rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: De** **<mark>p</mark> endent Variable is Intervention Index or PRI**<sup>**1**</sup> **<mark>(</mark> E** **<mark>q</mark> uation 4a** **<mark>)</mark> :**

|**All variables in natural logs or**<br>**log first differences**|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|External liabilities to<br>Assets (1 quarter lag)|-0.100|-0.091|-0.149|-0.089|-0.069|-0.090|
||(3.60)**|(3.57)**|(3.29)**|(3.38)**|(2.95)**|(2.18)*|
|External liabilities to<br>Assets (1quarter lag) squared|-0.048|-0.038||-0.059|-0.035||
||(4.46)**|(4.26)**||(5.74)**|(3.96)**||
|Volatility in stock market index<br>(overpreviousfourquarters)|0.161|0.087|0.264||||
||(3.12)**|(1.92)|(3.46)**||||
|Change in GDP (over previous<br>four quarters)|-0.455||-0.397|-0.489||-0.463|
||(4.70)**||(2.88)**|(5.15)**||(3.38)**|
|Real Effective Exchange Rate<br>Index|-0.215|-0.124|-0.224|-0.236|-0.211|-0.247|
||(2.42)*|(1.44)|(1.67)|(2.72)**|(2.58)*|(1.96)|
|Change in ratio of M0 to total<br>reserves (over previous four<br>quarters)|0.077|0.242|0.168|0.115|0.249|0.160|
||(1.42)|(5.28)**|(2.05)*|(2.22)*|(6.47)**|(1.93)|
|Change in stock market index<br>(overprevious two quarters)||-0.013|||-0.015||
|||(1.67)|||(2.07)*||
|Coefficient of variation in external<br>liabilities to asset ratio (over<br>previous four quarters)|||0.187||||
||||(1.25)||||
|Growth in the change in inflation<br>difference overthe US|||0.153|||0.170|
||||(1.48)|||(1.63)|
|Volatility in change in external<br>liabilities to asset ratio (over<br>previous four quarters)||||||-0.056|
|||||||(1.17)|
|Constant|1.479|1.092|1.393|1.662|1.530|1.717|
||(3.60)**|(2.71)**|(2.21)*|(4.16)**|(4.04)**|(2.95)**|
|Observations|696|733|313|759|758|343|
|F-testforoverallsignificance|<1%|<1%|<1%|<1%|<1%|<1%|

Absolute value of t statistics in parentheses; * Significant at 5%; ** significant at 1%; Marginal effects reported with z- statistics in parentheses; Results of stationarity tests on transformed variables available from authors upon request. Natural log of Real Effective Exchange Rate Index is marginally I(1).

20
# **Tables and Figures**

Table 1: Summary Statistics, by Higher Education Choice

|**Variable**|**Short-Cycle Program**<br>**Bachelor’s Program**|**Not Enrolled**|
|---|---|---|
|Female|0.481<br>0.548|0.542|
|Age at Saber 11|16.782<br>16.550|17.292|
|Siblings|2.347<br>2.132|3.040|
||_Household Income Level_||
|<1 Minimum Wages (MW)|0.187<br>0.149|0.343|
|1-2 MW|0.506<br>0.353|0.463|
|2-3 MW|0.205<br>0.201|0.131|
|2-3 MW|0.102<br>0.296|0.063|
||_Mother’s Education Level_||
|Primary|0.368<br>0.221|0.532|
|Secondary|0.431<br>0.325|0.357|
|Short-Cycle program|0.136<br>0.178|0.072|
|At least Bachelor’s program|0.066<br>0.276|0.039|
|_Standardize_|_d Test Scores from the High School Exit Exam_||
|Math|0.003<br>0.361|-0.149|
|Reading|0.132<br>0.555|-0.240|
|Biology|0.044<br>0.506|-0.218|
|Physics|0.005<br>0.340|-0.144|
|History|0.070<br>0.464|-0.202|
|Chemistry|0.030<br>0.504|-0.214|
|Geography|0.056<br>0.424|-0.182|
|Philosophy|0.064<br>0.407|-0.173|
||_Educational Attaintment_||
|Short-Cycle Incomplete|0.495||
|Short-Cycle Complete|0.342<br>0.012||
|Bachelor’s program Incomplete|0.101<br>0.502||
|Bachelor’s program Complete|0.062<br>0.485||
||_Formal Labor Market Outcomes_||
|Works in 2013|0.517<br>0.547|0.473|
|Avg. Monthly Wage (2013)|909,590.9<br>1,120,830.8|548,288.1|
|N|30,803<br>96,504|201,051|

Note: The sample corresponds to the universe of students who took the high school exit exam (Saber 11) in 2005. The information on educational attainment comes from SPADIES (System for Dropout Prevention of Higher Education), and labor market outcomes for higher education graduates are from OLE (Labor Market Observatory for Education). For high school graduates and those with incomplete higher education, we impute labor market participation and wages using household survey data from SEDLAC (Socioeconomic Database for Latin America and the Caribbean); see Appendix A.3 for details. Avg. Monthly wages are in Colombian pesos (COP).

24
selection gravity model (cf. Helpman et al., 2007) to examine the impacts of EU Harmonization Directives and Mutual Recognition Agreements on intra- and extra-European trade. These effects are captured using dummy variables. Baller (2007) adopts the same approach using data on both EU and ASEAN harmonization and mutual recognition agreements. Empirical results generally suggest that harmonization boosts trade among harmonizing countries, as well as imports from third countries. The present paper builds on and extends these results by focusing on product variety, which is not the object of analysis in either of these two previous papers.

The plan of the paper is as follows. The next section provides some theoretical motivation for the empirical hypotheses to be tested later on, using a heterogeneous firms model of trade. I provide an explicit definition of harmonization within this framework, and use simulations to examine its impacts on export product variety in harmonizing and non-harmonizing countries. Section 3 describes the dataset, a new World Bank database of EU product standards, as well as measures of export variety covering up to 200 countries for the period 1995-2003. The empirical model is presented in section 4, along with estimation results, robustness checks, and details of instrumental variables estimates. Section 5 concludes, and offers some suggestions for further research in this area.

2 Theoretical Motivation This section develops a simple theoretical framework that is used to produce predictions which the remainder of the paper tests empirically. Since the model is essentially a three-country version of Melitz (2003), it is presented in outline only.! The key feature of this model is the inclusion of fixed market entry costs that are allowed to vary bilaterally. I motivate the existence of such costs by relating them to the need to modify products and production processes in order to meet foreign standards.” The available firm-level evidence suggests that foreign standards can indeed impose <u>~ |The presentation here is closest to the version of Melitz</u> (2003) that appears in Helpman et al. (2004) or Baldwin and Harrigan (2007). Baller (2007) also applies a heterogeneous firms framework to the analysis of product standards. The model does not directly address the distinction drawn in the policy literature between mandatory and voluntary standards. The fixed costs modeled here can be seen as related to standards that are either a commercial or legal

5
nighttime lights where the DMSP-OLS data observes no light (see Appendix S3 for a comparison of DMSPOLS data and DMSP-OLS-like data). This difference results from VIIRS’s ability to capture low-level light that DMSP-OLS cannot capture; when simulating DMSP-OLS-like data from VIIRS data, some of the lowlevel light is retained. However, these differences should not have a large impact on results. First, these differences are concentrated in low-lit regions; Appendix S4 shows that average nighttime light trends are relatively consistent across DMSP-OLS and DMSP-OLS-like data. Second, as the empirical strategy section later describes, we control for factors common to all units in a given time period, such as a difference in the satellite used to capture nighttime lights.

While nighttime lights are now a staple of the economic literature on roads programs, including in Africa (Donaldson and Storeygard, 2016), the GlobCover data is a more recent addition to the toolbox. This dataset is produced by the European Space Agency (ESA), and is available annually from 1992 to 2018 at 300m resolution (European Space Agency, 2017, 2019). It classifies each pixel into one of the 36 land cover classes defined in the United Nations Land Cover Classification System. From this dataset, we use two land cover classes. First, we use land classified as urban, defined as 300m pixels covered mostly ( _>_ 50%) by artificial surfaces and associated areas. Second, we create a cropland category, defined as 300m pixels where over 50% of the pixel is cropland (this aggregates three cropland categories in the underlying data).

# **3.2 Aggregating Data**

We rely on Kebeles (Ethiopia’s smallest administrative unit) as our primary unit of analysis. We use a dataset that includes the boundaries of Kebeles for all regions in Ethiopia except Somali Region (15,670 Kebeles).<sup>6</sup> For Somali Region, we rely on boundaries of administrative units one higher than Kebeles—Woredas (N=44). However, for simplicity, we refer to the units of the combined dataset as ”Kebeles” throughout the paper. Kebeles are small enough to capture granular changes in the road network; 15,714 Kebeles cover Ethiopia, and the average size of a Kebele is 70km<sup>2</sup> (the median size is 24km<sup>2</sup> ). Appendix S5 shows a map of Kebeles. When aggregating data to Kebeles, we take the average values of nighttime lights and the total number of urban and cropland pixels.

To test the sensitivity of results to the unit of analysis, when relevant, we also estimate models using 1x1km pixels—the original resolution of DMSP-OLS data (1.1 million 1x1km pixels cover Ethiopia). As GlobCover landcover data are captured at a 300m resolution, we aggregate cropland and urban to the 1x1km pixels by creating binary variables indicating whether any portion of the 1x1km pixel contains urban or cropland area. However, we use Kebeles as the primary unit of analysis because they represent a more

> 6The Kebele shapefile is available at `https://ethiopia.africageoportal.com/datasets/africageoportal::kebeles-level-4/ about`

10
18 Do interest rates ever exceed growth rates? Figure 4 shows a scattergram of 1965-85 averages of output growth and real interest rates. For this period, average interest rates are below growth rates for all countries. However, over shorter periods averaged interest rates exceed averaged growth rates in a few cases. One such case is Chile: in 1974-85, a period marked by massive financial crisis and costly debt relief, interest rates averaged 4.5% and growth only 2.5%. Chile seems to have been on the right hand side of A in Figure 1 in this period. But growth rates for the slowest-growing countries are well below the average real interest rates which are sustained in the fastest growing countries. Unless liberalizing interest rates is itself assumed to sharply raise the growth rate, attempts to hold interest rates at world market levels in the sloqest growers or in countries experiencing growth crises could therefore result in periodic episodes during which their governments seek to write down the real value of debt to restore profits.<sup>21</sup> Figure 4 suggests that real interest rates and growth rates are quite strongly correlated. Table 2 shows the pattern of cross-correlation between average growth rates (DYY) RR, RFSTS, IOCR and the investment rate, IGDP pooling the averages over the two sub-periods. All coefficients are positive and significant at the 5% level except those between IGDP and RFSTS and IGDP and IOCR which are not (and were not expected to be) significantly different from zero; the relationship between RR and IGDP is also not significant at the 5% level.

21 An example of such a strategy was Brazil in 1982-83. As output fell and the country entered severe recession, monetary correction was held below inflation and real interest rates fell on a wide spectrum of financial assets and loans. Together with measures to insulate the private sector from losses on foreign exchange denominated debts, this policy allowed private firms to markedly strengthen their balance sheets over 1981-84.
were able to rule out the effect of unobserved time-invariant country heterogeneity and timespecific shocks using fixed effects, the decision to implement gendered laws may be highly correlated with unobservable country-level time variant factors that also affect financial inclusion. If this unobserved heterogeneity is not statistically accounted for, its effect will be captured by the variables measuring legal equality and inflate its statistical magnitude. Furthermore, greater access to financial services might induce a greater level of development in general which may also be reflected in more egalitarian legal frameworks. This endogeneity resulting from simultaneous effects might bias our main estimations.

To address endogeneity concerns, we first utilize an instrumental variable estimation of the WBL index using two different variables. The exclusion restriction underlying the use of an instrumental variable implies that it needs to be correlated with our dependent variables of interest, but must not have any direct effects on financial inclusion. Thus, selection of relevant variables is carefully made considering their prior use and reliability demonstrated in extant literature. First, the signature of the Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) has been used by Perrin and Bertrand (2022) to instrument the implementation of laws against discrimination in the credit market. The CEDAW is an international treaty adopted in 1979 by the United Nations General Assembly in order to eliminate discrimination against women in political and public life. The 99 States Parties committed themselves to being a society that promotes policies, laws, organizations, structures and attitudes that ensure women are guaranteed the same rights as men. Thus, this prior commitment is likely to affect the implementation of gendered laws in the present without direct consequence on financial inclusion. As such, the dummy _CEDAW_ can be used as a conventional external instrument. Secondly, if one believes, like Merryman (1985, p. 2), that “the legal tradition relates the legal system to the culture of which it is a partial expression,” then the origin of a country's legal framework may rely on characteristics of its informal institutions. Thus, unequal legal treatment of women may be the product of a conservative culture of patriarchy and the consolidation of power in men (Cherif, 2010). We capture the cultural dimensions that reflect differences in gender by using an index provided by the World Atlas of Linguistic Structures that indicates the intensity of gender marking in language ( _Language_ ). Grammatical gender marking has been associated with a wide range of women’s economic and social outcomes (see Mavisakalyan and Weber (2018) for a complete survey). Moreover, language’s grammatical structure offers the advantage of being a stable feature inherited from the distant past, therefore ensuring the respect of the exclusion restriction.

17
# 1. **The Institutional Setting**

Institutional disorder and legal permissiveness makes it difficult to define tEe public sector in Brazil in any precise waj. Consequently, onlyj approximate measures of the size of the public secto r And of fiscal actions are possible. The lack of transparency in public accounts and actions is frequently put to political use; it is a convenient set up for the administration to accomodate conflicting groups and win political support without the close scrutiny of other special interests and of society as a whole. It is not accidental that no substantial progress in the organization and modernization of the public sector has been made. Fiscal reform in a broad sense would necessarily involve the partial dismantling of the network of privileges, preferential treatment, entitleme,its tc; income sources, transfers and the multitude of government actions that benefit special groups in societN. This is the political dimension of fiscal policy that is usually overlooked in economic analyses of developing economies'-. In a broad sense, the public sector comprises four branches the central (federal) government, the monetary authoriti, the state-owned enterprises (both financial and non-financial), and the state and municipal governments (with their own microcosms of financial and non-financial enterprises). The interaction betweem these branches is complex, in the sense that the real direction of subordination and control is loose and informal, and the 1. Lao's description of the "transfer state" captures the political economn aspect of fiscal policy in many developing countries. Cf. D. Lao, "The Political Econouy of Economic Liberalization", The World Bank Economic Review, V.I, n. 2 (January, 1987).

-1 -
!COL=!COL+1 NEXT 'Check Row Totals !NRWS=@ROWS(IDMACSAM) FOR !R=1 TO (!NRWS-1) ROWVECTOR RV{!R}=@ROWEXTRACT(IDMACSAM, !R) VTOT(!R)=@SUM(RV{!R}) DELETE RV{!R} NEXT COLPLACE(IDMACSAM,VTOT, !NRWS) DELETE VTOT 'Turn the Matrix into a Table FREEZE(IDTABSAM) IDMACSAM SETLINE(IDTABSAM,3) SETCOLWIDTH(IDTABSAM,1,12) !COL=2 !RW=4 FOR %LB ACTIVITY COMMODITY HOUSEHOLD GOVERNMENT SAVING WORLD TOTAL SETCELL(IDTABSAM,1,!COL,%LB,"C") SETCELL(IDTABSAM,!RW,1,%LB,"L") !COL=!COL+1 !RW=!RW+1 NEXT '==========Specify the CGE Model============================================= MODEL IDGSS 'Generalized Salter-Swan Model for Indonesia '*****Production Side***** 'Exports are Derived from the CET Function IDGSS.APPEND XE = XD*( (PE / PDT) * (1 - alpha_x) / alpha_x )^(1 /(phi_x - 1)) 'Domestic Sales as a Residual IDGSS.APPEND XD=XS - XE 'Domestic price of exports IDGSS.APPEND PE=EXR*PWE*(1+ te) 'Producer Price of Composite Output (GDP Deflator) IDGSS.APPEND PX=(PE*XE + PDT*XD)/XS 'Price of Domestic Good IDGSS.APPEND PDT*XD=(PQ*QQ - PM*QM)'Tax inclusive IDGSS.APPEND PD=PDT/(1 + td)' Before tax '*****Consumption Side***** 'Domestic Price of Imports IDGSS.APPEND PM=EXR*PWM*(1+ tm) 'Domestic price of imports 'Supply of and Demand for Composite Consumption Good defined from Armington Aggregation of Imports and Demand for Domestically Supplied Good IDGSS.APPEND QQ=b_q*( beta_q*QM^(-rho_q) + (1-beta_q)*XD^(-rho_q) )^(-1/rho_q) 'Imports are derived from Armington Aggregation IDGSS.APPEND QM = XD * ( (PDT / PM)*beta_q / (1 - beta_q) )^(1 / (1 + rho_q)) 'Price of Composite Consumption Good 'IDGSS.APPEND PQ= (CONS_hh + CONS_gov+ INV)/QQ Numeraire '*****Government Account***** IDGSS.APPEND TARIFF=(tm*PWM*EXR*QM) IDGSS.APPEND INDTAX=(td*PDT/(1 + td)*XD) IDGSS.APPEND HHTAX=ytx_hh*Y_hh IDGSS.APPEND Y_gov = TARIFF + INDTAX + HHTAX - (te*PWE*EXR*XE) 30
Table 9: Real income gains by country.

|||R<br>|eal Income Grow<br>|th<br>|
|---|---|---|---|---|
||Armington|Monopolistic<br>competition|Increasing<br>returns|Increasing<br>returns & labor<br>mobility|
||(1)|(2)|(3)|(4)|
|China‐3|1.2%|2.0%|2.4%|2.4%|
|Kazakhstan|1.6%|2.0%|2.5%|5.6%|
|Kyrgyzstan|4.9%|4.4%|3.9%|5.1%|
|Pakistan|1.8%|2.3%|2.5%|2.7%|
|Tajikistan|1.7%|1.5%|1.2%|0.9%|
|Turkmenistan|0.3%|0.0%|‐0.2%|‐0.3%|
|Uzbekistan|0.8%|0.9%|1.1%|1.5%|
|Aggregate|1.4%|1.9%|2.1%|2.7%|

Table 10. Growth in GVA, by sector

||Mono<br>comp|polistic<br>etition|Increasin|g returns|Increasing<br>labor m|returns &<br>obility|
|---|---|---|---|---|---|---|
||Primary|Manuf.|Primary|Manuf.|Primary|Manuf.|
||(1)|(2)|(3)|(4)|(5)|(6)|
|China‐3|‐2%|4%|‐2%|5%|‐2%|5%|
|Kazakhstan|3%|3%|2%|4%|5%|8%|
|Kyrgyzstan|7%|‐6%|8%|‐9%|8%|‐5%|
|Pakistan|2%|4%|2%|5%|2%|5%|
|Tajikistan|4%|‐1%|5%|‐2%|8%|‐9%|
|Turkmenistan|3%|‐1%|3%|‐1%|3%|‐1%|
|Uzbekistan|2%|1%|2%|2%|3%|0%|

Clustering and localization economies occur at the cell level, and population movement is between cells within countries. It is therefore at the cell level that the story appears most clearly, as shown in figures 6 and 7 (shown on smaller vertical scales than preceding scatter plots). With increasing returns and labor mobility, cells in manufacturing areas of Western Kazakhstan experience increases in real incomes in excess of 10%. Driving this is large growth of manufacturing, which doubles in size in Aqtobe, Aqtau, and increases to four times its initial output in Qostanay in Kazakhstan. This occurs alongside population growth, enabling scale economies to be realized. Similar, though smaller, transformations occur in Quetta in Pakistan and Osh city in Kyrgyzstan.

This is also clear in the Table 11, which shows the correlation between the falls in trade costs and real income growth under Armington, Monopolistic Competition, and Increasing Returns with Labor Mobility. First, while base manufacturing shares have little impact on income growth in the Armington setting (and in fact, a slight negative one), once increasing returns to scale are considered, cells with higher manufacturing employment shares are better able to reap the benefits of declines in trade costs. Second, the impact of reductions in import costs remains similar in the three settings, 21
# _4.1. Firm-Specific Information_

Databases, such as Reuters, obtain financials for these listed companies from the exchanges. To the extent that these exchanges have different reporting requirements, financial definitions might vary. Differences in currency value are avoided by using ratios, which are comparable across countries. Ratios are obtained by scaling by total assets unless otherwise noted.

As many empiricists have attributed size as a determinant of capital structure, which could have implications on capital issuance, I assign size categories based on total assets. Korajczyk and Levy (2003) and Baker and Wurgler (2002) find a positive relation between leverage and size. Titman and Wessels (1988) find that size influences not only the extent of leverage, but also the type.<sup>18</sup> Firms that have total assets less (more) than the country-year median are classified as Small (Large).

Leverage itself plays a role in the timing of issuance for many firms as found in the market timing literature (Leary and Roberts, 2005; Korajczyk and Levy, 2003; Baker and Wurgler; 2002). I include this variable to control for any implications market timing or capital structure may have on firm issuance.

Profitability of firms would be an obvious influence since it impacts how well a firm could fund positive net present value (NPV) projects internally (i.e., avoiding external fundraising). Following Titman and Wessels (1988), I calculate this variable as operating income divided by sales. Another obvious influence is the perceived level of risk for a firm. I calculate this as the standard deviation of the return on assets for time t-3 through time t-1.

Also relevant is asset tangibility. This variable refers to how palpable the assets of a firm are and relates to a small firm's access to capital concerns through its limitations on debt levels based on the ability to provide collateral. A firm with fewer tangible assets is thought to have an increased probability of bankruptcy since it would have less ability to raise funds through liquidation in times of financial distress. I calculate this variable by dividing fixed assets by the book value of assets (following Rajan and Zingales, 1995).

To correct for any additional access a firm might have in other nations (Lins, Strickland, and Zenner, 2005), it is vital to include an indication of whether a firm has listings in other countries (e.g., an 12
The Economic Lives of Young Women in the Time of Ebola: Lessons from an Empowerment Program<sup>_∗_</sup> Oriana Bandiera, Niklas Buehren, Markus Goldstein, Imran Rasul, Andrea Smurra<sup>_†_</sup>

> _∗_ We thank all those at BRAC Sierra Leone and IPA Sierra Leone, Gieltje Adriaans, Abdulai Bah and Fernando Fernandez for excellent research assistance. We have benefited from comments from Chris Blattman, Aureo De Paula, James Fenske, Erica Field, Elizabeth Foster, Sebastian Galiani, Rachel Glennerster, Francesco Giovannoni, Jessica Goldberg, Scott MacMillan, James Robinson, Sanchari Roy, Matthias Sutter, Frank Windmeijer, Yanos Zylberberg and numerous seminar participants. We are grateful to JPAL, IGC, UNICEF and the World Bank Group’s Umbrella Facility for Gender Equality for financial support. The views presented are the authors’ and do not represent those of the World Bank or its member countries, or DfID. This is an output of the Africa Gender Innovation Lab. Human subjects approval was obtained from the Institutional Review Board at IPA (#:13October001). The study is registered on the AEA registry (AEARCTR-0000300). All errors remain our own.

> _†_ Bandiera: LSE, o.bandiera@lse.ac.uk; Buehren: World Bank, nbuehren@worldbank.org; Goldstein: World Bank, mgoldstein@worldbank.org; Rasul: UCL, i.rasul@ucl.ac.uk; Smurra: UCL, a.smurra.11@ucl.ac.uk.