rafmacalaba/datause-encoder-data huggingface dataset:
The coefficients of the choice model γ and α are not dependent on the AEZ. However, the marginal impact of climate on the probability of selecting a crop depends on the climate conditions in each AEZ and so will vary by AEZ.

# **3. Description of Data**

A typology of AEZs was developed by the FAO as a mechanism to classify the growing potential of land using the length of the growing season (FAO 1978). The growing season is defined as the period where precipitation and stored soil moisture is greater than half of the evapotranspiration. The longer the growing season, the more crops can be planted (or in multiple seasons) and the higher are the yields (Fischer and van Velthuizen 1996; Vortman et al. 1999).

Figure 1 maps AEZs across Africa. AEZs are classified by climate, soils, and altitude. They are divided into five zones depending upon the length of the growing period: semi-arid, dry savannah, moist savannah, sub-humid, and humid forest. Each of these five zones is again divided into three zones depending upon elevation: lowland, mid-elevation, and high elevation. The remaining AEZ is desert. The Sahara desert occupies a vast amount of area in the north. There is also a desert in the south-western edge of the continent. South of the Sahara desert is semi-arid zones followed by dryland savannah, moist savannah, and humid forest. In central Africa around Cameroon, it is mostly humid forest in high elevation with high rainfall. This high-elevation humid forest turns into mid-elevation and then into dry savannah as it stretches east toward Kenya. South of the humid forest is moist savannah followed by dry savannah. The AEZs of South Africa are mostly moist savannah in the east, dry savannah in the center, and desert in the west.

The economic data for this study were collected by national teams as part of the GEF/World Bank project on climate change in Africa (Dinar et al 2008). The survey asked detailed questions on crops and livestock operations during the agricultural period of July 2002 to June 2003. The data were collected for each plot within a household and household level data were constructed from plot level data. In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. In each chosen district, a survey was conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling cost. A total 6
Table 16 uses FI index and FI effective index as dependent variables in Tobit regressions. Countries with lower income per capita levels, after controlling for deposit and loan penetration, have more topics under the mandate of the regulators. This result also holds for countries with a positive economic outlook. The existence of explicit deposit insurance is negatively related to both indices, and loan penetration is positively related to the FI effective index. Hence, poorer countries seem to have a more loaded financial inclusion agenda.

The extent of reforms, measured by a similar index constructed using the ten areas of financial inclusion reforms surveyed, is also negatively related to GDP per capita, and not related to the degree of deposit or loan penetration. However, once the items in the financial inclusion agenda are controlled for using the two indices described above, the effect of GDP per capita is no longer observed. Countries that have a more loaded financial inclusion agenda seem to have a higher number of reforms in 2009 (Table 17), although it is not possible to comment on the effectiveness of the reforms.

When examining specific areas of financial inclusion topics, we find that countries with lower GDP per capita and lower deposit penetration are more likely to have promotion of basic accounts as a mandate of the regulator with a designated team (Table 18), whereas regulators in countries with lower GDP per capita and a positive economic outlook tend to have promoting access to finance in rural areas as a mandate (Table 19). Financial consumer protection and financial literacy are the two topics that are likely to receive more attention in countries where loan penetration is higher (Table 20), but countries with explicit deposit insurance tend to be less likely to have financial literacy as a mandate for the regulator with a designated team. Lastly, countries with lower GDP per capita levels are more likely to have their regulators work effectively on promoting access to finance by SMEs (Table 21).

# **5 Conclusion**

Using the _Financial Access_ database by CGAP and the World Bank Group, this paper (i) counts the number of unbanked adults around the world, (ii) analyzes the state of access to deposit and loan services as well as the extent of retail networks, and (iii) discusses the state of financial inclusion mandates around the world.

The findings indicate that there is yet much to be done in the financial inclusion arena. Fifty-six percent of adults in the world do not have access to formal financial services. The situation is even worse in the developing world with 64 percent of adults unbanked. Nevertheless, highincome countries also have to worry because approximately one in every five adults is unbanked. On the contrary to conventional wisdom, poor people indeed need and use financial services, albeit in small amounts and usually from informal sources as it is costly for formal providers to provide services for such small amounts. Anecdotal evidence suggests that informal financial 16
# **3.2.3.2 Estimating the Effect of E-filing on TCC**

Table Uk.3 provides estimates from Equation 2 for Ukrainian firms. The first two columns report the results for the effect of the controls on total tax compliance cost in UAH. In the next four columns, annual “tax accounting cost (TAC)” is employed as it is the most relevant component of TCC to e-filing. In the last two columns we employ TCC for only filling out and submitting tax forms and TCC for only VAT with a sample of only VAT payers. Although we tried all dependent variable definitions with different model specifications (as in TAC), we report the statistically significant and economically relevant results only.

49
**Table 4a. Impact of EPAG on Participation on Earnings, by Training Track**

|||**Job Skills Traine**<br>**(n= 607)**<sup>**+**</sup>|**es**||**BDS Trainees**<br>**(n=990)**<sup>**+**</sup>||
|---|---|---|---|---|---|---|
||Baseline-|OLS||Baseline-|OLS||
||Mean|(w controls)|Observations|Mean|(w controls)|Observations|
|Total earnings last|729.5|200.1|3178|680.1|768.0***|3178|
|week (LD)<br>unconditional|[2322.7]|(185.6)||[1474.8]|(169.6)||
|+ Table reports the su<br>type is “job skills” or “<br>adolescent, education|m of coefficient<br>BDS”. Controls<br>al status of adol|s and joint p-valu<br>used are househo<br>escent, marital st|es of the interaction t<br>ld size, sex of househ<br>atus of respondent,|erms “post x trea<br>old head, asset b<br>parental status of|t” and “post x tre<br>ased quintiles, co<br>respondent (indic|at x type” where<br>mmunity, age of<br>ator variables|
|for adolescent having<br>and month of intervie|a child and bein<br>w at midline; all|g pregnant), orph<br>controls are from|an status of respond<br>baseline data excep|ent (one parent o<br>t for month of mi|r both parents be<br>dline interview.|ing deceased),|

**Table 4b. Heterogeneous Impacts of EPAG on Participation on Earnings—by Wealth, Education and Age**

|||**Panel 1: By **|**WealthQuintile**<br>Quintiles of|<br>OLS<br>asset-based we|lfare index||
|---|---|---|---|---|---|---|
||All|Q1|Q2|Q3|Q4|Q5|
|Total earnings|563.216***|444.259**|521.720**|436.218**|805.215***|477.683*|
|(LD) unconditional|(124.839)|(219.093)|(228.871)|(210.061)|(216.182)|(277.570)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3178|601|641|653|631|652|
|||**Panel 2:**|**By Education**<br>Educ|OLS<br>ation level of th|e Girl||
|||None/<br>Informal||Junior high||High school<br>graduate|
||All|only|Primary|school|High school|and above|
|Total earnings|563.216***|809.749|724.562***|646.673***|341.710*|559.497***|
||(124.839)|(772.746)|(217.737)|(219.883)|(196.143)|(200.076)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3178|87|604|753|833|901|
|||**Panel 3:**|**By Age Group**<br>Ag|OLS<br>egroupof the|girl||
||All||16-19|20-24|25-27||
|Total earnings|563.216***||730.499***|578.846***|461.749***||
||(124.839)||(162.166)|(178.138)|(158.549)||
|Controls|Yes||Yes|Yes|Yes||
|Observations|3178||478|1742|958||

Controls used are household size, sex of household head, asset based quintiles (excluded for “by wealth” regressions), community, age of adolescent, educational status of adolescent (excluded for “by education” regressions), marital status of respondent, parental status of respondent (indicator variables for adolescent having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except month of midline interview.

38
**Figure 12: Index of Diversification of Destinations for Peruvian Exports: 1993-2009**

<!-- Start of picture text --> 0.21<br>0.19<br>0.17<br>0.15<br>0.13<br>0.11<br>0.09<br>0.07<br>0.05<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>Traditionals Non Traditionals Total<br>index HH<br><!-- End of picture text -->

# **5. Conclusions**

Peru‟s exports exhibited rapid growth during the past decade, largely as a rapid increase in external demand for Peruvian exports – associated with the substantial pace of growth of the global economy -- and of growing supply as a result of the implementation economic reforms since the early 1990s. This has resulted in a tangible increase in the degree of openness of the Peruvian economy (as measured for example by the export/GDP ratio). However, the country has not yet caught up with what would be expected for its income level, as indicated by an empirical analysis that adjusts for a number of other factors associated with openness.

Based on a breakdown of the price and volume effects in traditional and non-traditional exports, this study finds that a significant part of the export value increase of Peru is related to the growth of volumes. The effect of export supply growth has been particularly important in explaining the expansion of the value of non-traditional exports, for which prices have played only a marginal role. Even for traditional exports, and despite booming international price levels after 2000, the increase of volumes has accounted for around 30% of the growth -- around 45% for the entire period since 1990. This suggests that the Peruvian export boom has also stemmed from investments in supply expansion, many of which made prior to the commodity price boom.

The analysis also finds that in the aggregate Peruvian exports have not diversified since the early 1990s, a results that seems to be associated with the booming supply and price of traditional mining exports. However, when isolating non-traditional exports, there is a clear trend towards greater diversification within this group as well as a striking boom in the appearance of new export products in recent years. Although the share of non-traditional exports fell to 23% of total exports in 2009, chiefly as a result of the unprecedented boom in the price of traditional commodities, in nominal terms the value of this export grouping was quintupled and the number of subheading-country relations increased from 9,826 in 1993 to 28,307 in 2009.

19
Table A8: Respondent-level correlates of “types”

|||(1)|(2|)||(3)||T-test||
|---|---|---|---|---|---|---|---|---|---|
||T1: Low-level u|sers reserved space|T|2|T3: High-level u|sers reserved space||P-value||
|Variable|N/[Clusters]|Mean/SE|N/[Clusters]|Mean/SE|N/[Clusters]|Mean/SE|(1)-(2)|(1)-(3)|(2)-(3)|
|Age 18-25|67|0.284|73|0.301|98|0.327|0.808|0.528|0.755|
||[35]|(0.052)|[37]|(0.060)|[42]|(0.044)||||
|Age 26-30|67|0.119|73|0.274|98|0.235|0.064|0.025|0.599|
||[35]|(0.041)|[37]|(0.071)|[42]|(0.043)||||
|Age 31+|67|0.597|73|0.425|98|0.439|0.041|0.035|0.856|
||[35]|(0.058)|[37]|(0.066)|[42]|(0.046)||||
|Years of schooling|67|12.522|73|13.329|98|12.878|0.165|0.439|0.367|
||[35]|(0.391)|[37]|(0.446)|[42]|(0.315)||||
|Single|68|0.515|78|0.551|104|0.587|0.706|0.429|0.679|
||[36]|(0.079)|[37]|(0.065)|[42]|(0.048)||||
|Employed|68|0.676|78|0.692|104|0.760|0.830|0.168|0.307|
||[36]|(0.056)|[37]|(0.053)|[42]|(0.043)||||
|High self-reported socio-economic status|68|0.088|78|0.090|104|0.125|0.974|0.460|0.424|
||[36]|(0.039)|[37]|(0.029)|[42]|(0.031)||||
|Average monthly household income in census tract|65|3080.136|77|2692.339|103|2734.577|0.245|0.405|0.872|
||[35]|(377.935)|[36]|(186.063)|[42]|(225.469)||||
|Home station crimes / year / 100,000 people|101|1545.080|112|1323.169|112|1292.011|0.040|0.051|0.620|
||[38]|(127.419)|[38]|(82.366)|[41]|(70.072)||||
|Home station violent crimes / year / 100,000 people|101|48.433|112|46.456|112|44.170|0.303|0.018|0.059|
||[38]|(1.814)|[38]|(1.482)|[41]|(1.349)||||
|Home station theft / year / 100,000 people|101|307.821|112|254.343|112|245.993|0.040|0.051|0.596|
||[38]|(30.396)|[38]|(20.283)|[41]|(17.978)||||

_Note:_ Unit of observation is an individual. Groups are terciles of takeup of the reserved space in the revealed preference experiment, where 1 is the lowest and 3 is the highest takeup. Demographic characteristics (age, education, marital status and employment) are observed for the subsample who responded to the demographic survey; home station characteristics are observed for the full sample of participants. Standard errors are clustered at the level of the participant’s home station.
**Table 12: Trade and the Productivity Intensity of Growth**

||**Agricultur**<br>|**al Productivity-i**|**ntensive Gro**|**wth**|||
|---|---|---|---|---|---|---|
|Openness to Trade|-0.00214<br>(-0.77)|||||0.000114<br>(0.01)|
|Terms of Trade||-0.000453**<br>(-2.17)||||-0.0000282<br>(-0.07)|
|Real Effective Exchange<br>Rate|||0.000245<br>(1.11)|||0.000272<br>(0.66)|
|Foreign Direct Investment||||-0.000296<br>(-0.19)||0.000592<br>(0.21)|
|Landlocked|||||-0.0127<br>(-0.69)|-0.0621***<br>(-5.16)|
|Constant|-0.00153|0.0423**|-0.0270|-0.00142|-0.00151|-0.0259|
||(-0.65)|(2.10)|(-1.18)|(-0.26)|(-0.85)|(-0.33)|
|Observations|43|43|19|43|44|16|
|AdjustedR-squared|-0.020|0.035|-0.032|-0.022|0.023|0.357|
|*p<.10,**p<.05,***p<.0|1.T-statistics|inparentheses|||||

# **5 Conclusions**

In this paper we analyzed the relationship between poverty and the employment and productivity profile of growth, both at the aggregate level and by sectors. We decomposed per capita value added growth among labor market components (employment, productivity and demographic changes) and empirically analyzed how each component affects poverty. We also explored the role of labor market characteristics, the structure of employment and the investment climate and trade in the observed growth pattern.

We complement the existing literature in several ways. First, we go beyond the sectoral pattern of growth or its overall employment intensity and we analyze the role of sectoral productivity and employment intensity in poverty alleviation. Second, we use a decomposition methodology to construct a measure of employment-intensive growth and an analogous measure of productivity-intensive growth. We believe this measure addresses some conceptual and empirical weaknesses of the more commonly used measure: the employment elasticity of growth. Third, our study includes a larger fraction of countries than previous studies. Fourth, we go beyond the correlation between poverty and the pattern of growth, and explore the correlation between economic policies and outcomes, and the observed growth pattern.

32
material composition of the affected infrastructure). Data scarcity has prevented systematic analysis for poor countries and, to our knowledge, this is the first such exercise for Bangladesh.

Our assessment draws on two new information sources: panel measures of salinity from 41 stations in coastal Bangladesh, provided by the Bangladesh Soil Research Development Institute, and information on road maintenance expenditure, income, road network length and road surfaces compiled by our project study team from the annual budget books of 20 coastal municipalities. We use spatial interpolation of station measurements to estimate groundwater salinity at the geographic centroids of the sample municipalities, and combine these with the municipality-level information to construct our estimation database.

Using this information, we estimate a log-log expenditure model that relates road maintenance expenditure to groundwater salinity interacted with the paved road share of the road network; the paved road share separately; the length of the road network; and two sources of municipal income (local revenue and development income from outside sources). To test robustness, we fit alternative models using robust regression, generalized least squares (allowing for variable error variances across municipalities) and fixed-effects panel regression. We find large and significant effects for salinity in all model specifications, including fixed effects, and significant effects for municipality income in the robust and GLS regressions.

To assess the overall impact of salinity, we use our regression model to predict the effect of within-sample salinity variation on road maintenance expenditure shares, holding municipal income and road length constant at sample mean values. For the median paved road share of municipality road networks, we find that increasing salinity from its sample minimum to its sample maximum increases the predicted road maintenance expenditure share by 252%.

18
**Table 3. Pearson correlations of concentration indices**

||_CI_|_IMR_<br>_C_|_lforU5MR C_|_ILOWHFA_|_CILOWWFAC _|_IDIA_<br>_C_|_IARI_|
|---|---|---|---|---|---|---|---|
|CIIMR|Pearson<br>Correlation|1.000|.954**|.564**|.552**|.359*|.244|
||Sig.(2-tailed)|.|.000|.000|.000|.020|.125|
||N|42|42|38|38|42|41|
|CIU5MR|Pearson<br>Correlation|.954**|1.000|.657**|.644**|.439**|.329*|
||Sig.(2-tailed)|.000|.|.000|.000|.004|.036|
||N|42|42|38|38|42|41|
|CILOWHF|A Pearson<br>|.564**|.657**|1.000|.898**|.512**|.210|
||Correlation|||||||
||Sig.(2-tailed)|.000|.000|.|.000|.001|.206|
||N|38|38|38|38|38|38|
|CILOWWF|APearson<br>|.552**|.644**|.898**|1.000|.449**|.235|
||Correlation|||||||
||Sig.(2-tailed)|.000|.000|.000|.|.005|.156|
||N|38|38|38|38|38|38|
|CIDIA|Pearson<br>Correlation|.359*|.439**|.512**|.449**|1.000|.787**|
||Sig.(2-tailed)|.020|.004|.001|.005|.|.000|
||N|42|42|38|38|42|41|
|CIARI|Pearson<br>Correlation|.244|.329*|.210|.235|.787**|1.000|
||Sig.(2-tailed)|.125|.036|.206|.156|.000||
||N|41|41|38|38|41|41|
|Correlatio<br>*Correlation|nissignificantatthe<br> issignificantatthe|0.01level (2-t<br>0.05level (2-ta|ailed).<br>iled).|||||

**Table 4. Spearman correlations of concentration indices**

|||_CIIMR_|_C/forU5MR_|_CILOWHFA_|_CILOWWFA_|_CIDIA_|_CIARI_|
|---|---|---|---|---|---|---|---|
|CIIMR|Pearson<br>Correlation|1.000|.954**|.564**|.552**|.359*<br>|.244<br>|
||Sig.(2-tailed)||.000|.000<br>|.000<br>|.020<br>|.125<br>|
||N|42|42|38|38|42|41<br>|
|CIU5MR|Pearson<br>Correlation|.954**|1.000|.657**|.644**|.439**|.329*|
||Sig. (2-tailed)|.000||.000|.000|.004<br>|.036<br>|
||N|42|42|38|38|42|41|
|CILOWHFA|Pearson<br>Correlation|.564**|.657**|1.000|.898**|.512**|.210|
||Sig.(2-tailed)|.000|.000||.000|.001|.206|
||N|38|38|38<br>|38<br>|38<br>|38<br>|
|CILOWWFA|Pearson<br>Correlation|.552**|.644**|.898**|1.000|.449**|.235|
||Sig.(2-tailed)|.000|.000|.000||.005<br>|.156<br>|
||N|38|38|38<br>|38<br>|38<br>|38<br>|
|CIDIA|Pearson<br>Correlation|.359*|.439**|.512**|.449*|1.000|.787**|
||Sig.(2-tailed)<br>|.020<br>|.004<br>|.001<br>|.005<br>||.000<br>|
||N|42|42|38|38|42|41|
|CIARI|Pearson<br>Correlation|.244|.329*|.210|.235|.787**|1.000|
||Sig.(2-tailed)|.125|.036|.206|.156|.000||
||N|41|41|38|38|41|41|

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

_9_
investment receivables (often representing shadow credits of nonbank affiliates) reached 16 percent of the total assets of smaller banks, compared to 1.6 percent for the Big 4 (Figure 23). Adding to financial vulnerabilities, many wealth management products (WMPs) were held by banks off-balance sheet. WMPs equaled 18.5 percent of the assets of smaller (joint stock and city commercial) banks in 2016, as opposed to 8.7 percent for the Big 4.

**Figure 22 Growth of total assets, 2015-16 Figure 23 Selected commercial bank average indicators, 2016**

<!-- Start of picture text --> (annual average growth in total assets, percent)<br>25<br>20<br>15<br>10<br>5<br>0<br>Big 4 Joint stock and city<br>commercial banks<br><!-- End of picture text --> Source: PBC; WIND Info; authors’ calculations.

<!-- Start of picture text --> (as a share of balance sheet assets, percent)<br>20<br>15<br>10<br>5<br>0<br>Big 4 Small and Big 4 Joiny stock<br>medium-sized and city<br>banks commercial<br>banks<br>"Other investment" Off-balance sheet WMPs<br><!-- End of picture text --> Source: CEIC; PBC; Wind Info; authors’ calculations.

The recent rapid expansion in credit to the nonfinancial sector is likely to have hindered productivity growth from two important perspectives. First, the allocation of credit toward infrastructure and housing has reduced the resources available to higher-productivity sectors and firms. For example, LGFV borrowing through loans and bonds crowded out investment by domestic private firms (Huang et al., 2017). Second, the growing burden of nonfinancial sector debt, at 250 percent of GDP in 2019, will itself slow growth in the medium term if it is not addressed through deleveraging. Empirical evidence suggests that the negative impact of a high debt burden on growth can be considerable even in the absence of a financial crisis (Reinhart et al., 2012). Significant debt service costs increasingly crowd out resources available for investment. Furthermore, high debt levels tend to raise risk premia and financial market stress, which drives interest rates higher and discourages investment.

# **3. China’s still high growth potential**

Even after four decades of 10-percent growth per year, China’s growth potential remains high. Per capita income in China is less than a quarter of the high-income country average at market exchange rates and less than a third in PPP terms. Despite advances in sectors such as e- commerce, fintech, high-speed trains, renewable energy, and electric cars, China generally remains distant from the global technological frontier. TFP is less than half that in the United States and lags the TFP levels in a number of middle-income countries (Figure 24). Hence, there is scope for China to catch up to global leaders through the transfer of technology and state-of-the-art management practices. In addition to catch-up growth, China will continue to build capacity for innovation to extend the global technology frontier (World Bank and DRC, 2019).

18