rafmacalaba/datause-encoder-data huggingface dataset:
- Christiano, L. J. (1989). Understanding Japan's saving rate: the reconstruction hypothesis. Quarterly Review, (Spr):1025.

- Cirera, X., Fattal Jaef, R. N., and Maemir, H. B. (2017). Taxing the good ? distortions, misallocation, and productivity in Sub-Saharan Africa. Policy Research Working Paper Series 7949, The World Bank.

- Corrado, C., Hulten, C. R., and Sichel, D. E. (2006). Intangible capital and economic growth. Finance and Economics Discussion Series 2006-24, Board of Governors of the Federal Reserve System (U.S.).

- Da-Rocha, J.-M., Tavares, M. M., and Restuccia, D. (2017). Policy Distortions and Aggregate Productivity with Endogenous Establishment-Level Productivity. NBER Working Papers 23339, National Bureau of Economic Research, Inc.

- Fattal-Jaef, R. N. (2018). Entry and Exit, Multiproduct Firms, and Allocative Distortions. American Economic Journal: Macroeconomics, 10(2):86112.

- Fattal Jaef, R. N. (2019). Entry Barriers, Idiosyncratic Distortions, and the FirmSize Distribution. Policy Research Working Paper Series 9027, The World Bank.

- Feenstra, R. C., Inklaar, R., and Timmer, M. P. (2015). The next generation of the penn world table. American Economic Review, 105(10):31503182.

- Hausmann, R., Pritchett, L., and Rodrik, D. (2005). Growth accelerations. Journal of Economic Growth, 10(4):303329.

- Hsieh, C.-T. and Klenow, P. J. (2007). Misallocation and Manufacturing TFP in China and India. Discussion Papers 07-006, Stanford Institute for Economic Policy Research.

- Hsieh, C.-T. and Klenow, P. J. (2009). Misallocation and manufacturing tfp in china and india. The Quarterly Journal of Economics, 124(4):14031448.

- Hsieh, C.-T. and Klenow, P. J. (2014). The Life Cycle of Plants in India and Mexico. The Quarterly Journal of Economics, 129(3):10351084.

- Imrohoroglu, S., Imrohoroglu, A., and Chen, K. (2006). The Japanese Saving Rate. American Economic Review, 96(5):18501858.

38
|(10)<br>(11)<br>RD<br>SE<br>(Within School)<br>0.236<br>0.263<br>-0.121<br>0.073<br>-0.166**<br>0.071|hool characteristics. The<br> years before 1998 and 3<br>ool level for the OLS re-<br>effect (depending on the<br>ard error estimate) for the|
|---|---|
|(9)<br>SE<br>0.248<br>0.434<br>0.125<br>0.067<br>0.153<br>0.076<br>0.088<br>0.101<br>0.087<br>2.012<br>5.492<br>0.372<br>0.116<br>0.081|nt and sc<br>s hired 3<br>d the sch<br>ool fxed<br>(or stand|
|(8)<br>RD<br>(4 Year)<br>0.274<br>-0.922**<br>-0.295**<br>-0.170**<br>-0.200<br>-0.087<br>0.134<br>-0.131<br>-0.211**<br>-0.772<br>2.784<br>0.453<br>-0.001<br>-0.134|ts on stude<br>es teacher<br>results an<br>cher or sch<br>t estimate|
|(6)<br>(7)<br>RD<br>SE<br>(Within School)<br>0.154<br>0.316<br>-0.073<br>0.093<br>-0.154*<br>0.082|ect of temporary contrac<br>1. The 3 year RD includ<br>e regression discontinuity<br>zed by calculating the tea<br>. Each cell is a coeffcien|
|(5)<br>SE<br> <br>0.270<br>0.582<br>0.178<br>0.087<br>0.188<br>0.079<br>0.101<br>0.126<br>0.113<br>2.615<br>6.273<br>0.473<br>0.129<br>0.094|for the eff<br>s after 200<br>evel for th<br>re normali<br>xed effects|
|(4)<br>RD<br>(3 Year)<br>0.433<br>-1.025*<br>-0.350*<br>-0.145*<br>-0.296<br>-0.075<br>0.117<br>-0.118<br>-0.218*<br>-0.295<br>-1.399<br>0.693<br>0.020<br>-0.130|ity results<br>and 4 year<br>nth hired l<br>l years we<br>for year f|
|(3)<br>SE<br>0.059<br>0.115<br>0.031<br>0.017<br>0.026<br>0.019<br>0.029<br>0.034<br>0.035<br>0.786<br>1.127<br>0.091<br>0.023<br>0.026|scontinu<br>e 1998 <br>the mo<br>r severa<br>trolling|
|(2)<br>OLS<br>0.066<br>-0.480***<br>-0.118***<br>-0.040**<br>-0.117***<br>-0.030<br>-0.029<br>-0.085**<br>-0.099***<br>0.438<br>1.701<br>0.044<br>-0.077***<br>-0.042|regression di<br> years befor<br>e clustered at<br>ple times ove<br>bserved), con|
|(1)<br>Mean<br>-0.473<br>-0.607<br>-0.039<br>-0.136<br>-0.076<br>-0.048<br>-0.117<br>-0.129<br>-0.102<br>0.294<br>6.671<br>-0.236<br>-0.065<br>-0.057|d fuzzy<br>rs hired 4<br>errors ar<br>ved multi<br>ristic is o<br>effect.|
|**School**<br>**Attributes**<br>Basic School Facilities<br>Extra School Facilities<br>Library<br>Computer<br>Sports<br>Hall<br>Wall<br>Fans<br>Electricity<br>Number Teachers<br>Student Teacher Ratio<br>**Parental**<br>**Attributes**<br>Student Household Assets<br>Student Mother Education<br>Student Father Education|This table presents OLS an<br>4 year RD includes teache<br>years after 2001. Standard<br>sults. Characteristics obser<br>level at which the characte<br>temporary contract teacher|

41
those applicants scoring above the agreed cut-off score, the potential assignment to treatment or control (with 50-50 probability) was also calculated by the server at the same time; but the assignment was kept on the server, hidden from the loan officers and applicants.

— If an applicant passed the documentation check and business appraisal a process — that typically took around three weeks only at that moment was her treatment status transferred from the server onto the branch computer system and hence revealed to the loan officer. The loan officer immediately informed those women who were assigned to the treatment group that they would receive the loan, and those assigned to control that they had passed but unfortunately would not receive the loan due to the experimental set-up to test this new product that had been explained to them. The treatment group typically received their loan around a month after being informed of their status.

# **4 Sample and data**

In total 357 women showed interest in the loan product over the baseline survey period. The volume of applicants increased steadily, after dedicated staff at the MFI were trained to promote the WEDP-X product among women business owners who were seeking a loan but lacked access to collateral. All 357 applicants completed a psychometric test on a tablet, followed by additional questions on basic business and demographic characteristics whose responses were also made available to the credit scoring technology. Of these women, 339 also completed a short additional baseline survey for the purpose of our study, with more detailed questions on the business, access to finance, and household decision-making. Of the total 357 women, 259 women scored above the cut-off and were invited to proceed. However, 128 of these women did not subsequently pass the regular screening of a documentation check and a business appraisal, and hence were excluded from the impact evaluation. Columns 8 and 10 of Table A1 in the Appendix shows that these firms who passed the psychometric cutoff but did not then pass the regular screening subsequently performed worse than the impact 8
18

**The commitments** and timing of the various sources are shown in Table 4 below. The Table outlines which of the various commitments will be available in February/March 1990 **and** the extent to which bridging facilities will be needed. Credit enhancement is scheduled to be implemented in March 1990.

# Table 4 AVAILABILITY OF ENHANCEMENT MONEYS (US$ millions)

||IMF4/|IBRD|Mexico|Japan|Total|
|---|---|---|---|---|---|
|Available Feb/March 199|0|||||
|Interest support|606|1260||||
|Scheduled Set Asides|272|375||||
|Advancing 1990IMFSet<br>Asides to Feb/March 90<br>Advancing a Portion of|364|||||
|IBRD 1990 Set Asides<br>to Feb/March 90||375||||
|Japanese Parallel fina<br>and Cofinancing|ncing|||14001,2/||
|Enhancements<br>Available<br>Feb/March 20|12240012|4||L401/|59|
|Enhancemen<br>available later|455|||6501/|1105|
|Total Enhancements<br>Available|IQ.'|2010|1243|_2050Q_<br>_2_|7000|
|Total Enhancements<br>Recu|ired||||z725|

Total Available for Debt Reduction Only (IMF and Bank Set Asides) :1841 Total Available for Interest Support Only; Interest Facility IMF and Bank:1866

# Notes:

- 1/ Japanese money includes cofinancing for $1.25 billion, whi-h is not directly available for credit enhancement purposes, and a bridge loan of US$150 million by Japanese counmercial banks.

- 2/ The dollar value of the IMF contributions has been calculated on the basis of a SDR value of US$1.30.

For the World Bank's contribution, a total of US$750 million was set aside from adjustment loans. The set asides could be used to provide principal or interest collateral for the discount bonds issued by the Governnent. The level for set asides amounts to 25% of all adjustment loans the Bank has made or expects to make to Mexico in the three-year period FY89-91. The Bank's contribution for interest coverage on discount and par bonds have been made available through an "interest support loan". US$950 million of the US$1.26
# **2. STYLIZED FACTS**

This section presents stylized facts on the evolution of export diversification which help us to disentangle the relative importance of the intensive and extensive margins of trade. We begin with a description of the dataset used for both, the stylized facts, and the econometric analysis.

## 2.1. **_The dataset_**

The ideal dataset to deal with export diversification issues from a microeconomic point of view would consist of firm-level export data for each country pair; however, such data are not available to researchers. We use instead the highest level of internationally comparable disaggregated country-level trade data that are currently available, namely the 6-digit level of the Harmonised System (HS) from Comtrade database.<sup>3</sup> Our time frame is the period from 1990 to 2005, and the sample includes a selection of 24 developed and developing economies. The set of countries encompasses Argentina (ARG), Australia (AUS), Bangladesh (BGD), Brazil (BRA), Canada (CAN), Chile (CHL), China (CHN), EU (EEC15), Ghana (GHA), Indonesia (IDN), India (IND), Japan (JPN), Kenya (KEN), Morocco (MAR), Mexico (MEX), Mauritius (MUS), Malaysia (MYS), Peru (PER), Thailand (THA), Tunisia (TUN), Turkey (TUR), Uganda (UGA), USA and South Africa (ZAF).

The group of countries that has been selected accounts for a very significant share of world trade. In year 1995, the selected group of countries accounted for two-thirds of world trade. When looking at trade among developing countries alone, the developing countries included in the sample also account for two-thirds of the total amount of trade among developing nations.

## 2.2. **_How do we measure diversification?_**

Export diversification has been measured in many ways. For instance, it has been measured using concentration indexes, counts of exported products or even indexes that take into account the productivity content of the export basket.<sup>4</sup> We use some of these measures in the first part of this section, but most of our analysis rests on a much more straightforward way to look at diversification. We follow AmurgoPacheco (2006), and Baldwin and Di Nino (2006) to look at the intensive versus extensive composition of trade.

With respect to this latter, we define what is meant by intensive and extensive margin. The _intensive margin_ of trade refers to the growth of exports in goods that are already being exported. We can refer to these as "old products". The _extensive margin_ is defined as the growth of exports in new categories. In a similar way we can think of it as "new products". For the purpose of this analysis, we have defined old products as

> 3 For completeness we use mirror data (imports) from Comtrade.

> 4 See Hausmann and Klinger (2006) for a diversification measure considering productivity content.

3
# )OClVi INTRODUCTION

Table 7 Population Aged 0-14, 15-64, and 65 and Over for the W,orld, Less Developed and More Developed Countries, and Continents, 1985, 2000, and 2025 (millions)

|Year and<br>age group|Wortd d|Less<br>eveloped<br>de|More<br> <br>veloped|Africa|America|<br>Asia|Europe,<br>USSR|Oceania|
|---|---|---|---|---|---|---|---|---|
|1985|||||||||
|0-14|1,634|1,374|260|253|208|994|171|7|
|15-64|2,924|2,139|784|289|409|1,699|511|16|
|65+|287|153|134|17|49|131|88|2|
|2000|||||||||
|0-14|1,937|1,685|252|382|230|1,154|164|8|
|15-64|3,860|3,015|845|462|534|2,305|540|20|
|65+|407|239|168|26|62|206|110|3|
|2025|||||||||
|0-14|2,163|1,915|248|564|229|1,202|160|9|
|15-64|5.498|4,649|848|964|688|3,279|541|25|
|65.|754|514|240|60|122|420|147|5|

Note: "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comprises the rest of the Fertility and mortality. Underlying these projections are fertility and mortality rates summarized in Table 8 for the world, less developed countries, and more developed countries. (Rates for continents are in Table 12.) The expected reduction in the world population growth rate of .2 percentage points in the next decade is based on simultaneous reductions in the crude birth rate of 3 points per thousand and in the crude death rate of 1 point per thousand, the bulk of these reductions taking place in less developed countries. The crude birth rate in less developed countries, 31 per thousand (double the rate in more developed countries), will decline 4 points per thousand in each of the next two decades. In more developed countries this rate will decline at a considerably slower pace. The crude death rate, on the other hand, is actually lower in less developed countries, with their younger populations, than in more developed countries, and will decline in the former while it rises in the latter.

Unlike the crude birth rte, the total fertility rate is not affected by differences in age structure. This rate for the world as a whole, estimated at 3.4 for 1985-90, will fall 1 percent a year until 2000 and .8 percent a year between 2000 and 2025. The decline will be almost entirely in less developed countries, where total fertility will go from 3.9 in 1985-90 to 3.1 in 2000-05 to 2.5 in 2020-25. In effect, the average woman in less developed countries will have one-and-a-half fewer children in 2025 than in 1985. These declines may appear relatively moderate. However, given that fertility is unlikely to decline much further in China and a few other large countries, the reductions required in other countries will still be fairly substantial. For instance, for Africa a substantial reduction in total fertility from 6.1 to 5.1 is expected within 15 years, and an even larger reduction to 3.3 within the following 20 years. Such reductions are possible, on the basis of past experience with fertility transition, but in the recent past have not happened automatically but required
share of health expendLtuee in the government budget (which comprises both the budget of the miniLtries and national administrations and that of the regLons) was nearly 9S at the beginning of the 1970s and has fallen by half since then. Only artful presentation can mask this situation, which is summarized in the following graph: <!-- Start of picture text --> 6e It 1'S 69 10 -S 4<br>4~~~~~~~~~~<br><!-- End of picture text --> _HEALTH_ SHARE OF NATIONAL BUDGET The fact is that health expenditures are financed essentially by families. The major expendittre component by far is purchases of pharmaceutical products, which represent a much heavier drain on private budgets than is generally imagined: whereas the value of sales iS officially of the order of CFAF 6 billion (in 1986 and 1987), equal to about CFAF 800 per capita, the pharmaceutical expenditure per capita indicated by the direct population surveys is much higher--more than double. The old surveys ([32a, (331) Lndicate drug expenditures four to eight 0'*es higher, and the more recent surveys ((341) two to three tlmes higher, than the official pharmaceutical sales figure.

These differences are explained by a number of well-identified phenomena. These include the sale of products of the PPM _(Pharuarcle_ Populaire _du Mal,_ a public agency with a monopoly right to import, and until 1986 to market, drugs) at above their official prices, sale of products imported clandes.inely from several neighboring or European countriest and sale to the public of products forming part of the supplies allocated to public health unLts or projects. Whatever the mechanism, the drug always ends up being paid for by the patient or tba family. It has to be concluded that the direct surveys glve a better pi..ture of the situation than the incomplete statistics normally used and that families pay at least CFAF 12 billion for modern drugs, whether prescribed or self-administered.

The second major item is traditional medicine. Some figures put forward reflect more the favor it enjoys among politicians and certain experts--at least to the extent that it asks nothing more of the Government than fine words--than a concern to arrive at a realistic assesst -t. On the basis of the very few direct population surveys that exist (cf. (3d1, (331, (341, (351), we estimate that expenditures connected with traditional medicine do not exceed CFAF 2 billion a year.
# **The Costs and Benefits of Regulation: Some Implications for Developing Countries**

# Table of Contents

|||_Page No._|
|---|---|---|
|Exec|utive Summary||
|I.|Introduction ........................................|_1_|
|II.|Regulation: Definition, Retionale and Problems .....................|.<br>_2_|
|Ill.|The Costs and Benefits of Regulation .............................|_5_|
|IV.|Assessing the Gains from Regulatory Reform .......................|13|
||Social Regulation ...................................|13|
||Economic Regulation .................................|16|
|V.|Conclusions................................|.........<br>25|
|Refe|rences<br>............................|29|
relatively more people have formal bank loans, VF lending is also more widespread; these are likely areas with a high demand for credit, and where the VF may complement, rather than supplant, other sources of funds.

Our next exercise tries to measure the determinants of the number of loans extended to „poor‟ households for every loan extended to a non-poor household, for rural areas. The results are presented in Table 27, and here too an endogenous switching model was not warranted. The first column in Table 27 shows a strippeddown set of estimates; the right-hand column shows the full results of a model that also includes dummy variables for each province (coefficients not shown). This is one of the few sets of results where the inclusion of provincial (or regional) dummies had an appreciable effect on the fit.

**<mark>Table 26.  Estimation Results of Model of the Determinants of Lending Density</mark>**

|**Lending Density**|Robust OLS,stepwise|
|---|---|
|Supply: Fund Capital<br>Initial VF capital|0.007**<br>_(0.003)_|
|Supply: Characteristics of VF Committee||
|Proportion who have accounting experience|0.047*<br>_(0.026)_|
|Demand: Village Size and Organization<br>Number of households in village|-0.043***<br>_(0.007)_|
|Demand: Household Characteristics||
|Proportion of households who are self-employed farmers|0.035***<br>_(0.007)_|
|Proportion who have a loan from a formal bank|0.036***<br>_(0.006)_|
|Proportion of borrowers who are women|-0.046***<br>_(0.010)_|
|Expenditure per capita|-0.002**<br>_(0.001)_|
|Loan terms and conditions||
|VF borrows to on-lend to members|0.017***<br>_(0.003)_|
|Memos||
|Number of observations|2,237|
|R2|0.189|
|Source: Village Fund Survey 2010, and SES 2009. Robust sta<br>parentheses. Stepwise regression uses p=0.2 cutoff for drop<br>* p<0.05, ** p<0.01, *** p<0.001.|ndard errors in<br>ping variables.|

These results are interesting in that they hint at a tradeoff between equity and efficiency. Village Funds that have computerized their accounts – which we interpret as effective management – are relatively less likely to lend to the poor, other things being equal. Lending to the poor is relatively lower in large villages; one might imagine VFs in such areas funding that there is plenty of demand by low-risk non-poor households, so they are less obliged to serve the poor. Where farmers are common, lending to the poor is more widespread; this 37
ier 10; jes kee(tivnas15 —tijno3)15 je95 _ 15 95 15 95 ier INT; = ». >. (thiaas - t?? kos) (hjz 7) jes KEE <!-- Start of picture text --> 95<br><!-- End of picture text --> 95 15 <u>j</u> JES KEE J 15 95 <u>j</u> J y y Ciik1s Ci i,k,03 y = [1995, 2015] y jk T S E
Figure 3: External Links vs. Ownership Integration (U.S. non-Fortune 500 Entities) <!-- Start of picture text --> 20 2.5<br>15 2<br>10<br>1.5<br>5<br>1<br>0<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Customer & suppliers Ownership links Customer & suppliers Ownership links<br>Ownership links to agribusinesses Ownership links to agribusinesses<br>US Non-Fortune 500 firms US Non-Fortune 500 firms<br>Av. chg. rel. to firm's 2014 baseline<br>Median chg. rel. to firm's 2014 baseline<br><!-- End of picture text -->

**Note:** This graph compares the evolution of a firm’s number of customers and suppliers to the number of firms that it is linked to via ownership ties (either parent, subsidiary, or common parent). For each firm, the change in the number of externally and internally connected firms is computed relative to its 2014 value. The left panel shows the yearly average for U.S.-based non-Fortune 500 firms, and the right panel shows the median. Constantly observed actively covered companies are included. By construction, firms with no ownership links in 2014 are dropped (equivalent for the series depicting relative changes of agribusinesses in a firm’s corporate family).

# **4.2 The Global agri-food Network**

Our analysis of the broader global agri-food industry confirms that the most connected firms are not only maintaining but significantly increasing their network of customers and suppliers over time. This trend underscores the pivotal roles that few highly connected firms play within the network, affirming their central position in the agribusiness industry’s connectivity landscape. Figure 4 provides a vivid illustration of this dynamic. The left panel of the figure reveals a relatively stable number of links for the bottom percentiles of firms in the degree distribution over time, indicating minimal change in connectivity among less connected firms. However, there is a marked and sharp increase in the number of links at the 90th percentile, highlighting that the most connected firms in a given year are ever more connected than their less-connected counterparts. The right panel of Figure 4 further elucidates the dominance of these top-tier firms, showing a significant share of the direct customer and supplier links in the agribusiness network attributable to firms from the highest percentiles in terms of degree distribution. This suggests that a small set of highly connected firms not only maintains but also intensifies their influence on the overall network structure over time.

19