rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

What makes a survivor? For exporters, this question has become a focus of study since the seminal work of Besedes and Prusa (2006a) showed that the median duration of USbound export spells was, strikingly, only two years. Subsequent work has identified the importance of the sustainability margin of exporting in addition to the classical margins: the intensive (growth in volume) and the extensive (diversification of products and markets). Low-income countries in particular face very high exit rates from exporting, despite having entry rates similar to developed economies (Brenton, Saborowski and Uexkull 2009). It is for this reason that in developing countries a key element in achieving higher aggregate export growth is to reach higher survival rates for existing trade flows (Besedes and Prusa 2006a).

The growing literature which looks at the determinants of export survival and the duration of trade has followed several strands. Theoretical developments (using both representative and heterogeneous firms) such as the search-cost theory (Rauch and Watson 2003) or the literature on sunk costs have some explanatory power, and the importance of sunk costs in firms’ export decisions has been empirically verified (Roberts and Tybout 1997). With sunk costs one would expect long spells, but this tends to conflict with the low values found in the literature. It has therefore been argued that there is currently no given theoretical model that can fully explain the short trade durations that have been observed (Brenton, Saborowski and Uexkull 2009, Hess and Persson 2011b).

Empirical studies have focused on macro trade data at the country and industry level (usually from UN’s Comtrade database), the dominant focus being on specific determinants of export survival. Research in this vein has analyzed, for example: the impact on duration spells of differentiated versus homogeneous products (Besedes and Prusa 2006b); the effect of the GDP in origin or destination country (Hess and Persson 2011b); the importance of initial export values and fixed costs to export (Fugazza and Molina 2009); and contract institutions (Araujo, Mion and Ornelas 2012).

Another type of empirical investigation into survival determinants uses firm-level data, including panels of firms and customs records, but these are generally much harder to obtain. They have tended to be country or sector specific, looking, among others, at the impact of diversification and receiving support from the export promotion agency in Peru (Volpe and Carballo 2008), sequencing of destinations for firms in Argentina (Albornoz, et al. 2011) and the importance of networks of exporting firms in regions of Colombia (Tovar and Martinez 2011). More recently, with increasing availability of customs data, cross-country comparisons of firm-level data have allowed a more refined understanding of local specificities (Cadot, et al. 2010). This culminated 2
Table 10 Olley and Pakes Regressions for Grouped Data with Contemporaneous Subsidized and non-Subsidized Spillover Variables (calculated based on value added tax) and Tariff Controls: non-SOEs vs. SOEs (all firms, foreign-invested, domestic firms)

||All|firms|Foreign-inv|ested firms|Domes|tic firms|
|---|---|---|---|---|---|---|
||non-SOEs|SOEs|non-SOEs|SOEs|non-SOEs|SOEs|
|Foreign share (by HK-Taiwan-Macau)|-0.0041|0.0371|0.003|-0.0765|||
||(0.0031)|(0.0579)|(0.0044)|(0.0716)|||
|Forein share (b other countries)|0.0050*|0.121***|0.0130***|0.0829|||
|g y|(0.0026)|(0.0357)|(0.0048)|(0.0698)|||
|State share|-0.001|-0.0250***|-0.0016|0.0312|0.0007|-0.0253***|
||(0.0033)|(0.0045)|(0.0091)|(0.0318)|(0.0035)|(0.0046)|
|Hor_subsidized|0.0398|0.230*|0.0177|-0.683|0.0662|0.249*|
||(0.0871)|(0.134)|(0.0865)|(0.416)|(0.0963)|(0.135)|
|Bw_subsidized|0.831|1.133**|1.138*|2.560***|0.636|1.086**|
||(0.534)|(0.523)|(0.572)|(0.793)|(0.574)|(0.535)|
|Fw_subsidized|1.068***|0.685***|1.161***|0.785***|0.943***|0.659***|
||(0.130)|(0.114)|(0.140)|(0.183)|(0.147)|(0.118)|
|Hor_non_subsidized|0.240**|0.116|0.214**|-0.518|0.255**|0.117|
||(0.0928)|(0.111)|(0.0966)|(0.497)|(0.0978)|(0.112)|
|Bw_non_subsidized|0.765**|0.695|0.666**|2.763**|0.842*|0.663|
||(0.376)|(0.527)|(0.314)|(1.109)|(0.456)|(0.537)|
|Fw_non_subsidized|-0.0597|0.00991|-0.00319|0.408|-0.105|0.0161|
||(0.102)|(0.136)|(0.111)|(0.258)|(0.0990)|(0.144)|
|lnTariff|-0.0394**|-0.0515**|-0.0295*|-0.0408|-0.0424**|-0.0510**|
||(0.0165)|(0.0209)|(0.0175)|(0.0355)|(0.0187)|(0.0209)|
|lnTariff_backward|-0.0324|-0.0135|-0.0263|0.0151|-0.0361|-0.0122|
||(0.0198)|(0.0369)|(0.0185)|(0.0737)|(0.0223)|(0.0371)|
|lnTariff_forward|-0.00487|0.0129*|-0.0134|0.0568**|-0.0017|0.0123|
||(0.0079)|(0.0076)|(0.0110)|(0.0242)|(0.0072)|(0.0076)|

51
**Table 7. Components of livestock overall score at the beneficiary level**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Livestock<br>Score|Livestock<br>Quality<br>Score|Livestock<br>Quantity<br>Score|Animal<br>Bought at<br>The Correct<br>Age (0/1)|Animal Is an<br>Improved/Crossed/Hybrid<br>Breed (0/1)|Animal Is<br>Productive<br>(0/1)|Animal<br>Health by<br>Mean<br>Number of<br>Illnesses|Fraction of<br>Animals Not<br>Observed<br>(Dead/Stolen/Sold)|
|Training|0.067|0.035|0.125*|-0.014|0.020|-0.032|0.032|-0.045**|
||[0.080]|[0.077]|[0.073]|[0.040]|[0.013]|[0.042]|[0.025]|[0.018]|
|Control means|0.055|0.06|-0.029|0.354|0.161|0.448|0.892|0.184|
|N|5,061|5,127|5,471|5,524|5,515|5,127|5,127|5,524|
|R-squared|0.403|0.453|0.353|0.346|0.821|0.356|0.293|0.391|

Notes: This table reports the OLS regression results for the treatment effect on household-level outcomes for livestock projects only at the first endline survey. The dependent variable in column 1 is an aggregate index of columns 2 and 3. Column 2 is composed of columns 4 to 8. Column 3 is an indicator of the number of animals a participant received. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

43
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 15. Botswana’s infrastructure spending needs are among the lowest in the region, relative to GDP**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>LIC-NoFragile<br>SADC<br>Resource-Rich<br>MIC<br>Congo, Dem. Rep.<br>Ethiopia<br>Kenya<br>Sudan<br>Uganda<br>South Africa Capex O&M<br>Cape Verde<br>Botswana<br>% of GDP 0 10 20 30 40 50 60 70<br><!-- End of picture text --> Source: Foster and Briceño-Garmendia 2009.

Note: LIC = low-income country; MIC = middle-income country; SADC = Southern African Development Community; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

Botswana’s existing spending amounts to around 7.8 percent of GDP (figure 17). This is close to the average for middle-income states and for the Southern African Development Community (SADC). Relative to its peer group, Botswana is more heavily reliant on the public budget for ICT, transport, and water and sanitation investments. Botswana’s spending effort on transport and water and sanitation is substantially higher than the respective average for MICs (figure 17). Consequently, the largest share of infrastructure spending goes to water and sanitation (40 percent), followed by transport (35 percent) and ICT (18 percent).

The composition of infrastructure financing in Botswana differs markedly from African peers. In particular, infrastructure is funded almost exclusively by public investment, reflecting the country’s strong public finances. Particularly striking is the low level of private participation in infrastructure; even in the ICT sector where such funding typically dominates.

32
53 Table 24 RegressLon **IStimtes** for **the Private** Sector Dependent Variables _TIP:_ t-values **La parentheses**

|Dependent<br>**Variable**|Kodel 1|Model 2|
|---|---|---|
|Intercept|0.006 (0.2)|-0.012**(0.6)**|
|GOUT<br>IKEP|0.557 (14.7)<br>0.026 (2.7)|0.553**(15.3)**<br>0.02X (2.6)|

|Year Dummy**Variabler**|||
|---|---|---|
|1978<br>-0.014|(.5)|-0.014 (0.5)|
|1979<br>-0.07|(2.3)|-0.072 (2.4)|
|1980<br>-0.05|(0.2)|-0.005 (0.2)|
|1981<br>-0.017|(0.6)|-0.016 (0.5)|
|1982<br>-0.026|(0.9)|-0.026 (0.9)|
|1963<br>-0.055|**(1.6)**|-0.055**(1.9)**|
|1984<br>-0.016|(0.5)|-0.016 (0.5)|
|1985<br>-0.043|(1.5)|.0.043 (1.5)|

|Industry Dumy Variables|
|---|
|311<br>-0.021 (0.5)<br>|
|313<br>-0.050 (1.0)<br> <br>|
|321<br>-0.007 (0.1)<br>|
|323<br>-0.049 (1.0)<br>|
|324<br>-0.019 (0.4)<br>|
|331<br>-0.036 (0.7)<br>|
|332<br>-0.031 (0.6)<br>|
|341<br>-0.061 (1.2)<br>|
|342<br>-0.028 (0.6)|
|351<br>-0.036 (0.8)|
|352<br>0.020 (0.4)|
|354<br>-0.086 (1.8)|
|355<br>-0.003 (0.1)|
|356<br>0.007 (0.1)|
|361<br>-0.027 (0.6)|
|362<br>-0.032 (0.7)|
|369<br>-0.011 (0.2)|
|371<br>0.010 (0.2)|
|372<br>0.001 (0.0)<br>|
|381<br>-0.003 (0.1)<br>|
|382<br>-0.009 (0.2)|
|383<br>-0.025 (0.5)|
|384<br>0.000 (0.0)|
|A2<br>0.6770<br>0.6394|
|F-test<br>11.560<br>39.120|
|GOUT- Rate of growth of real outputs|
|GIMP*Rate of growth of import penetration.|
had 8 percentage point higher survival rates in the first year, 13 percentage point higher survival rates in the second year, and 20 percentage point higher survival rates in the third year. The results are similar for the experimental and propensity score estimates.

Although treatment has large impacts on the likelihood the winners are operating a business relative to the controls, the observable characteristics of the winners operating a firm and the control group operating a firm are similar (appendix 5). In particular, winning does not appears to have resulted in a change in the sectors individuals operate in, nor in the education and initial wealth profiles of the entrepreneurs who operate businesses.

# **5.2 Impacts on Employment**

Generating employment was one of the main goals of the program. Our main measures of employment come from survey reports. The surveys asked detailed questions on employment in different categories, and questions on the most recent workers hired. Appendix 6 compares these reports to administrative data on the winners that came from program reports at around the same time as the second survey. The employment reported to the program is larger than that reported to our surveys in the majority of cases, and appears as if may suffer from over-reporting biases. In contrast, comparison of our survey measures with physical observation where available suggests more confidence in the survey measures.

Table 4 reports the impact of winning on key employment outcomes, while Figure 1 shows the CDFs of total employment in round 3 by treatment status. These are unconditional estimates, which code employment as zero for individuals not operating firms. Appendix table 6 shows estimates conditional on the firm being in operation.<sup>14</sup> Appendix table 7 reports estimates on other measures of employment that were contained in the pre-analysis plan.

The first and fifth columns of Table 4 consider the impact on the employment of the owner for new firm applicants and existing firm applicants respectively. We see increases in these own employment rates, with the difference between these estimates and the impacts on start-up and

> 14 Note that this conditions on an outcome (business operation) which has already shown to be impacted by the treatment. As such it no longer represents a causal treatment effect, but is presented for descriptive purposes to examine how the characteristics of the firms operating differ between treatment and control groups.

21
DUMMY(96+) = Takes the value 1 for 1996 onward, and zero otherwise.

PUBINV/TOTEXP represents the variable λ , TAXREV/GDP is a proxy for the tax rate τ , and ODA/GDP is foreign aid T, in our theoretical analysis. We have added other explanatory variables such as EXP/GDP and LAB as control variables. As mentioned before, the economy of Rwanda went through very significant changes after the genocide of 1994; in particular many of the changes in terms of the policy regime came into effect in 1996. Thus, the qualitative effect of aid on the endogenous variables can be different after1996 compared to what it was before then. To capture this effect we have included an intercept dummy (DUMMY(96+)) as well as a slope dummy that interact with ODA/GDP. Based on proposition 1 and 2, we expect a negative signs for both β1 and β7.

Before discussing the results, it is useful to discuss the data.

# **_Description of Data_**

The data used in this study come from the World Development Indicators (WDI, 2006). Table 2 summarizes the mean, median and standard deviation of the variables used in the regression. As can be seen from the above table, all the variables display fair degree of variations.

**<u>Table 2: Descriptive Statistics of the Variables</u>**

|**PU**|**BINV/TOTEXP**|**TAXREV**|**ODA/GDP**|**LAB**|**EXP/GDP**|
|---|---|---|---|---|---|
|Mean|40.81|9.85|20.29|2.99|8.32|
|Median|39.42|10.08|17.24|2.82|7.65|
|Maximum|60.29|12.67|95.05|4.18|14.44|
|Minimum|19.23|3.62|9.54|2.28|5.15|
|Std. Dev.|10.31|1.92|18.3|0.59|2.71|
|Skewness|0.21|-1.32|3.12|0.75|0.66|
|Kurtosis|2.55|5.81|12.65|2.29|2.29|

24
estimate of the impact of duration of school closures finds that for every week that schools were closed, learning levels declined by almost 1% of a standard deviation (Patrinos 2023).

# **6. Conclusions**

We estimate the global impact of COVID-19 on student learning on standardized tests over time. We model the effect of closures on achievement by predicting the deviation of the most recent results from a linear trend in mathematics and science achievement using data from all rounds.

COVID-19-induced school closures led to significant student learning losses. Math scores declined from 2018 to 2022 by an average of 12 points, or 14 percent of a standard deviation (SD), roughly equivalent to seven months of learning. Larger declines are recorded for students in schools that faced relatively longer closures and for lower-achieving students. Countries with the shortest closures experienced relatively small losses, while countries with the longest closures experienced losses of around 20 percent of a SD, or up to 12 months of learning. Depending on the closure length measure, each week of school closures was associated with 0.15-0.17 points or around 0.2 percent of a SD of additional decline in achievement for boys. At around 40 weeks of full school closures, the overall learning loss for boys is the same as for girls and increases for longer closures. Students with an immigrant background lost 0.33 points or 0.4 percent of a SD with every week of full school closures and while in countries with shorter closures their learning loss was smaller, for countries with around 25 weeks of closures their learning loss is like the learning loss for natives, and it increases for longer closures.

Distributional analyses reveal significant differences in the learning losses between students at different achievement levels, but also across countries that vary in the length of school closures. For countries with the average length of school closures the learning loss is similar for low-, average-, and high-achieving students. However, in countries with relatively short closures, the best students lost very little in terms of achievement and the decline is mostly 21
is based on the aggregate food expenditure deflated by the Paasche index, computed with the median aggregate price at the level of each data collection stratum (A. Deaton and S. Zaidi, 2002). Following Deaton and Zaidi (2002), we compute the Paasche index for each sample household with the following equation: where _PP_ is the Paasche index;<sup>_s_</sup> _i_<sup>is the budget share for commodity i;</sup><sup>_p_</sup> _i_<sup>is the</sup> aggregate price for commodity i; and<sup>_p_</sup> _i_ 0<sup>is the reference aggregate price for commodity i, which</sup> is set to the median aggregate price in each stratum.

**<u>Table 8: Welfare Loss from the VAT by Food Expenditure Quintile</u>**

||**Bottom**|**2nd**<br>**quintile**|**3rd**<br>**quintile**|**4th**<br>**quintile**|**Top**|
|---|---|---|---|---|---|
|||8 % Increase in<br>|cereal, meat, a<br>|nd other foods<br>|<br>|
|Compensating variation (x1000)|-39.138|-67.136|-91.616|-123.922|-213.267|
||[0.145]|[0.109]|[0.148]|[0.208]|[1.241]|
|Share of aggregate loss|0.073|0.125|0.171|0.232|0.399|
|||12 % Increase in<br>|cereal, meat,<br>|and other foods<br>|<br>|
|Compensating variation (x1000)|-45.434|-77.802|-106.694|-145.849|-257.681|
||[0.167]|[0.125]|[0.170]|[0.246]|[1.574]|
|Share of aggregate loss|0.072|0.123|0.168|0.230|0.407|
|Observations|4,266|4,230|4,248|4,250|4,245|
|Standard errors in brackets, 100 boo<br>Source: 2012 1-2-3 Survey, DRC|tstrap replic|ations||||

Table 8 presents welfare losses from the introduction of the VAT by quintile of food expenditure. The magnitude of the welfare loss increases from the bottom to the top quintile. The bottom quintile only accounts for 7.3 percent of the aggregate welfare losses, while the top quintile accounts for 39.9 percent of the aggregate welfare loss. The results from the table cleary highlight that the current regime of the VAT is progressive.

Although our results suggest that the VAT is progressive, we do not know whether exemptions are well-targeted. To address this issue, we assess budget share devoted to exempted goods. Engel curves plotted in figures 3.2a-3.2e show the relationship between log-household food expenditure and budget share devoted to specific food commodities. Indeed, these graphs can help to access whether exemptions are well targeted. Because cereals, including bread and rice, are necessities one can infer that these exemptions are well-targeted. However, based on Figure 3.2b, exemption of meat does not seem to be pro-poor because meat is a luxury good in this context.

21
mechanisms, most of the crop that is commercialized (around 20% of the total output) is sold shortly after harvest. Therefore, maize prices show strong seasonal variation due to the timing of the production cycle, usually peaking between December and March. Transaction costs between markets are considerable (World Bank, 2018a).

Results of the month-price effects (γm in equation 2) are presented in Figure 3 and Table 7. Overall, the findings indicate that maize prices in markets close to affected areas are very sensitive to the occurrence of weather shocks. Yet, the direction and persistence of the effects depend on the weather shock and the timing of the event with respect to the crop cycle. In the case of the drought in 2005, the findings show that prices begin to rise as the shock unfolds following the harvest season and peak at month 9, increasing by 78.9% per a reduction of a standard deviation in the precipitation index. After this, the inflationary effect begins to dissipate. However, a year after the onset of the drought, maize prices remained 29.3% higher in markets within a 50kilometer radius from affected villages compared to markets in areas less or not affected. The upward trend for the price effects associated to Cyclone Jokwe (2008) is quite similar although the increase in maize prices starts to level off and reverse earlier.

The pattern of the effects associated with the floods in 2007 is the opposite. The point estimates show a reduction in maize prices of up to 46.9% in affected areas during the first three months following the disaster. This interval of time overlaps with the lean season in affected areas, a period where the demand-side effects brought about by the shock are likely strongest. The results show that the deflationary pressure starts to dissipate as the harvest season progresses and the supply-side effects begin to take effect.

# **4. Robustness Analysis**

This section presents results of tests performed to check the robustness of the main findings. One of the threats to the internal validity of the study lies with the definition of treatment, namely the triple interaction ( _Shock_ x _After_ x _Growing Cycle_ ), which may be capturing changes in the outcomes of interest caused by factors other than the weather anomalies, or simply a spurious relationship. The lack of comparable household surveys to measure the outcome variables for at least two data points over a pre-shock period forbids testing the identifying assumption of common 17
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