rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: panel regression of CPIA clusters A and B average on cumulative conditions

|equation no.|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|variation|quad.+Arg.|linear|quad|log|
|number of cumulative conditions|.010|.004|.007|.|
||(.003)<sup>_∗∗∗_</sup>|(.001)<sup>_∗∗∗_</sup>|(.003)<sup>_∗∗_</sup>||
|number of cumulative conditions (squared)|-.00003|.|-.00002|.|
||(.00001)<sup>_∗∗∗_</sup>||(.00001)||
|log of number of cumulative conditions|.|.|.|.111|
|||||(.061)<sup>_∗_</sup>|
|year|-.018|-.017|-.018|-.016|
||(.008)<sup>_∗∗_</sup>|(.008)<sup>_∗∗_</sup>|(.008)<sup>_∗∗_</sup>|(.008)<sup>_∗∗_</sup>|
|log GDP per capita (PPP)|.807|.792|.794|.816|
||(.155)<sup>_∗∗∗_</sup>|(.154)<sup>_∗∗∗_</sup>|(.154)<sup>_∗∗∗_</sup>|(.158)<sup>_∗∗∗_</sup>|
|aid over GDP|1.697|1.682|1.690|1.689|
||(.520)<sup>_∗∗∗_</sup>|(.535)<sup>_∗∗∗_</sup>|(.526)<sup>_∗∗∗_</sup>|(.521)<sup>_∗∗∗_</sup>|
|Political Rights|-.016|-.019|-.016|-.016|
||(.021)|(.021)|(.021)|(.021)|
|_country fxed efects_|_yes_|_yes_|_yes_|_yes_|
|Observations|1761|1748|1748|1748|
|Countries|139|138|138|138|
|_R_<sup>2</sup>|.14|.124|.127|.123|
|Adjusted _R_<sup>2</sup>|.137|.122|.124|.12|
|AIC|1102.59|1090.29|1085.58|1092.87|
|BIC|1135.43|1117.46|1118.38|1120.12|

Note: * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample for equations (2) through (4).

24
**Figure 5: Composition of Turkish agricultural exports by destination region, 1993-2010**

<!-- Start of picture text --> Share of total exports<br>100%<br>MENA<br>80%<br>US & Canada<br>60%<br>Europe<br>40%<br>20%<br>East & South Asia Pacific<br>Latin America<br>Central Asia<br>0% Sub-Saharan Africa<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Source: FAO (2012) and World Bank staff calculations.**

**Figure 6: Activity of firms exporting to the EU and to MENA by product type and  level of processing, 2002-2010**

<!-- Start of picture text --> Exporting to the EU Exporting to the Middle East and North Africa<br>Number of firms Number of firms<br>5,000 5,000<br>Tobacco, cigarettes, etc.<br>Tobacco, cigarettes, etc.<br>Fish, crustaceans, etc.<br>Fish, crustaceans, etc.<br>4,000 4,000 Beverages, spirits and vinegar<br>Fats and oils (animal and vegetable) Beverages, spirits and vinegar<br>Animal and dairy product Fats and oils (animal and vegetable)<br>Other prepared food Animal and dairy product<br>3,000 3,000<br>Cereals and milled prod ucts Other prepared food<br>Prepared cer eal a n d m ilk pro du cts Cereals and milled products<br>Othe r Prepared cereal and milk products<br>2,000 2,000<br>Prep ar ed f ruit, nuts & vegetables Other<br>Choc ol ates , sweets , co ffe e, te a, spi ces, etc Prepared fruit, nuts & vegetables<br>F resh vegetables Chocolates, sweets, coffee, tea, spices, etc<br>1,000 1,000<br>Fresh fruit and nuts Fresh vegetables<br>Fresh fruit and nuts<br>0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Source: World Bank staff calculations based on Enterprise Survey data from TurkStat.**

22
village hosts a good nursing school will presumably be correlated to a higher taste for expenditure on health. To rule out similar possibilities, I regress a number of village specific characteristics on the instruments to check that there is no correlation between the two. The results are reported in table 13 and show no clear pattern of correlations that give enough confidence about the exogeneity of the instruments.

**Table 13.** Robustness Checks: Exogenous Instruments.

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|---|---|---|---|---|---|---|---|
||% of<br>households<br>with<br>electricity|elementary<br>schools|junior<br>high<br>schools|number<br>of<br>midwives|health<br>posts|distance to<br>coach<br>station|distance<br>to<br>post office|
|(<br>) /<br>(<br>)<br>_d_<br>_d_<br>_f_<br>_m_<br>_E w_<br>_E w_|-5.901|-0.599|0.316|-0.115|0.950|23.984|-0.950|
||(4.430)|(0.499)|(0.283)|(0.106)|(1.233)|(21.131)|(1.940)|
|(<br>) /<br>(<br>)<br>_d_<br>_d_<br>_f_<br>_m_<br>_Var w_<br>_Var w_<br>|<sup>3.782**</sup>|0.293|-0.336**|0.015|0.264|-11.480|-1.190|
||(1.815)|(0.226)|(0.163)|(0.048)|(0.540)|(8.781)|(0.872)|
|Observations|12,217|12,766|12,686|8,796|12,190|9,229|10,183|
|_R_<sup>2</sup>|0.0188|0.0046|0.0217|0.0056|0.0155|0.0105|0.0299|

**Notes:** Unit of observation is household. Standard errors robust to village level clustering in parenthesis. *** p <0.01, ** p <0.05, * p <0.1

As a last check I performed some Montecarlo simulations to assess the robustness of the estimates. I have drawn 1,000 random samples, adequately calibrated to reproduce the correlation between endogenous and outcome variable of the real sample, and I estimated the TSLS and the LIML coefficients for each drawn. Table 13 reports the averages and standard deviations of the coefficients estimated, together with the corresponding _F_ statistic, the p-value of the test of over identifying restrictions and the confidence interval corresponding to the test of equality between the coefficient estimated from the simulated sample and the ones estimated from the real sample and reported in table 12. The coefficients estimated are very similar to those of table 11, I do not reject that they are equal in more than 95% of the cases ( _coverage_ ). Moreover, the _F_ statistic is now systematically larger than 10, which is above the 20% level of the Stock and Yogo (2002) critical values for the TSLS, and above the 10% one for the LIML estimator. Finally, the Hansen J test of overidentifying restrictions leaves us little doubt about the possibility that the instruments are not exogenous.

26
# **_3.2. Effects of Capital Controls_**

To examine the impact of capital controls on financial market integration we perform event studies on a stock level basis.<sup>18</sup> The results of these tests are summarized in Table 2, showing the number of cases in which the post-event mean is significantly different from the pre-event mean. In addition, we show the behavior of the average cross-market premium around changes in controls on outflows (Figure 1) and changes in controls on inflows (Figure 2).<sup>19</sup> Figures 1 and 2 and Table 2 suggest a common pattern. Before capital controls on outflows are introduced, the cross-market premium is close to zero with a very low volatility, however it jumps significantly after controls are imposed. For example, in Argentina the average cross-market premium goes from -0.02 percent to 11.54 percent. In the case of Venezuela, it goes from -1.29 percent to 24.56 percent. When controls are lifted, the reverse happens. For example, when Argentina removes the controls on capital outflows, the mean of the average premium decreases from 2.33 percent to 0.76 percent. In the case of South Africa, the mean of the average premium decreases by 15.79 percent, from 17.71 percent to 1.92 percent. This result is highly consistent across stocks (as noted in Table 2). The introduction of capital controls significantly increases the cross-market premium for all stocks tested. The lifting of controls results in a drop of the premium in all but two stocks (in the case of Argentina).

> 18 In some cases, the underlying stock or the DR trades very infrequently in either the pre- or post-event period, limiting the number of observations. When less than 15 observations are available to calculate the mean, the stock is not used in the event study.

> 19 Note that Table 2 and Table 4 show the average change of the mean across stocks (the cross-market premium mean difference is first calculated per stock and then averaged across stocks per country and event). Meanwhile, the figures display the change of the mean of the average cross-market premium (the cross-market premium is first averaged across stocks per country and then the pre- and post-event mean difference is calculated). As a result, the mean change for each event differs slightly between the table and the figures.

15
names. NREGS cards have been given to 9 villages. NREGS cards should definitely be given to differently-abled persons. Tey should be paid salary even when they simply stay at the site. Tis mission is mainly to identify the differently-abled persons. All should participate in the peoples status survey. Ten only we will be able to differentiate the poor and the differently abled persons.” “I now read the newly included voters name at No.14, Seliambedu village. Amsaveni daughter of Ramakrishnan. Suganya daughter of Gnaprakasam. Gayathri wife of Kamalakannan. Kanimozhi wife of Devaraj. Aruldass. Babu son of Gnanaprakasam. Sridhar son of Ragupathi. Kalaiselvan son of Madasami. Arul son of Panneer Selvam. Tangamuthu son of Arumugam. Te voters ID cards are with me. If anybody’s name is omited, you get the form from me and fill up the form.”

# **4.1.1 Topic Validation**

While the topics identified by this method are largely consistent with what we would expect in a _gram sabha_ meeting, we further validate the topics generated in two ways. First, as a test of predictive validity, we examine whether the topics that capture proforma features of the assembly are indeed more likely to be discussed by officials, rather than citizens. More specifically, the topic model identifies a set of standard remarks — such as the reading of resolutions, the formal greetings and votes of thanks, and discussion of government funding allocation — as distinct topics. If these topics capture the rote features of assemblies as they are conducted, these should be primarily spoken by officials, who are responsible for convening and adjourning the meeting, as well as sharing information about recent public expenditures. Figure 2 plots the difference between the expected proportion of these proforma topics between citizens and officials (both elected and administrative) for the documents in the corpus. As expected, these proforma speeches are all _significantly_ more likely to be raised by officials, suggesting that the topics reflect our substantive interpretation of their content.

Second, we also validate the topics against the survey data collected by enumerators sent to each village. More specifically, as part of the data collection process, enumerators were asked to record information on the types of issues raised during the assemblies. Given this data, we can coarsely examine whether the type and frequency of issues counted in the survey-collected data correspond to their counterparts in transcript data. Tis comparison, while helpful, is necessarily 21
# **5 Discussion and Conclusion**

## **5.1 Explanations for the findings of the study**

This study has illustrated that, on average, the sales of formal firms in Zambia have recovered from the pandemic far more strongly than their employment levels. These findings suggest that formal firms have adjusted their operations in a way that reduces their need for labor to achieve the same level of sales. In other words, there has been a shift in the capital–labor mix that formal firms use, away from labor. If this is a persistent change, lower levels of formal employment in low- and lower-middle-income countries may be a concerning consequence of the COVID-19 pandemic that lingers for years to come. This pattern of a weak recovery of formal employment has also been observed in several emerging-market economies (Andaloussi et al. 2022). Ultimately, this means more people are in vulnerable employment situations that lack a formal safety net and there is less income tax being paid to fund the provision of public goods and services. Detailed investigation of the survey and tax administrative data does not suggest that the overall findings are due to improvements in formal sector labor productivity. There is no evidence of existing firms reporting higher average wages for the employees they retained or large shifts between relying on full-time compared to casual employees. In addition, the tax administrative data does not suggest there have been a large number of new firms entering the formal sector.<sup>5</sup> Rather, a more compelling explanation for what we observe is that many firms have tried to rebound from the crisis by reducing (in some instances potentially unproductive) labor inputs, which can often be challenging in a more stable setting. An unavoidable limitation of this study is that the focus is on only formal sector employment, which means that it is entirely possible that people who were previously in formal employment have moved to the informal sector and maintained (or even increased) their productivity.

> 5If this was the case, it could be possible that employees are leaving existing firms and moving to more productive new firms.

30
|||approval||||
|---|---|---|---|---|---|
||Works in agovernment<br>job|Respondent<br>approval|0.00<br>(0.04)|0.01<br>(0.04)|-0.01<br>(0.05)|
|||Community<br>approval|0.01<br>(0.04)|0.00<br>(0.04)|0.03<br>(0.05)|
||Works in a|Respondent|0.09***|0.10***|0.08**|
||restaurant/hotel|approval|(0.02)|(0.03)|(0.03)|
|||Community|0.05|0.08**|0.02|
|||approval|(0.04)|(0.03)|(0.05)|
||Jobpays verywell|Respondent|0.10|0.12|0.07|
|||approval|(0.08)|(0.08)|(0.13)|
|||Community|0.01|-0.02|0.06|
|||approval|(0.09)|(0.08)|(0.14)|
|Husband’s|Not working and no|Respondent|0.05|0.03|0.08|
|employment|income|approval|(0.05)|(0.07)|(0.08)|
|status||Community|0.05|0.00|0.11|
|||approval|(0.05)|(0.05)|(0.07)|
||Previously unemployed|Respondent|0.13***|0.15***|0.11**|
||but now employed|approval|(0.04)|(0.04)|(0.04)|
|||Community|0.08***|0.08**|0.09**|
|||approval|(0.02)|(0.03)|(0.04)|
||Migrant worker who|Respondent|0.08*|0.06|0.11**|
||does not send|approval|(0.04)|(0.04)|(0.04)|
||remittances|Community|0.09**|0.07*|0.11**|
|||approval|(0.03)|(0.04)|(0.04)|

_Notes_ : (1) Positive correlation means that approvals are associated with higher likelihood of women working for pay outside the house. (2) Respondent and community approval variables are binary variables coded as 1 if the respondent approves of the behavior. Outcome variable is coded as 1 if the female respondent answered yes to working outside the house for pay. In the case of male respondent, he was asked whether his wife works outside for pay and the variable was coded accordingly. (3) No controls and no regional fixed effects are included. (4) SEs clustered at the ward level. (5) Robust standard errors in parentheses. (6) *** p<0.01, ** p<0.05, * p<0.1.

52
# **<mark>Table A5 – Impact of FD Law on Bank Lending to Firms (North vs non-North States) Given State Public Debt</mark>**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms headquartered in Northern vs nonNorthern states of varying ex ante public indebtedness. North states are defined following the definition of the Mexican National Statistics Agency. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are <u>provided in Table A1 in the Appendix.</u>

||Vol<br>North<br>(1)|umef,b,m<br>Non-North<br>(2)|Invest<br>North<br>(3)|mentf,b,m<br>Non-North<br>(4)|Workin<br>North<br>(5)|g Capitalf,b,m<br>Non-North<br>(6)|
|---|---|---|---|---|---|---|
|Postm*DebtStates,16Q1|0.015**|0.017*|0.042***|0.092**|0.017**|0.009|
||(0.008)|(0.010)|(0.015)|(0.036)|(0.008)|(0.011)|
|Observations|631,424|620,646|113,370|93,220|566,800|564,604|
|R-squared|0.811|0.795|0.876|0.865|0.806|0.793|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|

70
coded so as to capture colonization by Great Britain is strongly positively correlated with our two transparency indices: the simple correlation coefficient is 0.72 for the ITI and 0.74 for the ETI. Since British colonization took place in this region largely in the 18<sup>th</sup> and 19<sup>th</sup> centuries, we can be confident that it is exogenous to current (i.e., 2004) bilateral trade flows. We therefore use two dummy variables, one for exporter colonization by Great Britain and another for importer colonization by Great Britain, as instruments for exporter and importer transparency respectively. They are sourced from the CEPII distance database (Mayer and Zignago, 2006).<sup>16</sup> Wooldridge (2002, pp. 663-665) sets out a straightforward methodology for instrumental variables estimation of Poisson models. In the first stage, the endogenous explanatory variables (ETI and ITI) are regressed by OLS on the exogenous explanatory variables (distance, GDP, tariffs, and NTBs) and the instruments (British colonization). The residuals from the first stage regressions are then included as additional regressors in the final Poisson regression.<sup>17</sup> We apply this approach to obtain the results in Table 6, treating ETI and ITI as the only endogenous variables. (For first stage regression results using the full sample, see Table 7.) Tariffs and non-tariff barriers are treated as exogenous in this case, because the reference group aggregation scheme we have used to produce HS 2-digit data means that the endogeneity problem is far more limited than would be the case if, for instance, simple trade weighting had been used.

Moving down column 2, we see that the estimated coefficients on both importer and exporter GDP retain their expected positive signs and are statistically significant at the 1% level, while tariffs and NTBs both impact negatively on bilateral trade. While NTBs are statistically significant at the 5% level, bilateral tariffs are only marginally significant at the 15% level (prob. = 0.151). In both cases, however, the impacts of these variables

> 16 It is also necessary for the instruments to be excludable from the gravity regression itself. It is arguable that our colonization dummies satisfy this criterion, since country-pair fixed effects already account for the impact of _common_ colonial history, which gravity models often find to be a significant determinant of bilateral trade. However, instrument choice and excludability are important questions for future work in this area to address.

> 17 Note that the estimated standard errors have not been adjusted to take account of the use of first stage residuals. Thus, they tend to understate reality.

25
Because Burkina’s smaller seed processing companies produce partly, ra‐ ther than fully, refined oil, concerns have been expressed that their oil is of poor quality and perhaps dangerous for human consumption. SN‐CITEC, which pro‐ duces fully refined (and high quality) oil with its own label, has complained re‐ peatedly that some of the smaller companies use its label. A USAID (2006) report commented (p. 43) that: “The large processors complain that competition from smaller, more artisanal production units also undercuts them in the market. These firms operate outside the fiscal net, so they typically do not pay taxes and they do not apply the appropriate ‘ _textes_ ’ (regulations). These firms reportedly use unhygienic production methods and their unrefined oil is dirty, contami‐ nated, and inferior to the vegetable oil of large processors.” The pricing of cottonseed bought by the smaller companies in Burkina Fa‐ so is pan‐territorial and pan‐seasonal—similar to that of seed cotton. During 2008 and 2009, SOFITEX sold cottonseed at CFAf 75,000 per ton net of any taxes; when taxes are included, a ton would cost the companies more than CFAf 90,000.<sup>**24**</sup> The prices charged for oil and meal, however, differed considerably among companies. Meal prices during 2009 varied from a low of CFAf 60,000 per ton to a high of CFAf 110,000 per ton (Figure D1) while oil prices varied from CFAf 8,300 per jc to 13,000 per jc (Figure D2), a range similar to those recorded in Uganda and Tanzania. There are numerous reasons for such price variability, in‐ cluding oil quality, location of company, time of sale, etc.

A comparison of the oil and meal prices received by the smaller compa‐ nies with those received by SN‐CITEC shows, surprisingly, that the large firm’s price premium for meal sales is larger than its premium for oil sales. SN‐CITEC sold its meal at CFAf 103,000 per ton in 2007/08 and 124,500 per ton in 2008/09. The average meal prices received by the smaller companies during these two seasons were CFAf 92,750 per ton and 90,300 per ton, implying that SN‐CITEC commanded a premium of 11 percent and 38 percent over the smaller companies. Such premium seems justifiable for a number of reasons, including that SN‐ CITEC is an established company using advanced crushing technology and bet‐ ter packaging, and has a larger marketing network.

> **24** Table D3 (Panel II) reports prices paid for seed by the 21 companies surveyed. When the price was quoted without tax, it was the same for all companies, CFAf 75,000/ton. When it was quoted with tax it was not the same (for example, company **_IX_** paid CFAf 92,270/ton while company **_X_** paid CFAf 90,270/ton.) It is not clear what explains the difference.

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17