rafmacalaba/datause-encoder-data huggingface dataset:
**Annex ‐ Table 2 ‐ SSA: Fiscal Regression for Model A**

|**_Dependent variable_**|||**Real GDP**|**Central**<br>**African**<br>**per capita**|**Cote**<br>||||||**Real**|**GDPper c**|**apita**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Benin**|**Botswan**|**a Cameroon**|**Rep. **|**d'Ivoire**|**Ethiopia**|**Gabon**|**Gambia**|**Ghana**|**Kenya**|**Mozambiqu**|**e**<br>**Nigeria**|**Rwanda**|**Senegal**|
||_Column 1_|_Column 2_|<br>_Column 3_|_Column 4_|_Column 5_|_Column 6_|_Column 7_|_Column 8_|_Column 9_|_Column 10_|_Column 11_|_Column 12_|_Column 13_|_Column 14_|
|Constant|6.958<br>(1.999)*|34.910<br>(2.927)**|26.494<br>* (2.63)**|22.230<br>(10.374)*|13.990<br>**(7.48)***|4.919<br> (3.158)**|24.507<br>* (4.824)**|7.917<br>* (7.898)**|2.983<br>*|7.048<br>(3.626)***|‐10.471<br> (‐2.384)**|‐21.091<br>(‐0.622)|7.021<br>(2.245)**|10.487<br>(9.805)***|
|Government revenue per capita|||||||||||||||
|(in constant US$)|0.001<br>(0.02)|0.019<br>(0.458)|0.026<br>(1.524)|0.077<br>(1.486)|0.113<br>(2.869)**|0.263<br> (4.308)**|0.277<br>* (6.035)**|‐0.005<br>* (‐0.074)|0.006|0.065<br>(1.174)|0.169<br>(2.424)**|‐0.001<br>(‐0.032)|0.177<br>(5.84)***|0.222<br>(1.853)*|
|Government expenditure per|||||||||||||||
|capita (in constant US$)|‐0.017<br>(‐0.985)|0.014<br>(0.445)|0.019<br>(1.405)|‐0.013<br>(‐0.456)|0.010<br>(0.543)|‐0.003<br>(‐0.081)|0.041<br>(2.812)**|0.020<br> (0.642)|0.027|0.014<br>(0.421)|0.077<br>(0.822)|0.016<br>(1.535)|0.048<br>(0.864)|0.078<br>(2.237)**|
|Private investment (in GDP)|0.072<br>(3.727)***|0.125<br>(2.885)**|0.051<br>* (1.767)*|0.037<br>(0.698)|0.039<br>(3.036)**|0.010<br>* (1.287)|‐0.196<br>(‐3.517)*|0.124<br>**(1.944)*|0.004|0.027<br>(0.751)|0.014<br>(1.348)|0.045<br>(1.206)|0.027<br>(0.562)|0.007<br>(0.161)|
|Log of labor force|0.332<br>(1.356)|‐1.603<br>(‐2.173)*|‐0.930<br>* (‐1.487)|‐0.776<br>(‐5.786)*|‐0.179<br>**(‐1.867)*|0.037<br>(0.392)|‐0.853<br>(‐2.192)*|‐0.039<br>* (‐0.536)|0.673|0.150<br>(1.502)|1.384<br>(4.291)***|1.765<br>(0.943)|0.131<br>(0.617)|‐0.078<br>(‐0.923)|
|AR(1)|0.860<br>(10.498)***|0.985<br> (84.165)*|0.874<br>**(7.531)***|0.598<br> (1.853)*|0.495<br>(1.985)*|0.160<br>(0.586)|0.875<br>(9.505)**|0.180<br>* (0.677)|‐0.204|0.737<br>(2.862)***|0.642<br> (2.784)**|0.932<br>(11.409)**|0.883<br>* (7.556)***|0.587<br> (1.824)*|
|MA(1)|0.139<br>(0.548)|0.959<br>(20.923)*|0.997<br>**(9.808)***|‐0.058<br> (‐0.134)|0.472<br>(1.755)*|0.931<br>(17.952)*|‐0.997<br>**(‐13.782)|0.221<br>**(0.599)|1.602|0.360<br>(1.345)|0.173<br>(0.557)|0.445<br>(2.132)**|‐0.032<br>(‐0.116)|‐0.183<br>(‐0.439)|
|**Adjusted R2**|0.948|0.998|0.986|0.935|0.950|0.829|0.856|0.590|0.998|0.761|0.970|0.844|0.879|0.815|

The estimation technique is ARIMA. t‐statistics are given in paranthesis.*** stands for 1% significance level, ** 5% significance level, and * 10% significance level. AR(1) is autoregressive variable. MA(1) is moving average variable.

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conclude that trade costs, not migration frictions, represent a major constraint to the efficient distribution of economic activity and to high sustainable economic growth in Latin America.

# **6. Conclusions**

Transportation costs can hinder economic development with uneven effects across space within countries and regions. Barriers to the mobility of workers in the economy could have analogous results, by not allowing labor to allocate optimally across space. In this paper, we assess the magnitude of migration frictions in Latin America c. 2000 and quantify the effects of cuts in trade and migration frictions on spatial development in the region. We show that reductions in trade costs in an optimal way can be key for the long-term spatial economic development in the region and the potential to speed up its convergence to advanced countries’ standards of living.

Using the dynamic spatial general equilibrium model in Desmet et al. (2018) and fine-grained spatial data, we first calibrated migration entry costs using information on differences in initial utility across regions within the Latin American countries. We find that in most Latin American countries, migration entry costs calibrated based on spatially differential initial utility are on average substantially lower than those obtained assuming uniform within-country initial utility. Their within-country dispersion is also mostly lower. International comparisons suggest that the adjusted migration frictions are also smaller on average in Latin America than those in advanced economies, but trade frictions are considerably higher, even after adjusting for distance.

We then use two counterfactual simulations to assess the relative importance of the two types of mobility frictions. The first counterfactual explores the effects of reductions in transportation costs due to optimal expansion of the domestic road networks in most Latin American countries. We show that such a reduction increases the present discounted value of real per capita income in the region on average by 15.1% – a much larger magnitude than the one obtained with static quantitative trade models because of dynamics gains. The optimal improvements in connectivity also tend to stimulate the economic development of the relatively more populous and productive locations in the Latin American countries. By contrast, making it easier to migrate to such top locations has no effect on the present discounted value of the region’s real per capita incomes, reflecting the relatively low dispersion in migration entry costs in most Latin American countries and the relatively low entry barriers in these locations. In both counterfactuals, the welfare increases are significantly larger than the increases in 26
completion of the Chrysler bailout in mid-2009. There is no perceptible differential in returns in first half of 2008, and the differential does not change in the second half of 2009.

We also examine cumulative returns on a risk-adjusted basis. Following Bessembinder et al. (2009) we group all bonds into four S&P rating categories, and compute value-weighted returns for all bonds falling into each of the rating groups.<sup>91</sup> We do not further partition by size or time-to-maturity due to the fact that a number of bonds do not trade on a daily basis; thus finer partitions result in small sample sizes within each partition. Cumulative risk-adjusted returns are shown in Figure 4. Cumulative riskadjusted returns for both unionized and non-unionized firms drop with the worsening of the financial crisis in late 2008. But the cumulative returns of unionized firms recover more quickly. The spread in the returns widens during the spring of 2009, with the fashioning of the auto bailout. As with the raw returns, the difference in cumulative riskadjusted returns amounts to about 30% over the period studied.

Over the period we study, there is no evidence that investors were worried about political interference in unionized firms as a consequence of the bailouts. To the contrary, investors reacted positively to the prospect of public support.

We also undertake an event study of bond returns in order to pinpoint when and how the bailout impacted the credit market. Key event dates relating to the bailout are set forth in the Appendix hereto. To compute abnormal returns, we use the five-factor bond model developed in Fama and French (1993). This model is an extension of the commonly used three-factor stock return model, which includes factors for the market risk premium (or MKT), firm size (small minus big, or SMB), and value (high minus low, or HML). The bond model adds two additional factors: TERM, which represents the slope of the Treasury yield curve, and DEF, the default premium measured as the difference between the returns on long-term corporate bond indices and long-term Treasuries. Formally, the model developed by Fama and French to estimate expected bond returns is:

> 91 The rating groups are (i) AAA to AA-; (ii) A+ to A-; (iii) BBB+ to BBB-; and (iv) BB+ to C.

27
effectively credit constrained. Additionality also refers also to the developmental impact of the scheme, including the survival rate of firms, investment, growth, and job creation. Additionality also has another dimension, namely, the depth of financing: longer term financing instead of short term, financing of immaterial and intangible assets, financing of project type traditionally excluded by the banking sector.

**Assessing additionality remains technically challenging** . Assessing additionality entails comparing the financial and economic performance of guarantee users (the “treatment group”) to those of non-users (the “control group”). The main challenge is to identify a correct control group, consisting of firms with similar characteristics to those of the guarantee users. As shown in Table 17, there are several methods used to assess additionality (see Annex 2 for more details). Some country studies conclude that their guarantee schemes generate significant additionality (Table 18), but assessing additionality remains technically challenging (see e.g., World Bank 2008).

**Table 17: Alternative Methods to Assess Additionality**

|**Methodologies**|**Description**|
|---|---|
|Interviews|The basic way of measuring additionality consists of asking guarantee users and bankers:<br>“would you have obtained (extended) the loan without using the guarantee scheme?”|
|Descriptive statistics|<br>The share of guarantees extended to riskier types of borrowers (e.g. small firms, start-ups,<br>firms seeking long-term finance, or SMEs with low risk scoring results) can be used as a<br>proxy to assess additionality.|
|Propensity score<br>matching|This method is based on a survey of enterprises, and employs econometric techniques to<br>make the “control group” more comparable to the group of guarantee users, controlling for<br>firms’ characteristics.|
|Regression<br>discontinuity|Many guarantee schemes use credit scoring to allocate guarantees to applicants. This<br>method compares firms around the cut-off point. The “control group” is composed of firms<br>just above the line, while the “treatment group” is composed of firms just below.|
|Natural and quasi-<br>natural experiment|This method is based on a naturally occurring event that differently affects different groups<br>of firms (e.g. change of eligibility criteria, use of guarantees by different banks, at different<br>point of time or in different regions)|
|Randomized<br>experiment|A control group is selected using a random process. Some eligible firms applying for the<br>guarantee are randomly rejected. Additionality is measured by comparing these firms to a<br>random sample of guarantee users.|

29
**<u>Appendix table 2: Prices for key farm inputs, 2021 and 2022</u>**

||<br>**National**|<br>**Center**|**North**|**Regional**<br>**South**|**East**|**West**|
|---|---|---|---|---|---|---|
|**Panel A: 2021**|||||||
|Fertilizer price (US$/50kg)|15.57|15.45|15.28|15.91|15.28|15.94|
|Diesel (US$/liter)|0.78|0.78|0.78|0.78|0.79|0.79|
|Machinery services (US$/ha)|37.52|35.42|31.85|30.56|28.65|61.15|
|Daily wage (US$/day)|11.55|13.89|12.50|7.98|12.36|11.03|
|**Panel B: 2022**|||||||
|Fertilizer price (US$/50kg)|40.17|38.90|40.28|40.42|38.19|43.06|
|Diesel (US$/liter)|1.49|1.48|1.49|1.49|1.50|1.48|
|Machinery services (US$/ha)|49.23|45.30|39.18|42.03|39.76|79.88|
|Daily wage (US$/day)|14.38|15.30|12.50|13.89|16.32|13.89|

_Source:_ Survey of community leaders and local dealers’ adverts, complemented by completed auctions for machinery services on the Prozorro sales platform. Prices are for ammonium nitrate and urea for fertilizer and the cost of harvesting and ploughing/soil preparation for wheat and maize, respectively, for machinery services. Wages are for agricultural labor.

32
and paid labor income for all adults increase by 5% (p-value 0.684) and 8% (p-value 0.389), respectively.

Increases in revenues from livestock come mostly from an increase in livestock sales of USD 32 PPP (USD 9 nominal, p-value 0.002) compared to the control group, followed by increases in the value of milk produced by USD 23 PPP (USD 6 nominal, p-value < 0.0005), and yogurt by USD 7 PPP (USD 2 nominal, p-value < 0.0005) (Table A6 in the <u>Online Appendix). Since</u> these figures reflect the revenues during the four weeks preceding the follow-up survey, our measures of income and revenues may miss long-term cycles and important smoothing dynamics of consumption. However, these results indicate that livestock sales are an important source of income. To what extent reproduction of the livestock vs. decrease in the value of the assets due to liquidation for consumption occurs is an important question to assess the sustainability of these impacts in the longer term.

# Psychological Well-Being

Table 6 shows the impact of the program on the psychological well-being of the primary woman and man. The treatment effects on both indices are statistically significant and large, both in absolute terms and compared to other programs. The index increases by 0.58 SD (FWER-corrected p-value < 0.0005) for the primary woman and 0.26 SD (FWER-corrected p- value 0.010) for the primary man. An important difference with other studies stems from the use of a more comprehensive set of tools to measure psychological well-being. We use an index including six measures: five subjective indicators and one objective measure. The subjective measures include self-reported life satisfaction (WVS), self-esteem, depression (CES-D), self-reported happiness (WVS), and stress (Cohen). The objective measure is salivary cortisol. Other studies of TUP programs typically measure well-being through a smaller set of indicators. Table 6 shows the breakdown of the individual indicators; the results are consistent with the overall index. For primary women all indicators of psychological wellbeing improved: All subjective measures improved and were statistically significant, while the objective measure of salivary cortisol showed a decrease (improved), but the change was not statistically significant. For primary men, all indicators improved, although cortisol and selfesteem were not statistically significant.

# Women’s Empowerment

We present two sets of impacts on women’s empowerment, as described in Section 4. Our first index of women’s empowerment, focused on household finances and expenditures, increases by 0.09 SD (p-value 0.140), which is not statistically significant, in line with results in other countries, which have largely failed to find treatment effects in this dimension across all countries studied (Table 7).<sup>18</sup> One possible reason for this lack of measured impact is that

> 18 Banerjee et al. (2015) find impacts on women’s empowerment in their 2-year endline but it is mostly driven by one country (Pakistan) and in the 3-year endline, estimates are not statistically significant for each country.

24
Other control variables include output, _Y._ The coefficient sign of output _Y_ , _ß1_ , is not unambiguously predictable. An increase in _Y_ normally leads to a higher total wage bill. If the cost increase is equally distributed between female and male labor, there should be no influence on _S_<sup>_F_</sup> . If the wage bill of male labor increases more than proportionally, e.g. due to better bargaining power, this results in a higher _L_<sup>_M_</sup> and/or _w_<sup>_M_</sup> , and _S_<sup>_F_</sup> is expected to fall ( _ß 1_ < 0).

One expects a lower _S_<sup>_F_</sup> and thus a lower cost share of _L_<sup>_F_</sup> in total wages when relative wages for male labor, _w_<sup>_M_</sup> _/w_<sup>_F_</sup> , as part of _CV_ rise ( _ß 2_ < 0). An increase in the capital intensity, _k_ , will increase _S_<sup>_F_</sup> , if capital is a substitute for male labor ( _ß 3_ > 0), but reduce _S_<sup>_F_</sup> , if capital is a substitute for female labor ( _ß 3_ < 0). A higher _tfp_ could increase labor demand in favor of female ( _γ2_ > 0) or male workers ( _γ2_ < 0). The influence of _trade_ on _SHS_ is not easily predictable, as trade could increase the relative demand for female ( _γ1_ > 0) or male labor ( _γ1_ < 0).

In the absence of gender‐specific wage data at the firm‐level, the model is modified in two ways: First, the analysis on trade and the female wage share combines the Enterprise Survey dataset with sectoral wage data by gender, available from Household Surveys. This assumes equal relative wages across all firms in a sector. Such wage data are available for 30 countries in the Enterprise Surveys, allowing to estimate the following female wage share equation: Second, the analysis assumes equal wage rates for female and male workers, _w_<sup>_F_</sup> _= w_<sup>_M_</sup> _= w._ Since _S_<sup>_F_</sup> = _w_<sup>_F_</sup> _L_<sup>_F_</sup> _/ wL_ , the dependent variable becomes the female labor share in total employment, _femsh= L_<sup>_F_</sup> _/ L._ Similarly, the model no longer controls for relative wages, since we assume ln( _w_<sup>_M_</sup> _/w_<sup>_F_</sup> ) = ln1 = 0. While assuming equal wage rates between female and male workers is a strong assumption, we argue that the fixed country‐sector effects included in the model partially correct for this. The female labor share equation takes the following form: Equation (2) becomes our baseline specification, allowing us to include all 64 countries in the analysis, while equation (1) serves as a robustness test. Both models consider four types of trading firms: (1) 5
AUSTRALIA

# 2. Projections - NRR-l by 2030

|Tow|**PgaWatIin**|SW.<br> rtio|w'i|rwth<br>rote|<br>C|CR|TFR|Note|o<br>**Fmtsin**|1|a1|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>1995<br>2000<br>2005<br>2010<br>20Th<br>2020<br>|17010<br>16327<br>19506<br>20551<br>21434<br>22158<br>22715<br>|49.0<br>49.2<br>47.9<br>47.5<br>48.0<br>50.6<br>54.1<br>|1990-94<br>1995-99<br>2000-04<br>2005-09<br>2010-14<br>2014-19<br>2020-24<br>|1.49<br>1.25<br>1.04<br>0.84<br>066<br>0.50<br>0.42|14.8<br>14.1<br>13.7<br>13.2<br>12.8<br>12.5<br>12.3|7.1<br>7.1<br>7.1<br>7.3<br>7.5<br>7.7<br>8.1|1.85<br>1.85<br>1.89<br>1.92<br>1.96<br>1.99<br>2.03|74.0<br>75.0<br>76.0<br>76.6<br>77.3<br>77.9<br>78.6|80.4<br>81.4<br>82.3<br>82.9<br>83.6<br>84.i<br>85.0<br>|9<br>8<br>7<br>6<br>5<br>s<br>4|11<br>10<br>9<br>a<br>7<br>6<br>5|
|2025<br>|23196|58.7<br>|2025-49|0.14|11.9|10.4|Z.07|80.0|86.4|3|4|
|2050|24043|73.6<br>|2050-74|-0.03<br>|11.8|12.0|2.07|81.3|87.9|2|3|
|2075<br>2100<br>2125<br>2150|**23886**<br>24031<br>24173<br>2424|72.9<br>73.7<br>74.3<br>74.7|2075-99<br>2100-24<br>21;3-49|0.02<br>0.02<br>0.01|11.8<br>11.7<br>11.6|11.5<br>11.5<br>11.5|2.07<br>2.07<br>2.07|82.0<br>U2.5<br>U2.7|8.8<br>89.4<br>89.6|2<br>2<br>2|3<br>3<br>3|

# 3. **Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> E% llllB ~~~75 + 2R Z l<br>70-74<br>EMM llX 65-69<br>60-64<br>55.59<br>50.54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>lo8 I . T 4 2 0 2 4 6 8 10 10 8 .. .. 6 4 . .. 2 . 0 2 4 .... 6 al1<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

TFR **eO** Growth rate <!-- Start of picture text --> CoLotry Region Icmem grou.p<br><!-- End of picture text --> <!-- Start of picture text --> Country legian Incomw group Country Region Income group<br><!-- End of picture text --> 37
this analysis. Textiles are also considered by themselves because, arguably, the textile industry is one of the most mature and sophisticated export sectors in Lao PDR and this is where the experience effects are expected to be strongest – this is indeed what is found (i.e. the diminution of the hazard rate from experience variables is the largest).

The last two specifications look at the impact of changes in the definition of death (i.e. the gap between two consecutive transactions is considered too long for it to be the same contract). The standard in the literature is 12 months (also to account for seasonal exports), but it is also customary to investigate whether the effect holds for 6 or 18 months. A death after only a 6 months break could be too radical because it would exclude, for example, exports of fruit that grow only once a year, or exports of warm clothes to Europe. In fact this alteration produces the most significant change, but even this is quite close to the Main specification.

Overall, the findings are confirmed and therefore strengthened by these checks. The coefficients do change, but not to a large extent, and the significance levels are sometimes diminished as compared to the specification of choice (Main). More importantly, though, the direction of the impact on the odds of termination is not changed in either of these alternate setups.

# **6 Discussion and Conclusions**

This paper has analyzed the exporter population of Laos for the period 2005-2010 at the level of monthly activity of individual firms based on a recent dataset of Customs transactions. Its analysis has focused on the impact of firm experience and network effects on the odds that a trade contract (operationalized as a firm-product-destination triplet) survives in subsequent months. This “micro” approach to export survival (looking at contracts rather than firms) was justified by the higher level of refinement with which such variables could be constructed. The analysis has not looked at changes in investment climate or trade facilitation because the timeframe is small and the data insufficient. Furthermore, investment climate variables are more likely to impact export entry and discovery than survival, although improvements will make it easier for firsttimers to acquire experience and enable them to be more reliable, thereby gaining the trust of their partners.

The main tool used for analysis is a discrete-time duration model which accounts for unobserved heterogeneity. This choice of the model and the importance of accounting for unobserved heterogeneity were validated by comparisons with the simpler ones generally used in the literature, and which have several shortcomings outlined in the methodological section. In line with results found elsewhere in the literature, it was also confirmed that the wealth of the trade partner (as measured by 28
Table 2-4 Summary Statistics for Subsidized and non-Subsidized Spillover Variables (calculated based on value added tax)

|||||Sub<br>|sidized<br>|||||Non-Sub<br>|sidized<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Hori|zontal|Bac|ward|For|ward|Hori|zontal|Back|ward|For|ward|
||Number||||||||||Std||Std|
|Year|<br>of Obs|Mean|Std. Dev.|Mean|Std. Dev.|Mean|Std. Dev.|Mean|Std. Dev.|Mean|.<br>Dev.|Mean|.<br>Dev.|
|1998|95,879|0.053|0.062|0.014|0.011|0.009|0.018|0.151|0.078|0.045|0.024|0.059|0.085|
|1999|103,945|0.049|0.056|0.013|0.012|0.007|0.012|0.169|0.089|0.052|0.027|0.069|0.107|
|2000|102,465|0.049|0.053|0.013|0.011|0.009|0.019|0.182|0.094|0.058|0.030|0.076|0.118|
|2001|114,461|0.049|0.050|0.013|0.011|0.008|0.017|0.187|0.095|0.057|0.029|0.080|0.123|
|2002|122,218|0.063|0.064|0.017|0.014|0.008|0.016|0.178|0.088|0.055|0.028|0.081|0.127|
|2003|138,158|0.070|0.075|0.018|0.018|0.013|0.038|0.177|0.083|0.056|0.027|0.085|0.130|
|2004|202,551|||||||||||||
|2005|194,120|0.061|0.058|0.017|0.015|0.014|0.035|0.207|0.102|0.064|0.034|0.101|0.162|
|2006|216,924|0.054|0.054|0.015|0.014|0.015|0.045|0.214|0.103|0.069|0.034|0.102|0.153|
|2007|254,905|0.047|0.056|0.013|0.015|0.012|0.038|0.214|0.097|0.068|0.032|0.105|0.159|
|_Notes_:|Since the i|nformatio|n on value a|dded is m|issingfor 20|04,we lea|ve the summ|arystatisti|cs for 2004|with blan|k.|||

37