rafmacalaba/datause-encoder-data huggingface dataset:
As was stated in the introduction, in May 2005 King Mohammed VI of Morocco launched the National Human Development Initiative (INDH) and underscored his desire to have this initiative represent a cornerstone of his reign. This initiative builds on previous efforts in Morocco to encourage decentralization and to improve the government’s efforts to respond to local needs. The initiative was allocated an initial resource envelope of the equivalent of US$1 billion for the period 2006 to 2010, and these resources are devoted evenly to four broad components: a rural component that targets 403 of the poorest rural communes, an urban component targeting 264 of the poorest urban neighborhoods, a component that focuses specifically on the reduction of vulnerability, and a cross-cutting component aimed at building capacity, information exchange and communication. The rural component aims to finance activities that support access to basic services, development of economic and social infrastructure and income-generating activities. The design of the project is explicitly not an income transfer scheme, but rather one that aims to galvanize the economic and social environment in the poorest rural communities. The selection of the targeted communes in this rural component was based on a number of criteria including their estimated poverty rates – based on the poverty map that was developed with the 1994 census and 1998 survey data.

It is clear that the analysis in the present study cannot be used directly to assess the design and operation of the rural component of the INDH. There are too many ways in which the stylized antipoverty strategy considered in this study differs from the INDH. However, it may remain of interest to examine how a given budget would be distributed across the provinces of Morocco if an optimal commune-level targeting of income transfers were to be implemented. This budget distribution could be compared against the provincial distribution of the current INDH program. While there is no presumption that the current INDH provincial distribution should be the same as that of the hypothetical anti-poverty scheme examined here, it may be helpful to the authorities if it were observed that there 18
Exporter Dynamics Database fills this gap and establishes two novel stylized facts on how exportsector characteristics and dynamics vary with country characteristics.

The data point to systematic ways in which the micro structure of exports changes as countries develop. Larger countries and richer countries have both more and larger exporters on average, with greater concentration in the top 5 percent of firms. Together with results from previous studies showing that exporters are more productive than non-exporters and larger exporters are more productive than smaller exporters, our results are consistent with allocative efficiency in export markets improving as countries develop, with more resources flowing to the largest and most productive firms.

Of interest is how reallocation happens. In particular, exporter dynamics are also closely linked with stage of development, with richer countries experiencing higher entrant survival rates and lower entry and exit rates. This is consistent with allocative efficiency improving as countries develop: only the more productive firms grow and enter the export market in more developed countries, and these relatively good firms are less likely to exit. The results suggest that the distortion that affects exporter dynamics across countries is more closely related to variation in the idiosyncratic uncertainty about the profitability of exporting than to the costs of entry into exporting.

Overall, we hope that the measures in the Database will allow the examination of several interesting cross-country questions, cross-country cross-sector questions, and within-country questions. The measures can also be used as controls in estimations that require exporter characteristics at the country-sector level. In particular, as the measures in the Database offer the first opportunity to study exporter behavior on a global basis, some of the facts described above open the door to questions such as: Does trade promote growth via firm size or firm count? How 27
## Appendix **IV.C** Honduras

Percentage of Demographic Group in Poverty or Absolute Poverty*

|Demographic<br>Group|Percent in<br>Poverty|Percent in<br>Absolute Poverty|
|---|---|---|
|Young (14 or under)|27.5|15.8|
|Prime Age 1 (20-25)|17.8|10.2|
|Prime Age 2 (26-35)|17.7|9.5|
|Prime Age 3 (36-60)|23.0|13.4|
|Over 60 Total|27.8|17.0|
|Female|27.2|17.4|
|Male|28.3|16.7|
|Over 75 Total|30.2|20.9|
|Female|27.3|19.5|
|Male|33.0|22.2|

* Poverty = _<_ 50% median income per family _member;_ Absolute Poverty = < 35% median income per family member. Source: See table IV.A

## Appendix IV.D Panama

# Percentage of Demographic Group in Poverty or Absolute Poverty*

|Demographic<br>Group|Percent in<br>Poverty|Percent in<br>Absolute Poverty|
|---|---|---|
|Young(14or under)|30.9|22.8|
|Prime Age 1 (20-25)|23.7|16.3|
|Prime Age 2 (26-35)|19.6|13.1|
|Prime Age 3 (36-60)|26.1|19.8|
|Over 60 Total|30.2|23.5|
|Female|28|21.3|
|Male|32.4|25.7|
|Over 75 Total|32.5|26.0|
|Female|27.2|20.6|
|Male|38.1|31.7|

* Poverty = < 50% median income per family member; Absolute Poverty = < 35% median income per family member. Source: See table IV.A

12U
<!-- Start of picture text --> Figure 4<br>Forecasted fossil energy share in t+30 according to the year of publication Forecasted share Forecasted share =<br>0.39 x Publication Year- 694.38<br>Robust Tukey smoother<br>R 2 = 0.25<br>Linear regression line<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Fossil energy share (%x100)<br><!-- End of picture text --> 10
# **Figure 2. Schematic to represent production activities**

<!-- Start of picture text --> Export Domestic supply<br>CET<br>Aggregate commodity produced from the<br>formal and informal sectors<br>CES<br>Gross output from Gross output from<br>formal sector Informal sector<br>CES<br>Value-added & Energy Aggregate of non-<br>Composite energy inputs<br>CD<br>Non-energy input 1<br>Capital-energy Labor<br>composite Non-energy input n<br>CES<br>Capital<br>Energy aggregate<br>CES<br>Fossil fuels Electricity<br>CES<br>Natural gas Petroleum<br>Leontief<br><!-- End of picture text --> 12
To get more robust results, it is useful to look at the aggregation of RoE per categories of countries classified according their income levels where sample sizes are large enough. This is done in Figure 8 which shows that none of the country groups reached their corresponding hurdle rates. However, it can be seen that low-income countries, which have a riskier (i.e.: higher) hurdle rate have had, on average, quite low returns (5.1%). It is surprising though, that returns in upper-middle income countries (1.1%) have been lower than in lowincome countries (5.1%). However, low-middle income countries have, on average, the highest returns (7.6%), which is still below the _global developing countries_ ’s hurdle rate(around 14%)—i.e. the one calculated for the whole sample. As a result, investors in lowmiddle income level countries, where the RoE relative to their respective hurdle rates is relatively low, experienced the least significant losses between 1998 and 2002. This conclusion is strengthened by the analysis of the volatility of returns across income groups. This analysis shows that the LMIC also enjoyed the lowest standard deviation (0.06) among the three groups. In fact, LIC and UMIC saw relatively strong volatility in their returns with standard deviations of 0.15 and 0.17 respectively.

**Figure 8  Average profitability per countries according to Income Levels(1998-2002)**

<!-- Start of picture text --> Cost of Equity vs. RoE<br>per countries according to Income levels<br>21%<br>18%<br>15%<br>12%<br>9%<br>6%<br>3%<br>0%<br>Low Income Low Middle Income Upper Middle Income<br>Cost of Equity RoE<br><!-- End of picture text --> In sum, the analysis of the match between risk and returns and the stability of the returns contributes to explain the major slow down in capital flows relatively well for most regions and all income groups. The only exception may be Africa where the huge wedge between return on equity and cost of equity is reflected in the low absolute levels of investment but not in the somewhat unexpected increase in the flows of investments observed over the last three years.<sup>18</sup> Overall, only 32 of the 120 companies covered by this sample enjoyed an RoE larger than their CoE during the post Asia crisis period, implying that two thirds had bad experiences and would probably be reluctant to disburse or even commit significant new investments. This is particularly true for the rail and water sectors since thye represented only 25% the successful companies. The rest were in energy and ports.

> 18 World Bank (2003), PPI database

15
# **Table 4: Round 1 outcomes by gender of household head, control group**

||**Male headed**<br>**households**|**Female headed**<br>**households**|**Difference**|**N**|
|---|---|---|---|---|
|Food consumption from market purchases (birr)|77.5|54.8|22.6***|672|
|Total value of assets (birr)|1,107|562|545***|672|
|Non-food consumption (birr)|3,557|2,214|1,342***|672|
|Nb of household members who contribute to income|2.37|1.77|.603***|672|
|Nb of household members who work on household farm|4.37|3.32|1.05***|672|
|Total plot area (ha)|2.32|1.86|.460***|672|
|<br>Livestock ownership (=1)|.980|.895|.085**|669|
|Total value of livestock owned (birr)|8,965|4,117|4,848***|669|
|Total agricultural sales (birr)|4,536|2,312|2,224***|672|
|Productivity (harvest value/ha, birr)|5,955|4,069|1,886***|668|
|Tried new agricultural activities in the past year (=1)|.540|.280|.259***|653|
|<br>Intercropping (=1)|.256|.181|.075**|667|
|<br>Irrigation (=1)|.163|.099|.064**|670|
|<br>Crops with improved seeds (number of crops)|.888|.636|.252**|671|
|<br>Crops with purchased seeds (number of crops)|1.58|1.26|.320***|671|
|Fertilizer or pesticides (=1)|.873|.841|.032|667|
|Number of crops grown by the household|4.70|3.72|.984***|671|
|<br>Marketable crop farming (=1)|.249|.156|.093**|671|
|<br>Marketable crop selling (=1)|.149|.077|.072**|664|
|<br>Tree farming (=1)|.185|.133|.052|671|
|Share of plots owned|.411|.418|-.007|670|
|Share of plots fertile|.848|.971|-.123***|672|
|<br>Contact with extension officer for advice or information (=1)|.869|.684|.185***|653|
|<br>Contact with extension officer was regular (=1)|.502|.333|.169***|642|
|<br>Rated DA advice very useful (=1)|.596|.437|.160**|403|
|<br>Received advice from peer (=1)|.684|.578|.106**|654|
|<br>Received advice from contact farmers (=1)|.380|.225|.155***|652|
|Received advice from extension officers (=1)|.890|.688|.202***|655|
|Received advicefromanNGO (=1)|.071|.058|.014|649|

Notes: * indicates significance at 10% level, ** at 5% level, *** at 1% level. Table shows means at Round 1 of data collection for the control group only.

19
13

**Table 3**

**Estimated Rates of Return to an Additional Year of Schooling**

|Years:||||19|88|||||19|92|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.130_|0.099|0.092|0.117|0.146|0.169|_0.107_|0.090|0.086|0.095|0.119|0.126|
|2|20|_0.126_|0.093|0.093|0.117|0.144|0.164|_0.103_|0.084|0.084|0.095|0.116|0.122|
|2|30|_0.122_|0.088|0.095|0.117|0.141|0.158|_0.099_|0.079|0.082|0.094|0.112|0.118|
|2|40|_0.118_|0.082|0.096|0.118|0.138|0.152|_0.095_|0.073|0.081|0.093|0.109|0.114|
|6|10|_0.162_|0.129|0.136|0.159|0.179|0.190|_0.139_|0.113|0.120|0.134|0.153|0.160|
|6|20|_0.158_|0.123|0.137|0.159|0.177|0.185|_0.135_|0.107|0.118|0.134|0.149|0.156|
|6|30|_0.154_|0.118|0.139|0.159|0.174|0.179|_0.130_|0.102|0.117|0.133|0.146|0.152|
|6|40|_0.150_|0.112|0.140|0.159|0.172|0.173|_0.126_|0.096|0.115|0.132|0.142|0.149|
|11|10|_0.202_|0.167|0.191|0.211|0.221|0.217|_0.178_|0.141|0.163|0.183|0.195|0.203|
|11|20|_0.198_|0.161|0.192|0.211|0.218|0.211|_0.174_|0.136|0.161|0.182|0.191|0.199|
|11|30|_0.194_|0.156|0.193|0.212|0.216|0.205|_0.170_|0.130|0.160|0.181|0.188|0.195|
|11|40|_0.190_|0.150|0.195|0.212|0.213|0.200|_0.165_|0.125|0.158|0.181|0.184|0.192|
|16|10|_0.243_|0.205|0.245|0.264|0.262|0.243|_0.217_|0.170|0.206|0.232|0.237|0.246|
|16|20|_0.238_|0.199|0.247|0.264|0.260|0.237|_0.213_|0.164|0.205|0.231|0.234|0.242|
|16|30|_0.234_|0.194|0.248|0.264|0.257|0.232|_0.209_|0.159|0.203|0.230|0.230|0.238|
|16|40|_0.230_|0.188|0.249|0.264|0.254|0.226|_0.205_|0.153|0.201|0.229|0.227|0.234|
regulation of the financial sector and **by** strong incentives to borrow abroad, due to high domestic funding costs.<sup>1</sup> The prospect of diminishing returns, after a long process of growth without productivity gains, may induce international lenders to be cautious in taking long-run positions." This may increase long-term risk premiums and create incentives for firms to bias their maturity structure to the short-term.

Even thought an emerging economy might move to a shorter maturity structure, the financial liberal[ization variable can only capture the effects of financial integration at the aggregate level. This variable cannot identify the effect of the actual participation in developed financial markets on the firms' financing choices. To study this effect, we showed in the previous section how financial structure change when firms issue bonds or trade equity in international markets. Firms with access to international capital markets might behave differently than firms constrained to finding investment through domestic markets.

The financial liberalization variable and the variable capturing access to international capital markets suggest that financial integration does not seem to have a uniform effect across firms. On the one hand, access to international bond markets during the 1990s is associated with an extended maturity structure of firms that participate in these markets. C the other hand, the maturity structure shrinks for the whole economy. These two facts suggest that firms constrained to local financial markets increase short-term

> **"1** In fact, large current account deficits in emerging economies are usually interpreted as evidence of new investments **in** projects with high-expected returns.

> **17** See, for example, World Bank **(1998).**

> **18** Claessens et al. **(1998)** argue that relatively low profitability in some of the Asian economies forced

> firms to look for external financing during the decade previous to the financial crisis, with short-term debt playing an important role.

21
program initiation. Virtually all barangays had received the program by Round 3, with one exception (N = 32) which we also excluded from the analysis.

Based on information on the start of the program provided by the project implementation office that controls procurement and the release of funds, the duration of exposure apparently was dependent on administrative lags in central procurement rules and centralized actions rather than on preferences in the program areas. Data from this office indicate that due to lags in disbursements, in 70 percent of program barangays, the program did not get started until after the first round of data was collected, and in another 29 percent, not until after the second round. In the second round, lack of funds or delays in releasing funds were a problem reported by 28 of the 33 municipality-level ECD project teams with available data, and was the most common implementation problem they named (OPS 2005).

**Table 7. Percentage Distribution of Children across Exposure Categories by Age, Philippines ECD Study**

|Age in years<br>(Round 3)|<4 months|4-12 months|13-16 months|17+ months|Total|
|---|---|---|---|---|---|
|2|4.42 (24)|33.2 (180)|32.6 (177)|29.8 (162)|100 (543)|
|3|6.13 (53)|33.7 (291)|28.4 (245)|31.8 (275)|100 (864)|
|4|6.51 (50)|34.4 (264)|28.4 (218)|30.7 (236)|100 (768)|
|5|5.67 (49)|39.2 (339)|24.1 (209)|31.1 (269)|100 (866)|
|6+|6.92 (76)|36.7 (404)|28.0 (308)|28.3 (311)|100 (1,099)|
|Total N|252|1,478|1,157|1,253|4,140|

Notes: Numbers of children are in parentheses.

Our estimates of impact are based on nearest-neighbor propensity score matching of a set of treatment and control observations using the variables described in Appendix C. Using the “nnmatch.ado” routine in STATA 9 (see Abadie, _et al._ 2004), we calculate the sample average treatment effect (or the difference-in-difference estimator in our case). We condition our estimates on the joint distribution of children’s age (at Round 3) and a discrete measure of their duration of program exposure—distinguishing according to 4-12 months, 13-16 months, and 17+ months duration<sup>17</sup> —and we specify robust standard errors. This strategy allows us to uncover potentially valuable information about how children of different age groups respond to varying program exposure. Table 7 shows that there is a sufficient number of children in program areas in all the age/duration combinations to estimate impacts within each class.

# **4. Results**

________________ 18
developing and transition countries; the data are reported in World Bank (2004) (though I supplemented this source with data from _PovcalNet_ ).<sup>10</sup> The poverty rate is the most recent estimate provided it is no earlier than 1995 (the median survey year is 1999), though the poverty data predate the death rate data. Using the BR method the implied mean CDR for the poor is 22.6 per 1,000 people with a (heteroscedasicity-corrected) standard error of 2.5; the CDR for the non-poor is 8.1 (standard error of 0.5).<sup>11</sup> The implied value of _DRD_ is 2.2.

One region that looks notably different in the relationship between death rates and poverty incidence is Sub-Saharan Africa (SSA). Indeed, if one just focuses on the 18 observations in this data set for SSA then one finds no correlation between death rates and the incidence of poverty. In other words, one cannot reject the null hypothesis that the CDR is the same for the poor as the non-poor. (The estimates implied by the BR method are 19.3 for the poor, with a standard error of 7.1, and 17.1 for the non-poor, with a standard error of 1.56; the difference is not significant; t-statistic=0.6.) South Africa is an outlier, however, with a high CDR relative to its poverty rate ( _DR_ =20; _H_ =7.1%). The correlation between the CDR and the poverty rate is stronger if one drops South Africa, but it is still not statistically significant. Dropping South Africa the estimates CDR for the poor in Africa is 20.3 (standard error of 2.7) while that for the non-poor is 16.2 (standard error of 1.7); the implied _DRD_ is slightly over 1.1.

Since Africa will be prominent in later results in this paper, it is worth exploring further why one finds a low _DRD_ for this region using the BR method. A plausible explanation lies in the fact that SSA has a higher $1 a day poverty rate than average for the developing world — slightly under 50% in SSA versus about 20% on average (Chen and Ravallion, 2004). So a

> 10 _PovcalNet_ is a data tool accompanying Chen and Ravallion (2004) that allows users to access the underlying distributional data from all 500 surveys used for the Bank’s global poverty measures; see <u>http://iresearch.worldbank.org/povcalnet</u> .

> 11 All standard errors reported in this paper are corrected for heteroscedasticity using White’s method.

12
Figure 5 Dominican Republic and Haiti: Terms of Trade Index (2000=100), 1963–2007 **___** Dominican Republic ___ Haiti --- United States <!-- Start of picture text --> 400 1400<br>350<br>1200<br>300<br>1000<br>250<br>800<br>200<br>600<br>150<br>400<br>100<br>200<br>50<br>0 0<br>1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007<br><!-- End of picture text --> Source: IMF and World Bank.

All three indicators suggest therefore that the Dominican Republic and Haiti are in general affected by asymmetric shocks. By itself, this would suggest that the formation of a CCA between these countries would not be advisable. However, before reaching this conclusion, there are two issues worth highlighting. First, in general two countries might exhibit low business cycle synchronicity not because using different currencies creates underlying structural asymmetries, but simply because the two monetary authorities are following different policies. Conversely, two countries might exhibit a relatively high level of synchronicity only because they are subject to common external shocks. What is needed therefore is to look at _conditional_ business cycle correlations, instead of simple correlation coefficients; this is what is done in the next section.

Second, the existence of asymmetric shocks (which are likely to remain, even in a well integrated union) is not sufficient to establish the case for retaining a separate currency. A CCA may still be viable in the presence of asymmetric shocks as long as there are alternative adjustment mechanisms available to deal with them. These mechanisms include most notably the degree of wage and price flexibility, the degree of labor mobility, and the scope for discretionary fiscal policy—as discussed next.

17