rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>**Table 10: Do the results generalize to other countries?**</sup> Dependent Variable: Monthly Microenterprise Profits (US dollars)

||All<br>industries<br>(1)|Industries<br>which are<br>predominantly <br>one gender<br>(2)<br>MEXICO|Industries<br>in which<br> both genders<br>participate<br>(3)|All<br>industries<br>(4)|Industries<br>which are<br>predominantly <br>one gender<br>(5)<br>BRAZIL|Industries<br>in which<br> both genders<br>participate<br>(6)|
|---|---|---|---|---|---|---|
|Capital Stock (excluding land and buildings)|0.166***|0.180***|0.178***|0.400***|0.411***|0.153***|
||(0.0131)|(0.0164)|(0.0227)|(0.0173)|(0.0376)|(0.0433)|
|Capital Stock*Female|-0.0764***|-0.123***|-0.0816***|-0.132***|-0.190**|0.0174|
||(0.0231)|(0.0465)|(0.0314)|(0.0309)|(0.0964)|(0.0649)|
|Female|-66.49***|-63.75***|-39.17***|-63.64***|-66.15***|-52.03***|
||(4.499)|(9.988)|(6.072)|(4.606)|(18.56)|(13.51)|
|Observations|9773|6017|3305|15875|4581|3952|
|R-squared|0.186|0.170|0.161|0.221|0.145|0.259|

_Notes:_ Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 All regressions also include controls for age and education of the owner and own hours worked. Mexico regressions also include year of survey dummies.

Industry subsamples in (2) and (5) are industries which are either less than 25% female, or more than 75% female Industry subsamples in (3) and (6) are industries which are between 26% and 74% female. Industry subsamples restricted to industries with at least 50 firms in the sample.

42

-  -
<!-- Start of picture text --> US$ billion Share of GDP, %<br>1,200 9<br>8<br>1,000 7<br>Share of GDP<br>800 \ 6<br>5<br>600<br>4<br>400 3<br>2<br>200<br>Total capital inflows 1<br>0 0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>8 i Po<br>v4 i‘ A A ry %.<br>\ fv '<br>a a gh by \ Developing countries 7<br>ms fh<br>55 cy* ‘ omA is 4- ' * ;<br>4 ; y Py) 7 : pe a aes r] ' \ .<br>‘< . ie fess wr 44 ‘<br>3 .\ ‘i v\ MH =a 7LA *oyf ‘rf ’. nt<br>2 cs +17 \ 453 ‘ / i<br>Ls nt ye we<br>1 4 a! BP<br>Ny High-income countries<br>Le)<br>1962 1967 1972 1977 1982 1987 1992 1997 2002 2007<br><!-- End of picture text -->
trade ban coupled with the binding export quota caused low prices of rice in the South and high prices in the North. The increase in export quota and the lifting of internal restrictions has lead to increased rice prices (in real terms) everywhere but proportionally more in the South (Benjamin & Brandt, 2004; Niimi et al, 2003; Seshan, 2005).

The unit prices in the household data also confirm this. The prices in Vietnam have changed substantially between 1993 and 1998. The general inflation rate was 45.6% (which corresponds to a yearly inflation rate of 7.8%). The increase in nominal rice price has been more than the inflation rate in all regions for the sales unit price and almost all regions for the production unit price (Table 1) pointing to an increase in real rice prices at the household level.

**Table 1. Change in Producer Rice Prices from 1993-1998, by Region.**

|**Rice price**|**Increase in Sales unit prices**|**Increase in Production unit prices**|
|---|---|---|
|Northern Uplands|57%|43%|
|Red River Delta|62%|35%|
|North Central Coast|72%|52%|
|South Central Coast|98%|56%|
|Central Highlands|130%|86%|
|South East|62%|54%|
|Mekong Delta|65%|69%|
|Total|67%|49%|

Source: Author’s own calculations using VNLSS 93 and 98. The percentage change in the mean unit price of one kilogram of rice: from sales and production values and quantities respectively.

# **_2.1 Data_**

Our analysis is based on the panel data from Vietnam Living Standards Survey (VNLSS) for 1992/1993 and 1997/1998. This is a national household level survey with detailed information of household activities (i.e. agriculture, non-farm enterprise, etc.), in addition to the general characteristics (health, education etc.). It also includes a community survey where information about rice prices and locational characteristics are collected. The first round of VNLSS was conducted from September 1992 to October 1993<sup>10</sup> . It includes 4800 households with a self-weighted sample design, and two strata: urban and rural. In the second round of VNLSS all the initial households were sought after and some 1200 additional households for interview, for a total of 6000 households.

> 10 For clarity purposes we’ll refer to this as VNLSS 1993, and the second round as VNLSS 1998.

7
Most such surveys simplistically label the status of a water point’s functionality using a binary definition: either the water scheme was “working” or “not working” at the time of its assessment. A few list failure rates over the entire survey period, but they are the exception. A review of relevant surveys (Wilson et al., forthcoming) indicates that: (1) there is no widely agreed upon definition for functionality, and so it is difficult to compare the results of various surveys, and (2) most surveys use the binary definition of “working” or “not working,” though some refer to “partial functionality.”<sup>4</sup> Until recently, studies of the failure of water schemes in Nigeria were also basically non-existent. However, a recent study of water points (Andres et al., 2018) offers some findings relevant to schemes. Though location—both in terms of political region and underlying hydrogeology—is the major factor affecting the functionality of water points during their first year, factors that can be controlled in the design, operational, and implementation stages also contribute significantly to failure rates. Then, as water points age, their likelihood of failure is best predicted by those factors that cannot be modified, as well as technology. The presence of a water, sanitation, and hygiene committee (WASHCOM) and regular repair and maintenance decline in importance over time.

A recent study (Cronk and Bartram, 2017), using Bayesian analysis, finds that water systems in Nigeria are more likely to be functional if they are used by both livestock and humans. The authors suggest doing more to monitor systems and analyze the data gathered. Meanwhile, several studies focus on sustainability. In the urban area of Ibadan, most water schemes that involve shallow wells are unsustainable. Hence these are supplemented by rainwater and water vendors (Enabor, 1998). Another study of Nigeria suggests a pragmatic strategy that involves all stakeholders in a collaborative effort to raise the sustainability of community water and sanitation programs (Ademiluyi and Odugbesan, 2008). A study of rural water schemes in Akwa Ibom State lists several factors behind failure: a lack of maintenance, poor community participation, little or no coordination and cooperation among stakeholders, political factors, inefficient monitoring, and a lack of maintenance and oversight of public property (Ibok and Daniel, 2014).

> 4 <u>http://nora.nerc.ac.uk/514658/1/Handpump.pdf.</u>

6
To account for other factors that might influence the primary balance, the vector **_X_** _k,it_ contains several control variables. First, we include two lags of the primary balance. Following Montiel Olea and Plagborg-Møller (2021), we add an additional lag to address serial correlation in the regression residuals. Specifically, their findings demonstrate that lag-augmented local projections are asymptotically valid across both stationary and nonstationary data and at long horizons (i.e., horizons that are a non-negligible fraction of the sample size). Furthermore, lag augmentation eliminates the need to correct standard errors for serial correlation in the regression residuals.<sup>14</sup> Second, we account for the broader macroeconomic environment. We include the lagged public debt-to-GDP ratio to capture the responsiveness of fiscal policy to debt sustainability challenges. Additionally, we control for real GDP growth, inflation, and the current account balance (all lagged by one period to address endogeneity concerns).

Third, we control for the institutional environment. Amongst others, we incorporate a variable that considers the presence of an election year to account for the potential existence of political budget cycles. Moreover, we control for the strength of political institutions. However, since no single variable fully captures this concept, we employ Principal Component Analysis (PCA) and take first principal component to construct a summary measure. We use data from the International Country Risk Guide (ICRG) database, incorporating variables on the regulatory quality of the government, the preservation of the rule of law, the level of democratic accountability, and the control of corruption. <u>Appendix 2</u> describes the PCA and the outcomes in detail.

In addition to the quality of political institutions, we include variables that control for the effect other macroeconomic policies on the primary balance. Specifically, we incorporate measures that capture the presence of independent fiscal councils and sovereign wealth funds. Furthermore, we account for the presence of an inflation targeting regime, the prevailing exchange rate regime, and the extent of capital account openness. Detailed definitions and data sources for all variables are provided in Table A1 in <u>Appendix 1.</u>

> 14 We opt for clustered standard errors to deal with potential heteroskedasticity in our analysis over Driscoll-Kraay standard errors (Driscoll and Kraay, 1998) for two main reasons. First, the global wave of fiscal rule adoption occurred gradually over time (see Figure 1), mitigating concerns about cross-sectional dependence in the response of the primary balance to fiscal rule adoption. Second, Driscoll-Kraay standard errors require large _T_ , which is not the case in our dataset. Nonetheless, our results—which are available upon request—remain robust when using Driscoll-Kraay standard errors.

12
# **Table 2: Doing Business Legal Rights Index in MENA in 2009**

||**ALG**|**BHR**|**DJI**|**EGY**|**IRN**|**IRQ**|**JOR**|**KWT**|**LB**|**MOR**|**OMN**|**QAT**|**SAU**|**SYR**|**TUN**|**UAE**|**WBG**|**YEM**|**%**<br>**of**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Index**<br>**(0-**|**3**|**4**|**1**|**3**|**4**|**3**|**4**|**4**|**3**|**3**|**4**|**3**|**4**|**1**|**3**|**4**|**0**|**2**|**Yes**|
|**Rank**|128|106|178|128|106|128|106|106|128|128|106|128|106|178|128|106|182|168||
|1|Y|Y|Y|Y|Y|Y|Y|Y|N|Y|Y|Y|Y|N|Y|Y|N|Y|83.3|
|2|N|N|N|N|N|N|N|N|N|N|N|N|N|N|N|N|N|N|0.0|
|3|Y|N|N|Y|N|N|N|Y|Y|Y|N|N|N|N|Y|Y|N|N|38.9|
|4|N|N|N|N|N|N|N|N|N|N|N|N|N|N|N|Y|N|Y|11.1|
|5|Y|Y|N|Y|N|N|N|Y|Y|Y|Y|Y|Y|N|N|Y|N|N|55.6|
|6|N|Y|N|N|N|N|N|Y|N|N|Y|N|N|N|Y|N|N|N|22.2|
|7|N|N|N|N|Y|N|Y|N|N|N|N|N|Y|N|N|N|N|N|16.7|
|8|N|N|N|N|N|Y|Y|N|N|N|N|N|N|N|N|N|N|N|11.1|
|9|N|N|N|N|Y|Y|Y|N|Y|N|Y|N|Y|Y|N|N|N|N|38.9|
|10|N|Y|N|N|Y|N|N|N|N|N|N|Y|N|N|N|N|N|N|16.7|

_Source: Doing Business_ 2010

## **Index Components:**

1 Can any business use movable assets as collateral while keeping possession of the assets; and any financial institution accept such assets as collateral?

2 Does the law allow businesses to grant a non possessory security right in a single category of revolving movable assets, without requiring a specific description of the secured assets?

- 3 Does the law allow businesses to grant a non possessory security right in substantially all of its assets, without requiring a specific description of the secured assets?

- 4 May a security right extend to future or after-acquired assets, and may it extend automatically to the products, proceeds or replacements of the original assets?

- 5 Is a general description of debts and obligations permitted in collateral agreements, so that all types of obligations and debts can be secured by stating a maximum amount rather than a specific amount between the parties?

- 6 Is a collateral registry in operation that is unified geographically and by asset type, as well as indexed by the grantor's name of a security right?

- 7 Do secured creditors have absolute priority to their collateral outside bankruptcy procedures?

- 8 Do secured creditors have absolute priority to their collateral in bankruptcy procedures?

- 9 During reorganization, are secured creditors' claims exempt from an automatic stay on enforcement?

10 Does the law authorize parties to agree on out of court enforcement?

When compared across regions, MENA is clearly behind all of the other regions, including Sub-Saharan Africa (see Table 3). Egypt, Jordan, West Bank and Gaza, Bahrain and Qatar have begun to consider substantial overhaul of their systems, and are in early stages of reform.

11
wave. At the national level, 76 percent and 24 percent of rural smallholder farmers are vulnerable and resilient, respectively (Figure 1). Eastern, Muchinga, Northern, and NorthWestern Provinces had, on average, a larger proportion of vulnerable households than the national average; while Copperbelt, Central, and Lusaka Provinces had, on average, a larger proportion of rural households that were resilient than the national average (Figure 1).

Figure 1: Proportion of vulnerable and resilient rural households between 2012 and 2019 in Zambia <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40% 68.0 71.7 71.9 74.4 75.3 76.6 77.6 77.9 80.3 81.0 76.3<br>30%<br>20%<br>10%<br>0%<br>Vulnerable Resilient<br><!-- End of picture text --> Notes: Own calculations from RALS (2012-2019).

Further disaggregated results in Table S3 in the online appendix show that only about 19 percent of female-headed households were resilient compared to 25 percent for maleheaded households. About 81 percent of female-headed households were vulnerable compared to 75 percent among male-headed households. We further disaggregated the results by three household types. Male adult-only households, female adult-only households, and a third category in which the household is comprised of both male and female adults. Among these gender types, about 80 percent of female adults-only households were vulnerable compared to 47 percent for male adults-only, and 77 percent for mixed male and female adult households. A smaller proportion of female adult-only households were less resilient at only 20 percent compared to 53 percent for male adult households, and 23 percent for both male and female adult households (Table S3). Thus, female-headed and female adultonly households are more vulnerable and less resilient. Households that used CSA, as defined in this paper, were more resilient and less vulnerable, but the differences are small.

Figure 2 presents transitions in and out of poverty over the three survey waves in 2012, 2015, and 2019. At the national level, 46 percent of smallholder farmers were poor in all three survey waves in 2012, 2015, and 2015, while 2 percent were never poor (Figure 2). The rest of the farmers transitioned from being poor to non-poor status, or vice versa, over the three survey waves. For example, 13 percent of smallholders that were not poor in 2012 fell into poverty in 2015 and 2019, and about 14 percent of smallholders that were poor in 2012 and 15
# Research Copimnents of World Bank Education Projects, since 1962.

|Area|CountryandLoan/Credit<br>|<br>iNumber|Approved|<br>|<br>Research Components<br>|<br>I<br>__||Current Status ofStudies,<br>including<br>CompletedDocum-nts/Data|
|---|---|---|---|---|
||||(iii)<br>Implement training<br>needs assessment model|(ii)<br>Training<br>**study**91received;<br>study *2 dueMay<br>1990. Institute<br>for Construction<br>Training<br>nd<br>Development (ICTAD) (19U8). "Fourth<br>tracer<br>study<br>of graduates."<br>(doc. S F19220)<br>(iii)<br>Training needs assessment carried out;model<br>developed.<br>initial<br>run data factored<br>IntoICTAD<br>training<br>progrm<br>plirming.<br>R.<br>Ihitlock (1985).<br>"Draft<br>final<br>reportontraining<br>nee<br>**ssesinnt**<br>formaagementtraining."<br>(doc.<br>6F01024)<br>aLIso:<br>Conrultants<br>in Technology.Manmgnt<br>**nd**<br>Devtoopment(1968).<br>Training n<br>assessmntfor<br>craftsman,<br>mechanics<br>nd operators<br>in the<br>construction<br>industry."(doc.**_I_**F21842)<br>**_M._Fernando**(1989).<br>"Training<br>andeoploVmentne<br>of woen in the contruction<br>industry<br>of Sri<br>**Lanka."**(doc. * F35771)|
|ASIA|Thailand LN 2178-TN|1982|Sixth (Secondary) Edukation<br>Undertake study on<br>incentives for rural teachers(pars.<br>3.11p.26)<br>Three areas for studies: (i) vocational<br>andtechnical educationalandmanpower needs;<br>(ii)<br>preservice and in-service teacher<br>training;<br>(iii)<br>educationaL plaming.<br>(iv) Provision for other studies (3.18, 31).|Caqileted.<br>0i)C. Buripakdi(1987).<br>"Models for the<br>development of technical teachers in Thailand.0<br>(doc.1F16905)<br>(ii)<br>MOE(1988). "Research for plarming educational<br>resource distribution."<br>(doc.SF16906)<br>(iii)<br>MOE(1968)."Astudy of the improvement of<br>vocational and technical progrmes<br>for for_al<br>andnon-forml education." (docRf16907)<br>(iv)NESDO(1985). "Long term projection of the<br>labour supply."<br>(doc.1F16911)<br>(v) NESDB (1966)."Narpouerplaming in the public<br>sector.<br>"(doc.**1**f16915)|

48
Equation (5) enables us to decompose the variability of commodity prices into various components by selecting the appropriate frequencies, _k_ .<sup>**8**</sup> In the context of the present analysis, we decompose the transitory component into two cycles ( _k_ ∈ [8-20] and [2-8]) and short-term fluctuations ( _k_ ∈ [0-2]). Thus, identity (1) becomes: [8,20] [2,8] ≡P . (6) tt tt tt tt tt P , which represents the permanent component introduced earlier, could be character-pp + TTPP + TTPP + SSTT ized by a linear trend, perhaps subjected to structural breaks, if price behavior is analyzed tt in the context of declining Terms-of-trade hypothesis (Baffes and Etienne 2016), or it could include non-linearities consistent with supercycles (Cuddington and Jerrett 2008; Fernández, Schmitt-Grohé, and Uribe 2020). The empirical section elaborates further on [8,20] the nature of P . The second component, , denotes the medium-term cycle with a periodicity of 8-20 years as proposed by Blanchard (1997) and popularized by Comin tt tt TTPP[2,8] and Gertler (2006). The third component, , represents the business cycle with a periodicity of 2-8 years, following traditional definition applied by the National Bureau of tt TTPP Economic Research (Burns and Mitchell 1946). The last component, captures fluctuations which may reflect short term movements in the economic activity or other macroett SSTT conomic variables (such as exchange rates and interest rates) or seasonality and weather patterns (in the case of agriculture). Often these fluctuations are analyzed with Vector Autoregression models (Kilian and Murphy 2014; Baumeister and Hamilton 2019) and GARCH models by utilizing high-frequency data, focusing mostly on volatility (Engle 1982). Following decomposition, the logarithm of the price was regressed on each component to measure its relative contribution to price variability. For example, the from [8,20] the regression of the price on the medium term cycle, , gives the RR<sup>2</sup> relative contribution of the medium term cycle to total price variability. Note that the sum tt tt tt pp = μμ+ ββTTPP + εε of the respective s adds to unity since the sum of the four components adds to the price. Monthly data for 27 commodity prices covering the period January 1970 to DeRR<sup>2</sup> cember 2020 (612 observations) were used in the analysis. They come from the World **8** Even after removing the linear trend before detrending, as in equation (4), the cycle still contains a timevarying component, , leading to inconsistent results. Corbae and Ouliaris (2006) show that, in addition to consistency, (5) has no finite sampling error, has superior end-point properties, with a much lower TT pp /√TT mean-squared error compared to popular time-domain filters and other band-pass filters such as Baxter and King (1999) and Christiano and Fitzgerald (2003).

_— 8 —_
<u>Fuel consumption of cars:</u> For the Latin America region, the SMP spreadsheet indicates that cars will consume 8.3 liters/100km in 2010. We interpolate their data so that cars consume 8.2 liters/100km in 2012 and reach 6.5 liters/100km in 2050. This corresponds to a 20% decrease over the 2012 – 2050 period.

<u>Fuel consumption of buses: We here again draw our data from the SMP model, so that buses in the model</u> consume 26 liters/100km in 2012. We make the supplementary assumption that technological progress will allow the consumption of buses to decrease at the same pace as for cars so that consumption of buses reaches 21 liters/100km in 2050.

<u>Load factors: cars are supposed to carry 1.72 passengers/trip on average in 2012 and buses approximately</u> 19. These average load factors decrease at the same pace reaching 1.51 for cars and 16.68 for buses in 2050. This corresponds to a little more than a 12% decrease over the 2012 – 2050 period.

<u>Vehicle unitary emissions: With a CO2 content of 2416 gCO2/liter of gasoline, vehicles emit on average 114</u> and 33 grams of CO2/passenger.km for cars and buses respectively in 2012, i.e. a ratio of 3.5. These figures go down to 104 gCO2/pkm for cars and 30 gCO2/pkm for buses in 2050. The ratio between unitary emissions remains constant throughout time as gasoline consumptions and load factors evolve in parallel for cars and buses. These figures are in line with those reported by Carbontrust<sup>16</sup> .

# 10.2.5 Land use and building restrictions

<u>Land uses incompatible with urbanization: In order to be able to reproduce the location and extent of</u> urban areas in the Buenos Aires region correctly, NEDUM-2D accounts for regulations or land uses that are incompatible with the development of residential areas. NEDUM-2D therefore requires a map of land use constraints which is used as a filter to exclude some zones from potential urbanization. For example, it excludes zones occupied by water or protected natural areas such as forests. Water bodies (sea, rivers and lakes) are excluded from potential urbanization as a first step. In a second step a detailed land use map (see Map 11) is used to identify supplementary zones that need to be excluded from potential urbanization. The exclusion map encompasses any area occupied by protected natural areas, equipment such as the military zones in Campo de Mayo and infrastructure (port, railway station, Ezeiza international airport for example). While the total area of exclusion is non-negligible, it remains, by far, lower than the area that could be potentially urbanized. This is indicative that although the exclusion map can avoid major discrepancies between the simulation and the data, it is not a main driver behind the overall accuracy of our results regarding the match between simulated and observed urbanized areas.

> 16 https://www.carbontrust.com/conversionfactors

49