br>Z<br>5<br>'<br>0<br>1970 =1975 1980 = 1985 1990 1995 2000 2005 2010 2015 2020<br>@ With face value reduction Rescheduling only<br><!-- End of picture text -->

<!-- Start of picture text --> 20<br>— 15<br>5<br>on<br>a=|<br>3<br>:<br>3 10<br>=<br>°<br>3<br>&<br>5<br>a5<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>@ With face value reduction Rescheduling only<br><!-- End of picture text -->