rafmacalaba/datause-encoder-data huggingface dataset:
fiscal solvency of the pension system, but it is also likely to lead to a shift in consumer demand toward services (notably health-related).

The version of the LINKAGE model used here has a 2007 base year and relies on the Global Trade Analysis Project (GTAP) 7 database<sup>5</sup> to calibrate initial parameters. A scenario is developed by solving for a new equilibrium in each subsequent year through 2030. The long term projections rely on assumptions about developments in population, factors of production (capital and labor) and productivity. Population and labor force projections are exogenous and based on the United Nations’ population forecast revision from 2008<sup>6</sup> . According to UN projections, the world populations will increase from 6.5 billion in 2005 to 8.3 billion in 2030. The evolution of labor force is assumed to be in line with the growth of working age population- i.e. population between 15 and 64<sup>7</sup> . According to the UN forecast, high income countries and East Central Asia (ECA) will see their labor force declining from 2015 onwards. The labor force in South Asia and Sub-Saharan Africa is expected to increase by 40% or 80%, respectively, over 2005-2030 period. The growth of working age population in Latin America and the Caribbean (LAC) over this period is much slower at 25 percent. From 2015 onwards the Chinese labor force declines at an average annual rate of 0.2% over 2005-2030.<sup>8</sup> The assumptions on changes of productivity are more complex. Productivity in agriculture is assumed to be factor-neutral and exogenous and is set to estimates from empirical studies (e.g., Martin and Mitra, 1999). Productivity in manufacturing and services is labor-augmenting; it is skill-neutral but sector-biased. In the case of agriculture, productivity growth averages 2.5 percent per annum for all countries. In manufacturing and services changes in productivity are country-specific and based on the past trends. Following the broad findings of Bosworth and Collins (2007) we assume that productivity growth in manufacturing is about 3 percentage points faster than in services.

While the national savings are determined by the growth in per capita income and demographic factors, foreign savings are exogenous in the baseline. We assume that current account imbalances will tend to zero in the long run.

> 5 See www.gtap.org for details.

> 6 The UN has released a new projection in May 2010. There are relatively modest changes with respect to the older projection with the total population in 2050 increasing to 9.31 billion versus 9.15 billion in the 2008 projections. 7 This is a simplistic assumption given that the overall labor force participation rates are likely to increase as more women enter the labor force in developing countries or due to the rise in the retirement age in high income countries. 8 The slowing of the labor force growth along with ageing population will likely pose a challenge to the continuation of the fast economic growth in China and several other developing countries.

6
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Figure 20

# Impact of APEC FTA on the Japanese Rice Market

NO PRICE REDUCTION (8 = 0.6) <!-- Start of picture text --> 100%<br>80%<br>40%<br>20% /<br>20%<br>TARIFF REDUCTION (percent)<br><!-- End of picture text --> PRICE REDUCTION (, = 0.6) <!-- Start of picture text --> 1W%<br>60%<br>*ROWV<br>40% *~~~~~~~~~~~~~~~~~APEC<br>40%<br>0%<br>- ~~~~~~~~~~~~~w<br>TARRIFF REDUCTION (percent)<br><!-- End of picture text --> 53
58 of the World Development Indicators (WDI) database of the World Bank (2021).<sup>53</sup> More recent versions of the WDI do not report these GDP shares for earlier decades, only the older versions of the WDI do. The Beta version has the merit of showing all available estimates for all versions of the WDI. For each we yearly simultaneously country-year, then take the mean of the available estimates. To maximize the number of available estimates for the years 1960, 1965, ..., 2015, 2020, and in order to minimize fluctuations due to year-specific measurement issues, we rely on five-year moving averages.

After doing so, of the 1,508 observations in our data, for 189, 159 and 195 observations we still do not have an available estimate for the GDP share of MFG, services (SERV), and MFG+SERV, respectively. For the 2010-2020 period, we complete the data using estimates from Central Intelligence Agency (2021) and reports from international organizations or governmental agencies. Even after doing this, for 188, 153 and 189 observations we still do not have an available estimate, respectively.

For the long-difference regressions, we need data c. 1960-1970. For these years, we use United Nations (1960-1980). However, for 87, 75, and 88 observations, we do not have an available estimate for the GDP share of MFG, SERV, and MFG+SERV, respectively.

The _System of National Accounts_ (SNA) - _Analysis of Main Aggregates_ (AMA) database of United Nations (2020c) reports the GDP share of aggregated sectors, including MFG and services, for all countries from 1970 to 2020. When needed, we use this database to complete the missing country-years of our main data set, after verifying that the newly added estimates are consistent with the estimates that we already had for other years.<sup>54</sup> **GDP Share of FIRE.** The _National Accounts Official Country Data database_ of United Nations (2020a) reports when available the GDP share of various sectors – using both the ISIC Revision 3 and Revision 4 – from the 1960s to date. The data are patchy, however, and we employ them only to obtain the GDP share of FIRE c. 2020 (observations from 2015-2020).

> 53For manufacturing, we use the series “Manufacturing, value added (% of GDP)”. For services, we use as our baseline the series “Services, etc., value added (% of GDP)”. When estimates of the service share are not available, we rely on another WDI series: “Services, value added (% of GDP)”.

> 54 We do not use SNA-AMA as our baseline database. Indeed, when comparing WDI + the yearbooks and SNA-AMA, it appears that many SNA-AMA estimates were extrapolated.
# **Table 2. FinScope Survey Results on Access to Finance and Financial Literacy in Sub-Saharan Africa and Pakistan**

|COUNTRY<br>|USEF<br>|INANCIALP<br>|RODUCTS<br>|CURRENTLY<br>SAVE ORPUT|BORROWED<br>MONEY INPAST|USE AN<br>INSURANCE|AWARE<br>SAVINGS|NESS(“HASNEV<br>INTEREST ON|ERHEARDOF...<br>|”)<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|(YEAR OFSURVEY)|TOTAL|FORMAL|INFORMAL|MONEYAWAY|12 MONTHS|PRODUCT|<br>ACCOUNT|<br>SAVINGS|INSURANCE|LOANS|
|Southern Africa|||||||||||
|Swaziland(2011)|63%|50%|13%|66%|33%|22%†|--|--|--|--|
|Lesotho (2011)|81%|58%|23%|51%|66 % (“have<br>borrowed”)|61%†|--|--|--|--|
|South Africa (2010)|77%|63%|14%|37% (“save<br>regularly”)|33% (“have credit<br>product”)|50%†|--|--|--|--|
|Mozambique(2009)|22%|13%|9%|20%|8%|5%|82%|90%|88%|22%|
|Botswana(2009)|67%|59%|8%|--|--|--|--|--|--|--|
|Zambia(2009)|37%|23%|14%|46%|24%|4%|--|--|--|--|
|Namibia(2007)|48%|47%|1%|45%§|15%§|18%§|--|--|--|--|
|Malawi(2008)|45%|26%|19%|74%|22%|3%|54%|66%|--|48%|
|East Africa|||||||||||
|Kenya (2009)*|67%|41%|26%|52%|38%|7%|9% (“don’t<br>understand<br>product”)|49%|48% (“don’t<br>understand<br>product”)|--|
|Uganda (2009)|70%|28%|42%|67%|45% (“currently<br>borrowing”)|25%†|--|--|--|--|
|Rwanda(2008)|47%|21%|26%|54%|27%|82%‡|--|--|--|--|
|Tanzania(2006)|46%|11%|35%|--|--|--|27%|35%|25%|9%|
|West Africa|||||||||||
|Ghana(2010)|56%|41%|15%|64%|19%|5%|--|--|--|--|
|Nigeria (2008)**|47%|23%|24%|67%|7% (“currently<br>borrowing”)|2%|42%|36%|48%|22%|
|South Asia|||||||||||
|Pakistan (2009)***|44%|12%|32%|56%|35% (currently<br>has a loan)|2%|--|--|--|--|

* FinAccess. **Access to Finance Survey. ***Access to Finance Study (A2FS). §Estimate for use of formal product only.

- †In South Africa, this includes 45% who have funeral coverage, and 26% who use other products. In Lesotho, over a third of adults have funeral insurance. Funeral coverage is also common in Swaziland and Uganda.

‡Reflects access to government-sponsored medical insurance. Use of other types of insurance products is 0-1%.

8
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# **Table 6: How much difference does keeping books make?**

Treatment Effect for the Treatment of Receiving an account book

||<br>KMS S|eptember|<br>SLMS r|<br>ound 3|<br>SLMS|<br>round 4|
|---|---|---|---|---|---|---|
||Pure effect|controlling|ITT effect|TOT effect|ITT effect|TOT effect|
|_Outcome:_|f|or lagged outcome|||||
|Total Revenues|4123|5418|1050|2141|4352|7689|
||(0.82)|(1.56)|(0.57)|(0.57)|(1.62)|(1.61)|
|Purchase of materials and items for resale|6209|7412|726|1461|3094|5344|
||(1.32)|(2.25**)|(0.46)|(0.46)|(1.64)|(1.63)|
|Total Expenses|6191|7286|755|1541|2288|4038|
||(1.29)|(2.14**)|(0.48)|(0.48)|(1.17)|(1.16)|
|Revenue - Expenses|-2068|-1988|356|726|2363|4178|
||(1.02)|(0.98)|(0.28)|(0.28)|(1.57)|(1.56)|
|Value of goods used for home consumption|783|708|12|25|-12|-22|
||(2.02**)|(1.89*)|(0.09)|(0.09)|(0.09)|(0.09)|
|Profits|103|379|716|1431|-117|-204|
||(0.17)|(0.72)|(1.42)|(1.41)|(0.29)|(0.29)|
|Sample Size|173|173|418|418|413|413|

Notes: For firms not keeping financial records in baseline survey All SLMS regressions also include the round 2 (pre-treatment) lagged outcome. Robust t-statistics in parentheses.

*, **, and *** indicate significance at the 10%, 5% and 1% levels respectively.

**Table 7: Correlation between Revenue-Expenses and Profits for those with and without book treatment**

||KMS<br>September|SLMS<br>round 3|SLMS<br>round 4|
|---|---|---|---|
|Pearson Correlation: Treated with books|0.126|0.168|0.453|
||[-0.08, 0.33]|[-0.02, 0.36]|[0.25, 0.66]|
|Pearson Correlation: Untreated|0.231|0.277|0.458|
||[-0.04, 0.50]|[0.02, 0.54]|[0.30, 0.62]|
|Spearman Correlation: Treated with books|0.075|0.535|0.624|
||[-0.11, 0.26]|[0.42, 0.65]|[0.52, 0.73]|
|Spearman Correlation: Untreated|0.431|0.468|0.492|
||[0.20, 0.67]|[0.34, 0.60]|[0.37, 0.61]|

Note: 95 percent bootstrapped confidence intervals in parentheses. For firms not keeping financial records in baseline survey

**Table 8: Do firms deliberately misreport?**

% of firms saying that many enterprise owners tend to:

||KMS|SLMS|
|---|---|---|
|Expenses:|||
|firms overstate expenses|54|45|
|firms understate expenses|10|13|
|firms state expenses correctly|36|42|
|Revenues:|||
|firms overstate revenues|1|4|
|firms understate revenues|86|60|
|firms state revenues correctly|13|36|

27
TABLE 1: OLS, firm-level elasticity of substitution across suppliers

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|log<br>�<br>ˆ_p_<br>ˆ ̃_p_<br>�|0.2171|0.2222|0.3045|0.2114|0.3030|0.2441|0.3370|
||(0.0133)|(0.0147)|(0.0151)|(0.0192)|(0.0196)|(0.0352)|(0.0365)|
|_ε_|0.7828|0.7777|0.6954|0.7885|0.6969|0.7558|0.6629|
|Obs|2028039|1966591|991187|2021334|816122|993583|341903|
|R<sup>2</sup>|0.417|0.460|0.404|0.478|0.411|0.495|0.420|
|HS digits|4|4|4|6|6|8|8|
|Feenstra term|Y|Y||Y||Y||
|Buyer-time FE|Y|Y||Y||Y||
|Product-time FE|Y|Y||Y||Y||
|Buyer-product FE||Y||Y||Y||
|Buyer-product-time FE|||Y||Y||Y|
|Seller-product FE||Y|Y|Y|Y|Y|Y|

**Notes:** OLS estimates come from estimating Equation (11). Time is monthly frequency. The first row reports the estimates associated with changes in log relative unit values. Standard errors are two-way clustered at the origin and destination state level and are reported in parentheses below each estimate. The third row reports the implied value for _ε_ , which is 1 minus the estimate on the first row. The fourth row reports the number of observations. The fifth row reports the goodness of fit ( _R_<sup>2</sup> ). The table contains seven columns: Each corresponds to different specifications on how we define a product (4-digit, 6-digit, or 8-digit HS codes), which fixed effects are included, and whether the Feenstra (1994) term is included. These combinations are reported by the last seven rows of the table. All specifications include the controls mentioned in the paper.

term. Each specification reports a high Kleibergen-Paap F-statistic, indicating that our instruments are statistically relevant. In columns (1) and (2), we use the seller-level and seller-buyer-level instruments separately. The elasticities are 0 _._ 50 and 0 _._ 61, respectively, which also reflect complementarity. In column (4), we also include buyer-by-product and seller-by-product fixed effects, and the elasticity rises to 0 _._ 66. Columns (5)-(6) show that our complementarity results remain after including stronger sets of fixed effects. All our estimates provide evidence for _ε <_ 1.

The 2SLS estimates for _ε_ are smaller than the OLS estimates. As discussed in Section 5.3, the bias is in the expected direction if we expect the Covid-19 shock to also induce negative demand shocks, thereby biasing up OLS estimates of _ε_ . We may expect that our estimated elasticity will be lower for the sub-sample of buyers who did not have more than one supplier to source inputs from. In Table A5, we restrict our sample to cases when a buyer traded with at least two sellers in two consecutive periods. Column (5), again yields an elasticity of substitution of 0 _._ 56, very close to the estimate from our main specification.

To account for the extensive margin, we show the results from estimating Equation 14 in Table A6. Again, we find evidence of high levels of complementarity in the short-run, with the estimated elasticities ranging from 0.23-0.81 depending on the range of fixed effects used.

27
_Brazilian spatial labor dynamics Page 22_

**Table 8:  Regression results, Brazilwide**

|Dependent Variable:<br>Delta ln Wage|2SLS Est.|2SLS SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-.23969856|.06883838|-.56879473|.30864844|
|ln Teacher qualification in 1991|.00548926|.00230455|.00416922|.0057475|
|Years of Schooling in 1991|.06204049|.00435942|.05989964|.01154468|
|Total Rainfall|.00005344|9.645e-06|.00006334|.00002661|
|Government Accountability|.02233476|.01293292|.01689856|.03458774|
|Delta ln Employment|-.49393135|.05272667|-.60942137|.21566178|
|ln Transport Cost SP in 1995|-.03205953|.00590571|-.02147947|.02295913|
|ln Transport Cost Capital in 1995|-.04812287|.00601824|-.01909574|.02022743|
|Delta ln Transferences|.29663134|.03118072|.20129193|.11094594|
|Delta ln Market Potential|.54819572|.09031346|.98877352|.29459548|
|crit. fn. test of overid. restrictions|----|----|24.106339|24.106339|

|Dependent Variable:<br>Delta ln Wage|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1)|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|---|---|---|---|---|
|Intercept|-.71932918|.39269048|-.89149666|.46470872|
|ln Teacher qualification in 1991|.00313908|.00588544|.00077822|.00636871|
|Years of Schooling in 1991|.06201083|.01286978|.06663528|.01474433|
|Total Rainfall|.0000674|.00002795|.00007651|.00002947|
|Government Accountability|.02179158|.03504303|.03300684|.035756|
|Delta ln Employment|-.74141878|.24696933|-.89418358|.27639595|
|ln Transport Cost SP in 1995|-.01160188|.02949758|.00306388|.03553532|
|ln Transport Cost Capital in 1995|-.00840394|.02391369|.00218674|.02606355|
|Delta ln Transferences|.13809767|.12475475|.05152986|.13939323|
|Delta ln Market Potential|1.1562178|.34490082|1.3035762|.36538869|
|crit. fn. test of overid. restrictions|19.804367|19.804367|14.762519|14.762519|
banks but not for foreign banks. Their model can provide a rationale for our findings. These authors argue that models featuring only portfolio investors ignore the important role of banks in intermediating capital flows. In their model both banks and sovereigns play a role in EM, consistent with our data.

# **6 Conclusion**

We construct a new data set for gross capital flows during 1996–2014 for a large set of countries at a quarterly frequency. We decompose debt inflows and outflows by borrower and lender type: banks, corporates and sovereigns. We use the standard BOP data from IMF (BMP6) as the starting source. In order to get a larger, longer, and balanced panel of countries with debt flows split by sector, we proceed with a data filling exercise. When the BOP data by sector is missing, we use other publicly available data from IMF, WB, and BIS. Our data captures fairly accurately the volume and variation of aggregate flows for most countries and allows us to extend the coverage of the standard samples substantially.

To gauge how well our constructed estimates capture the true flows, we undertake a counterfactual exercise. We take a of countries where BOP data sector is non- sample by missing over 2006q1–2013q4. Then we compare this data to our estimates done for this period as if the BOP data was missing. We match pretty well the aggregate patterns – the correlation between the the two series for the total debt inflows is over 86 percent. At the sector level, our external filling procedure makes a large difference. Some 25-40 percent of observations for EM and 75-90 percent of observations for developing countries that are missing in the BOP inflow data are filled. A sizable number of observations for advanced 41
## **ABBREVIATIONS AND ACRONYMS**

CA Central American CAFTA Central America Free Trade Agreement CAS Country Assistance Strategy CCAD Central American Development and Environmental Commission DFID Department for International Development ESSD Environmentally and Social Sustainable Development Department FAO Food and Agriculture Organization FAO/CP Food and Agriculture Organization Cooperative Program GIS Geographic Information Systems IADB Inter-American Development Bank IFAD International Food and Agriculture Fund IFPRI International Food Policy Research Institute LAC Latin America and Caribbean NGO Non-governmental Organization ODI Overseas Development Institute RNFE Rural Non-farm Employment UNDP United Nations Development Programme USAID United States Agency for International Development WB World Bank

# **Acknowledgements**

This paper was prepared for the Central American regional study, “Identifying Drivers of Sustainable Rural Growth and Poverty Reduction in Central America,” carried out for the Central America Environmentally and Socially Sustainable Development (CA ESSD) Department in the Latin America and Caribbean (LAC) Region of the World Bank. Martin Raine was the Sector Manager and Francisco Pichon the Task Team Leader for the study. Helpful comments were received from Professor Jeff Alwang, from Virginia Tech. The original version of this paper was prepared October 2002.

iii
important in our data set.

We test only specifications that either have been used in the literature or are natural extensions of those previously used. An example of a natural extension is applying a specification previously used to study foreign acquisitions to study privatization. We do not use specifications that would not be used by others. For example, we do not allow for the possibility of including only selection effects, which never occurs in the literature.

We employ a very simple standard for nonrobustness that can be applied to each coefficient. If either the full model or the model with exclusions yields a statistically significant coefficient, and the other model does not also yield a statistically significant coefficient with the same sign for the corresponding coefficient, then the finding is “nonrobust.” These are the cases in which researchers using classic statistical inference looking at the results of the two models would reach different conclusions for rejecting the null in favor of a specific alternative hypothesis. For our application, we use the lowest significance level shown in the tables for rejecting the null hypothesis, the 10% level. When we analyze whether a particular set of exclusions from the model is likely to create problems, we evaluate the proportion of the relevant coefficients that are nonrobust.

We summarize the nonrobustness checks in Table 3, and show the more detailed discussion and regression tables in the Appendix. We use the same 5 bank performance measures as in the main results, and always include the Quarters Since indicators, the most complete specification. Thus, these specifications represent exclusions relative to the complete model on the right side of Table 2, and we evaluate the proportions of coefficients that would yield different conclusions from this model.

Panel A of Table 3 summarizes the results in Appendix Table A1 where we examine each type of governance in a separate model and exclude all of the other governance types. Because we have two different types of dynamic governance changes for state-owned banks, we measure the effects of state governance two separate times – once with the variables for privatization and once with the state restructuring variables. Each entry in the table gives the proportion of 5 coefficients that are nonrobust, where the 5 coefficients represent one regression each for the 5 performance measures used as the endogenous variables. Thus, in each case, we test for the nonrobustness of 5 static coefficients, 5 selection coefficients, 5 coefficients for undergoing dynamic governance changes, and 5 coefficients for the quarters since those dynamic changes. We follow the conventional practice of excluding the domestic static effects as the base case, so the domestic static effects are not applicable (n.a.).

21
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